DACO Approved Projects

Below is a list of projects approved by the Data Access Compliance Office (DACO) which are accessing the controlled data of the International Cancer Genome Consortium (ICGC). Upon acceptance, approved projects are granted a one year period of access to the controlled data starting from the date of acceptance of their application.

Principal InvestigatorPrimary AffiliationCountryDate Approved for Accesssort iconValid UntilTitle of Project
1.Adam ShlienThe Hospital for Sick ChildrenCanada2018-01-292019-01-28
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
2.Adam ShlienThe Hospital for Sick ChildrenCanada2018-01-292019-01-28
Each cancer is associated with a unique spectrum of mutations in its genome. In most cases the timing and exact mechanism that gave rise to these mutations are unknown. We will use bespoke computer algorithms to understand these mechanisms in the ICGC data and to compare it to the cancer genomes of patients treated at the Hospital for Sick Children.
3.Ashwini PatilMedGenome Inc.United States2018-01-302019-01-29
In the last five years, a new generation of cancer drugs - checkpoint control inhibitors - have significantly improved the survival of cancer patients. These drugs increase the anti-tumor function of some immune cells. Unfortunately, these drugs provide no survival benefit to colorectal cancer, except a small subset of tumors that harbor a large number (>1000) mutations that change protein structure. This observation suggests that colorectal cancer could be a good target for developing cancer vaccines that can be used in combination with checkpoint inhibitors. The goal of the project is to discover effective cancer vaccines in colorectal cancer. We would like to apply our cancer vaccine discovery pipeline and the tumor microenvironment analysis platform on the large colorectal cancer dataset that ICGC has generated to discover features to select patients who would benefit from checkpoint inhibitor treatment, thereby fulfilling an important unmet clinical need in this cancer.
4.Rajat DeIndian Statistical InstituteIndia2018-01-312019-01-30
The present project aims at investigating molecular foundations of multi-tumor types, through combined use of ICGC data at different levels (genomic, gene expression, methylation). The objectives are as follows. • Deep learning algorithms will be developed, by exploiting these data, to define gene signatures relevant to multi-tumors and tissue specific tumors. • Data generated from normal and tumor samples will be analyzed under deep learning framework to identify tumor-specific patterns of the said data, and structural variations in genes between normal and tumor samples. This will enable to identify genes mediating a cancer, which will be further integrated with clinical meta-data of patient samples available in ICGC. • Methodology will be developed for prediction of development and prognosis of a cancer. • Results obtained from the said multi-level and single level data will be compared. • Relevant proteins and small molecules will be identified for novel drug targeting.
5.Peter RoganCytoGnomix IncCanada2018-01-312019-01-30
There are no excuses for missed or molecular genetic misdiagnosis.The current molecular diagnostic rate for inherited breast and ovarian cancer is 15-20%. We need to aspire to improve this. First, we need to determine DNA sequences of complete genes and then try to interpret the consequences of any gene variants that are found. Second, support computational approaches that recognize all DNA variants that can cause disease. This project will use data from the ICGC to identify new changes that have consequences to the structures and the amounts of expressed genes. Third, we must recognize that most variants will be private and not shared among populations, so global genomic databases won’t have the statistical power to reveal whether they are disease causing.
6.Wei LiBaylor College Of MedicineUnited States2018-02-012019-01-31
We have developed a novel bioinformatics tool to identify 3’ untranslated region (3’ UTR) from RNA-seq. With access to the International Cancer Genome Consortium (ICGC) dataset, we will be able to provide a comprehensive landscape of 3’ UTR usage across different cancer types. This 3’ UTR landscape may provide a new direction to discover cancer driver genes, many of which are predicted to favor shortened 3′ UTRs.
7.Ravi GuptaMedGenomeUnited States2018-02-012019-01-31
The immune system is constantly protecting us from invading infectious agents by recognizing them as foreign and eliminating them. Although they are equipped to attack and eliminate growing tumors, they are prevented from doing so by elaborate defense mechanisms put up by the tumor cells. The rapidly growing field of cancer immunotherapy aim to deactivate these protective mechanisms so that immune cells can identify and eliminate tumors effectively. However, in many cancers such as pancreatic cancer, immunotherapy drugs fail to show efficacy because of a lack of understanding of the protective mechanisms employed by the tumor cells to counter immune attack. In this study, we propose to characterize the tumors at the molecular level to identify genetic and environmental factors that protect cancers from immune attack. Findings from this study can be translated to approaches that can make pancreatic cancers sensitive to treatment by cancer immunotherapy drugs.
8.Gary HardimanMedical University of South CarolinaUnited States2018-02-012019-01-31
Research from the Medical University of South Carolina, USA confirms the population – as well as the economic and regional – disparities in cancer detection, incidence, and mortality in the Palmetto State: African-American women are about 60 percent more likely than white women to die from breast cancer after diagnosis. That is the largest disparity in the USA. African-American men are almost 80 percent more likely to get prostate cancer than white men, and about two and a half (2.5) times more likely to die from it. Understanding gene-environment interactions and their interplay with population disparities is an overarching goal of the proposed research. My laboratory is developing computational tools to facilitate identification of biological signatures in the context of these diseases and understand how gene/environment interaction contributes to cancer progression. We would benefit greatly from large-scale analysis, such as the controlled data that is available in ICGC.
9.Nuria Lopez-BigasInstitute for Research in Biomedicine (IRB Barcelona)Spain2018-02-012019-01-31
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
10.Sara CooperHudsonAlpha Institute for BiotechnologyUnited States2018-02-012019-01-31
We will be trying to understand the functional consequences of the DNA alterations identified in the ICGC genome sequencing data. We will use a variety of computational tools to predict which of the mutations might be the most important for the development, progression and treatment of human cancers. We will explore commonalities between cancers and also identify changes that are cancer-specific. Promising genes will be pursued by functional genomics to validate predictions.
11.Erwin Van MeirEmory UniversityUnited States2018-02-022019-02-01
Medulloblastoma (MB) is the most aggressive and malignant brain tumor occurring in children and frequently results in death or in long-term cognitive impairment due to side effects of radio- and chemo-therapies. New and efficacious anti-tumor agents for MB therapy are urgently needed. Addressing this major challenge depends on a better understanding of the biological mechanisms of MB formation and their exploitation for therapeutic purposes. Our research focuses on evaluating the potential role of some tumor suppressors in MB formation. We want to first analyze the expression of these genes in the ICGC controlled datasets, then try to find out how they are regulated in MB. The results will advance our understanding of the expression and function of tumor suppressors in normal cerebellum development and tumor formation.
12.Christopher WardellUniversity of Arkansas for Medical SciencesUnited States2018-02-062019-02-05
Cancer is a genetic disease, meaning that it is caused by changes in a cell’s DNA. Scientists use DNA sequencing to discover these changes, which help us better understand and treat the disease. However, the list of changes that is currently produced isn’t entirely accurate; there are many mistakes, which make further analysis much more difficult because the real changes are being lost in a sea of noise. These ICGC sequencing data are extremely high quality and will act as a gold standard with very few mistakes. This will help us develop software to filter out the mistakes in other samples, allowing other cancer scientists to clean up their DNA sequencing data.
13.Ivan MerelliCNR-ITBItaly2018-02-082019-02-07
During the life cycle of cells, DNA can accumulate errors. One of the most critical error is the translocation of part of a chromosome into another chromosome and vice versa. This can either contribute to the origin of cancers or govern their subsequent behaviour, especially in the ovaries. We aim at studying these chromosomal translocations using a novel sequencing approach, relying on a molecular biology technique that is able to describe the spatial conformation of the DNA inside the nucleus of cells. This is very important because the proximity of two DNA strands increases the possibility of their reciprocal translocation. In particular, we want to study the relationship between the spatial conformation of the genome with the sequence of the genome itself, both in normal and cancer cells, to see if there are correlations between some specific genome defects.
14.Francisco De La VegaFabric Genomics, Inc.United States2018-02-082019-02-07
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
15.Martin TaylorThe University of EdinburghUnited Kingdom2018-02-082019-02-07
Some regions of DNA appear readily changed by mutation whereas others appear protected. We have found that some important genetic switches in DNA are particularly susceptible to mutational damage both in cancer and to be inherited by our children. By studying the genetic changes in the ICGC data we hope to better understand why these genetic switches are so prone to damage and if such changes make an important contribution to the development or progression cancer.
16.Christoph BockCeMM Research Center for Molecular Medicine of the Austrian Academy of SciencesAustria2018-02-082019-02-07
Childhood cancers have few genetic defects compared to most adult cancers, suggesting that mechanisms other than genetic damage are important. We study the role of epigenetic mechanisms in childhood cancers, in particular for the Ewing sarcoma family of tumors. The term epigenetics refers to mechanisms of gene regulation that are not written in the DNA sequence, but rather act by controlling which genes can be activated in a given cell. We will use the ICGC data to investigate the biological function of genes that we identified as targets of epigenetic defects, and we will use epigenetic data for other cancers to search for Ewing sarcoma specific epigenetic changes.
17.Jeffrey BaileyUniversity of Massachusetts Medical SchoolUnited States2018-02-092019-02-08
We are investigating the molecular and genetic causes of cancer that are related to blood and viral infections by analyzing and comparing related and unrelated tumors. Our primary focus is lymphoma and herpes viruses including understanding the causes of Epstein Barr Virus-associated Burkitt lymphoma, particularly the endemic form which is the most prevalent pediatric cancer in sub-Saharan Africa. We are also searching for new viral associations with cancer. Combining our data with ICGC data, we will compare and contrast different cancer and cell types to improve diagnosis and treatment.
18.Luonan ChenShanghai Institutes of Biological SciencesChina2018-02-092019-02-08
Considerable evidence suggests that during the progression of complex diseases, the deteriorations are not necessarily smooth but are abrupt, and may cause a critical transition from one state to another at a tipping point. To achieve that purpose, we have developed a model-free method, which can be used to detect such early-warning signals of critical transitions, even with only a small number of samples. Based on our method, we plan to use the transcriptome data from ICGC, to demonstrate the effectiveness of our method on cancer prediction and prevention.
19.Ton SchumacherNetherlands Cancer InstituteNetherlands2018-02-092019-02-08
It has been shown that over the course of tumor development, mutations can accumulate in malignant cells. These mutations can lead to changes in endogenous proteins, which can be processed and recognized by cytotoxic ‘killer’ T cells, a subset of cells of the adaptive immune system. In this project we aim to describe the contribution of mutations acquired during the development of human tumors to the recognition of said tumors by the adaptive immune system. We have developed a bio-informatic pipeline capable of assessing the effect of tumor mutations and determining whether immune recognition through altered proteins is likely. We would like to use the ICGC datasets, as they comprise a wide range of tumor types as well as the information necessary to accurately determine the tumor-specific changes that can lead to immune recognition. The knowledge gained will provide insights into the breadth of applicability of cancer immunotherapy.
20.Cooper RoddeyEdico Genome Corp.United States2018-02-092019-02-08
Whole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge, along with other ICGC-controlled datasets, is part of an international effort to create standard methods for identifying cancer induced mutations in whole-genome sequencing data. Such datasets allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
21.Laxmi ParidaIBM ResearchUnited States2018-02-092019-02-08
A core challenge in understanding and treating cancer is the molecular complexity and heterogeneity observed in and across patient diseases. To date, a great deal of research has been focused on regions of the genome that encode genes, leaving the vast majority of the genome under-studied with respect to its prognostic or diagnostic power. Our goal is to apply computational methods and artificial intelligence algorithms on ICGC genomic and clinical data in the hopes of discovering patterns among all the different parts of genome that can distinguish between different major and minor types of cancer. Such insights could lead to improved diagnosis and care of cancer patients.
22.Trever BivonaUniversity of California, San FranciscoUnited States2018-02-092019-02-08
My research focuses on Ewing’s sarcoma (ES), a bone tumor that primarily affects children and young adults and is characterized by a unique genetic change that creates a tumor-specific protein EWS-FLI1. Despite dismal outcomes for patients with recurrent or metastatic disease, treatment regimens have remained largely unchanged for decades – intense non-specific chemotherapy combined with surgery or radiation. My preliminary data has uncovered a unique ES specific vulnerability in a core pathway involved in repairing DNA - this vulnerability may represent an opportunity to design more targeted and less toxic therapies for patients and is the subject of my proposal. The ICGC Data is essential to test if what we are discovering in the laboratory is actually happening in ES patients, allowing us to hopefully understand the biology to improve treatments.
23.Zhe Jinorthwestern universityUnited States2018-02-132019-02-12
The vast majority of cancer-associated mutations are located in noncoding regions (DNA sequences that do not encode protein sequences). However, studies have revealed that many of those noncoding regions are functionally important. Using genomic sequencing datasets in the ICGC database, we will develop novel computational tools to compare genetic information encoded in cancer patient samples vs. normal tissues, and systematically characterize the genetic mutations in noncoding regions. Our research can reveal novel cancer driving mutations and novel cancer therapeutic strategies.
24.Mark GersteinYale UniversityUnited States2018-02-132019-02-12
Despite the discovery of tens of thousands of mutations in cancer patients, few are readily interpretable in terms of their effects on known cancer genes. It is unclear how these newly discovered variants relate to cancer. Are they simply neutral passengers created by error-prone DNA replication in cancer genomes? Alternatively, are key cancer-driving variants lurking among this pool of mutations? Or perhaps, do some variants regulate the expression of known cancer genes? Using data from the ENCODE Consortium, which has catalogued connections between hundreds of regulatory proteins and their targets throughout the genome, we are searching for mutations that might disrupt the regulation of key genes and thus help cause cancer. As part of the Pan-Cancer Analysis Working Group, we are also examining whether, in addition to key driver mutations, the thousands of other mutations in each tumor might have subtle effects that together impact cancer progression and patient survival.
25.Nicola AcetoUniversity of BaselSwitzerland2018-02-152019-02-14
Cancer is caused by genetic changes (mutations) in the DNA. These mutations might be inherited or could be accumulated during the lifetime of an individual. The latest are called somatic mutations, and are caused by errors in the DNA duplication process during in the cell division or exposures to cancer causing factors (such as air pollution, tobacco or ultraviolet rays from the sunlight), yet these changes are only in some circumstances associated with cancer development. After the initiation of cancer, new mutation causing mechanisms could arise. Our aim is to use pan-cancer ICGC somatic mutations set to investigate patterns of these mutations and causes that are correlated with those patterns.
26.Josep VilardellBarcelona Molecular Biology InstuteSpain2018-02-162019-02-15
The ribosome is the factory where the cells maintain and build themselves before dividing. They number in the millions per cell, and making them will take most of the cell's energy. Alterations to this effort cause cellular stress and illness, including cancer. We have gathered computational evidence indicating that the potential to make ribosomes is diminished in Chronic Lymphocytic Leukemia. This is unexpected, since it should lead to an abated capability for growth, and needs to be explained. Ribosomes work as instructed by the mRNAs, a product of the spliceosome, possibly the most complex machine of the cell that works extracting the coding information of pre-mRNAs. Together, these two machines will be key to define the cell. Mutations in both are linked to disease and we propose that in cancer their composition and function are tweaked to promote the survival of tumor cells. We aim to uncover these strategies.
27.Thomas GruenewaldLudwig Maximilian University of MunichGermany2018-02-162019-02-15
Newly occurring mutations in the genome (somatic mutations) can alter genes that protect from cancer (tumor suppressors), or that are strictly regulated as they drive cancer (oncogenes). Whether such mutations lead to cancer or not (tumor initiation), and the aggressiveness and growth rates of the cancer cells (tumor progression) appear to be dependent on preexisting heritable genetic variations (germline variants). We aim to decipher the interaction of somatic mutations and germline variants on tumor initiation and progression with data from the International Cancer Genome Consortium (ICGC) on prostate carcinoma. As ICGC provides data from prostate carcinoma patients on somatic mutations, germline variants and clinics, genetic interactions and their impact on clinical course can be well investigated. Therefore we expect to get further insights into the cooperation of somatic mutations and germline variants and its impact on tumor initiation and progression, and, moreover, detect new prognostic biological indicators for prostate carcinoma.
28.Claes WadeliusUppsala UniversitySweden2018-02-162019-02-15
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
29.Matthew HayesXavier University of LouisianaUnited States2018-02-162019-02-15
Double minute (DM) chromosomes are small fragments of DNA that are responsible for increasing the lethality of cancers. Effective methods to discover DMs would give researchers the information needed to develop therapeutic regimens that target the DMs, which can improve patient prognosis. The goal of the project is to develop algorithms and software that can discover DMs. The data used from ICGC will help to measure the effectiveness of these algorithms by testing their ability to predict the presence of the DMs contained within the tumor genomes. Specifically, since the ICGC contains medulloblastoma (a type of brain tumor) data embedded with known DM coordinates and structure, we can evaluate our method's ability to computationally reconstruct DMs by comparing the coordinates and structure of the true DMs to our method's predicted DMs. This project is conducted in collaboration with Dr. Kamal Al Nasr at Tennessee State University.
30.Kamal Al NasrTennessee State UniversityUnited States2018-02-192019-02-18
Double minute (DM) chromosomes are small fragments of DNA that are responsible for increasing the lethality of cancers. Effective methods to discover DMs would give researchers the information needed to develop therapeutic regimens that target the DMs, which can improve patient prognosis. The goal of the project is to develop algorithms and software that can discover DMs. The data used from ICGC will help to measure the effectiveness of these algorithms by testing their ability to predict the presence of the DMs contained within the tumor genomes. Specifically, since the ICGC contains medulloblastoma (a type of brain tumor) data embedded with known DM coordinates and structure, we can evaluate our method's ability to computationally reconstruct DMs by comparing the coordinates and structure of the true DMs to our method's predicted DMs. This project is conducted in collaboration with Dr. Matthew Hayes at Xavier University of Louisiana.
31.Gangqiao ZhouNational Center for Protein Sciences · BeijingChina2018-02-192019-02-18
As a heterogeneous disease with high prevalence and mortality rate, hepatocellular carcinoma (HCC) is developed by the interaction of genetic and environmental factors. Lots of studies have characterized the risk factors of HCC which arise from environment exposure. However, the inherited genetic alterations of HCC still remain largely unrevealed. The goal of this project is to assess the genetic susceptibility to HCC, which may be helpful for effective prevention and treatment of this malignancy. Some genetic variants significantly associated with HCC have been identified by analyzing our in-house data, and the sequencing data of HCC samples in International Cancer Genome Consortium (ICGC) would be used as valuable independent datasets in our replication stages. We hope this project will facilitate the advanced understanding of genetic susceptibility to HCC and the improvement of cancer treatment.
32.Malachi GriffithWashington University in St. LouisUnited States2018-02-202019-02-19
Recent advances in sequencing technology have allowed for rapid and comprehensive characterization of the genetics of individual patient tumors. Initial analysis of these data has focused almost entirely on small mutations that affect the protein sequence of each gene. Other kinds of mutations that affect the abundance or structure of genes have been largely overlooked. Furthermore, the clinical consequence of most mutations remains poorly understood and the resources needed to determine clinical relevance are sorely lacking. The research proposed here will use the ICGC Controlled Data to develop new tools. We will apply these tools to each cancer type to identify novel mutations that determine the individual behavior of tumors and how they respond to therapy. We intend to publish and share the findings of our work with the scientific community and public at large. All tools and source code will be made freely available under an open source license.
33.Juan Fuxman BassBoston UniversityUnited States2018-02-202019-02-19
Cancer is caused by changes in the DNA sequence of our genome. Most changes have been identified in regions that produce proteins; however, less is known about the consequences of changes in regions that control when and where proteins are being produced (regulatory regions). In this project, we aim to identify DNA changes in regulatory regions that have implications in cancer, either as a general cancer mechanism or as differences between multiple cancer types. To achieve this main goal, we will use ICGC collected DNA sequence data and measurements of the extent at which genes are turned on, and integrate this data with computational analyses and experimental studies. Altogether, this project will identify novel cancer mechanisms which will pave the way for the development of new therapeutics.
34.Rameen BeroukhimBroad InstituteUnited States2018-02-232019-02-22
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
35.Rameen BeroukhimBroad InstituteUnited States2018-02-232019-02-22
Recent efforts to profile the genomes of pediatric brain tumors have revealed great insights into the genes that drive growth of the tumors, and those that can be potentially targeted in new treatments. However, to find alterations that occur less commonly, larger numbers of tumors need to be included in analyses. Here we plan to combine newly generated sequencing data with previously published data including genetic data from the ICGC to increase the number of tumors included in the analysis.
36.Bissan Al-LazikaniInstitute of Cancer Research, UKUnited Kingdom2018-02-232019-02-22
The discovery of novel and more effective cancer drugs with lessened treatment side effects and lower likelihood of cancer relapse is becoming increasingly more difficult and challenging. To do so, it is required to (i) develop new computational approaches that draw strength from state-of-the-art knowledge in the fields of chemistry, pharmacology and biology, (ii) apply these on the vast amount of genome sequencing data generated within the ICGC consortium so that drug candidates can be identified and prioritized by cancer type and (iii) evaluate the candidate validity in the lab. As part of the Cancer Research UK Cancer Therapeutics Unit at the Institute of Cancer Research, a world leading academic drug discovery organization, we are in a strong position to address all three requirements. Being an academic institution entails that our findings will be made available for the greater benefit of cancer research and the drug discovery community.
37.Sae-Ock OhPusan National University School of MedicineSouth Korea2018-02-232019-02-22
To develop new diagnostic and therapeutic targets in cancer patients, profiling of gene expression and mutations in cancer tissues is required. Based on this profiling, we can build an accurate prognostic system which is important for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology. To develop a new prognostic systems which can overcome previous limitations, an accurate external validation is required. Because TCGA consists of only one cohorts, independent cohorts are required for the external validation. Both TCGA and ICGC collected high quality genomic data using the same experimental methods, therefore we are going to make a new prognostic system for various kinds of cancers using TCGA and ICGC controlled data. During the development of a new prognostic system, we can find out new diagnostic and therapeutic biomarkers in various cancers.
38.Ka-Wei TangUniversity of GothenburgSweden2018-02-232019-02-22
Almost every fifth malignancy in the world is caused by an infection. Protein or DNA/RNA from virus, bacteria and parasites can sometimes be detected in the tumors. By utilizing sequencing data from the tumors in ICGC we can unbiasly detect microbial DNA or RNA hidden amongst the human sequence. We can thereby find new types of microbes, quantify the amount of microbial DNA or RNA in the tumors and characterize which microbial genes are expressed in the tumors. The presence of microbes during tumor development also causes very different types of mutations in the tumors. Our goal is to find new microbes that are associated with cancer and identify novel targets for cancer treatment.
39.Andrei GudkovRoswell Park Cancer InstituteUnited States2018-02-232019-02-22
Our interest is to study the ’dark matter’ of human genome, i.e the parts of genome other than genes. More than 40% of human genome is represented by the leftovers from ancient viral-like genetic sequences (called retroelements). They have mostly been rendered inactive in the course of evolution, but each individual still has copies of some potentially active retroelements that can potentially cause mutations of sorts in the genome. In several types of cancers such events have been shown to take place at a much higher rate, the fact we are trying to exploit in order to develop novel cancer diagnostics approaches. Our approach is based on novel algorithms which analyze cancer genome data in order to identify and measure the scale of retroelements activity.
40.Paul ScheetMD Anderson Cancer CenterUnited States2018-02-232019-02-13
One type of alteration in the human genome is called allelic imbalance (AI). These include duplication and deletion of different segments of the genome. Many of these AI events have been found in tumors and have been implicated in cancer development. Our lab has developed software that can detect these events in samples a with very small percentage of tumor cells - in samples with as low as 1% tumor cells for example. Given this ability, we will use ICGC data to build on the current knowledge of AI's role in cancer initiation and progression by studying the genomes of adjacent normal samples (relative to the tumor locations) of ICGC subjects. It has been shown that these adjacent normal samples are often not truly normal, but exhibit a small percentage of tumor cells. We will sensitively profile AI in these samples and attempt to determine their biological and clinical implications.
41.Sohrab ShahBritish Columbia Cancer Agency BranchCanada2018-02-232019-02-22
The International Cancer Genome Consortium (www.icgc.org) aims to identify patterns of variation in the tumour genomes of roughly 25,000 patients with common and uncommon cancer types. This project, called the Cancer Genome Collaboratory, will greatly accelerate research for effective treatments for cancer by making available to the world research community an unprecedented collection of more than 25,000 cancer genomes. Using this information, researchers will be able to search for common patterns in cancer genomes that are associated with tumour biology, and translate this information into new diagnostic tests, prognostic tools, and therapies.
42.Mark YandellUniversity of UtahUnited States2018-02-262019-02-25
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
43.Rebecca WattersUniversity of PittsburghUnited States2018-02-272019-02-26
One-third of pediatric patients with osteosarcoma (a cancerous bone tumor) will develop lung metastasis, which is a primary predictor of death. Osteosarcomas have unstable genetics, with more than half of tumors displaying significant structural rearrangements of the chromosomes (structures that carry genetic information). We will utilize the ICGC dataset to identify and estimate the recurrence of these structural variants in our samples, then we will examine how the structural rearrangements in osteosarcoma can serve as diagnostic biomarkers, identifiers of specific sarcoma subtypes, and drivers of metastasis in our own osteosarcoma sample set. This will help us uncover the important classes of structural rearrangements in pediatric sarcomas and potentially will highlight new therapeutic targets for intervention.
44.Christopher SaundersIllumina, IncUnited States2018-02-282019-02-27
We create methods to identify cancer-specific mutations from genomic data. We plan to improve these methods by assessing accuracy on real cancer samples from ICGC for which 'gold-standard' sets of cancer-specific mutations have already been found. Using real cancer samples to make such improvements increases the degree to which our methods perform as claimed when deployed to analyze cancer samples for clinical and research purposes. Our methods to identify cancer-specific mutations are freely provided to the cancer genomics community, and all improvements made using ICGC data will be provided as public updates to these tools.
45.sinisa volarevicUniversity of Rijeka, Faculty of MedicineCroatia2018-03-062019-03-05
Cancer cells have numerous mutations in their DNA sequences. However, only some of these mutations are responsible for cancer development and progression. Their identification is of paramount importance for understanding the molecular mechanisms involved in cancer, predicting the prognosis for cancer patients and the development of new therapies. Based on previous results from Siniša Volarevi´c group's analyses of colorectal cancer data, we wish to access ICGC datasets from several cancer types in order to examine mutations in a pair of genes called RPL5 and RPL11 which might contribute to cancer development. By using statistical, computational and biochemical approaches, we hope to provide insights into the significance of these mutations in human cancer.
46.Michael TaylorThe Hospital for Sick ChildrenCanada2018-03-092019-03-08
Medulloblastoma is the most common brain cancer in children. To understand how these cancers form, we need to discover the genetic changes that enable them to grow. Although the non-coding regions which do not directly record the sequences of proteins were considered to be of little significance, recent studies have revealed that some mutations in the non-coding region played an important role in cancers. Additionally, scientists have found that germline mutations, which were heritable variations, also contributed to tumor development. To date, mutations in the non-coding regions and germline mutations of medulloblastoma have not fully been investigated. We propose to analyze medulloblastoma data from our institute and from ICGC controlled data, and detect the important mutations related to cancer formation and therapy resistance. This research will show us what mutations are important and we can use this information to develop the therapies of medulloblastomas.
47.Arul ChinnaiyanUniversity of MichiganUnited States2018-03-092019-03-08
The whole genome sequence datasets generated by the ICGC will be used to identify those cancer-specific alterations where two regions of the genome are aberrantly fused together consequently leading to initiation and/or progression of cancer. This will enable the development of tests that can facilitate both the diagnostic and the treatment strategies in the clinic. Such methodology will be an addition to the techniques that are currently used for the MI-ONCOSEQ research study, the aim of which is to fully analyze patient biopsy samples, identify cancer-causing mutations and propose novel therapies based on the analysis
48.Julide CelebiIcahn School of Medicine at Mount SinaiUnited States2018-03-092019-03-08
A widely accepted genetic classification scheme of melanomas (a type of skin cancer) as aggressive and high-risk has not translated into the clinical setting. We propose to utilize a unique gene signature that we recently identified to classify melanomas as high- or low-risk and to identify the predictive value of our gene signature on patient outcomes. We will utilize ICGC controlled data to identify our gene signature and to identify critically mutated genes associated with primary cutaneous melanomas (the most common subtype of malignant melanoma).
49.Marcella AttimonelliDepartment of Biosciences, Biotechnology and Biopharmaceutics, Università degli Studi di BariItaly2018-03-092019-03-04
Ovarian cancer (OC) is the commonest cause of gynecological cancer-associated death. Most women are diagnosed with an advanced disease (stage III/IV) because symptoms do not develop during the early stages. Standard care is surgery followed by chemotherapy. The standard chemotherapy can damage the person's genetic material (DNA) and cause alterations that may involved in cancer development, progression and invasiveness. The goal of our research is to define the effects of these DNA alterations in pre- and post-chemotherapy OC cases.
50.Kenneth BuetowArizona State UniversityUnited States2018-03-092019-02-28
Tremendous progress has been made in understanding the basis of cancer. Large numbers of genes and the biologic processes with which they are associated have been observed to be altered in cancer. Common mechanisms are beginning to be observed to be altered across different cancers; however, to date, there has been little systematic effort to examine the relationship of these findings to the specific clinical characteristics of individual cancers. Even less is known about whether there are common underpinnings for clinical features across cancers. As clinical traits are produced by biologic processes, a logical place to look for common features is through analysis of alterations in these mechanisms. In this project, we will systematically look for associations between the clinical characteristics of ICGC individuals and systematic differences in biologic processes. We will first look within cancer types for these relationships. We will also look for common molecular associations across types.
51.Jin-Wu NamHanyang UniversitySouth Korea2018-03-122019-03-11
Long non-coding RNAs (lncRNAs), molecules that control gene expression, are now known to exert critical functions in cells even though they lack the ability to make proteins. Since one of the challenges in the study of lncRNA is that their locations and structures are mostly unknown, we recently developed a novel computational pipeline, CAFE (Co-Assembly of stranded and unstranded RNA-seq data Followed by End-correction) which enables the assembly of a confident set of lncRNA genes in various cancers. A huge amount of ICGC data would be a great test set for improving CAFE. Also, the recent breakthrough of immunotherapy in cancer has encouraged us to study the contexture of immune cells in cancers. Since ICGC Controlled Data possesses various cancer data from all over the world, it could be valuable for us to demonstrate general and distinct characters of different populations in cancers.
52.David GutteryUniversity of LeicesterUnited Kingdom2018-03-122019-03-11
Numerous studies have highlighted many genetic changes resulting in human cancers. Analysis of these changes and alterations gives the possibility of personalising treatment based on the changes found in each patient's cancer. Recent studies have focused on how cancers can evade the immune system and how scientist can find ways of combating this. The aim of this study is to determine how specific changes in a cancer patient's DNA can affect how the tumour is able to evade the immune system in breast, endometrial and colorectal cancers. Our analysis of the ICGC controlled data will generate a large bank of pilot data that will be used towards larger studies investigating how we can combat this and develop various options for treatment. Eventually, we will use this information to develop a blood test used to detect and monitor how cancer tries to evade the immune system.
53.Jorge Melendez-ZajglaInstituto Nacional de Medicina GenomicaMexico2018-03-122019-03-11
ICGC controlled data will be used to determine whether pancreatic tumors from Mexican patients belong to a specific subtype. Subtypes were described for European, Australian and American populations, but there are no reports describing pancreatic tumors subtypes in other populations. Assigning a tumor to one of these subtypes is important, since these subtypes could reflect differences in the reasons for cancer development and, in the future, help to decide which therapy would be best for each pancreatic cancer patient.
54.Gergely SzöllősiEotvos Lorand University BudapestHungary2018-03-132019-03-11
Cancer is a genetic disease fueled by evolution within subsequent generations of cells. Despite advances in the molecular biology of cancer-associated genes, our understanding of the mechanisms that lead to cancer is limited. Cancer death rates have changed little in the last few decades. To address these problems, a new field called "physics of cancer" has emerged. Most tissues have a hierarchical structure, with cell types ranging from stem cells, through more specialized cells, to fully specialized cells. Our aim is to understand the breakdown of the hierarchical organization of healthy tissues and the emergence of tumors, using evolutionary models, computer simulations, and sequence analysis. Using the data provided by the ICGC, we will evaluate how individual tumors evolve and branch into distinct lineages.
55.Ruibin XiPeking UniversityChina2018-03-162019-03-15
All cancers are results of DNA mutations and hence the study of mutations in tumor genomes can provide tremendous help in finding treatments of cancer. The ICGC data profiled DNA information of thousands of tumor genomes. Comprehensive analysis of these data can help us to identity critical mutations in tumor genomes. In this project, we will develop a series of tools that can efficiently and accurately analyze the DNA data. We will apply these tools to the ICGC data and look for new mutations that are important for tumor development.
56.Alexander WeberEberhard-Karls-University TübingenGermany2018-03-192019-03-18
Cancers that arise from certain white blood cells (called B cells) have been shown to harbor mutations that can affect genes that control the survival and function of an immune cell, and whether or how it is visible to the body’s immune system that usually scans cells to eliminate cancerous ones. Although in some cases it is known that such mutations are likely to cause cancer, the way in which they do so is not well understood. In this project we will study how certain mutations correspond to altered functions of the B cells and how the mutations affect the visibility of these cells to immune cells.Our aim is to verify these hypothesis in the ICGC datasets.
57.Alexander WeberEberhard-Karls-University TübingenGermany2018-03-192019-03-18
Cancers that arise from certain white blood cells (called B cells) have been shown to harbor certain mutations that can affect genes such as the genes called "SF3B1", "MYD88" and "NOTCH1". But how these mutations make a white blood cell become cancerous is not fully understood. SF3B1, MYD88 and NOTCH1 are known to affect how other genes are regulated. We have preliminary data that suggests a novel link in the influence of SF3B1 and MYD88. Additionally, we are interested in discovering whether these mutations are "visible" to the immune system. We suspect this is not the case, as cancer cells that visibly harbor mutations would be eliminated. Our aim is to verify these hypotheses in the ICGC datasets.
58.Alexander WeberEberhard-Karls-University TübingenGermany2018-03-192019-03-18
The innate immune system has been known for some time to affect the progression of colorectal cancer (CRC). We have found frequent genetic variants that affect one part of the innate immune system, namely the sensing of microbes such as those present in the human gut, to influence how patients with CRC survive. These variants were also shown to affect how well the innate immune system can "see" microbes. Therefore we think the variants directly influence the progression of the cancer. In this project we will analyze how CRC-relevant genes are regulated in carriers for these specific gene variants in order to find differences that could explain why certain variants promote cancer. This will help strengthen these genetic variants as diagnostic tools to identify high-risk patients or identify new ways in which cancer could be treated in certain individuals.
59.Bradley BernsteinBroad InstituteUnited States2018-03-202019-02-28
The human genome consists of DNA arranged in many long chromosomes which are compactly folded into the cell’s nucleus. The 3D organization of the DNA is tightly regulated because it can have a profound impact on which genes are active. One protein which affects DNA folding is called CTCF, which can change gene expression by shaping DNA into different types of loops. We will use ICGC data to determine whether mutations that affect CTCF’s ability to bind to DNA play a role in the development of a variety of cancers. We will particularly focus on endometrial cancer because it is frequently associated with CTCF mutations.
60.Jiri ZavadilInternational Agency for Research on Cancer (IARC)France2018-03-202019-03-19
Exposure to chemicals (from environmental pollution, lifestyle habits, occupational activities or medical interventions), food contaminants, radiation, and infectious agents, play major roles in human cancer development. Some of these exposures have been shown to leave specific alterations in the genome. The analysis of these alterations by current technologies has identified more than 40 so-called ‘mutational signatures,’ some of which could be attributed to exposure to specific human carcinogens or to deregulated biological processes. However, the origin of the majority of these signatures remains to be identified. Furthermore, humans are subjected to combined exposures at different stages of their lifetime, and our current understanding of how these exposures interact and contribute to cancer development is still limited. Through the analysis of ICGC controlled data, we aim to investigate the impact of various exposures on human DNA to better understand how these exposures may contribute to cancer development.
61.Kathleen MarchalGhent UniversityBelgium2018-03-212019-03-20
This is an international research project among the researcher of the International Cancer Genome Consortium (ICGC) and its regional components, including The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes (PCAWG) project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
62.Peter ParkHarvard Medical SchoolUnited States2018-03-222019-03-22
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
63.Luke BoulterUniversity of EdinburghUnited Kingdom2018-03-222019-03-21
Cholangiocarcinoma is an aggressive liver cancer, which is increasing throughout the world. There are currently few treatments for cholangiocarcinoma and only 7% of patients are suitable for surgery. Remaining patients are treated with chemotherapy, however the effects of chemotherapy in this cancer are limited. Because of this, only one-in-twenty patients survive for more than five years. Recently, DNA sequencing has uncovered some of the changes that happen to genes as this cancer forms. However there has been a surprising diversity in these genetic changes and it has been difficult to determine which mutations cause cholangiocarcinoma to develop. This project uses a combination of computing and gene editing to discover which of these mutations are causing cancers and which ones are there by chance. ICGC data will be used to make a list of DNA changes found in cholangiocarcinoma and these will then be tested experimentally.
64.Davide RobbianiRockefeller UniversityUnited States2018-03-222019-03-21
Chromosome translocations are DNA alterations that can initiate cancer. We previously identified a factor (Activation Induced cytidine Deaminase, or AID) that induces DNA breaks at dozens of genes in lymphocytes (a type of white blood cell), leading to their chromosome translocation and cancer. We will use genomic information available from ICGC to study features of the damage inflicted by AID to the DNA of lymphocytes, and to identify novel sources of DNA fragility associated with cancer development.
65.Richard HoulstonInstitute of Cancer Research, UKUnited Kingdom2018-03-222019-03-21
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
66.Sean GrimmondThe University of MelbourneAustralia2018-03-232019-03-22
Pancreatic cancer (PC) is projected to surpass colorectal cancer by 2020 to become the second leading cause of cancer death. The Australian Pancreatic cancer Genome Initiative (APGI, http://www.pancreaticcancer.net.au) is dedicated to accelerating the translation of scientific discoveries to improve outcomes for PC patients by: 1. furthering our understanding of the genetic diversity, complexity and heritability of pancreatic cancer; 2. evaluating the feasibility and clinical utility of genome sequencing; 3. building genomic resources and a collection of cryopreserved (frozen) tumour tissue that will be informative to selecting the most effective therapy for each individual pancreatic cancer patient. ICGC controlled data will be used to: 1. Harmonise additional pancreatic cancer samples recruited and sequenced through APGI, 2. Aid the development and/or improvement of analysis methods.
67.Carmen ArgmannIcahn School of Medicine at Mount SinaiUnited States2018-03-262018-12-13
Millions of people in the US are affected by Type 2 Diabetes which results from the loss of beta cells, which are the cells in the pancreas that produce the hormone called insulin which controls the body’s glucose levels. One therapy could be to increase the number of beta cells in diabetics, however, researchers to date, do not know how to make the beta cells replicate and expand in number. To find this ‘recipe’ we have been studying a unique collection of rare benign tumours of the beta cell, called insulinomas. Using state-of-the-art genomics and bioinformatics approaches we aim to understand the mechanisms enabling beta cells to expand in number.
68.Oliver ZillGenentechUnited States2018-04-032019-04-02
Cancers are composed of heterogeneous mixtures of cells that contain different sets of mutations. This tumor heterogeneity, and the presence of subsets of cells that are resistant to therapy, can allow a tumor to escape targeted therapy. We seek to evaluate and develop state-of-the-art computational methods to characterize tumor heterogeneity in clinical cancer samples, with the eventual goal of understanding how mutation patterns in heterogeneous tumors relate to patient outcomes. We will utilize specific ICGC cancer genome datasets with published results to benchmark our computational analysis methods.
69.Nada JabadoResearch Institute McGill University Health CentreCanada2018-04-032019-04-02
One in every 450 children will suffer from a cancer before the age of 15 years. High-grade astrocytomas (HGA) are a particularly lethal and disabling form of brain cancer, with barely 10% of children and young adults surviving 3 years after their diagnosis. We recently identified mutations in an important gene known as histone 3.3 in a significant fraction of children and young adults with this brain tumor. This histone gene is involved in regulating the development and growth of many body tissues, but particularly the brain. Blood tests for this gene will help for diagnosis. We are identifying genes(ICGC) which can be used for drug development, to improve immediate and long-term survival of children with HGA using genome analysis tools and animal models to reproduce the effects of these mutations. This will offer the real possibility of identifying promising treatment targets while already testing known drugs for their efficiency.
70.Eduardo ReisUniversity of Sao PauloBrazil2018-04-032019-04-02
Ductal adenocarcinoma (PDAC) is the most prevalent pancreatic tumor, extremely aggressive and whose only curative treatment available is the surgical removal in early stages of the disease. In this project we will sequence RNA and DNA from clinical samples of PDAC to search for changes in gene expression and/or DNA mutations with diagnostic or clinical relevance. Data analysis will allow us to pinpoint alterations in DNA and/or gene expression that are common in PDAC, pointing to novel candidate biomarkers for early diagnosis and prognosis. The ICGC controlled data from PDAC cases will be valuable to search for independent validation of our findings.
71.Eduardo ReisUniversity of Sao PauloBrazil2018-04-032019-04-02
Ductal adenocarcinoma (PDAC) is the most prevalent pancreatic tumor, extremely aggressive and whose only curative treatment available is the surgical removal in early stages of the disease. In this project we will sequence RNA and DNA from clinical samples of PDAC to search for changes in gene expression and/or DNA mutations with diagnostic or clinical relevance. Data analysis will allow us to pinpoint alterations in DNA and/or gene expression that are common in PDAC, pointing to novel candidate biomarkers for early diagnosis and prognosis. The ICGC controlled data from PDAC cases will be valuable to search for independent validation of our findings.
72.Jos JonkersNetherlands Cancer InstituteNetherlands2018-04-032019-04-02
Invasive lobular carcinoma (ILC) is a breast cancer subtype, accounting for 8-14% of all cases. The majority of human ILCs are characterized by loss of the protein E-cadherin, an important protein in structures that bind cells within tissues together. However, we have shown that in our mouse model loss of E-cadherin by itself in the mammary gland does not result in breast cancer. Therefore, we developed a model in which random mutations are introduced in the mammary gland in combination with loss of E-cadherin expression. We identified recurrent alterations in specific genes that resulted in the formation of tumors. The ICGC controlled datasets containing the characterization of 560 human breast cancers will allow us to compare the alterations we identified in our mouse models with human breast cancer.
73.Ian WatsonMcGill UniversityCanada2018-04-032019-04-02
Our research project aims to understand how mutated genes identified in melanoma sequencing data promote disease progression in order to develop novel therapeutic strategies to treat metastatic disease. Our primary objective is to identify significantly mutated genes that promote cancer progression. This task is challenging due to the fact that, in comparison with other cancers, melanoma has a high number of mutations caused by UV exposure. We will use ICGC data to better characterize this mutational process and develop algorithms that discriminate between driver mutations (those that contribute to disease progression) and passenger mutations (those that do not affect disease progression). Our second objective is to determine the mechanisms of action of these mutated genes by performing integrative analyses with clinical data.
74.Martin LoewerTRONGermany2018-04-042019-04-03
Recent research has demonstrated a complex interaction between tumors and the patients immune system, which may result in the tumor evading the control of the immune system. Therapies which restore this immune surveillance have the potential to improve the outcome of cancer patients. We are running clinical trials testing therapeutic vaccines, in which a patient's immune system is triggered to attack cancer cells. Therefore, we seek to identify tumor specific genes, as well as mutations, which are found only in the tumor cells. Using the ICGC data will be another rich source and enables us to find new tumor specific genes, cancer specific genes and will help us to further optimize our computational pipelines.
75.Chris GoodnowGarvan Institute of Medical ResearchAustralia2018-04-042019-04-03
20% of patients with lung cancer who are treated with immune-therapy have a very good response to treatment, the remaining 80% either have a small effect or no effect at all. We don't know who will and who will not respond, so many patients get treatment that doesn't work instead of more effective therapy. People who respond well to immune therapy may have changes in their genes that make them more likely to respond. By reading the code of lung cancer patients who have responded to immune therapy (recruited) and comparing it to patients who have not responded (recruited) and to lung cancer patients in general (ICGC data), we can find differences that might explain this response. With this information, we may be able to help identify which new patients should get immune therapy and which patients would be better getting alternative therapies first.
76.Luca MagnaniImperial College LondonUnited Kingdom2018-04-052019-04-04
Mutations that alter a single character of an individual’s genome are known as Single Nucleotide Variants (SNVs). SNVs are the most common mutation identified in the genomes of cancer cells, and profiling them across multiple patients is therefore crucial for understanding the mechanisms of cancer. Traditional software for identifying SNVs rely on matching corresponding locations between a patient’s genome and a healthy reference genome. However, this matching stage is often error-prone due to the presence of mutations in the patient’s genome, which alter the patient’s genomic information relative to the reference. To tackle this issue, we developed software that bypasses the need to match the patient’s genome to the reference, thereby eliminating this error-prone matching stage. Ultimately, we hypothesise our developed software should be more sensitive at mutation identification than software that includes the error-prone stage. Accordingly, our software will provide deeper insight from the same input data.
77.Bahram KermaniCrystal Genetics, Inc.United States2018-04-052019-04-04
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
78.Zhenyu XuSophia GeneticsSwitzerland2018-04-052019-04-04
Cancer is caused by an accumulation of mutations in the genome of cells, eventually leading to the aberrant growth of these cells and the formation of a tumor. It is therefore crucial for clinical diagnostics to detect and understand the role of these mutations in detail. The technology developed at SOPHiA GENETICS helps hospitals to detect critical mutations by analyzing the genetic data of their patients. In clinical practice, however, these analyses commonly target only a limited number of regions of the genome. In contrast, the ICGC Controlled Data provides data of the whole genome of cells obtained from various cancers. In this project, we will compare analyses applied to constrained regions of the genome to those applied to whole genome data, to better understand the limitations of targeted approaches and improve their design for an optimal leverage of targeted clinical data in the treatment of cancer patients.
79.Colin SempleInstitute of Genetics and Molecular Medicine, University of EdinburghUnited Kingdom2018-04-052019-04-04
Mutations, or changes in the DNA in a person's cells, can lead to cancer. Looking at the DNA of tumors can help us to understand the process by which mutations accumulate and eventually drive a cell to become cancerous. We can identify these mutations by DNA sequencing of tumor and a normal sample from the same individual. ICGC has a large dataset of DNA sequencing that we plan to use to develop new ways of looking at and analyzing cancer mutations. The pattern of mutations, both small and large scale, is of particular interest, and could lead to identification of which patients are likely to respond best to different treatments.
80.Ken ChenThe University of Texas MD Anderson Cancer CenterUnited States2018-04-062019-04-05
One type of alteration in the human genome is called a structural variation (SV). These include deletion, duplication, inversion, insertion, fusion and translocation of different segments of the genome. Some of these SVs have been found in tumors and are implicated in cancer development. Still more can be identified by using new tools to examine data from cancer genome sequencing projects. We will use ICGC data to develop such tools and characterize the SVs found in major types of cancer, potentially leading to improvements in diagnosis and treatment.
81.Sepp HochreiterJohannes Kepler University LinzAustria2018-04-062019-04-05
We are developing new machine learning and bioinformatics methods for the detection of copy number variations and segments that are identical by descent (IBD) on genetic data. Copy number variations play an important role in the development of cancer therefore reliable programs are needed to detect this kind of variation such as our recently developed method. Our IBD detection method is able to find segments of DNA that are shared by multiple individuals because they have inherited them from a common ancestor and are therefore identical by descent. IBD detection on the DNA of cancer patients may help to find underlying predispositions for developing the disease. We intend to test the newly developed methods on datasets such as those available on the ICGC controlled data portal and hope that they lead to results which verify or complement the outcomes of existing studies.
82.Yang ShiChildren's Hospital BostonUnited States2018-04-062019-04-05
Pediatric brain tumors are the leading cause of cancer-related deaths in children under age 10. The overall goals of our research are to better understand the biology of these tumors so that we can apply this knowledge towards the development of new treatment strategies. In the lab, we have identified multiple factors that can promote the growth of pediatric brain tumor cells by regulating which genes are turned “on” and “off.” Our goals are to compare the data we have generated in lab to controlled ICGC tumor data, which will help us to identify the most promising potential targets for new therapies. Specifically, we will ask if the genes that are turned “on” and “off” under laboratory conditions are similarly regulated in patient tumors. We will also analyze tumor data to identify novel factors and pathways potentially regulating brain tumor growth and biology.
83.Jakob PedersenAarhus UniversityDenmark2018-04-062019-04-05
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
84.Andre NussenzweigNational Cancer InstituteUnited States2018-04-092019-04-08
It has been well documented that many cancers are associated with changes in the number of copies of specific genes. This is a phenomenon called copy number variation. The goal of this project is to examine DNA fragility or breakage and their contribution to copy number variation. We have recently developed a method (END-seq) to map stress-induced DNA damage in primary, non-tumor cells. We have discovered genomic features that are associated with DNA damage in response to environmental stress that creates genome instability leading to cancer. We plan to mine the ICGC cancer data sets for genomic features surrounding fragile sites near copy number variations. Combining this data with END-seq in primary cells, we hope to understand the mutational processes that create copy number variations in the cancer genome.
85.Nina BhardwajMount Sinai School of MedicineUnited States2018-04-092019-04-08
Myeloproliferative neoplasms (MPN) are disorders characterized by excessive production of blood cells. Mutated-calreticulin (CALR) is a tumor-associated peptide (a compound consisting of two or more amino acids) that is present at high frequency in MPN patients. Our laboratory has demonstrated that this peptide induces immune responses in those patients. The objective of this study is to fully characterize the properties of the mutated-CALR in MPN patients and develop predictive therapy models using ICGC Controlled Data.
86.Kyeong-Kyu KimSUNGKYUNKWAN UNIVERSITYSouth Korea2018-04-112019-04-10
The goal of the current study lies in the detailed understanding of particular cancer-causing mutations and their potential in the genomic context. Additionally, method-development for predicting cancer-causing mutations in the regulatory regions (regions of the genome that control gene expression) followed by experimental validation is the long-term goal of the project. Hence, information on the diseased genome’s mutations with sampling details is mandatory for such research and analysis. Initially, we will consider all genomic regions and later more detailed analysis will be carried out. The significance of those mutations will be judged in contrast to the important factors like their frequency, position, and relation to the regulatory region of the genome. The ICGC controlled data would be helpful for our project to locate recurrent cancer mutations in genomic regions.
87.Brian RoodChildren's National Medical CenterUnited States2018-04-122019-04-11
We have identified small runs of repeated DNA code that seem to be able to distinguish individuals with the brain tumor medulloblastoma from healthy people. These elements are called microsatellites. Most interestingly, the DNA we used to find these elements is from the normal cells in the body, not the tumor DNA. Therefore, these cancer associated microsatellites exist before the tumor is formed and thus could mark a predisposition to cancer. The objective of this proposal is to attempt to determine the effect that these microsatellites may be having on the way that DNA instructions are interpreted by the cell to create proteins. We will first analyze DNA sequences from each subject to count the number of repeats in each medulloblastoma associated location. We will then look at the RNA sequences from the nearby genes to determine if there are changes in the way it is processed.
88.Chad CreightonBaylor College Of MedicineUnited States2018-04-122019-04-11
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
89.Jonathan GoekeGenome Institute of SingaporeSingapore2018-04-122019-04-11
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
90.Jose Luis Bello LopezInstituto de Investigacions Sanitarias de Santiago de CompostelaSpain2018-04-132019-04-12
Chronic Lymphocytic Leukemia (CLL) is a frequent haematological cancer that predominantly affects elderly individuals. It has been known for years that CLL cells harbour genetic alterations which have prognostic impact. Nevertheless, recent studies of CLL genomes have enabled the detection of a myriad of mutated genes and the inference of various common mutational patterns, reinforcing the idea that CLL is a genomically complex disease. In this project, we will integrate several layers of biological complexity in order to identify new genes implicated in CLL biology and to study their association with clinical events. We will use the ICGC CLL genome database as our initial genomic data input to characterize CLL genomes. Further experimental studies will be formulated on the basis of the results that we may obtain.
91.Eliezer Van AllenDana-Farber Cancer InstituteUnited States2018-04-132019-04-12
Previous studies aimed at genomic characterization of melanoma has enabled melanomas to be classified into four genomic subtypes based on the presence of significantly mutated genes (e.g. BRAF). The most recent study performed this characterization using 333 melanoma samples. Here we aim to further stratify and redefine the genomic subtypes of melanoma by collecting over 1000 samples, which will include the Brazilian and Australian cohorts hosted on ICGC. This large increase in sample size could easily change the prevalence of significantly mutated genes, and thus our approach to understanding the observed biological and clinical differences between samples. Other implications of analyses performed on this increased sample size includes the ability to identify significantly mutated genes at a higher power, identify genes not known to be mutated in melanoma, explain the underlying biological mechanisms of melanoma, identify druggable targets, and provide guidance about therapy.
92.Jinming LiNational Center for Clinical Laboratories,Beijing Hospital,Peking Union Medical College, Chinese Academy of Medical Sciences.China2018-04-132019-04-12
The "whole-genome sequencing" technique for cancer genome analysis has already became a clinical tool for the detection of tumor-specific (somatic) mutations. The computational analysis pipeline is a key component for identifying somatic mutations in whole-genome sequencing data. However, computational analysis pipelines used for extracting information from raw sequence data remains in their infancy and their outputs are highly divergent. Benchmark data is very important for laboratories to benchmark and calibrate their analysis pipelines. Recently, we have developed a tumor genome simulator to create benchmark data for somatic mutation detection. The ICGC real tumor data will help to verify the practicability of our synthetic tumor genomes and improve the fidelity of our cancer genome simulator.
93.Hong XueHong Kong University of Science and TechnologyHong Kong2018-04-162019-04-15
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
94.Lara MangraviteSage BionetworksUnited States2018-04-162019-04-15
Cancer represents one of the most complex diseases because the groups of genetic mutations that can result in tumor progression can vary widely across individuals. The goal of our research is to use mathematical models based on genomic and clinical data (including ICGC controlled data) donated from patients around the world to provide a model of cancer pathology that can be used to understand the complex biology that underlies tumorigenesis (the biological process involved in the generation of tumors). The goal of this effort is to predict clinical outcomes and, ultimately, to guide the production of more effective therapies for future generations of cancer patients.
95.John PearsonThe Council of the Queensland Institute of Medical ResearchAustralia2018-04-172019-04-16
Our research goals are to identify new mechanisms of cancer development and progression and to identify novel gene targets for cancer treatment. We will use controlled access ICGC data to validate findings from our animal models and patient cohorts. We will also search ICGC data for common patterns in cancer to be used in testing in our cancer cell cultures. This may reveal novel processes critical to cancer development, progression and treatment, which could potentially translate into new tests to identify cancer earlier and choose better treatments faster.
96.Donavan ChengIllumina, IncUnited States2018-04-202019-04-19
Whole-genome sequencing of samples to find cancer-specific mutations by comparing tumor and normal DNA is becoming routine in cancer research. Illumina is conducting an algorithms benchmarking exercise, evaluating the precision and recall of small and large cancer-specific mutations on well-characterized reference datasets. The ICGC data profiled DNA information of thousands of tumor genomes. Analysis of this data will help the evaluation of cancer-specific mutations analysis pipelines using the BaseSpace tools.
97.EKTA KHURANAJoan & Sanford I. Weill Medical College of Cornell UniversityUnited States2018-04-232019-04-22
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
98.Christina CurtisStanford UniversityUnited States2018-04-232019-04-22
Our research is focused on the development of analytical tools to integrate across and mine cancer genomic datasets. We request access to individual-level data available through The International Cancer Genome Consortium in order to integrate these data with other large-scale public datasets, as well those from our own studies. These analyses will result in a more comprehensive molecular map of cancer, that may ultimately inform novel therapeutic strategies.
99.Dmitry GordeninNational Institute of Environmental Health Sciences, NIHUnited States2018-04-232019-04-22
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
100.Robert VonderheideUniversity of PennsylvaniaUnited States2018-04-262019-04-25
Cancer cells have errors in their genetic code that can be targets for the immune system, allowing the cancer cells to be destroyed by the immune system. Research has shown that some cancers have many times the average amount of these errors and that in these cancers the number of errors can predict how well a patients' cancer responds to therapy. However, it is not clear if this ability to predict response will exist in pancreatic cancer, whose cancer cells have fewer errors overall and thus potentially less immune targets overall. The aim of the study is to use available genome data and computer modeling to predict how many immune targets exist in pancreatic cancer based on genetic errors. Genome data will be downloaded from ICGC and analyzed using an established protocol to identify the average number of immune targets present in pancreatic ductal adenocarcinoma.
101.Joachim WeischenfeldtRigshospitaletDenmark2018-04-262019-04-25
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
102.Peter ParkHarvard Medical SchoolUnited States2018-04-262019-04-25
Genomic DNA is tightly packaged inside human cells. The special protein molecules and other factors facilitate this packaging. Specifics of the packaging determine how easy it is to read out the information from each gene in the genome. It was discovered that the DNA-packaging proteins are mutated and malfunction in a number of cancers, including pediatric brain tumors. Using the ICGC controlled data we will investigate in which ways the production of these proteins is disrupted in cancer cells. Our findings will help to shed new light into how genomic DNA is ‘handled’ in cell during normal development and into how its ‘mishandling’ can lead to cancer.
103.Vsevolod MakeevVavilov Institute of General Genetics, Russian Academy of SciencesRussian Federation2018-04-272019-04-26
Cancer transformation of a cell is believed to be caused by so-called 'driver mutations', changes in the cell's genome that disrupt mechanisms controlling the cell's identity. Recent findings demonstrate that most of these mutations are found in DNA segments that do not encode proteins by themselves but control what tissue and what time the protein is synthesized. The mechanisms of this control are facilitated by special regulatory proteins, interacting with particular DNA sites. The objective of our research is to understand how occurrences of different types of mutations of chromosomes in cancer depend on neighboring genome segments performing particular functions: 1) how mutations are correlated with each other; 2) how the mutation process depends on local genome activity and other processes; and 3) what factors determine the subsequent fate of the mutated cells and their progeny.
104.Steven JonesBC Cancer, Part Of The Provincial Health Services AuthorityCanada2018-04-272019-04-19
BC Cancer Agency's Genome Sciences Centre is involved in numerous genomics and bioinformatics research projects studying a variety of cancer types and other diseases. From these, we have identified a vast repertoire of molecular variants in the human genome. This application requests approval to add data from ICGC to our existing data sets in order to separate molecular variants associated with clinical conditions from the naturally occurring spectrum of genomic variation.
105.Franco SilvestrisUniversity of Bari, Department of Biomedical Sciences and Human OncologyItaly2018-04-302019-04-29
Tumor neoantigens are protein fragments of 8-18 amino acids capable of eliciting a specific anti-tumor immune response. The neoantigen landscape of a particular type of pancreatic cancer called pancreatic neuroendocrine neoplasm (panNEN) has never been investigated so far. In this project, we aim at investigating the presence and recurrence of neoantigens in order to i) provide a molecular-immunological classification of panNENs, and ii) identify targets for new drugs or novel immunotherapy strategies. ICGC Controlled Data (EGAS00001001732) will be used for mutation detection, evaluation of the quantity of mutated mRNA, and subsequent prediction of neoantigen presence/type by using bioinformatic pipelines. The results of such analyses will be validated by using an independent cohort of panNENs for neoantigen identification. Finally, results will be validated in vitro.
106.Christine DesmedtInstitut Jules BordetBelgium2018-04-302019-04-29
Increased body mass index (BMI) has been recognized as a risk factor for developing breast cancer and has also been associated with adverse survival. Here we aim to use the ICGC Controlled Data to investigate the associations between the patient’s BMI at diagnosis and the biological characteristics of the tumor using the “560 breast cancer genomes cohort” (Nik-Zainal et al. Nature 2016). Given the importance of the immune tumor microenvironment in the context of increased BMI, we will also evaluate the association and relationship between several immune variables and the available biological, clinical and pathological characteristics of these breast cancers.
107.Peter CampbellWellcome Trust Sanger InstituteUnited Kingdom2018-04-302019-04-29
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
108.Martin PeiferUniversity of CologneGermany2018-05-022019-05-01
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
109.Jong-Seo KimSeoul National UniversitySouth Korea2018-05-022019-04-24
Various processes are responsible for the growth and maintenance of cells. For example, regulating the expression of genes and controlling the production of proteins are the most crucial cellular processes. Hence, by better understanding the elements that control gene expression, we can obtain valuable insights to make advances in many biological fields including those related to cancer biology. Here, we attempt to extend the understanding of gene expression regulation across various cancer types by examining elements which have been shown to contribute to the regulation of gene expression. By obtaining and analyzing the extensive gene expression data available on the ICGC database from thousands of patient donors across numerous cancers, we hope to find interesting and potentially biologically relevant mechanisms in gene expression regulation in cancer through an integrative pan-cancer analysis.
110.Bettina KempkesHelmholtz-Center MunichGermany2018-05-032019-05-02
Historically, the Epstein-Barr virus (EBV) was the first human tumor virus that was shown to be associated with the development of cancer in African children. This discovery was based on an intense collaboration of physicians, virologists and immunologists in the 1960s and 1970s. It motivated numerous scientists to explore EBV epidemiology, genetics and the pathogenic molecular mechanisms the virus uses to reprogram normal healthy cells to become cancer cells. World-wide, 95% of the population is latently infected with EBV but the great majority will not develop a EBV-related disease or cancer since the immune system will prevent clinical symptoms. However, immunocompromised patients or elderly people are at elevated risk to develop EBV-associated cancers. The comparative analysis of infected and non-infected cancerous and normal tissues provided by ICGC will support the identification of biological factors which drive cancer development or can serve as differential diagnostic markers for EBV-associated malignancies.
111.Casey FrankenbergerTempusUnited States2018-05-032019-05-02
We will use the ICGC data (and other data sources) to optimize clinical options within or outside of the standard of care. Variation in response to therapy and survival of patients with similar clinical presentation can potentially be explained in part by somatic mutations (mutations that are not heritable) and inherited genetic variation. Somatic mutations drive tumor proliferation and malignancy, where inherited genetic variation has been associated with increased cancer risk and aggressiveness. Using the ICGC and other data sources, we will perform analyses with somatic mutation and inherited genetic variation to identify patients with similar molecular and clinical presentation. The results from these analyses will provide insight into refining prognosis and improving the way doctors decide how to treat patients.
112.Andrea SottorivaThe Institute of Cancer ResearchUnited Kingdom2018-05-032019-05-02
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
113.Jung Kyoon ChoiKAISTSouth Korea2018-05-042019-05-03
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
114.Piers BlomberyPeter MacCallum Cancer CentreAustralia2018-05-082019-05-07
Advances in genomic analysis techniques have improved our understanding of the basic biology of leukaemia and lymphoma. When genomic data is combined with conventional pathology investigations such as the formation and structural features of the cells, this can lead to more accurate diagnosis and thus better patient treatment. Our study will combine local data with controlled data from the ICGC to document the genomic variation seen in patients with blood cancer. By pooling a variety of data including mutations, structural variations and gene expression and combining this with other clinical metadata we will use machine learning to develop algorithms that improve diagnostic accuracy and uncover new biological indicators of prognosis in patients with blood cancers.
115.Qing LuCincinnati Children's HospitalUnited States2018-05-092019-05-08
Brain tumors are one of the most common tumor types among children. The genetic landscape of brain tumors is also heterogeneous, with subsets of tumors exhibiting frequent chromosomal alterations and others displaying only single aberrations. Whole genome sequencing or whole exome sequencing was performed in order to discover the important mutation and uncover the tumor formation mechanism. We will combine the data from the ICGC database and our samples to identify genes or additional genetic alterations that drive pediatric brain tumors.
116.Ranjan PereraSanford Burnham Prebys Medical Discovery InstituteUnited States2018-05-092019-05-08
Medulloblastoma is the most common malignant brain tumor in children and a rare brain tumor in adults. It starts in the cerebellum, which controls balance and other complex motor and cognitive functions. While medulloblastoma often grows quickly and may spread to other parts of the body, it usually responds well to treatment. Human cells are producing a large number of RNAs called noncoding RNAs (no protein is produced) and until recently they were regarded as "junk" or transcriptional noise. The primary focus in our laboratory is to identify the molecular function of noncoding RNAs in medulloblastoma and other human cancers. Our goal is to identify one such noncoding RNA group called long noncoding RNAs which are medulloblastoma specific, so we could develop biomarkers for early diagnostics. We will use ICGC controlled data to determine whether brain tumors from this population belong to a specific group.
117.Zechen ChongUniversity of Alabama at BirminghamUnited States2018-05-102019-05-09
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
118.Christopher BuckNational Cancer InstituteUnited States2018-05-102019-05-09
Some forms of cancer are caused by viral infections. Cancer-causing viruses damage cells in ways that lead to uncontrolled cell growth. We seek to identify genetic damage caused by viruses. This will be achieved by thoroughly searching ICGC and other data sources for viruses and then identifying significant correlations between changes in viral and human genomes and differences in disease outcomes or gene regulation patterns. The ultimate goal of this study is to better understand how virus-associated tumors develop and to develop vaccines against carcinogenic viruses.
119.Rebecca HalperinTGenUnited States2018-05-102019-05-09
Cancer is thought to be caused by changes in DNA (mutations) that enable the tumor cells to grow uncontrolled. State of the art DNA sequencing technologies enable detection of these mutations in individual patient tumor samples, which can be used for matching patients to targeted therapies, identification of resistance mechanism to targeted therapies, identification of new targets and cancer drivers, and understanding the progression and evolution of the tumor overtime. Taking the raw sequencing data and detecting these mutations is a surprisingly difficult problem, for which existing methods often disagree. We have developed a new approach for detecting these mutations which can jointly analyze multiple tumor samples from the same patient in order to enable better understanding of the relationships between tumors. The ICGC data will enable us to benchmark our method compared to other available approaches.
120.Natalia PellegataHelmholtz Center MunichGermany2018-05-112019-05-10
Neuroendocrine tumors (NETs) are a group of heterogeneous tumors able to secrete hormones. Usually rare, NETs have not historically been a focus of research aimed at developing novel therapies. However, their incidence keeps increasing and they can be aggressive, invasive, recurrent, or malignant, thereby causing significant morbidity. No effective therapies are available for NETs at advanced stages. Understanding the molecular mechanisms mediating NETs progression is required to identify novel treatment options. We work with a rat model developing multiple NETs, including pancreatic NETs (pNETs). Rat tumors recapitulate the pathological and physiological changes of human NETs and can help elucidating tumor development and progression. We have determined the gene expression signature of rat pNETs. We will compare these data with the genetic profile of human pNETs stored at ICGC to identify commonly dysregulated genes that might be studied in our model to assess their biological function and therapeutic potential.
121.Frederic CharronUniversity of MontrealCanada2018-05-112019-05-10
Medulloblastoma is one of the most common pediatric brain tumors. Although many mutations have been characterized as drivers of medulloblastoma formation, little is known about the mechanisms that control the growth and progression from precancerous lesions to malignant tumors. We recently found that one of the mechanisms controlling medulloblastoma progression is cell senescence (a phenomenon by which normal cells can no longer divide). This research project aims to find new mutated genes and signaling mechanisms that lead to inactivation of cell senescence in medulloblastoma. We will use the ICGC controlled datasets to identify medulloblastoma mutations in genes that have been linked to cell senescence in studies of other cancers. Sequenced tumors curated and maintained by ICGC will allow us to uncover novel regulators of medulloblastoma progression that could be used in the development of pre-clinical medulloblastoma models to explore novel targeted therapies.
122.Thomas IlligHannover Medical SchoolGermany2018-05-142019-05-13
Liver cancer is the fifth most frequent cancer type and, with more than 750,000 reported deaths per year, the second leading cause of cancer-related death worldwide. The ICGC liver cancer datasets provide detailed insight into the genetic landscape of liver tumors. Based on this knowledge, we want to analyze tumor-promoting mechanisms, investigate the tumor cell response to its environment and develop novel therapies for liver cancer. As liver cancer represents an ideal model system of solid cancer, our goals are of relevance beyond liver cancer for other tumor diseases.
123.zhen helatrobe universityAustralia2018-05-142019-05-14
Our project is detection of mutations in cancer with machine learning approaches. Accurate identification of such mutations is the first step to therapeutic precision, and plays a key role in clinical diagnosis. A number of tools have been developed to predict them from paired tumor/normal sequencing data. However, there is still space for accuracy improvement. Our work is using ICGC controlled data stored in EGA to develop a tool with machine learning techniques to accurately identify mutations associated with cancer.
124.Peter Van LooFrancis Crick InstituteUnited Kingdom2018-05-172019-05-16
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
125.Yixue LiShanghai Center for Bioinformation TechnologyChina2018-05-172019-05-16
The overall goal of the project is to evaluate difference of microbial profiling of prostate cancer in various ethnic groups by integrating genomic and gene expression data from different data portals including ICGC. We explored the methodology for detecting microbes using prostate cancer whole genome sequencing (WGS) data and developed an analysis pipeline for the detection of microorganisms from prostate tumor tissues and the corresponding blood samples with high confidence and sensitivity. Our results primarily depict the differential microbial profiling in prostate cancer tissues of 14 paired cases, but additional samples are needed for further study. So we would like to have access to the controlled whole genome sequencing data of prostate cancer in ICGC, which will help us validate the existence of microbes in the prostate cancer tissues and further compare the microbial difference among various ethnic populations.
126.Ryan GutenkunstUniversity of ArizonaUnited States2018-05-182019-05-17
The goal of this research is to develop a method to more reliably identify the genetic mutations that are present in the tumor of any cancer patient. With this information, researchers and oncologists will be able to design better treatment strategies. For instance, they will have more information to use when prescribing drugs that target genetic mutations specific to cancer of individual patients. They may also use this information to design treatment strategies involving multiple drugs, which can increase the chances that patients will continue to respond to the treatment over the long term.
127.Ying GaoShanghai Institutes of Biological SciencesChina2018-05-182019-05-17
Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. DNA methylation, which is a kind of chemical modification of DNA, is often known to be linked to cancer. Aberrant DNA methylation has been shown to play important roles in CLL. We have performed research on DNA methylation and discovered some candidate targets which may be involved in CLL development. Our group aims to integrate ICGC datasets for further validation and exploration. These findings may be useful to understand the development of CLL.
128.Natalie JaegerGerman Cancer Research CenterGermany2018-05-242019-05-23
In the cancer genome, different mutational processes generate unique combinations of mutation types, termed “Mutational Signatures” (http://cancer.sanger.ac.uk/cosmic/signatures). Some signatures are present in many cancer types, whereas others are confined to a single cancer class. These signatures can also be informative for therapy response, hence act as biomarkers. Mutational Signature 3, for example, is strongly associated with inherited and sporadic mutations in the BRCA genes in breast, pancreatic, and ovarian cancers, and so-called "BRCAness" in general, which implies sensitivity to a type of targeted cancer drug (so-called PARP inhibitors). Further, many pediatric cancer entities of high medical need due to limited therapeutic options, show a mutational signature compatible with "BRCAness". Therefore, mutational signature analysis across diverse pediatric cancer types from the ICGC project might inform new therapeutic options for childhood cancers.
129.Eila Arich-LandkofOriel ResearchUnited States2018-05-242019-05-23
Achieving personalized medicine requires an extensive research of integrated datasets of genomic and health information. Oriel Research will develop a predictive model that will take various patient data types as input and will identify presence of disease and also suggest therapies that are likely to have positive outcome. Oriel Research model will be available on www.orielresearch.org and will be accessible to anyone who might want to use it. ICGC sequence data will be used to train (construct) the model and evaluate its accuracy.
130.Richard MaraisCancer Research UK, Manchester InstituteUnited Kingdom2018-05-252019-05-24
UV radiation is the major environmental risk factor for cutaneous melanoma, and most cutaneous melanomas carry mutations in either the BRAF or the NRAS gene, both of which drive tumours to grow when mutated. However, the relationship between UVR damage and the activity of these so-called oncogenes (BRAF/NRAS) is unknown. Our studies show that tumours carrying BRAF and NRAS mutations present with distinct patterns of UVR damage. This damage is independent of familial risk or UVR exposure pattern. NRAS requires more UVR-exposure to drive melanoma formation than BRAF. Using ICGC data, our aim is to identify genomic differences between BRAF and NRAS tumours. We will perform additional analysis on this data to determine how UV interacts with BRAF and NRAS genes.
131.Sampsa HautaniemiUniversity of HelsinkiFinland2018-05-252019-05-24
Epithelial ovarian cancer is the fifth most frequent cause of female cancer deaths. High-grade serous ovarian cancer (HGSOC) is the most common and lethal subtype. While HGSOC patients initially respond well to the therapy, the treatment is very seldom curative and more than 50% of the patients die within five years. In this project our goal is to use computational methods to identify mechanisms that lead to HGSOC recurrence (and thus poor survival) after therapy. With the help of the ICGC data we will establish predictive tools that suggest the most effective therapy options for an HGSOC patient. We will further use mathematical models to suggest novel personalized treatments for an HGSOC patient. The ICGC data will be further used to evaluate the global effects of the discoveries based on Finnish HGSOC patients.
132.John EdwardsWashington University in St. LouisUnited States2018-05-252019-05-24
We are interested in understanding what role epigenetic changes play in cancer, more specifically in Non-Hodgkin’s Lymphoma (NHL). Epigenetic changes are processes that affect DNA without altering the sequence itself. Not every cancer cell in a tumor is the same, and these differences may be responsible for tumor growth, metastasis, and treatment resistance. In this project, we will use data from the ICGC to develop new analytical tools to not only understand the importance of epigenetic changes in cancer, but to also identify key subpopulations of cancer cells. We will integrate data from the ICGC with epigenetic data we have obtained from patients with Non-Hodgkin’s Lymphoma (NHL) to understand the contribution of epigenetic changes to NHL. This project will give us a broader understanding of how epigenetic changes affect cancer and potentially improve our abilities to determine which patient may best benefit from epigenetic targeted therapies.
133.Steven GallingerUniversity Health NetworkCanada2018-05-252019-05-24
Cancer can result from changes in a person's genetic material (DNA). By studying genetic changes, researchers can learn what causes cancer. This will lead to new ways to prevent, detect and treat cancer. The International Cancer Genome Consortium (ICGC) was created to coordinate a large number of research projects. The ICGC will develop a comprehensive catalogue of genetic changes that occur in cancer. These will be benchmarked against other cancer types to ensure data is of the highest quality. As a contributing member of the ICGC, the OICR will generate a comprehensive catalogue of genomic abnormalities found in pancreatic tumours. Our target is to collect and study 500 independent tumours and their matched controls. The ICGC collaboration (OICR and UHN) will allow members world wide to advance cancer research through analysis of a large number of genomes from multiple cancer types.
134.Michael DeanNational Cancer InstituteUnited States2018-05-252019-05-24
Genetic studies have demonstrated that some individuals have a higher underlying genetic risk of developing cancer. The tumors themselves arise from alterations in genes that encode crucial regulators of cell growth and genome stability. In this project, we plan to identify genetic alterations (germline and somatic) by using ICGC controlled data that influence cancerous growth and investigate how these alterations influence key cellular processes.
135.Lauri AaltonenUniversity of HelsinkiFinland2018-05-292019-05-14
Each year, approximately 2600 Finns are diagnosed with colorectal cancer and fifth of them will ultimately die from it. Finland is a small country in the north eastern edge of Europe with exceptionally well characterized population history which is well suited for genetic risk studies. We are studying genetic colorectal cancer risks in Finnish population and will use data from ICGC to evaluate the global effects of the discovered risk genes.
136.Gangning LiangUniversity of Southern CaliforniaUnited States2018-05-302019-05-29
Pediatric high-grade glioma (a malignant tumor that occurs in the brain or spinal cord) has a very low survival rate. This has remained mostly unchanged over many decades. The goal of this study is to compare sequencing data from patients collected from various sources (including ICGC controlled data) with in-vitro lab cultured cells. This will provide us with confidence that the lab cultured cells are physiologically similar to the patient cells. This will be useful in proving the validity of downstream experiments studying the efficacy of in lab drug treatment.
137.Didier TronoEcole Polytechnique Federale de LausanneSwitzerland2018-05-302019-05-22
Our genome contains a vast amount of virus-related sequences. Over 4 million fragments of our DNA derive from mobile genetic elements, some of which were once viruses that infected and integrated into the genome of our ancestors. These transposable elements (TEs) vastly outnumber genes with ~4 million TEs compared to approximately 25,000 genes in the human genome TEs were for a long time considered to be purely “junk” DNA, yet it has recently come to the fore that many pieces of them are still transcribed into RNA and provide unique patterns, high density “barcodes” of cellular states in health and disease. This opens up the prospect of identifying novel, uncharted biomarkers with broad applications in precision oncology. We aim at mining ICGC data to uncover novel diagnostic and therapeutic options for cancer patients.
138.Sherene LoiPeter MacCallum Cancer CentreAustralia2018-06-012019-05-31
Breast cancer arising in young women (aged 40 yrs or less at diagnosis) is rare and associated with a particularly poor outcome relative to older age groups, particularly those breast cancers where the estrogen hormone supports tumour growth. Using ICGC controlled data, we aim to investigate how mutations, groups of activated or inactivated genes and the influence of the immune system may explain their aggressive disease course, as well as provide potential treatment implications.
139.Murali BashyamCentre for DNA Fingerprinting and Diagnostics (CDFD), Hyderabad, INDIAIndia2018-06-012019-05-31
The SWI-SNF complex gene is composed of several proteins that help in the development of embryos. Research in the past decade revealed frequent aberrations in these proteins during cancer development. A detailed understanding of the role of these proteins in cancer development will help in designing drugs for treating cancer. Our analysis in a small number of cancer patients revealed interesting observations regarding the role of SWI-SNF proteins in cancer. In this proposal using ICGC controlled data, we aim to identify the frequency of aberrations in SWI-SNF complex genes in head & neck, colorectal and esophageal cancers. In addition, we will also identify genetic aberrations in other known cancer genes and their association with aberrations in SWI-SNF complex genes.
140.Olivier CinquinUniversity of California, IrvineUnited States2018-06-012019-05-31
This project aims to further develop software to detect "rare variants", i.e. changes in DNA sequence that are present in only a fraction of the cells in a cancer sample, and to apply that software to data derived from cancer samples. Specifically, the ICGC controlled data will be used to help develop the software and to identify new rare variants that might not have been detected with high confidence using other approaches. Overall, this may eventually allow more insights into cancer occurrence and progression to be derived from the ICGC controlled data.
141.Zhaoshi JiangGilead SciencesUnited States2018-06-012019-05-31
The human digestive track is colonized by large variety of bacteria which interact with human immune system in complex and to this day not well understood ways. So far, only very few studies have shown that certain bacterial species can influence patient’s response to cancer treatment. This effect of bacterial composition in the human digestive tract is, however, difficult to identify because the bacterial composition often changes due to unrelated factors. Data from ICGC controlled data study EGAS00001001689: EGAD00001001936, EGAD00001001943 and ICGC controlled data study EGAS00001002698: EGAD00001003797, EGAD00001003943 provide unique opportunity to study bacterial effect on cancer treatment because similar interactions are not frequently documented. Our efforts will focus on understanding bacterial differences between patients who experienced positive or negative bacterial effect on their cancer treatment and the variation introduced by life-style differences.
142.Junbai WangOslo University HospitalNorway2018-06-042019-06-03
We intend to design new computational models for detecting gene regulation in cancer by incorporating diverse information to the model, e.g. large publicly available datasets obtained under various conditions, the cancer genome atlas (ICGC tumor/normal matched genomes from cancers), and datasets of other modifications on gene regulation. The project aims to extend our current BayesPI program that includes protein-DNA interaction data, to an improved application that includes chromosomal interaction, DNA sequence variation, epigenetic data (heritable chemical marks on specific parts of DNA that control the gene regulation without affecting the underlying genetic sequence – A, C, T, G), and nucleosome occupancy (the fraction of DNA sequences is occupied by any histone proteins) information.
143.Javad NazarianChildren's National Health SystemUnited States2018-06-042019-06-03
Midline glioma is the tumor arising in the midline location of the brain and is very fatal due to its anatomical site. Recent studies have identified mutations in a gene (called "histone") that drive a subset of these tumors but there still remains the need to identify less commonly occurring mutations. In order to identify these changes we have collected published and unpublished data to increase the number of cases and we would like to request ICGC scientific committee to grant access to these published data EGAD00001001944 and EGAD00001002006. Our goal is to look at the genetic/biochemical changes in DNA of larger cohort of midline gliomas to identify differences within already-known subgroups of tumors. These datasets will be analyzed along with other data that we have collated to identify genes using genome analysis tools and animal models and test drugs that can specifically target identified subgroups.
144.Jacco van RheenenNetherlands Cancer InstituteNetherlands2018-06-042019-06-03
In this project we want to study the contribution of different types of cells to tumors and their influence on the outcome of patients. We will use the ICGC controlled data to study the differences in contribution of different cell types in the tumor on the survival of breast cancer patients, and in the future how this might influence treatment response.
145.Florian MarkowetzCRUK, Cambridge Research InstituteUnited Kingdom2018-06-052019-06-04
Cancers are driven by genomic mutations. We are focusing on structural variation (SV) of cancer genomes, which include deletion, duplication, inversion, insertion, fusion and translocation of different segments of the genome. Our project has two goals: First of all, we want to find out in how many cells of the tumour a particular SV appears. This is important to understand if an SV is early (appears in all cells) or late (appears in fewer cells). The second goal is to find out what mechanism cause genome mutations by deriving fingerprints (called mutational signatures). In previous work, we have found 7 copy-number signatures in ovarian cancer, each one representing a particular mutational process acting on the genome. Through analysing ICGC controlled data, we want to see if the same signatures appear in other cancers and if other cancer types exhibit signatures we did not find in ovarian tumours.
146.Kui WUBeijing Genomics Institute-ShenZhenChina2018-06-052019-06-04
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
147.Young Seok JuKAISTSouth Korea2018-06-072019-06-06
The research project is about learning "mutational signatures" from samples of mutations, especially from samples of cancer genome (L. B. Alexandrov, Nature 500.7463, 415-421 (2013)). Mutational signature can be thought of as a trail of mutational footprints left by mutational processes. Through careful analysis of mutational signatures, we can study and learn more about the nature of cancer and mutation in general. Our research project is to build a more robust framework to learn mutational signatures. For that, we need a sufficient amount of high quality samples. In this respect, more than 560 breast cancer samples of the ICGC controlled data will be invaluable resources. Lastly, we want to make sure that we do not intend to make use of any information possibly related to personal identification such as heritable information.
148.Mikita SuyamaKyushu UniversityJapan2018-06-072019-06-06
The variations in the genome are important factors to the development of cancer. In this project, we plan to use the controlled data from ICGC to conduct research to further our knowledge of the cancer associated genome- sequence variations in several cancer types. Moreover, we will use metadata (e.g. age, sex, pathologic stage and survival time) to explore cancer progression and prognosis with statistical approaches.
149.Steven RozenNational University of SingaporeSingapore2018-06-072019-06-06
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
150.Steven RozenNational University of SingaporeSingapore2018-06-082019-06-07
Cancer is caused by alteration (mutations) to a cell's genome. Recent advances in computational methods enable the identification of genetic alteration patterns or mutation signatures in individual cancer genomes. This project aims to identify these mutation signatures operative in cancer and begin to experimentally link identified signatures to a specific biochemical or biological process. This will improve our knowledge of specific process contribution to cancer development and allow for prioritization of regulatory and educational efforts to reduce or eliminate people's exposure to carcinogens. We will examine the somatic mutations (detected by ICGC) of each tumour in the database to understand the tumour's mutational signature and link this to possible exogenous mutagenic exposures or other somatic or germ-line genetic variants.
151.Marcin ImielinskiNew York Genome CenterUnited States2018-06-082019-06-07
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
152.Sabarinathan RadhakrishnanNational Centre for Biological SciencesIndia2018-06-112019-06-10
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
153.Anthony MathelierCentre for Molecular Medicine Norway, University of OsloNorway2018-06-122019-06-11
Within this project, we will develop computational resources and software tools to assist in understanding and prioritising personal variations in the DNA fragments that control when and where genes are expressed. Mutations occurring in these regions have recurrently been shown to be important for cancer initiation and cancer development and can cause susceptibility to adverse drug reactions. We will use controlled ICGC data to study how patients' cancer mutations can dysregulate the gene regulation of the cell, triggering the formation of cancer. Knowledge derived from the proposed work will enhance the quality of findings to improve the quality of outcomes for patients.
154.Arnaud DroitUniversité LavalCanada2018-06-122019-06-11
Prostate cancer (PCa) is the second leading cause of cancer death in American men. Once treated, about 20-30% of men will relapse. Causes of this relapse are poorly understood. Studies using new genomics technologies show that PCa is a complex cancer and no specific gene has been linked to relapse. Thus, new approaches are needed to explain PCa relapse. Currently, we have found a combination of genes that perform relatively well to predict relapse. But we are limited in the understanding of the gene modifications beyond their simple expression. We also suspect that some other kind of genetic variations and other types of biological molecules may be associated to the relapse. These can be identified by the analysis of ICGC sequencing raw data. Moreover, we will also characterize the immune profile of PCa by looking for other signatures associated to the infiltration of immune cells within the tumour.
155.Ola MyklebostUniversity of Bergen, NorwayNorway2018-06-142019-06-13
The Norwegian Sarcoma Consortium (NoSarC.no) was initiated as part of the Norwegian Cancer Genomics Consortium (NCGC, cancergenomics.no), and has now collected samples from most Norwegian sarcoma patients over 3 years. More than 300 tumour/normal sample pairs are being exome sequenced (all genes), and these data are analyzed for biolgical and cancer mechanistic insight, with the aim to find new treatments. As a partner in the ICGC Bone cancer Group, we know the value of international sharing of samples and data. This is essential for sarcomas, as the cancers are rare and there are around 80 subtypes. Thus the ability to validate our findings in independent patient cohorts is very important.
156.Patricio YankilevichIBioBAArgentina2018-06-142019-06-13
The Genetic Risk of an individual, which is the probability of carrying a specific disease associated to genetic mutations, can be assessed in different ways. The current development consider a Genetic Risk Assessment system based on machine learning models built from genetic datasets of the ICGC Data Portal. The objective is to develop classifiers for genetic profiles associated with cancer patients in order to help and support local medical doctors. The developed model will be able to assess risk of individuals depending on the classification between positive cancer diagnosis and healthy controls.
157.Hiroyuki AburataniThe University of TokyoJapan2018-06-142019-06-13
Genome sequence variations and mutations in regions that do not code for protein are known to affect gene expression at other sites. We have identified these regions by analyzing tumor cells and would like to compare their sequences between different cancer types using ICGC whole genome sequence data. We will identify common and cancer-specific sequences and mutations which could be involved in tumor formations or cancer progression.
158.Hiroyuki AburataniThe University of TokyoJapan2018-06-142019-06-13
Whole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
159.Dawei LiUniversity of VermontUnited States2018-06-142019-06-13
We aim to learn more about the genetic mechanisms underlying human cancers by analyzing the existing genetic data from the ICGC database. We plan to develop new methods for proposed data analyses. We are particularly interested to study the roles of viruses in cancer development. In this project, we will identify variations, particularly those related to viruses, which have not been well-studied before. We also plan to statistically test associations of newly-identified variations with corresponding cancer types based on the ICGC data.
160.Sven BorchmannUniversity of CologneGermany2018-06-142019-06-13
Many cancers are known to be associated with certain viral or bacterial infections, for example, human papillomaviruses with cervical cancer. Associations of certain viruses, such as Epstein-Barr with hematological cancers have also been found, but are less clearly defined. Therefore, we want to analyze ICGC data from hematological cancer patients, in order to further investigate if known and not yet discovered associations between viral and bacterial infections and hematological cancers exist and if they can be used to guide treatment, monitor treatments success or even prevent the respective cancers in the first place.
161.Akshitkumar MistryVanderbilt University Medical CenterUnited States2018-06-192019-06-18
Cancer is often a manifestation of one or more aberrations in the genetic code of our cells. These aberrations can lead to changes in the expression of genes that often carry out cancerous functions. Understanding the expression of over thousands of genes in pediatric brain tumors is necessary to identify therapeutically targetable genes. Using gene expression data from ICGC and data from other public repositories, this project aims to 1) quantify gene expression changes in pediatric brain tumors; 2) unveil any patterns of gene expression based on the type of brain tumor; and 3) reveal specific non-cancerous and environmental gene expression signatures that are present in pediatric brain tumors. The result of this work will help identify new cancer therapies.
162.Justin StebbingImperial College LondonUnited Kingdom2018-06-192019-06-18
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
163.Steven RobertsWashington State UniversityUnited States2018-06-192019-05-22
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
164.Carla Robles-EspinozaNational Autonomous University of MexicoMexico2018-06-212019-06-21
Melanoma is one of the most aggressive human malignancies, constituting only about 4% of dermatological cancers but being responsible for about 75% of deaths from these. Acral melanoma is a poorly studied subtype of the disease that constitutes a large proportion of melanoma cases in counties in Latin America, Africa and Asia. In this project, we are seeking to pool together all genome information across a number of studies, including ICGC controlled data, and perform various bioinformatics analyses in order to search for recurrent alterations and structural variants that may be driving the disease, and search for molecular subtypes that might inform treatment options in the future.
165.Alex KentsisMemorial Sloan Kettering Cancer CenterUnited States2018-06-262019-06-25
The current study will use ICGC's genetic
 data to analyze a rare type of bone cancer called Ewing sarcoma. Our goal is to find evidence of DNA rearrangements that may contribute to cancer by allowing different genes to become fused together. These results can help us to identify previously unknown causes of Ewing sarcoma, leading to improved treatments for patients.
166.Maurizio D'IncalciIRCCS Istituto di Ricerche Farmacologiche "Mario Negri"Italy2018-06-272019-06-26
Ovarian cancer is normally treated with surgery followed by chemotherapy. Most patients will respond to this treatment, however, in 70% of cases the tumor will relapse and become resistant to therapy up to the point of being incurable. There is no standard way to predict in a patient if and when the tumor will relapse, just empirical evidence based on the time passed between the end of the treatment and the appearance of a relapse. Our proposal is to study the process of relapse and chemotherapy resistance. We will evaluate the genetic changes occurring in tumors at the onset of the pathology and after relapse. This information will be used to model how the tumor becomes resistant, in order to potentially predict the outcome of the disease when it is diagnosed. To verify the efficacy of this model, we will test it on the ovarian cancer data from the ICGC.
167.Youngwook KimSUNGKYUNKWAN UNIVERSITYSouth Korea2018-06-272019-06-27
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
168.Guido FranzosoImperial College LondonUnited Kingdom2018-06-282019-06-27
Professor Franzoso and colleagues have developed a new cancer drug, known as DTP3, which they are conducting to trial in multiple myeloma patients. DTP3 kills myeloma cells in laboratory tests and mice, without causing any toxic side effects, the main problem with other cancer drugs. The team has discovered how DTP3 also kills other types of blood cancer, including acute myeloid leukaemia (AML). Over 20,000 people develop AML in the UK and US each year. Unfortunately, current therapies are toxic and ineffective for many patients, resulting in a high relapse rate and poor prognosis. The researchers have tested DTP3 in cellular models of aggressive AML subtypes, where other drugs do not work, and found it is highly effective. They now seek support from the ICGC to comprehensively study their drug in AML with the aim of starting a new trial to develop an effective treatment for currently untreatable AML patients.
169.Paul PharoahUniversity Of CambridgeUnited Kingdom2018-06-292019-06-29
Inherited differences in our genetic makeup - our genome - underlie the tendency for cancer to run in families. The aim of this study is to look for differences in the regions of the genome that we think are biologically (or functionally) important in order to identify variants that might be associated with risk of ovarian cancer. We will therefore analyse the genomes of women with ovarian cancer that have been generated as part of the ovarian cancer component of the ICGC and compare the frequency of variants in functionally interesting regions of the genome with less active regions of the geome to identify risk associated regions. These will then be validated by targeted sequencing in large-scale ovarian cancer studies that are not part of the ICGC.
170.Ludmil AlexandrovUniversity of California, San DiegoUnited States2018-06-292019-06-28
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
171.Cord UphoffLeibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbHGermany2018-07-052019-07-04
Growth and survival of tumor cells rely on continuous activation of cell signaling pathways. Mutations of signal transduction genes have been described in different forms of Non-Hodgkin lymphomas. Novel drugs for individualized therapies are being developed targeting mutant proteins and leading the malignant cells to apoptosis, i.e. inducing cell death. Involving a process called “alternative splicing” genes can be translated into proteins of different sizes. Novel studies have shown that signaling genes/proteins (e.g. STAT3) can be affected by alternative splicing and that the proteins generated can have different cellular functions. Our preliminary experiments show that genes in signaling pathways other than the JAK/STAT pathway can be targets of alternative splicing. We are applying to get access to ICGC data to find out whether these results can be confirmed for patients. Inhibition of the affected pathway may then turn out to provide novel treatment options for a subgroup of lymphoma patients.
172.Ran ElkonTel Aviv UniversityIsrael2018-07-052019-07-04
Somatic mutations are spontaneous genetic alternations accumulating throughout one’s life leading to different types of cancer. Until recently, research was focused on identification of somatic mutations that enhance cancer development by modifying genes that encode for proteins. However, these DNA regions cover only less than 3% of our genome. The maturation of novel technologies that allow rapid determination of the DNA sequence of whole genomes now enables for the first time the discovery of somatic mutations that occur in regions in the genome that do not encode for proteins. Somatic mutations that occur in these genomic regions are called "noncoding mutations". The ICGC Controlled Data already include thousands of tumor-normal paired whole genome sequences from numerous cancer types. We will use this rich data resource aiming at the identification of additional noncoding genomic regions that are frequently mutated in cancer, elucidating novel aspects in cancer biology.
173.David TorrentsBarcelona Supercomputing CenterSpain2018-07-052019-07-04
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
174.Rosalind EelesInstitute of Cancer Research, UKUnited Kingdom2018-07-092019-07-08
This is an international research project involving prostate cancer research groups from seven countries, which form the Pan Prostate Cancer Group: PPCG (http://panprostate.org/). Our primary objective is to integrate different data types based on measurements in cancerous and normal cells from over 2000 prostate cancer patients generated by ICGC, TCGA, and other groups. Our aim is to translate our findings to provide clinical benefit, such as accurately predicting aggressive disease that will need radical treatment, gaining new insights into tumorigenesis for better diagnosis; and determining differences in prostate cancer across different ethnic populations. We will apply a uniform set of algorithms to process and analyse the data, developed by the PPCG members and overseen by the Sanger Institute, DKFZ, and OICR. This will eliminate differences that are due to different ways of analyzing the data. This project is modelling itself on the successful ICGC PanCancer Analysis of Whole Genomes (http://docs.icgc.org/pcawg/).
175.James DaiFred Hutchinson Cancer Research CenterUnited States2018-07-102019-07-09
The incidence of esophageal adenocarcinoma (EAC) has risen drastically in Western countries. Genomic features of western EAC have been studied in US and UK, featuring a high mutation rate and particular mutation signatures that may be related to gastroesophageal acid reflux diseases. It was reported that most cases of EAC in Western countries arose from Barrett’s esophagus (BE), a serious complication of gastroesophageal acid reflux disease. We have studied EAC in China and found that BE is rarely associated with EAC in China. We have genomic data from 10 Chinese EAC cases and their matched controls. The ICGC ESAD-UK data will allow us compare genomic profiles and signatures between UK EAC and Chinese EAC. We hypothesize that Chinese EAC may arise via a different disease process and have distinct genomic features from UK EAC. Through the comparison study, the findings will be helpful to understand the mechanisms of developing EAC.
176.Shaoping LingGenome WisdomChina2018-07-102019-07-09
Mutations, or changes to the genome sequence of a cell, can lead to cancer. It is therefore of extreme importance to catalog all of the mutations observed in tumors to gain information about the disease. Dozens of methods exist to identify mutations. However, there is currently very little agreement between any two methods. The discrepancies make it difficult to compare and combine results from different studies. We will use controlled access ICGC data to develop a series of tools that can efficiently and accurately detect mutations in major types of cancer , and apply these tools to the ICGC data and look for new mutations that are important for tumor development.
177.Sigrid SkanlandUniversity of OsloNorway2018-07-122019-07-11
Chronic lymphocytic leukemia (CLL) is a common malignancy of immune cells which covers 40% of all leukemia cases in the Western world. The disease is initially slow-growing and a “watch and wait” approach is recommended for patients without symptomatic disease. Traditionally, frontline chemoimmune therapy has been the conventional choice. However, over the past three years, novel therapeutic possibilities have revolutionized CLL management and continuously demand a better understanding of the highly heterogeneous features of the disease so that maximum patient benefit can be obtained. We wish to analyze ICGC controlled transcriptional data from CLL patients in order to identify alterations which can indicate drug or drug combinations best suitable for the individual patient. The ultimate goal of the project is to assist clinical decisions in individualized cancer therapy.
178.Charles LangleyUC DavisUnited States2018-07-132019-07-13
The centromere is the locus on the chromosome that directs replicated sisters chromosomes to daughter cells. Thus plays a central role in inheritance. Centromeric regions of the human genome are highly repetitive and still not fully assembled. They remain enigmatic, difficult regions in which to analyze variation.  This is despite the obvious critical roles that they play in genome stability and the regulation of gene expression. Recently we have made an advance in the interpretation of population genomic data from these regions that now opens them up to much more powerful research.  Taking a fresh look at the potential associations of clearly classified centromeric region genotypes with appearance and development of cancers is our top priority and access to the data in the ICGC is opportune.
179.Benjamin LehnerCenter For Genomic Regulation (CRG)Spain2018-07-132019-07-12
Cancer arises from changes to genes that control the way cells grow and divide. Major risk factors that may contribute to cancer progression are environmental hazards and hereditary genetic mutations. Hereditary genetic mutations that exhibit highly increased risk of cancer are observed in ~ 5 to 10 % of cancers. Our project aims to integrate ICGC data with other data sets in order to help understand how these mutations contribute to tumor progression (e.g., early onset of cancer). We will also investigate how much the patients' mutation frequencies differ from those of healthy people without cancer in order to identify cancer-associated genes.
180.Marc-Henri SternInstitut CurieFrance2018-07-132019-07-12
Cancer is often linked with acquired abnormalities of the tumor genome, such as mutations, gains and losses of parts, and other aberrant structures. Some tumors are characterized by an increased rate for such abnormalities, a process named genomic instability. Our research project is devoted to unravelling the origins of genomic instabilities in cancers. Our approach consists in the systematic analysis of cancer genome architecture with relation to the genes altered in various types of cancer. By analyzing ICGC controlled data, we aim at deciphering associations and functional links between gene alterations and the genomic instability patterns. Taking into consideration genomic instability could improve tumor molecular classifications, prognosis and prediction of response to treatment.
181.Charles DavidTsinghua universityChina2018-07-132019-07-12
The past decade has seen a surge in studies designed to look for genetic variations that affect an individual's susceptibility to cancer. These studies have enabled researchers to zero in on small regions of the human genome where specific sequence variations predispose individuals to the development of cancer. Many such "hotspots" have been identified in a variety of cancers, but the exact function of only a tiny fraction of these is understood. In pancreatic cancer, a susceptibility hotspot resides next to a gene, KLF5, which is critical for the emergence of pancreatic cancer. We previously showed that switching off KLF5 appropriately is essential to block incipient tumors, and our data suggest that this genetic hotspot is may control the cell's ability to do this. To examine this, we will use the ICGC's human pancreatic cancer data to examine genetic variation around KLF5, and its impact on KLF5 regulation.
182.Jeffrey RosenfeldRutgers UniversityUnited States2018-07-132019-07-12
Normal cancer sequencing for personalized medicine and the determination of eligibility for immunotherapy is based upon the sequencing of a few hundred genes in a patient's tumor. The assumption is that this data is sufficient to guide treatment decisions without looking at the full 20,000 genes in the tumor, the non-coding parts of the genome (that don't make proteins) and the genome of the patients' healthy cells. We will use the ICGC data to determine whether this assumption is correct or whether the expanded data in ICGC which includes the whole genome of both a patients' normal DNA and the tumor will improve our results.
183.Israel SilvaA. C. Camargo Cancer CenterBrazil2018-07-182019-07-17
Certain viruses are detected in cancer. One of these viruses, named Epstein-Barr virus (EBV), is present in a small set of gastric adenocarcinomas (~10%). Whether EBV infection leads to DNA damage in gastric carcinogenesis, and if so by which mechanism, is a subject of ongoing investigation. Using ICGC controlled data, aim to develop an integrative analysis to identify DNA changes specifically related to DNA-modifying enzymes. Therefore, understanding the role of these enzymes on the immune response against EBV infection may provide us with an understanding of how EBV, and other virus infections, lead to overall mutations that lead to gastric cancer.
184.Israel SilvaA. C. Camargo Cancer CenterBrazil2018-07-182019-07-17
Certain viruses are detected in cancer. One of these viruses, named Epstein-Barr virus (EBV), is present in a small set of gastric adenocarcinomas (~10%). Whether EBV infection leads to DNA damage in gastric carcinogenesis, and if so by which mechanism, is a subject of ongoing investigation. Using ICGC controlled data, aim to develop an integrative analysis to identify DNA changes specifically related to DNA-modifying enzymes. Therefore, understanding the role of these enzymes on the immune response against EBV infection may provide us with an understanding of how EBV, and other virus infections, lead to overall mutations that lead to gastric cancer.
185.Benjamin Bermancedars-sinai medical centerUnited States2018-07-192019-07-18
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
186.Zeynep GumusIcahn School of Medicine at Mount SinaiUnited States2018-07-192019-07-18
Inherited genetic variation contributes to the risk of developing different cancer types. Here, I propose to use data from ICGC to identify and characterize genetic markers that predispose individuals to the development of cancer. Results of these studies will be widely disseminated to the scientific community through publication in peer-reviewed journals.
187.Justo Lorenzo BermejoUniversity Hospital HeidelbergGermany2018-07-192019-07-18
We are conducting a research project towards personalized prevention and treatment of gallbladder cancer in Chile, where we plan to generate genetic information from tumor tissue and blood. In order to compare the molecular profiles of gallbladder cancer diagnosed in Chile and Japan, we apply here for access to deposited ICGC controlled data on Japanese population described in the article by Nakamura et al. (2015) “Genomic spectra of biliary tract cancer” (doi:10.1038/ng.3375).
188.Oliver KohlbacherEberhard-Karls-University TübingenGermany2018-07-202019-07-19
In recent years, immune therapies have become important and especially immune checkpoint blockade (such as blocking membrane receptors of T-Lymphocytes to suppress immune response) has shown therapeutic potential leading to their introduction into standard care for cancer patients. The proposed study will provide new knowledge of the immune biology of cancers and which can be used to find new therapeutic targets for personalized medicine. Another goal is the development and improvement of methods for immune therapies, especially personalized cancer treatments. These methods include bioinformatics approaches (computer-assisted analyses performed by trained researchers) on next-generation-sequencing (technology to get DNA/RNA sequence information) and mutation data (as from ICGC controlled data). Furthermore, statistical analyses of mutations in different cancer types will be performed, including analyses that will be used to study the interactions of tumours and the immune system.
189.Fran SupekInstitute for Research in Biomedicine (IRB Barcelona)Spain2018-07-202019-07-20
DNA in human cells needs to be repaired as cells age and divide. Failures to do so may result in cancers and possibly in other age-related pathologies. Our interests lie in peforming large-scale statistical analyses using genomic data, including ICGC controlled data, to learn about the DNA repair mechanisms that safe-guard the information stored in the DNA. We are also interested in learning how commonly failures in DNA integrity affect different individuals and the consequences this has on cancer risk and the carcinogenesis mechanisms, as well as implications for therapy of tumors.
190.Roman JaksikSilesian University of TechnologyPoland2018-07-202019-07-19
Leukemia is a cancer that leads to abnormalities of white blood cells, caused by two types of mutations, which increase the proliferation rate of the cells and prevent them from fully maturating. Despite a similar background leukemia is a very diverse disease showing four distinct types and an even larger number of subtypes, with various symptoms and treatment responses, a background of which is still poorly understood. The main goal of this project is to identify abnormalities in acute myeloid leukemia (AML) cells, which is the most common cause of leukemia-related mortality among adults, and acute lymphoblastic leukemia (ALL), the most common cancer among children, responsible for over 30% of cancer-related premature deaths in Poland. For this purpose ICGC data will be used. This study may contribute to a much more effective leukemia treatment by allowing to develop new ways of classifying cancer and predicting treatment outcomes based on information contained in the DNA.
191.Marc van de VijverAMCNetherlands2018-07-202019-06-01
Breast tumors consist of a mixture of cancer cells and non-cancer cells. For example, cells belonging to the immune system can infiltrate the tumor and communicate with the cancer cells. This may lead to an alteration in the tumor and potentially have an effect on clinical outcome in the patient. The project aims to understand patterns of interaction between the cancer cells and immune cells and look for associations in the genome of the tumor. Genomic data of ICGC of invasive breast cancer will be used alongside new information of the immune infiltrate in these tumors. We will evaluate the association and relationship between several immune variables and the available biological and clinical characteristics of these breast cancers.
192.Eunjung Alice LeeChildren's Hospital BostonUnited States2018-07-232019-07-22
More than half of the human genome originated from transposable elements (TEs), also called ‘jumping genes,’ that copy their sequence into other chromosomal locations. Most TEs lost their jumping ability, but some retain this ability and generate heterogeneous genomic configurations in humans. Abnormal TE insertions are known to cause diseases such as hemophilia. Several studies including our own studies have also shown that TEs are desuppresed and generate extensive new insertions in some human cancers. However, whether they play an active role in tumor development remains unknown. We will address this question by analyzing a large set of cancer genome data from the ICGC using Tea (transposable element analyzer), a tool we have developed.
193.Lorenzo PasqualiInstitut de Recerca Germans Trias i PujolSpain2018-07-242019-07-23
Pancreatic neuroendocrine tumors (PNETs) are endocrine tumors arising in the pancreas. The major molecular mechanisms underlying PNETs have not been yet elucidated and research evidence points to the involvement of both genetic and epigenetic mechanisms. We now aim to integrate epigenetic markers and gene expression with PNETs genetics. Access to ICGC data will allow us to relate epigenetic changes to DNA modifications. Such analyses will help us to unmask molecular pathways that drive the formation of PNETs. The acquisition of deeper knowledge of genetic and epigenetic mechanisms underlying cancer development and progression can set the base for a new diagnostic classification and medical therapy for pancreatic neuroendocrine tumors.
194.Jan KorbelEuropean Molecular Biology LaboratoryGermany2018-07-252019-07-24
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
195.Angela BrooksUniversity of California Santa CruzUnited States2018-07-252019-07-24
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
196.Amnon KorenCornell UniversityUnited States2018-07-262019-07-25
The DNA sequences of tumors hold information with regards to specific genetic alteration that are present in any given tumor. In addition, tumor DNA sequences contain information with regards to their functional state. For instance, processes that affect the stability of DNA can be observed in tumor DNA sequences because they influence the relative abundance of sequences along the tumor's chromosomes. These processes represent underlying tumor biology; however, they have not been systematically studied and are not well understood. In this project we will study DNA sequence abundance along tumor genome sequences from ICGC and link them to various underlying biological processes in different cancer types.
197.Hieu NimAustralian Regenerative Medicine InstituteAustralia2018-07-262019-07-25
This project seeks to employ data-driven analysis techniques to reliably stratify between low-risk and aggressive tumours. Utilising our own laboratory data and next-generation sequencing data from ICGC, the project will apply bioinformatics techniques to search for the missing genetic links between a breast cancer mutation and poor prognosis in prostate cancer.
198.Dimitris AnastassiouColumbia UniversityUnited States2018-07-262019-07-25
The goal of this research project is to identify cancer-associated genomic components present in multiple cancer types by computational mining of the ICGC data sets. We recently identified multiple sets of genomic features that act in nearly identical patterns across multiple cancer types, using a computational method that we developed, called the "attractor" method. We also proved that these features are prognostic of the outcomes of patients with breast cancer. The method is designed to point to the core of those genomic features, thus shedding light on the underlying biological mechanisms. We plan to analyze the ICGC cancer data sets using the attractor method to identify new cancer-associated features and improve the accuracy of existing ones, which will potentially improve the accuracy of diagnosis and prognosis in cancer.
199.Gane WongUniversity of AlbertaCanada2018-07-262019-07-25
We are interested in the molecular mechanisms by which cancer cells evade chemotherapy and eventually become resistant to treatment. Our work on breast cancer patients has identified a novel mechanism whereby tumours exposed to chemotherapy acquire resistance by mutating the genes targeted by chemotherapy (i.e. the tubulin gene family) in many different ways that all produce much the same effect on the 3-dimensional shape of the proteins encoded by these genes. We want to confirm this result in other cancers, e.g. ovarian, through access to the tutor sequences available from the ICGC website.
200.Ivo GutCentro Nacional de Analisis Genomico (CNAG)Spain2018-07-262019-07-25
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
201.Ryan MorinBC Cancer, Part Of The Provincial Health Services AuthorityCanada2018-07-272019-07-26
This study aims to search through the massive amounts of ICGC data to find the mutations that are most important to cancer cells. These mutations can activate cancer-promoting genes known as oncogenes or inactivate other important genes known as tumour suppressor genes. Such genetic changes are what allow cancer cells to survive. Discovering the important changes of individual cancer types may allow us to develop new drugs that can effectively eliminate cancer cells with less toxicity than classic cancer treatments. These changes can also provide the basis of sophisticated clinical tests that can help us better personalize/individualize cancer therapy thus ultimately improving patient outcomes.
202.Jinhua WangUniversity of MinnesotaUnited States2018-07-272019-07-26
Understanding the functional impact of DNA mutations will help the research community to find better treatment for cancer. Mutations deposited in the ICGC controlled dataset will be used to help us build better computational predictive models for functional annotation of DNA changes in cancer patients.
203.Joshua SchiffmanHuntsman Cancer Institute, University of UtahUnited States2018-07-302019-07-29
Ewing sarcoma is a tumor that has relatively few mutations. It is likely, then, that the development of Ewing sarcoma is driven by other mechanisms. One such mechanism is through duplications or deletions of segments of chromosomes in cancer cells. We will use the ICGC Controlled Data from Ewing sarcoma tumors to determine which segments of chromosomes in Ewing sarcoma have been duplicated or deleted. We will combine that data with clinical outcome data, to determine which of these duplications or deletions are the greatest impact on patient survival. We hope to understand these mechanisms so that better treatments can be developed.
204.Kai YeSchool of the Electronic and Information Engineering, Xi’an Jiaotong UniversityChina2018-07-312019-07-30
The identification of DNA mutations causing various forms of cancer would shed light on disease progression and provide tremendous help in identifying personalized treatment strategies. In this study, we will develop a new methodology to discover various mutations in highly repetitive regions of the genome. This, along with other available tools on the market would provide us with a comprehensive catalog of DNA mutations. The ICGC controlled data includes numerous tumor DNA sequences in both protein coding and non-coding regions. We will apply our new methodology to all ICGC data and search for novel DNA mutations that are potentially vital for disease diagnose or treatment selection.
205.Lutz KrauseThe University of Queensland Diamantina InstituteAustralia2018-07-312019-07-30
Our aim is to investigate genetic variants that predispose to cancer as well as variants that are associated with cancer progression and patient outcome. Therefore, we will use the ICGA data to search for genetic variants that are enriched in cancer cohorts compared to control cohorts at selected locations in the genome. Our second aim will integrate ICGC data from cancer cohorts to identify changes on DNA and gene expression level that are associated with patient survival.
206.Anita GrigoriadisKing's College LondonUnited Kingdom2018-08-022019-08-01
The cancer genome is characterised by a variety of alterations that accumulated over time. We and others have shown that specific changes in the cancer genome can provide read-outs of defects in the DNA repair mechanisms. Moreover, these alterations can also be informative for the prediction of treatment response. We would like to use the ICGC data to perform a comprehensive analysis on cancer genomes of their alterations. This will further refine our methods and provide a deeper knowledge of their prevalence and potentially their formation. Ultimately we will then test these alterations in cancer genomes of clinical trial data available to us.
207.Francesca DemichelisCentre for Integrative Biology (CIBIO) - Universita degli studi di Trento - ItalyItaly2018-08-022019-07-13
Human cancer is a widely spread disease often characterized by the disruption of multiple genes that often become the driving force of tumor cells. The aim of this research project is to identify new potential drug targets based on disrupted genes. Specifically, the search for drug targets is based on a cell's property, named synthetic lethality, where the concomitant disruption of two genes is fatal for the cell. In practice, when such targets are identified, the corresponding treatment is selectively effective only on tumor cells. In order to enhance the chances of nominating these drug targets, we implement a search using mathematical methods to learn from the genomes of thousands of patients’ cancer cells and experimentally validate the most promising findings. The data generated within the ICGC project will be used to select pairs of likely synthetic lethal genes. Funded by the European Research Council (ERC-CoG-2014).
208.Josep M LlovetInstitut D'Investigacions Biomediques August Pi i Sunyer (IDIBAPS)Spain2018-08-022019-07-25
Hepatocellular carcinoma (HCC) is the most common liver cancer, with an estimated 750,000 new cases and 695,000 deaths per year, rating third in incidence and mortality in the world. Whilst incidence and mortality for other cancers are declining, this type of cancer increases. Several different liver diseases such as hepatitis contribute to the establishment of HCC. Our aim is to identify biomarkers that identify patients at risk and the cellular mechanisms that are aberrant in these patients. These findings will help establishing better prevention strategies, and better therapies for patients. We will conduct a genomic analysis to achieve these aims. The ICGC data will be incorporated in this genomic analysis.
209.Melissa FullwoodCancer Science Institute of SingaporeSingapore2018-08-032019-08-02
Chromatin interactions are when two remote regions of the genome come into close spatial proximity and form a physical loop. Chromatin interactions have been found to play important roles in regulating the expression of genes. We have developed a computational method to predict chromatin interactions. By applying the method to datasets from ICGC controlled data and other sources, we wish to understand the different chromatin interaction landscapes in different subtypes of diseases. By associating the predicted chromatin interaction changes with differentially expressed genes that can be identified from ICGC datasets, we wish to understand how chromatin interaction changes could lead to gene regulation failures.
210.Ashok VenkitaramanUniversity Of CambridgeUnited Kingdom2018-08-032019-08-02
Cancer cells differ from normal cells because they exhibit abnormal behaviours like uncontrolled growth. Cancer cells evolve these abnormal behaviours by rapidly changing the information encoded in their genomes. The propensity for such rapid change is known as genomic instability, but how it occurs is not known. In this study, we wish to identify factors that are associated with genome instability in different types of cancer. We will study DNA and RNA sequencing information from multiple cancers, including ICGC controlled data, to attempt to identify recurrent characteristics that are associated with genomic instability. We hope through this work to better understand the development of cancer, in order to find new approaches to detect and treat the disease.
211.Kenji KabashimaKyoto UniversityJapan2018-08-032019-08-02
This study aims to investigate why angiosarcoma (AS) develops. AS is a type of soft tissue sarcoma. In cancers and sarcomas, genetic information ("genes") is damaged. These damages are called mutations. Mutations cause normal cells to become malignant. In common cancers such as lung cancers and melanomas, researchers have found specific damaged genes. Such information has been used to develop new drugs that target damaged genes. However, mutations that cause AS are not studied well because AS is a rare tumor. We are using International Cancer Genome Consortium (ICGC) data to look for new mutations of AS. We hope that our study will help better understand biology of AS and eventually lead to development of new drugs for AS.
212.Elia BiganzoliUniversity of MilanItaly2018-08-082019-08-05
Increased body mass index (BMI) has been recognized as a risk factor for developing breast cancer and has also been associated with adverse survival. Here we aim to use the ICGC Controlled Data to investigate the associations between the patient’s BMI at diagnosis and the biological characteristics of the tumor using the “560 breast cancer genomes cohort” (Nik-Zainal et al. Nature 2016). Given the importance of the immune tumor microenvironment in the context of increased BMI, we will also evaluate the association and relationship between several immune variables and the available biological, clinical and pathological characteristics of these breast cancers. The project will be conducted in collaboration with the J.C. Heuson Breast Cancer Translational Research Laboratory, Université Libre de Bruxelles, Institut Jules Bordet, Brussels.
213.Sung-Soo YoonSeoul National University HospitalSouth Korea2018-08-092019-08-08
Mutations in the FLT3 gene drive the development of cancer. Variations in single base pairs in the DNA sequence of this gene are not clearly understood. This study aims to investigate the influence of these variations using the data deposited in the ICGC and the Korean acute myeloid leukemia cohorts.
214.Pierre-Etienne JacquesUniversité de SherbrookeCanada2018-08-152019-08-14
Cancer cells rapidly accumulate mutations in their genome, which facilitates their growth and allows them to invade healthy tissues and frequently resist therapy. One of the ways in which tumors accumulate these mutations is by decreasing their ability to accurately repair DNA damage. Indeed, cancer cells frequently have inactivating mutations in proteins that help repair their DNA. These defects may be leveraged by the tumors to inhibit the still intact DNA repair pathways. We will use cancer genomic data from ICGC to identify molecular signatures that are associated with specific defects in DNA repair proteins. These signatures will allow us to determine whether a cancer has a defect in specific repair pathways and will also inform us on the potential compensatory DNA repair strategies that allow tumors to sustain their uncontrolled growth. This knowledge may guide the development of new treatment methods based on the specific genetic alterations of cancers.
215.Amanda TolandThe Ohio State UniversityUnited States2018-08-172019-08-16
The frequency of mutations in genes driving tumor growth for cancers such as breast and colon differs between ethnic and racial groups and is known to affect tumor aggressiveness and prognosis. It is well established that ethnicity and race are factors that impact cancer rates and outcomes. Some of the cancer disparities between ethnic and racial groups are due to socioeconomic and environmental risk exposures (i.e. smoking, poor diet, high stress living conditions, place of residence, access to health care), but studies also suggest that even when these are adjusted for differences remain that are likely to reflect biological diversity. This study will test whether inherited variations, that differ in frequency between racial groups, influence the rate of TP53 and PIK3CA mutations in breast cancer. We will use ICGC breast cancer datasets that have both mutation data and genetic variation data.
216.Satoru MiyanoThe Institute of Medical Science, The University of TokyoJapan2018-08-172019-08-16
Detection of DNA mutations in cancer cells from DNA sequencing data of each patient is one of the most important steps in cancer research and precision medicine. We are developing a mathematical method that is able to use multiple information sources helpful for finding out such mutations accurately by combining different types of statistical models. Using the ICGC Controlled Data, we will test and improve the ability of the method.
217.Salvatore PiscuoglioUniversity of BaselSwitzerland2018-08-172019-08-16
Long non-coding RNA (lncRNA) is a type of RNA that has not be subjected to the same extent of attention and research compared to RNA from protein-coding genes. While protein-coding genes account for <2% of the human genome, a substantial proportion of the remaining genome leads to the production of lncRNAs, which are involved in many important cellular mechanisms that might lead to cancer. Compared to protein-coding genes, research into their role in cancer development is still at its infancy. Here we propose to leverage the extensive data generated by the LIRI-JP project, accessed through ICGC Controlled Data, to improve our understand of the role lncRNAs play in cancer development.
218.Sam MbulaiteyeNational Institutes of HealthUnited States2018-08-172019-08-16
Burkitt lymphoma (BL) is a cancer of immune cells that occurs as three forms: sporadic (rare), endemic (common), and immunodeficiency-associated (epidemic) BL. Discovered 50 years ago, BL became a good example to learn how changes in DNA and some infections may cause cancer. Recent discoveries of DNA changes in BL tumors have rekindled interest in heritable DNA changes that affect the chance of getting BL. We are requesting access to sequence information obtained from active DNA of normal samples from cases in Germany (n=33) studied under the ICGC protocol. We will add this information to that from 124 single or familial BL cases studied at NCI (65 from US and 22 from Guatemala; 28 single endemic cases from Tanzania 6 familial endemic cases from Uganda and Tanzania). Mathematical analysis of DNA sequences will be performed to identify changes that may explain why some may get BL and others not.  
219.Kui WuBeijing Genomics Institute-ShenZhenChina2018-08-202019-08-19
Endocrine tumours are relatively rare malignancies that are heterogeneous in terms of location, symptoms and response to treatment. This project aims to compare endocrine tumor subtypes systematically to investigate their tumor initiation mechanisms and identify genetic features that can be used in future clinical molecular diagnosis. We will combine controlled ICGC data with other data that we have collated for the analysis of pattern of mutations, structural variations and molecular subtype classification.
220.Ying XuUniversity of GeorgiaUnited States2018-08-222019-08-21
One of the complexities of studying cancer is its large variability across individuals. However, a hallmark of cancer tissue cells is that they all have lower intracellular pH and higher extracellular pH compared to normal human cells. Our research aims to systemically explore multi-level changes during cancer cell division, and gain insight into how this reversed pH may be at the root of cancer development. ICGC controlled data, a collection that provides cancer-related data from different countries, will be used to improve our current model and potentially explain cancer at a more fundamental level.
221.Ryan Van LaarGeneseq BiosciencesAustralia2018-08-282019-08-28
Despite public health efforts, the mortality rate of melanoma has steadily increased over the past 50 years. Geneseq Biosciences has developed and validated a genetic signature of melanoma that can be measured in skin tissue and/or blood. ICGC data will be used to further explore the clinical utility of our genomic signature for melanoma to increase or understanding of how the tumor changes from early to late stage disease. Our research may lead to new tools to reduce the over and under-diagnosis of melanoma, as well as providing doctors a new tool to personalise treatment options.
222.Kuei-Yang HsiaoNational Chung Hsing University, TaiwanTaiwan2018-08-302019-08-29
The development of colorectal cancer involves complexed molecular events. In the past decades, the scientists have focused on those genes producing proteins. However, the growing body of evidence indicates that ‘junk genes’ which don’t produce proteins may also play critical roles during the development of cancer. In this proposed study, we will use computational tools to analyze the ICGC Controlled Data to investigate the expression of those ‘junk genes’. Through completing this project, the potential novel therapeutic targets may be revealed and facilitate the development of new therapeutic strategy.
223.Ramon ParsonsIcahn School of Medicine at Mount SinaiUnited States2018-08-302019-08-29
The development, progression of cancer and response to drugs are known to be driven by a combination of genetic and non-genetic modifications in cancer cell chromosomes and the evolution of cells within the tumor. Using molecular and clinical data from ICGC, we are interested to investigate specific molecular patterns as well as micro-organisms composition (presence of microorganisms genomes in tumor samples) in several types of cancer; including to study the genomic landscape of triple negative breast cancer (the subtype of breast cancer less understood and druggable so far) and to decipher the p53 network and how it maintains expression of other tumor suppressor genes in colon cancers. The lab will concentrate mainly these studies on seven cancers but we will extend our studies to other cancers in specific projects to investigate if we find the same patterns.
224.Nikolaus SchultzSloan Kettering Institute for Cancer ResearchUnited States2018-08-312019-08-30
Tumor samples usually contain many gene mutations. However, typically only a small fraction of these promote tumor growth (they are often referred to as "drivers"), while the majority are neutral ("passengers"). Identifying the functional mutations (drivers) in a tumor sample remains a challenge. We have recently developed two novel methods for the identification of functional mutations, based on recurrence in large sets of cancer samples in specific individual amino acids (linear hotspots) or in amino acids with close proximity in a protein structure (3-D hotspots). We plan to apply these methods to data from the ICGC.
225.Jehoshua BruckCalifornia Institute of Technology (Caltech)United States2018-08-312019-08-30
Instabilities in repeat regions in DNA have been identified in cancer patients. Given a large amount of repeat regions in human DNA, our study aims at finding specific repeat regions which can be attributed to different kinds of cancer based on variations in the number of repeats and mutations. In this regard, we will use DNA data for cancer patients provided by ICGC by extracting tandem repeat (TR) regions from their DNA and estimating the mutation rate there. TR regions are periodic sequences in DNA. For example, ACACACAC is a TR region with “AC” being repeated. These regions constitute 3% of the human genome. The mutation rates in these regions are particularly high and their estimates can be used to differentiate between healthy and cancerous people and also to study the variation among different cancers. This study can be useful in early cancer detection at a minimal computation cost.
226.Kai RothkammUniversity Medical Center Hamburg-EppendorfGermany2018-09-052019-09-04
Human cancer frequently harbours changes in the DNA. Recent studies have demonstrated that such “mutational signatures” reflect previous genotoxic insults (e.g. smoking, sun exposure) and DNA repair deficiencies, i.e. an inability of cells to fully restore damaged DNA. When cancer patients receive chemotherapy or radiotherapy, the outcome is influenced by the capacity of tumour cells to repair DNA damage. The development of reliable methods for the detection of mutational signatures representing DNA repair deficiencies is the aim of the proposed study. These methods would aid the prediction of treatment outcome in cancer patients receiving radio- and chemotherapy. They would also enable each individual patient to receive the best possible treatment, based on the vulnerabilities of their tumour. The ICGC Controlled Data will be used to establish detection methods for different DNA repair deficiencies and determine how often such vulnerabilities affect different cancer types.
227.Mulin Jun LiTianjin medical universityChina2018-09-072019-09-06
Evidences indicate that structure variants (SVs) can affect gene expression (gene products) by disrupting Topological Associated Domains (TADs) of nuclear chromatin. To inspect whether cancer recurrent SVs could reorganize the gene expression pattern through affecting TAD configuration, we are going to develop a computational framework based on pan-cancer SV events. Using the ICGC controlled dataset, we will detect hotspot of recurrent SVs and then associate them with gene expression changes within same TAD. We believe that this methodology will facilitate the interpretation of biological function of SVs in the development of human cancers.
228.Iwei YehUniversity of California, San FranciscoUnited States2018-09-102019-09-09
Our project seeks to better characterize melanomas that arise at sun-protected sites. These melanomas have distinct features as compared to melanomas from sun-exposed skin. Melanoma occurs when a normal cell sustains damage to its DNA or genetic material which leads to abnormal function. Some of the DNA changes can be opposed by specific therapies. For this reason, analyzing the DNA of sun-protected melanomas may aid in treating patients. However, completely analyzing the DNA of a cancer is very expensive. The changes that arise in sun-protected melanomas are different that those in sun-exposed melanomas. We will use the ICGC data to determine which regions of DNA are most high yield to test in sun-protected melanomas. We will also attempt to identify additional treatable changes in these deadly tumors.
229.Daniel WilliamsonNewcastle UniversityUnited Kingdom2018-09-102019-09-09
Medulloblastoma is an aggressive brain tumor, it’s the most common brain tumor in children. We want to investigate the molecular “story” behind Medulloblastomas, the genetic code of a cell gives us clues as to how the disease works and how severe it might be. In various studies different groups of samples have been investigated using differing methods. We aim to consistently analyze many Medulloblastoma samples including data from the ICGC and elsewhere using up to date variant calling and structural variation software. We plan to use these observations to better understand which patients have more aggressive forms of the disease, different subgroups have different clinical outcomes, and patterns of mutations can indicate which subgroup a patient has. In addition to knowing the subgroup, individual mutations may also indicate disease progression. We will use our findings to develop more accurate biomarker based test to better inform treatment based on patient prognosis.
230.Paweł NiewiadomskiUniversity of WarsawPoland2018-09-102019-09-09
Medulloblastoma is the most common brain tumor in children. Genetically modified laboratory mice are commonly used to model human medulloblastoma and devise new therapies. We discovered that there are significant differences between mouse and human medulloblastoma. The goal of this project is to understand why the tumors in the two species are so different, and what implications this might have for the use of mouse models in preclinical studies of this disease. ICGC data will allow us to determine how gene expression (gene products) in medulloblastoma tumors changes as a function of mutations present in the cancer cells.
231.Dmitri PervouchineSkolkovo Institute of Science and TechnologyRussian Federation2018-09-102019-09-09
We believe that genome’s “dark matter” -- so called long non-coding RNAs (functional molecules transcribed from DNA that do not translate into proteins) -- can sometime encode short proteins. These proteins were not found before because they are too short (thus referred to as micropeptides). Using bioinformatics, we predicted a number of novel micropeptides and now would like to test their association with cancer followed by experimental validation. This project proposes a previously unexplored paradigm for cancer mechanisms, and will deliver new targets for cancer therapy.
232.Karthik RamanIndian Institute of Technology, MadrasIndia2018-09-102019-09-09
Although vast amounts of genomic data have been generated worldwide from human cells in diseases like cancer, analytic approaches used for extracting information from raw data remains in their infancy. This project aims at possible identification of previously unknown mutations and other changes that occur in the cell that drive tumour formation, to understand the progression of tumour in specific cancer subtypes and across all cancer types. To do this we require ICGC data at different levels(genomic, gene expression) and across cancer types. We aim to build an easy-to-use open source end-to-end tool for the analysis of cancer datasets.
233.Keisuke KataokaNational Cancer CenterJapan2018-09-122019-09-11
Recent scientific advances have enabled us to systematically identify tumor genomic abnormalities. However, detection of genomic abnormalities still remains challenging. Therefore, this study aims to comprehensively characterize the entire landscape of genomic abnormalities in a variety of cancers using our newly created pipelines. We would compare the characteristics of these genomic abnormalities both within and across cancer types, and we attempt to identify the critical abnormalities in tumor genomes. Clearly describing the genomic abnormalities and identifying the novel abnormalities would be of great significance for achieving Genomics-Driven Precision Medicine. To perform this research, we are planning to run our newly developed pipelines on the data deposited by ICGC.
234.Wyeth WassermanThe University of British ColumbiaCanada2018-09-132019-09-12
Rapidly-developed technologies are making it possible to routinely measure the entire DNA sequence, the genome, of cancer patients. By developing new analysis methods, we now have an opportunity to investigate beyond the 2% of the DNA that contains genes, and begin closing the discovery gap in the remaining 98% of the genome. We aim to deliver bioinformatics tools for analyzing changes in the DNA of cancer patients. Using the new tools, we will investigate how on/off switches in the DNA control the activity of genes in individual cancer patients and across different cancers, allowing us to form a picture about which changes cause illness. The tools will be developed, tested and benchmarked on different datasets of whole genomes, including cancer genomes that are part of the ICGC Controlled Data.
235.Bin ZhuNational Cancer InstituteUnited States2018-09-142019-09-13
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
236.Ivo GutCentro Nacional de Analisis Genomico (CNAG)Spain2018-09-172019-09-16
The ICGC data sets with the tumor-normal sample pairs is, to our knowledge, one of the most comprehensive datasets to assess the performance of the programs used to call non-inherited mutations (somatic mutations) to date. We would like to access the ICGC Controlled Data to download the sequenced reads and predicted variants of the chronic lymphocytic leukaemia (a type of blood cancer) and medulloblastoma (a cancerous brain tumor) control-tumor samples. These data sets are going to be used as a gold standard to benchmark all the steps of our variant calling workflows. We are interested in finding possible caveats and also ways to improve the quality of our pipelines, specially by identifying common technical patterns in false positive and false negative variants.
237.David HausslerUniversity of California Santa CruzUnited States2018-09-182019-09-17
The focal point of this project is comparing gene-level expression estimates of an individual pediatric patient's tumor to the gene-level expression estimates of thousands of pediatric and adult tumors, called the research compendium. The gene-level expression estimates for some of the ICGC controlled access datasets will be calculated and included in the research compendium. This comparison is called a pan-cancer analysis, because the individual's data is being compared to data from many different cancer types. Although pediatric cancer is rare, by comparing each case to the research compendium, similarities can be spotted. This approach could be an efficient method of determining which cancer fighting treatments developed for adult tumors might be good candidates in specific pediatric cancers.
238.Jesper AndersenBiotech Research and Innovation Centre (BRIC), University of CopenhagenDenmark2018-09-182019-09-17
Biliary tract cancer (CCA) is among one of the fastest growing cancers in the world. We have performed an analysis of the genomes of 142 samples of CCA, and detected commonly occuring genetic alterations as well as alterations unique to subsets of patients, which may have specific therapeutic implications. Applying advanced genetic methodologies together with translational research approaches, we will characterize the genetic landscape in CCA and compare to cancers in the upper gastrointestinal systme (pan-GI: liver, gallbladder, biliary tract and pancreatic). Unique and pan-GI alterations will be investigated for their implications on the causes of the malignancy. Increasing the size of our patient group (by including data from the ICGC) allows for further detailed analysis into the subsets of patients as well as novel affected groups. Furthermore, increasing our study to include patients in the upper pan-GI system allows us to understand the genetic variability and commonalities.
239.Kaja MilanowskaArdigenPoland2018-09-182019-09-18
Next-generation sequencing (analyzing DNA sequences) has become the preferred method for interrogating different types of sequence variation amongst collections of samples. Recently it has become a cost-effectiveness method to support treatment selection for patients, including patients suffering from different cancer types. Still it is a great challenge to find a precise and standardized method to select and direct patients to the treatment the most beneficial for them. We would like to retrospectively test our classification method on ICGC controlled data. The data will allow us to assess the accuracy of our method in the context of cancer. Our intention is use the data to test our method and potentially make some biological discoveries through our analysis.
240.Takashi ItoKyushu UniversityJapan2018-09-202019-09-19
All the cells in our body have a mechanism called DNA methylation, a chemical modification of DNA that controls gene expression and shuts off the activity of genes. Each cell uses DNA methylation to properly select a unique set of genes to establish its identity. However, DNA methylation improperly functions in cancer cells. For instance, cancer cells use DNA methylation to silence a group of genes that serve as a brake against cell division, thereby enabling their uncontrolled growth. In this project, we will use the ICGC data on DNA methylation to construct a database for the DNA methylation patten of various cancers and their corresponding normal tissues. The database will help researchers to understand the roles for DNA methylation in cancers. It will be also useful for the search of novel types of disease markers and drug targets.
241.Markku MiettinenNCIUnited States2018-09-202019-09-19
Angiosarcomas are very aggressive cancers originating from blood and lymphatic vessels either spontaneously or after radiation for treatment of other cancers. Since it is rare disease, we know little about its biology. Recently, we understood that many cancers happen because of alterations in genes that control different functions in the human cells, especially proliferation. Recent evidence suggests that angiosarcomas may arise from mutations that increase the vascular proliferation in a small number of patients. We want to perform a more comprehensive study on a larger scale using ICGC and other data sources to discover the underlying mechanism of angiosarcomas and identify specific targets for treatment
242.Mohamed TawhidThompson Rivers UniversityCanada2018-09-212019-09-20
One of the main reasons of death is cancer. Unfortunately, most of the cases diagnoses of cancer happen at the late stage, it appears late to heal which increases the number of people who die because of cancer. If the diagnose happens in its early stage, then there is a possibility to increase the curing of the suffered peoples. There is a desire and need to deal with cancer in its early stage. Our research aims to apply image segmentation and data mining approaches to predict and detect cancer in its early stage when the input data is in the form of images. Using ICGC datasets, we analyze various existing image processing techniques used for early detection and prediction of cancer. Also, we develop computational models via Predictive modeling in which we can analyze massive amounts of data in order to predict healthcare outcomes for individual patients.
243.Douglas HanahanEcole Polytechnique Federale de LausanneSwitzerland2018-09-212019-09-20
Pancreatic Neuroendocrine tumor (PanNET) is a rare type of Pancreatic cancer. In this project, we try to find out why some patients develop metastasis while some others have relatively benign tumors. For this sake, we study a mouse model of PanNET which represents the human disease. Our study shows that many genes are important for the tumor to be aggressive and metastatic. By inhibiting these genes, we could demonstrate that the mouse tumor will start getting smaller and eventually disappear. As a next step, we need to validate our findings with humans. For this reason, we need to have good quality human clinical data from patients. ICGC Controlled Data acquired very rich clinical data from 89 patients with PanNET. This dataset will be critically important for our research, especially in showing the applicability of our finding (mouse model) to human disease.
244.Arcadi NavarroPompeu Fabra UniversitySpain2018-09-272019-09-26
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
245.Soo-Hwang TeoCancer Research MalaysiaMalaysia2018-09-282019-09-27
Asian women have previously had a lower risk of breast cancer, but this is rapidly changing because of changing lifestyle choices. To date, although Asians have a different genetic makeup from Caucasians, this information is not used in how we screen for and treat breast cancer in Asia. We found that a genetic variant in the gene-editing protein APOBEC3B is present in 50% of Asians and 10% of Caucasians. Interestingly, this variant is associated with an increased risk to cancer, and the cancers which develop have a different immune profile. We are currently conducting a detailed analysis of Asian breast cancers. We plan to use data generated by ICGC to validate our methodology and as an external Caucasian dataset against which we can compare our results, in the hopes of providing definitive evidence on whether breast cancers in Asian women are similar to breast cancers in Caucasian women.
246.Aviad ZickHebrew University-Hadassah Medical CenterIsrael2018-10-012019-09-30
Cancer treatment can be guided by understanding which genetic mutations are present in the tumor. One type of mutation associated with cancer involves the presence of amplified numbers of certain genes. For instance, extra copies of the ERBB2 gene is a sign indicating aggressive breast tumors that can also predict which drugs are likely to produce a benefit. These mutations can be detected using standard tests, which allow us to classify different types of cancer based on their genetic characteristics. Using ICGC data from tumors with different kinds of amplifications, our goal is to create a sub-grouping of tumors based ERBB2 gene characteristics such as type, size and number. If this project identifies new correlations between genetic characteristics and clinical outcomes, it may contribute to health care by indicating the basis for new types of cancer treatment.
247.Tatsuhiro ShibataNational Cancer CenterJapan2018-10-012019-09-30
Recent studies reported that the total number of mutations was associated with clinical response to immune checkpoint inhibitors in melanoma (skin care), lung cancer and others. The non-self antigens ("from the external environment") produced by somatic mutations (acquired genetic alterations) are called neo-antigen. Therefore, the landscape of neo-antigen in individual patient is expected to contribute to the personalized immunotherapies. However, several previous studies reported more complex association between neo-antigen and anti-tumor immune responses. To better understand the biological significance of neo-antigens in immunological features of tumor, we will perform comprehensive analyses using various sequencing techniques and ICGC data. We will identify neo-antigen, and then investigate the critical factors that contribute to differences in immune system within each tumor type. Through this study, we attempt to uncover clinically useful biomarkers (indicators we use to examine biological processes) for cancer immunotherapy.
248.Sarka PospisilovaCEITEC Masaryk UniversityCzech Republic2018-10-042019-10-03
The aim of the study is to use the requested dataset to optimize our pipeline for investigating mutation patterns in chronic lymphocytic leukemia (CLL). We will apply a broad range of methods in an attempt to identify distinct CLL subgroups. We will leverage observations obtained in the requested ICGC dataset to comprehensively explore a dataset that has been generated at our institute. We plan to develop an algorithm for better classification of leukemia patients with a goal of effective personalized care.
249.Eilish MiddlehurstConcR LTD.United Kingdom2018-10-042019-10-04
Currently 50% of diagnosed of cancer patients will die of cancer, of these 50%, 90% of the deaths will be caused by treatment resistance. ConcR is developing computational methods of predicting which genetic mutations will arise in a specific patients cancer, and which therapeutics the patient will develop resistance to over the course of their disease. ConcR is developing a software based tool to enable clinicians to adapt treatments proactively, reducing the occurrence of treatment resistance and therefore mortality associated with cancer. ConcR software will allow for effective interpretation of this data and be an enabling technology for precision oncology. ConcR will be utilising the data sets on the International Genome Consortium (ICGC) platform to further develop and test our predictive capabilities. All results of the predictions made from the analysis of these data sets will be published to the ICGC.
250.Jinghui ZhangSt. Jude Children's Research HospitalUnited States2018-10-052019-07-19
Cancer chemotherapy treatments can cause changes (mutations) in DNA, which may lead cancer to become resistant to treatment. We recently found that chemotherapies used to treat childhood leukemia may cause these types of mutations, as determined by analyzing the "fingerprint" (signatures) of mutations in treated leukemia samples. However, leukemia patients receive many types of treatment, and it's unclear which chemotherapies are the culprit. Using ICGC controlled data, we will test whether the mutation fingerprints observed in childhood leukemia are also found in adult cancers, which may help narrow down which chemotherapies are causing these mutations in leukemia, and potentially determine whether the same thing also happens in adult cancers. Additionally, we identified deletions in DNA sequences upstream of adult breast cancer related genes in pediatric cancer patients. We wish to explore whether ICGC controlled data also contains such deletions. This may help us improve detection of breast cancer susceptible individuals.
251.Xose PuenteUniversity of OviedoSpain2018-10-092019-10-08
Recent advances in cancer genomics have improved our ability to identify the genomic mutations present in tumor cells. The presence of a specific mutation in a tumor can be used for diagnosis and to decide the best treatment for a particular patient, sometimes with drugs aimed at counteracting the effect that that particular mutation exerts on the cell. However, our understanding of the human genome is still limited, and it is difficult to predict what effect will have a particular mutation. In some cases, current algorithms fail to correctly predict the effect of a mutation, leading scientists to miss some important mutations. Our study will use ICGC Controlled data to integrate genomic data (genomic mutations) with functional data generated by ICGC to identify these mutations that are frequently missed by current approaches.
252.James LillardMorehouse School of MedicineUnited States2018-10-092019-10-08
The great success of cancer genomics studies is partly due to the development of rigorous data analysis pipelines created by highly qualified statistical geneticists. However, it is possible that more information can be extracted from these datasets using alternative approaches. We plan to explore the use of the ICGC controlled datasets to elucidate the importance of cell signaling molecules in cancer progression. In particular, we will apply validated statistical methodologies to gain new insights into the genetics and the molecular signaling networks of cancer. A major goal of our study is to use these findings as a way to better: 1) predict clinical outcomes of cancer patients and 2) identify patients that would benefit from more selective therapies.
253.Alona SosinskyGenomics EnglandUnited Kingdom2018-10-092019-10-08
The 100 000 Genomes Project aims to improve clinical care for cancer patients through personalised medicine. To date, thousands of patients have received reports based on analyses of their tumour genetics that could help doctors make diagnoses, pick therapies and enrol them in relevant clinical trials. Tumour genomes contain "germline" variants that are inherited from parents as well as "somatic" mutations that arise during the patient's own life. In order to uncover these variants, we are routinely collecting tumour and blood samples for each patient. We then apply computational algorithms that analyse this data and return a small subset of variants that may be driving tumour development. Blood cancers can be more challenging to analyse because they often contaminate the tissues we use as reference for the patient's normal genome. We are developing a new approach to overcome this obstacle and will test it on well-studied genomes from ICGC projects.
254.Bauke YlstraVU university medical centerNetherlands2018-10-092019-10-08
Recent research has revealed massive differences in the number and organization of chromosomes between healthy tissue and cancer cells. This has led to novel insights into how these changes occur, like the identification of chromothripsis, a process where chromosomes are shattered and stitched back together in a different order. We identified a new phenomenon where certain stretches of DNA are present in extraordinarily large numbers and called it chromorexis. This phenomenon was associated with poor prognosis, suggesting a possible role in cancer formation and progression. We will use the ICGC controlled data to identify how frequently this phenomenon occurs, what the underlying mechanism is and whether there is indeed a relationship with prognosis.
255.Mauno VihinenLund UniversitySweden2018-10-102019-10-09
The goal of all medicine is to treat individually each patient. This goal is difficult to achieve. But the facility to incorporate a patient’s genetic information into the picture provided by traditional signs, symptoms, personal and family history, imaging and other laboratory studies will for the first time allow medicine to become truly personalized. Based on the information provided by ICGC controlled data, this project aims at contributing to personalized medicine in cancers by developing novel accurate methods for stratification of cancer cases for clinical and research applications. Since cancer is unique for each patient, it is important to find subgroups of patients who can benefit of different therapeutic options available.
256.Yiwen ChenThe University of Texas MD Anderson Cancer CenterUnited States2018-10-102019-10-09
The goal of this project is to develop principle and computationally efficient algorithms that enable integrative analysis of ICGC data to elucidate the involvement of particular genes and pathways in carcinogenesis and therapeutic response. We will develop and test our algorithms using the ICGC data from all cancer types. This methodological work will advance the understanding of the genetic basis of each of the primary conditions included in the requested datasets.
257.Mamoru KatoNational Cancer CenterJapan2018-10-162019-10-15
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
258.Fredrik SwartlingUppsala UniversitySweden2018-10-162019-10-15
We are generating various models of childhood brain tumors in mice and have performed global analysis of the activity levels (expression) of genes in tumors from these models. We focus on cancer pathways that are often upregulated in the most aggressive subtypes of the most common malignant childhood brain tumor, medulloblastoma. Now we would like to compare our data with similar data obtained from human samples in ICGC Controlled Data. Comparing our samples with newly generated ICGC data of medulloblastoma would give us much better accuracy as compared to analysis performed using older published data of medulloblastoma publically available elsewhere. The goal of our studies is to generate clinically relevant models that closely resemble the tumors found in childhood brain tumor patients. The models can then be used to test new promising therapies for patients affected with these devastating diseases.
259.Robert RussellHeidelberg University, BioquantGermany2018-10-172019-09-27
In this study, we plan to characterize genetic variants from ICGC patients through Mechismo (mechismo.russelllab.org/) an approach that integrates various types of biological information into computational models that predict the functional consequences of mutations. We will initially focus our analysis on known cancer genes from the Cancer Gene Census (https://cancer.sanger.ac.uk/census), and then extend it to related genes within the same biological pathway (https://reactome.org/; in collaboration with Guanming Wu, OHSU, and Licoln Stein, OICR, groups). The ultimate goal of the analysis is to identify variants, genes and/or whole biological processes that might represent critical susceptibility factors either predisposing or driving cancer.
260.Han LiangThe University of Texas MD Anderson Cancer CenterUnited States2018-10-172019-10-16
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
261.Todd AguileraUniversity of Texas Southwestern Medical CenterUnited States2018-10-182019-10-17
Prediction and assignment of an experimental combination of cancer therapies to a group of patients well suited is a challenging and expensive task. New ways to make predictions and test clinically while being cost effective is critical. To generate hypotheses to address this problem we intended to use the available ICGC data to evaluate the genetics of cancers from patients that had differential survival. We seek to identify genetic characteristics that could be prognostic and potentially predictive of the optimal therapeutic intervention. The ICGC data will be used as a foundation for laboratory experiments on tissue in the lab and in animal models to verify computational findings from the database. We expect the outcomes of the current proposal will accelerate the development of new hypotheses that can make precision medicine based predictions that can be tested translationally in clinical trials.
262.Yiwen ChenThe University of Texas MD Anderson Cancer CenterUnited States2018-10-192019-10-18
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
263.Edwin CuppenUniversity Medical Center, Utrecht, The NetherlandsNetherlands2018-10-192019-10-18
Tumor metastasis involves the dissemination of cancer cells of primary tumors to distant organ sites. Within this research project, we will systematically compare the genetic characteristics of primary and metastatic cancer in ICGC data and search for shared and distinct mutation mechanisms that are active in individual patients. The results of this study will provide a better understanding of the factors that drive cancer formation and metastatic processes.
264.Steven JonesBC Cancer, Part Of The Provincial Health Services AuthorityCanada2018-10-192019-10-18
We have developed a classification model for predicting the correct cancer type of a patient solely using the genomic profiles of their tumour. We will be using the ICGC datasets to evaluate this model and to expand the knowledge-base of the classifiers. The classifiers can then be used to characterize rare/unknown/metastatic cancers.
265.Zhaoshi JiangGilead SciencesUnited States2018-10-192019-10-18
Hepatocellular carcinoma (HCC) is a complex cancer without effective treatment. A big reason for this is that many distinct factors, including virus infection and life style, can cause HCC. Clinically, HCC patients can be classified as subgroups based on these factors. Therefore, understanding HCC biology in subgroups may reveal novel disease mechanisms specific to certain population. This ICGC controlled data set (EGAD00001001880) contains genome-wide profiling for gene activity and detailed clinical information for 300 HCC patients. We will look into this invaluable large data set and identify genes that expressed specifically in one subgroup. Such research may help us understand disease mechanisms in HCC subgroups, and provide novel drug targets for HCC.
266.George BovaUniversity of Tampere Institute of Biomedical TechnologyFinland2018-10-222019-10-21
The University of Tampere, Finland Institute of Biomedical Technology (IBT) is a collaborator in the ongoing ICGC Prostate Cancer-UK project, whose principal aim is to define the genomic basis of prostate cancer, and to use this information to improve prevention, diagnosis, and therapy of this common disease. Prof. Bova is a PI in the prostate cancer-UK project group, and he is mainly focused on analysis of metastatic prostate cancer samples which are a critical component of the ICGC Prostate Cancer-UK project. At IBT, working together with Prof. Visakorpi and Nykter and team members, we will use a combination of the ICGC data and data generated locally to build models to support the development of a system to enable effective prevention, diagnosis, and treatment of cancer tailored to each patient and their unique characteristics.
267.Lincoln Stein Ontario Institute for Cancer ResearchCanada2018-10-262019-10-25
Cancer can result from changes in a person's genetic material (DNA). By studying genetic changes, researchers can learn what causes cancer. This will lead to new ways to prevent, detect and treat cancer. The International Cancer Genome Consortium (ICGC) was created to coordinate a large number of research projects. The ICGC will develop a comprehensive catalogue of genetic changes that occur in cancer. These will be benchmarked against other cancer types to ensure data is of the highest quality. As a contributing member of the ICGC, the Ontario Institute for Cancer Research will generate a comprehensive catalogue of genomic abnormalities found in pancreatic and prostate tumours. Our target is to collect and study 500 independent tumours of each type and their matched controls. The ICGC collaboration will allow members worldwide to advance cancer research through analysis of a large number of genomes from multiple cancer types.
268.Stephen BenzNantOmics, LLCUnited States2018-10-262019-10-25
Each gene in the genome interacts with other genes in two ways: a gene’s activity is regulated by others, and the biological activity of a gene in a cell may require the coordination of other gene’s. In tumors, mutations will aberrantly turn genes on or off, resulting in the abnormal behaviors of tumor cells. Using ICGC controlled data, we will map each tumor’s mutations onto the set of genes, and then the corresponding interactions, in an attempt to explain tumor cell’s behavior in terms of how these regulatory interactions have been modified. We will provide an overview of what sets of interactions are most often modified together, in order to better understand what biological processes must be co-regulated in cancer.
269.Gerald QuonUniversity of California, DavisUnited States2018-10-292018-12-13
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
270.Frank RosenbauerUniversity of MuensterGermany2018-10-292019-10-28
The proper function of a cell depends on the precise control of gene activation and inactivation. This process is coordinated by a large set of regulatory elements. Mutations in these regulatory elements have been implicated in the development and progression of various tumors. The ICGC datasets will be used to precisely identify these mutations across the genomes of a cohort of lymphoma patients. We aim to identify regulatory mutations that contribute to tumor development in order to find new approaches for cancer diagnosis and treatment.
271.Zemin ZhangPeking UniversityChina2018-10-292019-10-28
Cancer generation is an evolution process during which the genome of cancer cells accumulate large number of alteration. Some key functional alterations drive this process. We will analyze the ICGC data, which contains thousands of cancer samples across multiple cancer types, identify and characterize key functional alterations. These functional alterations' characterization will improve our understanding of cancer and also provide candidate drug targets for therapy.
272.Roland SchwarzMax Delbrück Center for Molecular MedicineGermany2018-10-292019-10-28
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
273.Rachel KarchinJohns Hopkins UniversityUnited States2018-10-312019-10-30
Many DNA mutations implicated in cancer causation are found in a substantial fraction of patients. However there exist a very large number of mutations that are only seen in a few patients. We are developing a computational method to predict which of these rare mutations are involved in cancer origination and progression. ICGC Controlled Data will be used as a source of mutations in cancers of multiple types.
274.Seishi OgawaKyoto UniversityJapan2018-10-312019-10-30
The dynamics of clonal evolution from non-malignant mammary epithelial cells to invasive breast cancers are poorly understood. In this study, we will analyze the genetic alterations of non-invasive breast cancers and precancerous lesions by analyzing DNA sequences. To investigate the alterations correlated with invasiveness, we would like to analyze the ICGC sequencing data of invasive breast cancer cohort and compare them to our non-invasive cohort data.
275.David WedgeUniversity of OxfordUnited Kingdom2018-10-312019-10-30
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
276.Hiroyuki ManoNational Cancer CenterJapan2018-11-012019-11-01
Cancer can result from genetic alterations (changes in a person's genetic material). Some of the genetic alterations can be good targets of drugs (therapeutic targets). Triple negative breast cancer (TNBC) is a type of breast cancer, and more likely to recur than other types of breast cancer. In this study, genetic alterations of sixteen cases of Japanese TNBC were analyzed in order to discover therapeutic targets of TNBC. We wish to access ICGC data on related types of cancer in order to compare them with this dataset. It is hoped that the comprehensive analysis of large dataset will contribute to the identification of new therapeutic targets.
277.Yu LiuPediatric Translational Medicine Institute, Shanghai Children's Medical Center, Shanghai Jiaotong University School of MedicineChina2018-11-022019-11-02
Some regions of the human genome contain code that the cell uses to produce proteins and other regions do not. Genetic variations in these "noncoding regions" could cause tumors by activating genes that are involved in cancer. We developed a computational pipeline to discover new variations of this type. We plan to use ICGC data to improve our pipeline, which should help to expand our insights into the human genome and the molecular mechanisms underlying cancer.
278.Nathan LackKoc UniversityTurkey2018-11-022019-11-01
Prostate cancer is an extremely common disease that affects an estimated one out of every seven North American men in their lifetime. It has been demonstrated in simple experimental models that the growth of prostate cancer can cause DNA damage at specific genetic locations. To test if these same mutations arise in patient tumours, this project will use ICGC controlled data to characterize the type and frequency of mutations that happen near certain genetic elements from clinical data.
279.Phillip FutrealMD Anderson Cancer CenterUnited States2018-11-022019-11-01
We use genetic profiling to determine whether mutations in cancer can predict response to various therapies and explain why some families have a lot of cancer. For those patients who are resistant to therapies, we would like to use their genetic profiles to find new alternative therapies that could work for them. We are studying multiple types of cancer and will use the ICGC datasets to confirm any observations that we see.
280.Steven JonesBritish Columbia Cancer Agency BranchCanada2018-11-032018-12-18
Cancer is a disease of DNA mutations. Not only do mutations cause cancer, but cancer cells also build up huge numbers of mutations. This is because cancer cells often lose the natural ability to self-repair DNA. However, their inability to repair DNA also makes them vulnerable to certain cancer-treating drugs. Unfortunately, when treated with these drugs, some tumours are capable of regaining DNA repair capabilities in order to continue to survive and grow. Using data from ICGC, we plan to study the consequences of impaired DNA repair on the cancer’s genome sequence. In particular, we will focus on a group of cancers which regained the ability to repair DNA. We hope to learn about the genetic features associated with this phenomenon to identify biomarkers which may help to track changes in the sensitivity of cancer cells to certain chemotherapies.
281.Stuart McIntoshThe Queen's University of BelfastUnited Kingdom2018-11-052018-12-20
Women who have a mutation in the BRCA1 gene are known to be at high risk of developing breast cancer. It’s not known whether there are any common changes (mutations) seen in the DNA within the breast cancers that develop in these women. We propose to look at publicly available complete genetic sequence data (the ICGC Controlled Data) from breast cancers in 35 women with a known mutation in the BRCA1 gene to see whether there are any mutations which occur commonly or very frequently and which are responsible for driving the formation of these tumours. This will allow us to examine unaffected breast tissue from these women to see whether the same mutations can be seen within this tissue before clinically detectable cancers start to develop.
282.Robert ClarkeGeorgetown University Medical CenterUnited States2018-11-052018-12-20
There is a subtype in breast cancer (those tumors that express receptors for the hormone estrogen), in which a patient would have high risk of experiencing a late recurrence (emergence from dormancy) decades after their initial diagnosis and treatment. Unfortunately, very little is known about the genes that determine the onset, maintenance, and emergence from dormancy. Our multidisciplinary team will apply state-of-the-art technologies to public clinical datasets, in-house clinical datasets and data from a rat mammary gland tumor model, and the controlled ICGC data to address two fundamental aspects of dormancy. We will (i) use our in house tools to accurately and robustly identify those breast cancer patients at greatest risk of experiencing a late recurrence (5 years after initial diagnosis) and (ii) identify the biological process of dormancy to help develop new treatment.
283.Jessica OkosunBarts Cancer Institute, Queen Mary University of LondonUnited Kingdom2018-11-052018-12-20
Follicular lymphoma and diffuse large B cell lymphoma are types of blood cancers that affect the immune system. The clinical behaviour of these cancers vary widely from individual to individual with patients whose cancers come back early proving the most difficult to treat. To understand this variability, it is important to determine the genetic changes that occur in these tumours. Abnormal genetic changes can occur both in the region of the cancer genome that makes proteins (coding genome) and those that regulate proteins (non-coding genome). Our study plans to use the ICGC data together with our own to understand how both of these coding and non-coding abnormalities in these two lymphomas contribute to how the cancers develop, progress, regulate the host’s immune system and ultimately affect clinical outcome.
284.Eamonn MaherUniversity Of CambridgeUnited Kingdom2018-11-052019-11-04
Renal cell carcinoma (RCC) is the most prevalent kidney cancer, responsible for >100K deaths/year and with a rising incidence in Western countries. If detected early, surgical removal of RCC can be curative but the prognosis for metastatic disease is extremely poor. Despite recent progresses in cancer genomics, RCC is one of the very few tumour types for which the new acquired knowledge has not been translated into new medicines. Whilst cancer has been mostly studied from the tumour perspective, scientific evidence is showing that inherited alterations may be crucial for the development of the disease and response to therapy. Thus, our project tackles a major challenge in renal cancer: to identify critical cancer players and to establish the basis for new therapies by exploring the inherited genome of RCC patients. ICGC Data will be a valuable source to complete our dataset and to get deeper knowledge of genome alterations.
285.Mario CaceresUniversitat Autònoma de BarcelonaSpain2018-11-062019-11-05
Inversions are one type of genetic variants that affect a large fraction of the human genome and that have been implicated in functional differences between individuals. Nevertheless, they have been poorly studied due to technical challenges in their detection, which has precluded determining their role in disease susceptibility. In addition, it has been recently shown that many inversions have appeared independently multiple times in different individuals and their effects have been largely missed by current studies. Therefore, this project aims to carry out a complete analysis of the functional effects of human inversions, including their association with different common complex diseases and other health related traits. In particular, by using the available ICGC Controlled Data corresponding to sequence information from different types of cancer, we will be able to check the role of inversions on the genetic predisposition to the disease, which can result in potential significant social benefits.
286.Edwin WangUniversity of CalgaryCanada2018-11-062019-11-05
Advanced technologies in producing genetic data from patients provide potentials in matching drugs for the treatment and prevention of cancer. This project will use genetic data from tumors available from the ICGC to develop tools that will help clinicians to (1) apply the 'right' drugs to the 'right' cancer patients and (2) detect tumors in early stages so that cancer patients could be better managed.
287.Jun QinShanghai Institutes for Biological Sciences, Chinese Academy of SciencesChina2018-11-092019-11-09
Prostate cancer (PCa) is the second most common cancer diagnosed in men. While the indolent prostate tumors may be treated easily, the patients with aggressive prostate cancer often have the worse clinical outcome with characteristics such as the rapid relapse. Our long-term objective is to elucidate the underlying mechanism driving the indolent tumors progressing into the aggressive PCa. Recent studies have found that a variation in a single nucleotide (single nucleotide polymorphisms, SNPs) that occurs at a specific position in the genome could be associated with PCa risk. In this project, using the ICGC data and patients’ clinical information, we aim to identify the PCa risk-associated SNPs which are also correlated with disease relapse. Subsequently, we are designed to determine how these SNPs affect disease progression by experimental approaches. We believe the knowledge acquired in our studies will potentially lead to the development of therapeutic approaches.
288.Dieter BeuleMax-Delbrück-Centrum für Molekulare Medizin (MDC) Berlin-BuchGermany2018-11-092019-11-08
The detection of genomic alterations in cancer is of paramount importance for the diagnosis of cancer types in the clinic. These genomic alterations can help clinicians to choose the most optimal treatment for their particular patients, and therefore improve their quality of life through treatment and even their possibility of survival. We will use ICGC protected data to evaluate which computational methodology is the most optimal to identify these genomic alterations.
289.Li DingWashington University in St. LouisUnited States2018-11-092019-11-08
Cancer results in each individual from a combination of inherited genetic susceptibility and environmental exposures. Two important goals of personalized medicine for cancer are to identify individuals at high risk for cancer due to their genetic make-up and to identify the best treatment plan based on specific mutations that are present in patients' tumors. These goals will only be realized when each individual’s inherited and tumor genetic code can be read and analyzed in the clinical setting. We will use ICGC data to assist with the development of computer tools and to conduct analyses that aim to discover genes with rare genetic variants that increase cancer risk and to understand how this variation affects genetic mutations in the tumor. This project will accelerate the overall understanding of cancer genetics and its application to human health.
290.Jason LuQIAGEN, REDWOOD CITYUnited States2018-11-092019-11-08
Cancer cells have changes in DNA sequences which play a fundamental role in maintaining cell growth and other normal functions. Identifying these genome alterations is essential for understanding the initiation of cancer, how a tumor progresses, and why a treatment is effective for some patients but not for others. Using ICGC data, we aim to develop computational methods and tools to identify genome changes specifically occurring in each patient, providing the basis for developing targeted therapy for cancer.
291.Hong QuPeking UniversityChina2018-11-122019-11-11
We will use ICGC data to help identify genetic sequences involved in cancer metastasis which may help doctors to diagnose different types of cancer or predict patient outcomes. We will also integrate our genetic findings with ICGC's clinical information, such as patient survival periods, in order to develop methods of diagnosing different types of colorectal cancer.
292.Bernard FoxProvidence Cancer Center, Earle A. Chiles Research InstituteUnited States2018-11-122019-11-12
Comparison of matched normal and cancer genomes has given insights to genomic variations which we propose to examine for correlations with protein, gene expression, and clinical data in our cancer immunotherapy research participants using both genomes for the cancers they are being treated for, and across tumor tissue type cancer genomes (using ICGC controlled data).
293.David TorrentsBarcelona Supercomputing CenterSpain2018-11-132019-11-13
This is a research project to determine the impact of how genomic analysis can be used for making oncology decisions and personalize treatments. The project aims to identify the specific genomic variants in a known location on a chromosome that can provide an increased knowledge of the genes that could be involved in the development and progression of the disease. By using dataset of tumour-normal sample pairs from chronic lymphocytic leukaemia (type of blood and bone marrow cancer) and medulloblastoma (cancerous brain tumor) (Alioto et al 2015), we will be able to calibrate the different programs by means of the comparison between their results, and the results verified in the ICGC dataset. That way will be possible to achieve the highest specificity and sensitivity for detecting the alterations that occur along the life in an individual. All this work will allow us to further our knowledge on the variants effects.
294.Sameek RoychowdhuryThe Ohio State UniversityUnited States2018-11-142018-12-14
Cancer is a complex disease with various genetic abnormalities. We would like to use these sequencing data from ICGC to study novel patterns in cancer that may facilitate a better understanding of this disease. We intend to publish finding from this study with the scientific community.
295.Daniel RenoufBC Cancer, Part Of The Provincial Health Services AuthorityCanada2018-11-152019-11-15
Pancreatic cancer is a devastating disease with very poor survival rates. Recent studies have identified a subset of pancreatic cancer patients whose tumors are more detectable by the immune system, and therefore benefit from greater survival rates. However, it remains unclear why this occurs for only a subset of pancreas tumors. Our group aims to determine why some pancreas tumors are more detectable by the immune system than others, by leveraging data from both primary and metastatic pancreas cancer patients made available by the ICGC consortium and the BC Cancer Agency, respectively. By utilizing state-of-the-art computational techniques, we hope to shed light on mechanisms accounting for the different immunological subtypes of pancreas cancer and generate an impetus for more effective treatment decisions for this debilitating disease.
296.Cigall KadochDana-Farber Cancer InstituteUnited States2018-11-162019-11-15
We study molecular machines, called chromatin remodelers, which bind to DNA and help regulate which genes are expressed in a cell. In particular, we study the mammalian BAF (SWI/SNF) complex, a large molecular machine that is made of numerous proteins. It has become apparent that many of the genes encoding proteins that make up the BAF complex are mutated in certain cancers. These mutations cause BAF complexes in cells to malfunction, leading to changes in the expression of various genes, contributing to the development of cancer. We aim to use the ICGC Controlled Data to ask if the sequences of DNA these machines bind to are also often mutated in cancer.
297.Xue-Song LiuShanghaiTech UniversityChina2018-11-162019-11-15
Cancer therapies that enhance human body`s immune response, so called immunotherapy, are transforming the treatment of cancer. However, only a fraction of patients show response to immunotherapy, and there is an unmet need for markers that will identify patients more likely to respond to immunotherapy. Our research goals are to identify novel features and patterns of DNA alteration in cancer, and use these features as marker for cancer immunotherapy response prediction. We will develop novel analysis methods to analyze ICGC controlled data, to identify novel signatures and patterns of DNA alteration. This study will help to determine which cancer patients will respond best to different types of therapy
298.Xingyi GuoVanderbilt University Medical CenterUnited States2018-11-162019-11-15
Both genetic variants and non-inherited genetic changes play important roles in the etiology of various cancer types. A large number of somatic changes have been identified in cancer genomes by the International Cancer Genome Consortium (ICGC). In addition to non-inherited genetic changes, previous large genetic studies have identified numerous common genetic locations associated with cancer risk. In this application, we propose to analyze the ICGC whole genome sequencing data to identify genetic changes and evaluate the association between inherited genetic variants and non-inherited genetic changes for all available cancer types from the ICGC.
299.Ruty ShaiSheba Medical CenterIsrael2018-11-162019-11-15
The central question addressed by the present study is the role of certain genetic regulatory factors in the prognosis of a type of cancer called medulloblastoma, which is the most common malignant brain tumor in children. Using data from the ICGC, we hope to highlight factors that can be used to predict tumor aggressiveness and that can be targeted in the treatment of medulloblastoma.
300.Duane MitchellUniversity of FloridaUnited States2018-11-192019-01-03
Immunotherapy is a fast-evolving field of science and medicine that redirects the potency of our body’s immune systems against cancer cells. One of the most challenging aspects of developing immune-based therapies is identifying features of cancer cells which are not present on healthy cells that can be targeted by the immune system. Utilizing human brain tumor genetic sequences obtained from the ICGC database, we aim to identify potential targets expressed on brain cancer cells that have the potential of being presented to and recognized by the immune system.
301.Eduardo EyrasPompeu Fabra UniversitySpain2018-11-192019-11-19
The analysis of cancer genomes has allowed the identification of targeted treatments. Still, a considerable number of cancer patients do not contain any of the known alterations described so far. These tumors are named pan-negative and these patients cannot benefit from the available therapies. It is therefore of utmost relevance to expand the catalogue of actionable alterations. Recent results have shown that alterations in pre-mRNA splicing bear major importance in the understanding of cancer. In this project we propose to investigate the role of splicing alterations in pan-negative tumors. Using ICGC controlled data, we plan to use information about genome-wide somatic mutations to identify those that fall in splicing regulatory sequences and will measure their impact in splicing using RNA sequencing data from the same samples. We hope this work will provide new insights into the mechanisms of pan-negative tumors and will help identifying novel targets of therapy.
302.Ji Wan ParkHallym UniversitySouth Korea2018-11-192019-11-18
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
303.Scott CarterDana-Farber Cancer InstituteUnited States2018-11-192019-11-18
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
304.Roel VerhaakJackson Laboratory for Genomic MedicineUnited States2018-11-202019-01-04
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project
305.Sergey NikolaevGustave RoussyFrance2018-11-202019-11-20
Basal Cell Carcinoma and melanoma are skin cancers which accumulate mutations in their genomes induced by ultraviolet light. We plan to perform genomic profiling of 100 genomes of Basal Cell Carcinoma and to use genomic profiles of ICGC as a reference data set in order to study the effect of ultraviolet light in skin cancers. Specifically would like to investigate the mechanisms of the reparation of the DNA damages induced by ultraviolet light, and to compare them between melanoma and Basal Cell Carcinoma.
306.Shakuntala BaichooUniversity of MauritiusMauritius2018-11-202019-11-20
This team of researchers aim at applying computational knowledge to the genomic study of cancers relevant to the African continent. Breast, Ovarian and Prostate cancer are very common on the African continent, including Mauritius. Unfortunately the ongoing therapies are often not adapted to the varied ethnic background of the African population, thus leading to a high rate of deaths related to cancer. This project will help build tools and expertise using worldwide cancer genomics data. This application requests approval to access ICGC controlled data in order to build a preliminary data bank for our analyses and tool development.
307.Christopher HovensThe University of MelbourneAustralia2018-11-222019-11-22
Prostate cancer is now the most commonly diagnosed cancer in the Western world but only 10% of men with it, will die from it. Our current ability to discriminate between cancers which are harmless and those that are life threatening is poor. This project will examine the genetic make up of cancer clones that are present in high risk prostate cancer and define and trace the spread of those cancers that break away from the prostate and lodge in distant sites, causing death. We utilize ICGC genomic datasets that have detailed genetic information on common cancer types that have spread around the body away from the primary organ where they first arose, this includes, prostate, breast and colorectal cancers.
308.Beifang NiuComputer Network Information CenterChina2018-11-222019-11-22
Microsatellites are repetitive DNA sequences. Microsatellite instability (MSI) is a form of mutation that occurs in some tumors due to defects in the cell's ability to repair DNA. The mutation of microsatellites will transform normal cells into malignant tumor cells and eventually lead to malignant tumors. MSI detection is significant for tumor early diagnosis and prognosis. In this project, we will use the data from the International Cancer Genome Consortium (ICGC) to develop a convenient and accurate tool to detect MSI status to assist cancer diagnosis and prognosis. Firstly, we will analyze the data from ICGC to find microsatellite sites. Then we will judge whether the sample is stable or not, basing on the stability of the satellite site we found.
309.Kazem MousavizadehIran university of medical sciencesIran2018-11-232019-11-22
Cancers are divided into two types: solid tumors and hematologic malignancies.Hematologic malignancies affect lymph and blood systems and are divided into types based on whether they are fast or slow-growing. Chronic myeloid leukemia (CML) is a kind of slow-growing hematologic malignancy that is based on blood forming cells in the bone marrow. However, it can convert to a kind of fast-growing malignancy called acute myeloid leukemia (AML) which is hard to treat. In this project we aim to find new targets for AML treatment which may help patients to survive longer and improve their quality of life. We would like to access the ICGC's recent data on CML in order to find these genetic targets more accurately.
310.Jonathan BondUniversity College DublinIreland2018-11-232019-11-23
The best way to improve treatments is to try to understand the reasons why they sometimes don’t work. This is not straightforward, as the ‘internal wiring’ of a leukemia cell is very complicated, and the leukemia cell is very good at ‘rewiring’ itself to escape being killed by the therapies we currently give. We use a scientific approach called ‘Systems Biology’ to try and better understand how this happens. This approach involves making computer models of the gene and protein networks that keep a leukemia cell alive. We wish to use ICGC controlled data to see whether some of the mutations found in leukemia cells might interact with each other. We think that this analysis will help to identify hidden ‘Achilles’ heels’ in the leukemia cells, which might help us find more precise and effective cures for children with blood cancers.
311.Trevor PughUniversity Health NetworkCanada2018-11-232019-11-22
Recent advancements in genomic approaches have given rise to a number of tumour sequencing and biobank initiatives such as the International Cancer Genome Consortium (ICGC). While gathering such data are valuable alone, there is great understanding to be gained through integration and comparison across different models and data types. Specifically, we will use ICGC data to understand the type of immune cells that recognize tumours in children. Our goal is to incorporate the data from ICGC and other international initiatives and comprehensively characterize immune features of childhood cancers that may inform outcome of treatment in children. Findings from our proposed project will lead to more effective research, treatments, and diagnostic tests for childhood cancer.
312.Antoine Van KampenAcademic Medical Center, University of AmsterdamNetherlands2018-11-252019-01-08
About 80% of all breast cancers are ‘ER-positive, which means that the cancer cells grow in response to the hormone estrogen. For these patients hormone therapy is a common treatment. Unfortunately, a large fraction of these patients become resistant to the drug and relapse. We want to understand why patients become resistant to hormone therapy. Therefore, we investigate epigenetic changes of the DNA. Epigenetics comprises a layer on top of the genetic code of the DNA that determines how the genetic blueprint is read. Using data from The Cancer Genome Atlas (TCGA) we identified specific epigenetic modifications of the DNA that may contribute to drug resistance. We aim to use the ICGC data to validate these findings.
313.Marc RemkeGerman Consortium for Translational Cancer Research (DKTK)Germany2018-11-252019-01-08
Pilocytic astrocytoma (PA) is the most common pediatric brain tumor. PAs are low-grade tumors that are typically treated with surgery. Incompletely surgically removed tumors recur frequently despite radiation and/or chemotherapy. All PA harbor genetic alterations, mutations or gene fusions, in the same signaling pathway and are normally considered a single type of tumor. Our lab is using different data types to obtain an overall understanding of the underlying biology of these tumors. We use a method called Similarity Network Fusion (SNF), which is designed to combine different data types to achieve a more accurate and stable classification of the PAs. Using this approach, we have identified subgroups with specific clinical and biological features. Our goal is to use the ICGC data and the associated clinical information as a validation sample set to confirm our findings and to help improve our knowledge of this disease.
314.Georg FuellenRostock University Medical CenterGermany2018-11-262018-12-18
The development of next generation sequencing opens new opportunities in personalized medicine such as predicting the best drug combinations with fewer side effects based on genetic data profiles of patients. We request access to ICGC data, focussing on colorectal cancer, leukemia and lymphoma, so that these can be analyzed alongside our data. This integrative approach will enhance novel ideas and will increase the size of the dataset. With its help, we are able to rank the different genetic data profiles by relevance and utility for our machine learning approach. This increases the power and robustness of predictions of our machine learning approach as well as our ability to identify additional information to be included in the profile. Also, we will use machine learning techniques that include imaging data (such as X-rays) from patients to provide an optimized classification of cancer subtypes. Here, ICGC data will be used as training data.
315.Marcela Davila LopezUniversity of GothenburgSweden2018-11-262019-11-25
We are conducting a research project towards personalized prevention and treatment of gallbladder cancer in Chile, where we plan to generate genetic information from tumor tissue and blood. In order to compare the molecular profiles of gallbladder cancer diagnosed in Chile and Japan, we apply here for access to deposited ICGC controlled data on Japanese population described in the article by Nakamura et al. (2015) “Genomic spectra of biliary tract cancer” (doi:10.1038/ng.3375).
316.Benedikt BrorsGerman Cancer Research CenterGermany2018-11-262019-11-25
Ninety percent of pancreatic cancers are driven by a mutation in a single gene called KRAS, which is difficult to address therapeutically. The remaining 10% of tumors are not characterized very well. We discovered a previously unknown driving mechanism in a subset of tumors lacking a KRAS mutation. We would like to corroborate the significance of our finding by screening the ICGC pancreatic cancer cohort for similar events to explore their prevalence.
317.Sheri HolmenHuntsman Cancer Institute, University of UtahUnited States2018-11-262019-11-26
Recently approved therapies have shown strong promise in treating advanced stages of melanoma, a form of skin cancer, but treating patients whose cancer has spread to the brain (brain metastasis) remains challenging and the prognosis for these patients is grim. The goal of our research is to use the ICGC-controlled data, in addition to other datasets, to identify prognostic genetic signatures for those patients at highest risk for the development of brain metastases. Additionally, analysis of these data will provide important insights in to the potential effectiveness of combining treatments that target common alterations in melanoma. Finally, our work using these ICGC-controlled data will enable initiation of future interventional studies that will use specific compounds that target melanoma directly, or immune system-modulating therapies, to determine whether these treatments are effective to prevent brain metastasis in those patients at highest risk.
318.Ramu AnandakrishnanEdward Via College of Osteopathic MedicineUnited States2018-11-272019-11-21
Cancer is known to result primarily from genetic defects. Yet, despite decades of research and the availability of extensive genomic data, the specific cause for individual instances of cancer can not generally be determined. One reason is that current computational methods focus on identifying individual "cancer genes", while cancer results from a combination of multiple genetic defects (multi-hit combinations). We are developing an algorithm for identifying multi-hit combinations instead of cancer genes. Information from ICGC controlled data will be used to differentiate between cancer-causing and non-cancer causing mutations. The multi-hit combinations identified by this project are likely to better explain the cause of cancer and suggest new ways to view, diagnose and treat cancers.
319.roberto puzoneIRCCS Ospedale Policlinico San MartinoItaly2018-11-272019-11-27
Most cancer are heterogeneous genetic disease in which many genes are involved, and some inherited gene mutations are already known to be associated with different therapies, cancer progression and prognosis. Our study investigates into a potential increase of the known specific risk associated with some inherited point mutations (SNV), in cancers which have acquired mutations in genes which are known to strongly promote specific cancer development (driver genes). Because these SNV are in totally different DNA positions than the driver genes no direct influence can be thought, thus the effect should involve the genes as a network (pathways). Using ICGC controlled data, we will focus on high incidence cancer such as lung, breast, ovarian, and colon cancer, to perform an investigation that will include comparison among the different cancers.
320.Paul SpellmanOregon Health and Science UniversityUnited States2018-11-272019-11-26
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
321.Benjamin Schuster-BoecklerUniversity of OxfordUnited Kingdom2018-11-292019-11-28
Changes to the genetic material that happen during our lifetime can lead to cancer. These so-called "mutations" occur spontaneously, but many different environmental influences and life-style choices can make mutations more likely. By using the ICGC controlled data, the goal of our research is to find out more about the mechanisms by which lifestyle and the environment influence the appearance of mutations, and thereby the likelihood of developing cancer.
322.Mazhar AdliUniversity of VirginiaUnited States2018-11-302019-01-14
Although a majority of cancer cases are initially sensitive to chemotherapy, patients' responses vary, and most patients eventually develop recurrence and succumb to chemoresistant diseases. Our lack of understanding of the key drivers that lead to different chemotherapy responses and resistant states has been a critical roadblock that impedes therapeutic progress in the cancer field. We aim to identify the relevant genetic factors that allow cancer cells to survive in chemotherapy, re-populate into new tumors and/or invade other tissues. These genetic factors will enable us to predict patients’ drug responses in advance and develop new combination therapies to treat cancer patients more effectively.
323.Norman LeeGeorge Washington UniversityUnited States2018-11-302019-01-14
Prostate cancer is the development of cancer in the prostate. We will study the different prostate tumor genomic variations in the Moroccan population in order to identify factors that predict recurrence following surgery, radiotherapy, and hormone therapy. Our research project seeks to identify different pathways of tumor progression, revealing the necessary genomic information to use in stratified treatment strategies and personalized follow-up. Such a study will enable the selection of the best possible treatment for each patient and avoid unnecessary interventions, pain and cost for the patients. We are developing an analysis computer tool and we will use the ICGC Controlled Data to create and validate it.
324.Altuna AkalinMax Delbrück Center for Molecular MedicineGermany2018-11-302019-01-14
Cancer is a disease of the genome. However, cancer diagnostics and research studies mostly focus on the cancer-specific alterations on the parts of the genome that encode for proteins, called "genes". This study will look at alterations on the regions that are not genes, but the regions that regulate the activity of the genes. We will identify how alterations in those regulatory regions can be involved in tumor formation and/or cancer progression for different cancer types. We will be using whole-genome sequencing, gene expression and methylation data from ICGC to measure the alterations on regulatory regions and also to measure the activity of genes and regulatory regions.
325.Audrey FuUniversity of IdahoUnited States2018-12-012019-01-15
It is a challenge to learn which gene regulates which other gene directly from genomic data. Correlation is often used as a proxy of such a causal relationship, but similar levels of correlation can arise from different underlying processes. Therefore we are developing statistical models and machine learning algorithms that infer gene regulatory networks by combining genetic data and molecular measurements, such as gene expression. Combining ICGC controlled data with data from the GTEx (health individuals) and TCGA (cancer patients) will be helpful for us to examine the effectiveness of the methods we are developing and comparing. We will apply our methods primarily to breast and ovarian cancers.
326.Ilya MazoArgentys Informatics LLCUnited States2018-12-022019-01-16
We are interested in studying the 'dark matter' of the human genome, i.e the parts of genome other than genes. More than 40% of the human genome is represented by leftovers from ancient viral-like genetic sequences (called retroelements). Most have been rendered inactive in the course of evolution, every individual still has copies of some potentially active retroelements that can reinsert or cause other genes to insert into other places, causing mutations of sorts. In several types of cancers such events have been shown to take place at much higher rates, the fact we are trying to exploit to develop novel cancer diagnostics approaches. This became possible with the advent of next generation sequencing technologies that can produce the whole human genome data, specifically from cancer patient samples. Our approach is based on novel algorithms that analyse the cancer genome data to identify and measure the active retroelements.
327.Timothy StarrUniversity of MinnesotaUnited States2018-12-042019-01-18
Ovarian cancer is the fifth leading cause of cancer related death among American women. The standard of care consists of surgery followed by treatment with chemotherapy. While over 70% of patients will initially respond to this approach, more than 60% will develop resistance and ultimately 80% of women will die of their disease. New treatments are urgently needed. Our proposal is to propagate tumor tissue from a patient in mice. We can generate multiple mice for one patient and use these different mice to test different treatments. By studying the response and molecular characteristics of the tumors in mice we hope to identify new therapies that will be more effective than the currently available treatments. To translate our findings into humans we will compare our findings with clinical and molecular data from human ovarian cancers, which is available from the ICGC.
328.Nidhi SahniMD Anderson Cancer CenterUnited States2018-12-042019-01-18
We will develop and implement an integrative computational platform to systematically analyze cancer genomes. We seek to explore key mutations on the whole genome scale that are responsible for breast cancer. The rationale is that, changes in patient genome sequences would influence cell signaling networks, affecting normal biological functions. In this aim, candidate cancer-causing mutations will be identified via integrating large-scale network data with cancer-specific functional and molecular data profiles available in ICGC. Selected candidates will be further validated by similar network analysis using breast cancer data in the public TCGA database.
329.Andrew HsiehFred Hutchinson Cancer Research CenterUnited States2018-12-042019-01-18
Genetic material in the form of DNA serves as a template at the start of a series of discrete steps that ultimately lead to the generation of proteins, the building blocks of cells, tissues, and whole organisms. Proteins perform critical cellular functions such as growth and division, and are required for the proper functioning of the cell. Although the role of mutations within DNA in prostate cancer has been well described, very little is known about how changes in protein synthesis promote cancer, despite the functional significance of this process. My work focuses on elucidating the functional role of critical regulatory regions upstream of the start site of protein synthesis in advanced prostate cancer. Currently, it is unknown how these regulatory regions contribute to cancer. The ICGC controlled dataset will be essential in helping us determine clinical relevance of these genomic regions in a larger cohort of patient samples.
330.Davide RobbianiRockefeller UniversityUnited States2018-12-042019-01-11
DNA is stored in chromosomes. Chromosomes can break, and mis-repair of such breaks can result in cancer. Importantly, why so many DNA breaks occur near genes involved in cancer is poorly understood. Identifying the sources of DNA damage near such genes is important, because it may lead to novel strategies for preventing cancer or reducing its progression. We previously discovered that two molecules of the immune system (Activation Induced cytidine Deaminase (AID) and RAG1/2) can attack DNA at dozens of genes in mouse cells, and this can lead to DNA errors associated with cancer. We will use information available from ICGC to study whether and how recombinases and deaminases are responsible for breaking the DNA of human cells, and whether this promotes human cancers originating from B lymphocytes (such as certain leukemias, lymphomas, and multiple myeloma).
331.Christoph WierlingAlacris TheranosticsGermany2018-12-062019-01-18
Cancer is a complex disease involving many different changes in the genome of the tumor cell. These differences cause tumors with the same pathological classification to respond very differently to the drugs, making therapy decisions difficult. In addition, different patients will show differences in they way they react to a drug, due to differences in their genome. In this project we would like to use the genome/transcriptome information of different tumors/patients to develop individual models, using ModCell, a modelling system available at Alacris Theranostics to predict the effect (and side effects) of individual drugs or drug combinations. This would identify groups of patients which could respond to a particular treatment, and help to improve cancer diagnosis and treatment.
332.Victor RenaultFondation Jean DAUSSET - CEPHFrance2018-12-072019-01-21
Losses/gains of genomic regions, also known as copy number variations (CNVs), are common in cancers. So far, no tool allows the visualization of CNVs in a group of samples or those with missing quantitative information (e.g. number of chromosomes). We developed aCNViewer, a visualization tool for CNVs in groups of samples, which facilitates the identification of recurrent events. Using aCNViewer on two hepatocellular carcinoma (HCC) (the most common form of liver cancer) datasets, we found the presence of recurrent CNVs with varying quantification among chromosomes. Importantly, these variations were consistent between both independent HCC cohorts, suggesting a possible biological implication. Using that observation, we hypothesize other recurrent CNV patterns may exist. Thus, we plan to identify such patterns on all cancer types in ICGC. This may ultimately lead to a better classification of some cancerous tumors.
333.Christoph PlassGerman Cancer Research CenterGermany2018-12-072019-01-21
Chronic Lymphocytic Leukemia is a type of cancer that develops from cells that become a type of white blood cell in the bone marrow. In CLL, some genes are expressed on a different level than in normal (non-leukemic) cells. Chemical changes to DNA (DNA methylation) determine whether a gene is switched on or off. To better understand the biology of the disease, we want to measure how chemical marks on DNA can differently affect the levels of genes in CLL and in normal cells. Thus, ICGC expression data from normal cells will help us to understand the difference between the cells that are affected by leukemia vs cells that are normal. This will further help us to define new ways to diagnose and treat CLL patients.
334.Benoit HedanCNRSFrance2018-12-072019-12-06
The "Canine genetics" team led by Catherine ANDRE has been working during the last 20 years on human/dog homologous genetic diseases and especially cancers. Canine cancers with similar to human subtypes are frequent in dogs but rare and not well known in human. The final objectives are to identify the genetic bases of these cancers and to propose therapeutic targets, clinical trials "first in dogs" for veterinary and human medicine benefits. We identified specific alterations in canine melanomas and sarcomas. Our specific aims are to test if these alterations are also involved in some human cancers, even in rare cases. In this context, the ICGC controlled data will be used to screen for these potential rare alterations in human cancers, but known to be important in cancer developpement based on canine cancer data.
335.Lihua Zounorthwestern universityUnited States2018-12-072019-12-06
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
336.John GordanUCSFUnited States2018-12-072019-12-07
Many human cancers arise in the context of viral infection, but the extent to which these infections persist in tumors, and/or whether ongoing viral infection within the tumor predicts tumor behavior or treatment response, is not well understood. We will apply new methods to characterize viral behavior in tumors, then use the results of this analysis to study cancer-associated mutations using ICGC controlled data, starting first in hepatocellular carcinoma, the primary tumor of the liver. This analysis is intended to help us discover new treatment targets in this challenging cancer and could later be applied to other virus-associated cancers.
337.Dafna Bar-SagiNew York University School of MedicineUnited States2018-12-072019-12-06
Pancreatic cancer is a devastating disease, with a dismal 6% 5-year survival rate and no existing treatment. The overall goal of our study is to understand how tumor cells evolve in the pancreas and to decipher how cells within and outside pancreatic tumors interact to promote and maintain tumor growth. We propose to apply informatics methods to pancreatic cancer patient level Controlled Data from the ICGC to infer how human pancreatic tumors have evolved. We will use these findings to inform creation of new pancreatic cancer cell models from normal mouse pancreatic cells. We will implant these cells into pancreata of immune-competent mice to study how these cells communicate with each other and with supporting cells in the pancreas. An improved understanding of pancreatic cancer in this context may reveal processes critical to tumor survival or immune evasion, potentially representing promising targets for therapeutic intervention.
338.Xi ChenUniversity of MiamiUnited States2018-12-092019-01-23
Triple negative breast cancer (TNBC) is a molecularly diverse disease. We have previously showed that TNBC can be separated into four distinct subtypes. We propose to use ICGC dataset to perform genomic data analysis to understand the biological differences between TNBC subtypes.
339.Sevin TurcanHeidelberg University HospitalGermany2018-12-092019-01-23
Gliomas are one of the most common types of primary brain tumors among adults. Collaborative research studies have identified genes that are commonly mutated in gliomas. However, we still do not fully understand how gliomas form, progress and become resistant to therapies. In order to understand glioma biology, we aim to utilize the genomic and epigenetic (changes that affect DNA without altering DNA sequence) data available for brain tumors, and cancers in general, to develop and apply computational methods to integrate multiple data sets. Our goal is to utilize ICGC brain tumor data to develop methods that can integrate different layers information (such as genomic and epigenetic data) that can inform our understanding of how brain tumors develop. Our hope is to identify gene targets that can ultimately be translated into an effective therapeutic approach for brain tumor patients.
340.Joakim CronaUppsala UniversitySweden2018-12-102019-01-24
The ICGC initiative has analysed the biology of a large number of cancer samples. Samples from different cancers have been compared and classified through their similarities and differences. Endocrine tumours are relatively rare malignancies that are heterogeneous in terms of location, symptoms and response to treatment. There are few validated medical therapies. The aim of this study is to execute a systematic comparison of endocrine tumours and to compare them to cancers of other origin. We want to study how endocrine tumours arise and metastasize, and characterize potential treatment targets as well as potential resistance mechanisms.
341.Claude ChelalaBarts Cancer InstituteUnited Kingdom2018-12-102019-01-18
Cancer is a complex malignancy involving alterations not only to the tumor cell but also the surrounding environment. We propose to use ICGC data from breast, ovarian, prostate, pancreatic and skin cancer to conduct comprehensive assessments of cancer samples and their “normal” counterparts. These data will be analyzed alongside those generated from in-house, and other publicly available, projects. Analyzing tumors both in isolation and with their matched tissues will enhance novel ideas and will help to rank candidate genes for experimental validation in our local cohorts and hypothesis testing. The ICGC dataset will be key to increasing the power to detect robust predictive alterations.
342.Leng HanUniversity of Texas Health Science Center at HoustonUnited States2018-12-102019-12-09
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
343.Nicola WhiffinImperial College LondonUnited Kingdom2018-12-102019-12-10
Identifying the genetic cause of a disease can be used for diagnosis, to identify at-risk family members, for pre-natal screening and to dictate clinical treatments. For many diseases, clinical genetic testing has therefore become widespread. However, for most diseases, we only find a suspected disease-causing variant in ~30% of suspected genetic cases. Most research has focused almost exclusively on regions of the genome that are known to code directly for protein, where predicting the effect of variants is relatively straightforward. This leaves about 98% of the genome un-studied. We know that much of this remaining sequence is involved in regulating the levels of proteins and that variants that perturb this regulation could be involved in disease. Here, we will identify small sub-classes of these un-studied variants, with predicted regulatory effects and will use the ICGC Controlled Data to test for their involvement in cancer.
344.Guillaume BourqueMcGill UniversityCanada2018-12-112019-01-25
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
345.Gunnar RatschEidgenoessische Technische Hochschule ZuerichSwitzerland2018-12-112019-01-25
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
346.Benjamin RaphaelPrinceton UniversityUnited States2018-12-112019-01-25
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
347.Sven NahnsenEberhard-Karls-University TübingenGermany2018-12-112019-01-25
We are working on establishing a hybrid infrastructure to enable the analysis of protected ICGC data in the cloud and integrate the results with our locally computed private data. The routine research of cancer data requires large investments in infrastructure for analysis. Despite the benefits of cloud-providers (e.g. highspeed computing), there are still technical difficulties in using these infrastructures to analyze medical data for privacy reasons. Cloud, in this context, refers here to ICGC repositories that are available through Amazon-AWS. One big issue is to ensure that individual patient data is kept private at all times. Our approach will be able to fulfill these requirements, as we can calculate important statistics on ICGC in the cloud and subsequently download and integrate the results with our locally computed data. This will have the benefit of not conflicting with regulations in a medical context while preserving the ability to access ICGC data.