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 Investigatorsort iconPrimary AffiliationCountryDate Approved for AccessValid UntilTitle of Project
1.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.
2.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.
3.Edwin WangUniversity of CalgaryCanada2017-11-092018-11-08
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.
4.Lauri AaltonenUniversity of HelsinkiFinland2018-05-142018-06-27
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.
5.Hiroyuki AburataniThe University of TokyoJapan2017-07-202018-07-19
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.
6.Hiroyuki AburataniThe University of TokyoJapan2017-07-142018-07-13
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.
7.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.
8.Mazhar AdliUniversity of VirginiaUnited States2018-01-152019-01-14
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.
9.Tim AitmanUniversity of EdinburghUnited Kingdom2017-11-282018-11-27
Cancer can arise from and result in changes to an individual's DNA. Studying these changes can give insight into the causes of cancer and help to identify targets for cancer therapies. When sequencing DNA from cancer tissue, contaminating DNA from microorganisms (e.g. bacteria) is often also sequenced. Because the DNA sequence of such microorganisms has little similarity to human DNA, this is not generally considered to be a problem in the analysis of cancer genomes as the true human sequences can be separated from the bacteria computationally. However, we have recently identified ways in which contaminating DNA can affect the detection of sequence changes in human genome data. We therefore intend to use ICGC data to identify whether contaminating DNA can lead to false detection of sequence changes and develop software to identify such errors, thus improving the quality of data and helping to improve our understanding of cancer genomes.
10.Altuna AkalinMax Delbrück Center for Molecular MedicineGermany2018-01-152019-01-14
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.
11.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.
12.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.
13.Yasmin Alam-FaruqueEagle Genomics LtdUnited Kingdom2017-08-102018-08-09
We are working on therapeutic antibody discovery and development. Access to the ICGC sequence collection will provide additional insight into the sequence profiles of target genes. These collections will also allow the gene sequences to be profiled in populations suffering from specific cancers.
14.Dimitris AnastassiouColumbia UniversityUnited States2017-07-242018-07-23
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.
15.Jesper AndersenBiotech Research and Innovation Centre (BRIC), University of CopenhagenDenmark2017-09-152018-09-14
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.
16.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.
17.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.
18.Ravshan AtaullakhanovBostonGeneUnited States2018-01-052018-11-02
The major research objective of the study is to discover mechanisms of anti-tumor immune response. The study will also focus on key factors affecting various anti-cancer immunotherapies. The data from ICGC will be processed through our original bioinformatics machinery resulting in an array of advanced molecular and immunological data and will let us comprehensively analyze predictive and prognostic factors of immune response.
19.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.
20.Davide BacciuUniversità di PisaItaly2017-08-022018-08-01
The project investigates the use of artificial intelligence and machine learning methods to model cancer progression by taking into account both information on the primary tumour as well as genetic information on its mutated version that are found in the metastatic cells. ICGC Controlled Data will be used to train such machine learning models to provide personalized predictions concerning cancer progression and its response to treatment, allowing clinicians to design therapeutic plans that are more tightly personalized to the patients.
21.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.
22.Dafna Bar-SagiNew York University School of MedicineUnited States2017-11-092018-11-08
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.
23.Elan BarenholtzFlorida Atlantic University Research CorporationUnited States2017-08-252018-08-24
Deep learning uses artificial models of the brain to learn about patterns in complex data that cannot be found with traditional methods. It has recently been proven to be highly effective in diagnosing certain diseases. Here, we aim to apply this technology in order to categorize different cancer types based on unique signatures in their genetic sequence. The goal of this research is to develop software for diagnosing cancer rapidly through the use of genome sequencing. These techniques could be used to provide fast and early disease diagnosis for earlier intervention. This research will use non-U.S. cancer genomic datasets from the International Cancer Genome Consortium, combined with publicly available genomic data from non-cancerous individuals to serve as controls.
24.Murali BashyamCentre for DNA Fingerprinting and Diagnostics (CDFD), Hyderabad, INDIAIndia2018-05-172018-06-05
SWI-SNF complex is comprised of several proteins that help in the development of embryo. 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 small number of cancer patients revealed interesting observations regarding role of SWI-SNF proteins in cancer. In this proposal, 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.
25.Nizar BatadaUniversity of EdinburghUnited Kingdom2017-12-012018-11-30
Cancer is a disease caused by mutations. Mutations are often caused when the cell's DNA damage repair machinery makes mistakes. We don't quite know why these mistakes are made in healthy individuals but it is thought that excess exposure to sunlight (UV), tobacco smoke and alcohol can overwhelm the DNA repair machinery by causing too much DNA damage. For patients with tumors with dysfunctional DNA repair, alternatives to chemotherapy, such as PARP inhibitors, can be efficacious while having fewer side effects. Using the genomics data from the ICGC, we will aim to discover non-random patterns of mutations that are associated with DNA repair dysfunction. This knowledge can be used not only to understand which gene's defects cause DNA repair dysfunction but also to help make cancer therapy more tailored to the individual patient.
26.Michael BaudisUniversity of ZurichSwitzerland2017-11-062018-11-05
Cancer is a class of diseases caused by abnormal genomic variations. A small number of these alterations are believed to be crucial to transform healthy cells into malignant ones, thereby initiating cancer. Such genes altered by specific mutations are referenced to as "driver" genes in cancer development. We hypothesise that besides major drivers with a well-established functional impact, there should exist a potentially large number of "minor drivers"; each with little influence on their own, but collectively providing a critical impact on overall cancer development. Using data from ICGC, we will develop a computational method that identifies an extended list of driver genes and quantifies their contribution to cancer development. Furthermore, we will analyze patterns of causation across different cancer types. The recognition of these patterns can promote new discoveries of cancer mechanisms, and provide valuable insights to design new treatment protocols by referencing proven therapies across different cancer types.
27.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.
28.Stephen BenzNantOmics, LLCUnited States2017-11-162018-11-15
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. 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.
29.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.
30.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.
31.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.
32.Dieter BeuleMax-Delbrück-Centrum für Molekulare Medizin (MDC) Berlin-BuchGermany2017-09-282018-09-27
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.
33.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.
34.Andrew BiankinWolfson Wohl Cancer Research Institute, University of GlasgowUnited Kingdom2017-11-242018-11-23
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.Andrew BiankinWolfson Wohl Cancer Research Institute, University of GlasgowUnited Kingdom2017-11-242018-11-23
A wealth of information can be generated by comprehensive study of the genome. In order to implement personalised medicine approaches in the clinic, it is important to identify as many of the changes in the DNA sequence of cancer patients as possible. By comparing the patient’s cancer DNA to their normal DNA, it is possible to identify which changes are inherited, and may therefore impact their immediate family members, and which are not. Pancreatic genomic analyses and the data generated can be complex and difficult for researchers and, more importantly, clinicians to understand. In this project, we will use ICGC data sets to develop informatics tools to help both researchers and doctors treating cancer patients to understand and mine this complex data in order to evaluate and subsequently recommend appropriate personalised treatment for patients on an individual basis.
36.Andrew BiankinWolfson Wohl Cancer Research Institute, University of GlasgowUnited Kingdom2017-11-242018-11-23
A wealth of information can be generated by comprehensive study of the genome. This information can be complex and difficult for researchers and more importantly clinicians to interpret. Using ICGC data, we will identify somatic (non-inherited) changes in the DNA sequence of cancer patients. These changes will be separated from germ-line (inherited) changes by comparing the patient’s cancer DNA to that of their normal DNA. We will investigate the spread of all genomics changes as they occur across the cohort and the context in which they appear. We aim to be able to help doctors understand this data and what it means so that precision medicine can become a reality for pancreatic (and other) cancer patients.
37.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.
38.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.
39.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.
40.Sven BorchmannUniversity of CologneGermany2018-05-172018-06-28
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.
41.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.
42.Guillaume BourqueMcGill UniversityCanada2018-01-262019-01-25
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.
43.George BovaUniversity of Tampere Institute of Biomedical TechnologyFinland2017-10-192018-10-18
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.
44.Adrian BrackenTrinity College DublinIreland2018-05-252018-07-09
This research project is focused on the genetics of breast cancer. We know that breast cancer often runs in families, and some women inherit a risk of developing breast cancer. Landmark studies on these families led to the discovery of cancer-causing mutations in the BRCA1 and BRCA2 genes. However, mutations in these two genes are only detected in one in five women with familial breast cancer. Therefore to identify the mutations responsible in the majority of women, we sequenced the DNA of Irish patients from families with strong patterns of breast cancer, but with normal BRCA1/2 genes. We discovered several additional mutations we believe are responsible for causing cancer in these women. We now wish to search for these inherited mutations in the ICGC data, to determine their frequency in a large international sample of patients.
45.Benedikt BrorsGerman Cancer Research CenterGermany2017-11-102018-11-09
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.
46.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.
47.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.
48.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.
49.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).
50.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.
51.Claude ChelalaBarts Cancer InstituteUnited Kingdom2018-01-192019-01-18
We propose to conduct comprehensive genetic assessments of cancer samples and their “normal” counterparts. These samples, comprising tissues from breast, pancreatic and haematological malignancies, will be acquired from our local tissue banks. We request access to ICGC data from pancreatic cancer, breast cancer and lymphoma so that these can be analysed alongside our data. This integrative approach will enhance novel ideas and will help to rank candidate genes for experimental validation in our local cohorts and hypothesis testing. This is because the power to detect robust predictive gene markers will be increased, as will the ability to identify genuine alterations not evident otherwise.
52.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.
53.Yiwen ChenThe University of Texas MD Anderson Cancer CenterUnited States2018-05-142018-06-28
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.
54.Yiwen ChenThe University of Texas MD Anderson Cancer CenterUnited States2017-09-212018-09-20
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.
55.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.
56.Rong ChenMount Sinai Genomics IncUnited States2017-12-072018-12-06
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.
57.Xi ChenUniversity of MiamiUnited States2018-01-242019-01-23
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.
58.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.
59.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
60.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.
61.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.
62.Weimin CiBeijing Institute of GenomicsChina2017-08-312018-08-30
We would like to use ICGC datasets of different cancer types to create a dynamic genome database which can present information of all the gene sites for all samples of a specific cancer simultaneously. Furthermore, we intend to use the molecular and clinical datasets to discover biomarkers for carcinogenesis, drug targets and complex molecular mechanisms underlying cancer progression. The goal is to predict clinical outcomes and to guide the production of more effective targeting therapies for future generations of cancer management.
63.Olivier CinquinUniversity of California, IrvineUnited States2018-05-212018-06-28
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.
64.Robert ClarkeGeorgetown University Medical CenterUnited States2017-12-212018-12-20
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.
65.Rainer ClausKlinikum AugsburgGermany2017-08-232018-08-22
The treatment of chronic lymphocytic leukemia (CLL) patients with novel, non-chemotherapeutic oral drugs (e. g. ibrutinib) has significantly improved their outcome. However, malignant leukemia cells can survive after treatment, and deep therapy responses are usually not achieved. We have proven that CLL cells acquire alternative strategies for survival after ibrutinib therapy, e. g. the activation of an alternative cellular pathway (IGF1R pathway) that stimulates leukemia cell survival and growth. Thus, blocking these alternative pathways that might hinder the the therapeutic effect of ibrutinib could be beneficial. In this project, we wish to demonstrate that IGF1R is relevant in CLL and that therapy which blocks the IGF1R pathway is an effective novel treatment approach in CLL. ICGC data will help to understand the role of the IGF1R molecule and its regulation in different groups of CLL patients, specifically when undergoing novel oral therapies.
66.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.
67.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.
68.Joakim CronaUppsala UniversitySweden2018-01-042019-01-03
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.
69.Edwin CuppenUniversity Medical Center, Utrecht, The NetherlandsNetherlands2017-10-062018-10-06
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.
70.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.
71.Maurizio D'IncalciIRCCS Istituto di Ricerche Farmacologiche "Mario Negri"Italy2017-09-132018-07-31
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.
72.Brandi Davis-DusenberySeven Bridges GenomicsUnited States2017-09-262018-09-25
The growth of large-scale DNA sequence data for cancer research and its routine use in translational science is rapidly out-stripping the required computational capacity for storage, processing, network transmission, and analysis. The ability to access and analyze genomic data and associated clinical annotations collected from various studies is critical for accelerating research and making new discoveries. This project aims to support the continued development of the Seven Bridges Cancer Genomics Cloud as an accessible and cost-effective platform for research involving major biomedical datasets, including those hosted by the ICGC. Groups ranging in size from single laboratories to large research consortia will be able to derive value from the ICGC datasets without the need to 1) transfer these data to their local site; 2) maintain local copies of these data; or 3) support the massive compute capacity necessary to perform analyses over these data.
73.Subhajyoti DeRutgers UniversityUnited States2017-10-192018-10-19
Tumor samples acquire a large number of changes in their DNA. Whole genome sequencing is a method for reading the complete DNA sequence of normal and cancer cells. We will use the cancer genomic data from the ICGC to evaluate the accuracy of our software, which in turn can help adopt standardized, carefully evaluated approaches for both research and clinical practice.
74.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.
75.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.
76.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.
77.Scott DehmUniversity of MinnesotaUnited States2017-11-232018-11-22
The androgen receptor (AR) is a good prostate cancer drug target because experience has shown that treatment with the hormone-mimicking drugs frequently leads to a significant remission of the disease. After awhile, however, the cancer comes back because the AR in the tumor changes to be resistant to the hormone-mimicking drugs. One of the ways the cancer can come back is by the cancer cells synthesizing forms of the AR protein that lack the precise region that binds to hormone-mimicking drugs. Using prostate cancer cell lines and patient tissues, we have found that prostate cancer cells can accomplish this by altering the genomic blueprint, or instructions, required for making the AR protein. The goal of our research is to use data from ICGC to understand how frequently the AR genomic blueprints are altered in prostate cancer. We will also try to understand if these alterations are reflective of a global ability of prostate cancer cells to alter the genomic blueprints for other proteins.
78.Francesca DemichelisCentre for Integrative Biology (CIBIO) - Universita degli studi di Trento - ItalyItaly2017-07-132018-07-12
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).
79.Mark DePristoGoogle, Inc.United States2017-10-272018-10-27
Accurate identification of somatic variants is a critical aspect of cancer genome analysis. DeepVariant is a variant caller created using deep neural networks, and will be released as open source software later this year. Thus far, DeepVariant has been optimized for germline SNPs and short indels in both whole genome sequencing as well as exome sequencing data. We aim to further develop DeepVariant as a state-of-the-art somatic mutation caller, and will use DREAM challenge data as part of this effort, specifically for the purposes of model training and model evaluation.
80.Emmanouil DermitzakisUniversity of GenevaSwitzerland2017-11-292018-11-22
Somatic mutations (non-heritable changes) found in regions of the genome that do not encode for proteins have not been as extensively investigated for their role in tumorigenesis as the ones in the protein-coding genome. These somatic mutations affect the expression of genes by inducing changes in gene regulatory regions and have attracted a lot of interest due to their potential importance in cancer development. Two complementary methods have been developed in our laboratory that investigate those changes in regulatory regions between normal and tumour samples and identify potential somatic mutations that drive cancer. The goal of this project is to compare and benchmark the two methods, check the overlap between the two and investigate which method provides more biologically plausible results. Then, we will extend the analysis to discover regulatory regions potentially driving tumorigenesis in other cancer types.
81.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.
82.Harshil DhruvTranslational Genomics Research Institute (TGen)United States2018-05-132018-06-27
Cholangiocarcinoma is a rare cancer, residing within a diverse group of epithelial cancers that share the late diagnosis, poor outcome, and limited treatment options. Patients with advanced Cholangiocarcinoma have an average survival of only 1 year. Standard of care for Cholangiocarcinoma patients is radiation and cytotoxic chemotherapy; there is a lack of guidance (known targetable mutations or empiric experience) using targeted therapies. Successful completion of this project will establish proof of concept for genomics guided therapy for treatment of Cholangiocarcinoma. Additionally, cell models developed from the patient tissue as described in this project will serve as a valuable resource for discovery of novel therapies for the treatment of Cholangiocarcinoma.
83.Federica Di PalmaEarlham Institute, formerly The Genome Analysis CentreUnited Kingdom2017-08-232018-08-22
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.
84.Li DingWashington University in St. LouisUnited States2017-11-162018-11-15
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.
85.Henrik EdgrenMediSapiens LtdFinland2017-12-132018-12-12
Modern technology allows efficient sequencing of cancer samples, but the interpretation and practical use of the resulting data remains a significant challenge. In this project, we aim to develop new tools to analyze and interpret large sets of sequencing and other types of data from cancer samples. The aim is that these tools and the data they generate will enable development of new cancer treatments, as well as promote personalized medicine by helping doctors to choose the correct drugs for a patient's tumor. We aim to use the data made available by ICGC as reference data for developing these new analysis methods, as well as for validating methods we have originally developed using other data sets.
86.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.
87.Ran ElkonTel Aviv UniversityIsrael2018-04-282018-06-12
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.
88.Eduardo EyrasPompeu Fabra UniversitySpain2017-10-272018-10-17
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. 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.
89.Rebecca FitzgeraldUniversity Of CambridgeUnited Kingdom2017-09-052018-09-04
Oesophageal and junctional adenocarcinoma (OAC) are types of cancer which affect the esophagus and the area where it connects to the stomach. The groups of Rebecca Fitzgerald and Simon Tavare, in collaboration with the OCCAMS network of health centres, are collecting and performing whole genome sequencing on up to 500 selected cases of OAC. We wish to access ICGC data on related types of cancer in order to compare them with this dataset. It is hoped that our comprehensive analysis of large numbers of patient samples will contribute to the understanding of the causes of OAC.
90.Bernard FoxProvidence Cancer Center, Earle A. Chiles Research InstituteUnited States2017-12-062018-12-05
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.
91.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.
92.Audrey FuUniversity of IdahoUnited States2018-01-162019-01-15
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.
93.Georg FuellenRostock University Medical CenterGermany2017-12-192018-12-18
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.
94.Phillip FutrealMD Anderson Cancer CenterUnited States2017-10-062018-10-05
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.
95.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.
96.Pedro GalanteHospital Sirio-LibanêsBrazil2017-08-302018-08-29
The emergence of cancer accounts for a large number of changes in DNA. This project aims to identify and validate specific elements from DNA, also known as retrocopies, that influence the progression of cancer in humans. For that, we will use whole genome sequencing data of normal and cancer cells. We will use the cancer genomic data from the ICGC to search for those elements and evaluate the accuracy of our software developed for this purpose.
97.Pedro GalanteHospital Sirio-LibanêsBrazil2017-08-302018-08-29
The emergence of cancer accounts for a large number of changes in DNA. This project aims to identify and validate specific elements from DNA, also known as retrocopies, that influence the progression of cancer in humans. For that, we will use whole genome sequencing data of normal and cancer cells. We will use the cancer genomic data from the ICGC to search for those elements and evaluate the accuracy of our software developed for this purpose.
98.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.
99.Carlos GalmariniPharmamarSpain2017-08-172018-08-17
We are interested in better understanding the link between cancer genetic variations, clinical data and response to treatment in cancer patients, and this is the reason why we are requesting access to ICGC data. In our Precision Medicine project we will make use of artificial intelligence (AI) to analyze ICGC genetic and clinical data to generate hypotheses for a better understanding of the molecular causes of cancer and to predict response and delineate mechanism of resistance to treatment. Once the neural networks have been trained to pick up responsive patients, we plan to analyze data from our own clinical studies in order to forecast the response to treatment with our anticancer drugs. Through this technology we will eventually create novel ways to improve cancer patient survival and care.
100.Qinglei GaoHuazhong University of Science and TechnologyChina2017-11-202018-11-19
The resistance of cancer cells to drugs is a major barrier in cancer treatment and represents a significant unmet clinical need in ovarian cancer treatment. The results of this study using ICGC controlled data will provide a better understanding of the epigenetics (heritable changes in gene function that do not involve DNA sequence changes) factors that drive ovarian cancer resistance and will be helpful for the treatment of ovarian cancer.
101.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.
102.Rocio Garcia-CarboneroHospital 12 de OctubreSpain2017-07-312018-07-28
Neuroendocrine tumors (NETs) are a heterogeneous group of tumors with diverse biologic and clinical behaviors and unclear molecular bases. To date, several studies support the essential role of angiogenesis pathway (the physiological process through which new blood vessels form from pre-existing vessels) in the development of different tumors, including NETs. The main objective of our project is to identify and validate genetic mutations associated with clinical features of NETs patients and define a prognostic and/or treatment response signature. In our data, we have identified some relevant genetic mutations related with angiogenesis pathway and now, we need additional samples of neuroendocrine tumors to perform the validation of our results. With this purpose we request the access to controlled data of neuroendocrine tumors of the International Cancer Genome Consortium (ICGC).
103.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.
104.Gad GetzBroad InstituteUnited States2017-11-092018-11-08
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.
105.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.
106.Vassilis GolfinopoulosEuropean Organisation for the Research and Treatment of CancerBelgium2017-08-162018-08-15
DNA in any given cell has over 3 billion bases with as many as 4-5 million of these being different from person to person (i.e. genetic variants). Although some of these genetic variants are known to cause disease, many exist as ‘normal variation’ where they appear to have little or no measurable effect. Among those variants are some associated with the amount of a gene, including genes known to be important for cancer. We will use ICGC Controlled Access Data to better understand how this background variation contributes to cancer risk and treatment response.
107.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.
108.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.
109.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.
110.Anita GrigoriadisKing's College LondonUnited Kingdom2017-08-312018-08-30
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.
111.Andrey GrigorievRutgers UniversityUnited States2017-08-102018-08-10
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.
112.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.
113.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.
114.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.
115.Xingyi GuoVanderbilt University Medical CenterUnited States2017-08-232018-08-22
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.
116.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.
117.Alexander GusevDana-Farber Cancer InstituteUnited States2017-08-222018-08-22
The mechanisms of cancer are driven by inherited variants as well as non-inherited variants that occur in the cell. Though specific examples exist for both types of variation acting as a cancer driver, the genome-wide interplay between these two features remains an important area of research. The goal of this project is to investigate the impact of inherited genetic variation on patterns of non-inherited variation and cellular activity. The project is divided into three main aims. First, we will use the ICGC controlled data to identify specific inherited variants that are associated (and likely driving) cellular activity. Second, we will assess whether these variants can explain the associations identified in large-scale studies of cancer risk. Third, we will use the ICGC controlled data to assess whether cancer risk variants have a significant effect on patterns of non-inherited changes.
118.Ivo GutCentro Nacional de Analisis Genomico (CNAG)Spain2017-08-312018-08-30
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.
119.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.
120.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.
121.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.
122.Leng HanUniversity of Texas Health Science Center at HoustonUnited States2018-01-192019-01-18
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.
123.Karolin Hansen NordLund UniversitySweden2017-09-112018-09-10
The most common type of bone cancer is called osteosarcoma. This disease is difficult to cure and about one-third of the patients die from their disease, despite that fact that they receive one of the most aggressive treatment protocols in oncology. Osteosarcoma, and many other types of cancer that are difficult to cure, display extremely complex genetic aberrations. This is somewhat surprising because osteosarcoma primarily affects children and adolescents, who have not had the long life behind them thought necessary for the accumulation of massive amounts of genetic defects. We believe osteosarcoma is the perfect model for the exploration of the origin and consequences of genetic chaos in cancer. Our ultimate aim is to develop new treatment strategies for patients with osteosarcoma and other aggressive cancers. We have analysed more than 100 osteosarcomas and will use the data from ICGC to validate our findings.
124.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.
125.Olivier HarismendyUniversity of California, San DiegoUnited States2018-05-142018-06-28
We would like to use ICGC datasets of different cancer types to improve existing algorithms and develop novel tools and methods to analyze large genomic datasets. Specifically, we intend to use the molecular and clinical datasets to discover biomarkers for cancer progression, discover new drug targets and characterize complex molecular mechanisms underlying cancer pathways
126.David HausslerUniversity of California Santa CruzUnited States2017-09-072018-09-06
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.
127.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.
128.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.
129.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.
130.Yen-Yi HoUniversity of South CarolinaUnited States2017-07-112018-07-10
In this project, we will study the genes found to be altered in cancer tissues among patients. We would like to use quantitative approaches to examine whether genes in certain chemical reactions in a cell are more frequently altered than expected by chance alone. We will compare the genetic alterations between in human cancer patients and in mice with cancer. Our goal is to identify common altered chemical reactions in a cell that might contribute to the development of cancer. We will use all information about cancer patients' genome obtained from ICGC to identify important chemical reactions that are common in all cancers as well as reactions that are specific to certain cancer types.
131.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.
132.Sheri HolmenHuntsman Cancer Institute, University of UtahUnited States2017-09-272018-09-26
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.
133.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.
134.Andrew HsiehFred Hutchinson Cancer Research CenterUnited States2018-01-192019-01-18
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.
135.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.
136.Marcin ImielinskiNew York Genome CenterUnited States2018-05-062018-06-20
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.
137.Takashi ItoKyushu UniversityJapan2017-10-052018-10-04
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.
138.Pierre-Etienne JacquesUniversité de SherbrookeCanada2017-07-212018-07-20
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.
139.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.
140.Roman JaksikSilesian University of TechnologyPoland2017-07-142018-07-13
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.
141.Scott JewellVan Andel Research InstituteUnited States2017-07-192018-07-18
Almost 100% of children with Recurrent Medulloblastoma (RM) die of their disease. Detailed understanding of underlying genomic and molecular defects in RM will enable new research to develop new therapies. The primary management in RM is palliative, which leads to a lack of available biospecimens for genetic studies and subsequently a limited availability of genetic data. Therefore, it is critically important to analyze data from existing databases to study varying molecular mechanisms underlying RM. We will use ICGC data for an integrative genomic analysis to determine the total number of mutations in these RM cases and list the type of mutations in RM. This data will be used to support the development of in-vivo RM animal models to test treatment methods. We will also identify mutations in DNA damage repair (DDR) pathways to understand the development of mutations and that may be used as secondary therapeutics targets in RM.
142.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.
143.Zhaoshi JiangGilead SciencesUnited States2017-11-092018-11-08
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.
144.Peng JinEmory University School of MedicineUnited States2017-09-282018-09-27
Medulloblastoma, a tumor of the cerebellum, is the most common malignant brain tumor in children, but only few genetic alterations involved in tumor progression have been identified. During the postnatal stage, DNA modifications without change in DNA sequence lead to brain development by regulating gene expression related to neural functions. Aberrant DNA modifications in cancer, however, can contribute to abnormal gene expression favorable to tumor growth and its invasive phenotype. In this project, we aim to understand how DNA modifications affect gene expression related to Medulloblastoma progression. Therefore, we would like to conduct a meta-analysis of ICGC datasets and our own experimental data.
145.Jane JohnsonThe University of Texas Southwestern Medical CenterUnited States2017-09-182018-09-17
Malignant gliomas are among the most aggressive and lethal of all solid tumors. The proposed research program will address fundamental mechanisms involved in controlling what genes are made into protein that are required for tumor growth. Targeting these mechanisms provides an entry point for discovering critical dependencies in these cancers. We will focus on proteins that are found normally in development of the nervous system but are aberrantly present in many high grade glioma. The ICGC Controlled Data will be used to identify these regulatory genes potentially leading to improvements in diagnosis and treatment of glioma.
146.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.
147.Steven JonesBritish Columbia Cancer Agency BranchCanada2017-10-192018-10-18
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.
148.Steven JonesBritish Columbia Cancer Agency BranchCanada2017-12-192018-12-18
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.
149.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.
150.Young Seok JuKAISTSouth Korea2018-05-122018-06-26
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.
151.Kenji KabashimaKyoto UniversityJapan2017-07-202018-07-19
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.
152.Cigall KadochDana-Farber Cancer InstituteUnited States2017-12-062018-12-05
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.
153.Chandrasekhar KanduriUniversity of GothenburgSweden2017-07-312018-07-30
Medulloblastomas are heterogeneous tumors and are the most malignant brain tumors in children. Though there is progress in our understanding of reasons underlying the medulloblastoma pathogenesis (path to disease), a significant portion of medulloblastoma tumors still remain untreatable. Hence we are interested in understanding the molecular mechanism behind medulloblastoma pathogenesis and find answers to previously poorly understood questions. Our initial investigations using the available cell lines representing different medulloblastoma subgroups show promising outcomes. In order to perform clinical investigations based on our preliminary observations, we would like to access data generated by the International cancer genome consortium. This will help in our efforts to understand how medulloblastoma develops and progresses into a difficult to treat disease.
154.Mamoru KatoNational Cancer CenterJapan2017-11-092018-11-08
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.
155.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.
156.Alex KentsisMemorial Sloan Kettering Cancer CenterUnited States2017-07-182018-07-16
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.
157.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.
158.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.
159.Youngwook KimSUNGKYUNKWAN UNIVERSITYSouth Korea2018-05-102018-06-24
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.
160.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.
161.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.
162.David KlinkebielUniversity of Nebraska Medical CenterUnited States2018-05-222018-07-06
This project seeks to better understand the biology of ovarian cancer, by relating how a gene functions based on the genes DNA methylation, which is a specific chemical change that occurs on DNA. To do this, we need to obtain ICGC controlled data that has values that are a measure of how each gene is functioning within the ovarian cancer samples. The major goal of this project will be to gain a better understanding of the mechanisms that drive changes in DNA methylation, and the association between this change and specific gene function that can lead to the development of the most aggressive and deadly type of ovarian cancer, known as high grade serous ovarian cancer. This research will allow us to target specific cellular mechanisms that are drivers of this disease and develop better therapies to battle ovarian cancer.
163.Richard KocheSloan Kettering Institute for Cancer ResearchUnited States2017-08-212018-08-21
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.
164.Amnon KorenCornell UniversityUnited States2017-08-172018-08-16
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.
165.Sita KugelFred Hutchinson Cancer Research CenterUnited States2017-07-142018-07-13
Pancreatic Neuroendocrine Tumor (PNET) is a rare and slow growing tumor, which may have a prognosis of many years. However, a subset of these tumors can be more aggressive and spread, or metastasize, to the other parts of the body. Our preliminary data suggests that a protein called SIRT6 may be involved in preventing this spreading, or metastasis from happening. The ICGC Controlled Dataset will be used to examine whether SIRT6 may be altered in the more aggressive or metastatic subset of PNET tumors.
166.Nathan LackKoc UniversityTurkey2017-11-162018-11-15
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 characterize the type and frequency of mutations that happen near certain genetic elements from clinical data.
167.Thomas LaFramboiseCase Western Reserve UniversityUnited States2018-01-082019-01-07
Cancer is largely driven by mutations in DNA. However, the vast majority of mutations are "bystanders", and do not drive the disease. It is challenging to determine which mutations are important. In our project, we hope to let the tumor tell us which mutations are important by statistically assessing which mutations are repeatedly duplicated across many patient cancers. To achieve this, we require the large patient data sets that the ICGC curates.
168.Norman LeeGeorge Washington UniversityUnited States2018-01-152019-01-14
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.
169.Brian LehmannVanderbilt UniversityUnited States2017-08-102018-08-09
Triple-negative breast cancer (TNBC) is a molecularly diverse disease. Unlike other breast cancers that can be treated with targeted therapies to hormone receptors or growth factors, TNBC lacks targeted therapy and the standard of care is chemotherapy based. We have previously demonstrated that while different, TNBCs share similar molecular patterns that can be used to separate TNBC into four distinct subtypes. Within one of these subtypes we demonstrated frequent mutations in a growth factor pathway signaling gene results in sensitivity to drugs that inhibit the protein. We will use the ICGC data resource to identify and verify additional mutations, and additional drug targets in TNBC subtypes.
170.Benjamin LehnerCenter For Genomic Regulation (CRG)Spain2017-07-132018-07-12
Cancer arises from changes to genes that control the way cells grow and divide. Major certain risk factors that may contribute to cancer progression are environmental hazards and hereditary genetic mutations. Hereditary genetic mutations in which germline mutations contribute highly increased risk of cancer are observed ~ 5 to 10 % of cancers. Our project aims to integrate ICGC data with other data sets in order to help understand how much the germline mutations contribute to tumor progression and their functional roles in tumor progression (e.g., early onset of cancer). We will also investigate how much the germline mutation frequencies are different from those of healthy people (non-cancer persons) to identify cancer-associated genes.
171.Ulf LeserHumboldt Universität zu BerlinGermany2017-07-122018-07-11
The MAPTor-NET project focuses on improving common understanding of neuro-endocrinal pancreatic tumors (pNET). PNETs are a predominantly benign subtype of cancer which however, shows a rising prevalence within the population. The research project uses an integrated approach by combining mutation, gene-expression and drug response data to identify and understand the specific molecular mechanisms of pNETs. To this end, the ICGC data serves as source for relevant high quality data. The data from ICGC is valuable to the project, becuase other researchers have made their important pNET data available on the ICGC data portal which in turn will be used by MAPTor-NET. Thus, the ICGC data allows to analyze the biology of pNETs from different perspectives what eventually allows to identify sub-populations which may respond differentely to e.g. drug treatment. In summary, the ICGC data allows to improve the patient treatment on a long-term perspective.
172.Eric LetouzeINSERM - National Institute of Health and Medical ResearchFrance2017-12-012018-11-30
The genome of cancer cells is modified by different mechanisms, including modifications of single bases in the DNA sequence (mutations) or chromosomal rearrangements. Whole genome sequencing of large tumor series, as performed by the ICGC project, allows us to analyze these changes with unprecedented precision, and to better understand the molecular mechanisms at the origin of these DNA modifications. In the past years, large-scale analyses of mutations have revealed signatures of known (tobacco, UV light) and new mutational processes operative in different cancers. Our team is specialized in liver cancers. We have identified different processes generating mutations and chromosomal rearrangements in liver cancers. The current project aims at verifying if these mechanisms are restricted to the liver or also operative in other cancer types.
173.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.
174.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.
175.Yilong LiSBGD IncUnited States2017-12-132018-08-08
Cancer is driven by inappropriately increased or decreased gene activities. The data generated by the International Cancer Genome Consortium revealed genetic mutations and genetic activity levels in tumors. The goal of our project is to understand how mutations in different genes lead to cancer-causing changes in gene activity levels, particularly when a mutation in one gene causes gene activity changes in other genes. We will create mathematical models for predicting gene activity changes induced by mutations in individual genes. These mathematical models will allow us to understand how cancer-causing gene activity level patterns can be reversed by using cancer drugs to block or mutate specific genes.
176.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.
177.Han LiangThe University of Texas MD Anderson Cancer CenterUnited States2017-09-282018-09-27
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.
178.Zhen LinTulane UniversityUnited States2017-11-242018-11-23
Infectious micro-organism such as viruses (Epstein-Barr virus (EBV)) and bacteria (helicobacter pylori) can cause cancers. To further explore the infectious pathogens associated with lymphomas, we propose to analyze the requested data sets and explore associations between infectious micro-organisms and the development of cancers. We plan to further elucidate the role of infectious pathogens in the development of lymphomas by investigating both viral and cellular genetic information in primary lymphomas.
179.Charles LinBaylor College Of MedicineUnited States2018-05-202018-07-04
Chordoma is a rare kind of tumor thought to arise from remnants of the notochord, a structure found in embryos that eventually develops into the vertebrae. It has been shown that chordoma tumors are driven by high levels of a gene called brachyury. Brachyury is a “transcription factor” gene, meaning it makes a protein which binds onto DNA and controls other genes by turning them on or off. We will use ICGC data on chordoma cancers to look for mutations in other genes that may work with brachyury. We will look for mutations in genes known to physically interact with the brachyury protein, as well as mutations in brachyury binding sites located elsewhere in the genome. By answering these questions, we hope to better understand how cancer mutations that occur in chordomas may affect how brachyury turns genes on and off. This work is part of a larger collaboration with the Broad Institute Target Chordoma Initiative and the Chordoma Foundation and our ultimate hope is to develop new treatment strategies for chordoma patients.
180.Shaoping LingGenome WisdomChina2018-05-122018-06-26
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. The ICGC-TCGA DREAM organization is launching the Somatic Mutation Calling Challenge to identify the best method(s). The most accurate techniques that emerge from this activity will be made available to the research community and adopted as the standards for large scale analysis of CGHub datasets and for a whole genome pan-cancer analysis conducted by ICGC in the coming years.
181.Stefano LiseInstitute of Cancer Research, UKUnited Kingdom2017-08-032018-08-02
Cancer is a disease of the genome and reliably identifying the associated DNA mutations has important clinical implications. We have developed a computational approach to call variants in cancer sequencing data and plan to use the curated dataset available at the ICGC to benchmark it and assess its accuracy. We also plan to evaluate other state-of-the-art computational methods and establish new sets of best practices if needed.
182.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.
183.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.
184.Justo Lorenzo BermejoUniversity Hospital HeidelbergGermany2017-08-012018-08-01
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).
185.Jason LuQIAGEN, REDWOOD CITYUnited States2017-12-142018-12-13
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.
186.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.
187.Kun MaBeijing Genomics Institute-ShenZhenChina2017-11-222018-11-21
Breast cancer is the leading cause of cancer-related death in women world-wide. Although it is agreed that breast cancer is a type of complex disease caused by changes in multiple genes in patient’s DNA, the exact causation of breast cancer initiation remains unclear. We are conducting a study to uncover the causation of breast cancer initiation. It is known that DNA changes that cause breast cancer initiation vary hugely among patients. In order to understand the mechanism that causes breast cancer initiation in as many patients as possible, we would like to analyze the ICGC Controlled Data. Our work will lead to new ways to predict, detect, and treat breast cancer.
188.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.
189.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.
190.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.
191.Hiroyuki ManoThe University of TokyoJapan2017-09-282018-09-27
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.
192.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.
193.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.
194.Ilya MazoArgentys Informatics LLCUnited States2018-01-172019-01-16
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.
195.Stuart McIntoshThe Queen's University of BelfastUnited Kingdom2017-12-212018-12-20
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.
196.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.
197.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.
198.Byung Soh MinYonsei University College of MedicineSouth Korea2018-04-052018-10-01
Aminoacyl tRNA synthetases (ARS) are essential for the production of amino acids and the maintenance of human life. Recent researchers have found that ARS has other 'atypical' functions that are important in some disease processes, such as cancer. Some enzymes in ARS play an important role in cancer development and progression. So, if we understand how the 'atypical' functions of ARS work, we can treat cancer by blocking it. There are different forms of each individual enzyme we call 'isoforms.' Isoform analysis is done through a mechanism called 'alternative splicing.' ICGC controlled data can be used to analyze the importance of ARS in the isoform and biological processes of ARS and the clinical impact on solid tumors. The results of this project can provide important clues to develop new strategies for cancer treatment.
199.Duane MitchellUniversity of FloridaUnited States2018-01-042019-01-03
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.
200.Satoru MiyanoThe University of TokyoJapan2017-11-232018-11-22
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.
201.Ryan MorinBritish Columbia Cancer Agency BranchCanada2017-07-272018-07-26
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.Quaid MorrisUniversity of TorontoCanada2017-11-092018-11-08
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 patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, the types and subtypes of cancer, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
203.Sven NahnsenQuantitative Biology Center TübingenGermany2018-01-262019-01-25
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.
204.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.
205.Arcadi NavarroPompeu Fabra UniversitySpain2017-09-152018-09-14
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.
206.Sergey NikolaevUniversity of GenevaSwitzerland2018-05-142018-06-12
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.
207.Hieu NimAustralian Regenerative Medicine InstituteAustralia2017-08-102018-08-09
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.
208.Duncan OdomUniversity of Cambridge Cancer Research UK Cambridge InstituteUnited Kingdom2017-11-022018-11-01
Liver cancer is one of the most prevalent and lethal human cancers. Mouse models of cancer are instrumental in the understanding of disease and the development of new treatments for patients. This project has two aims. Firstly, we will compare similarity of chemically induced mouse liver cancers to human liver cancers. Secondly, we will use both the mouse and human cancer data to explore how the epigenome - features of the genome which affect gene expression without changing the DNA sequence – can shape the development of cancer.
209.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.
210.Jessica OkosunBarts Cancer Institute, Queen Mary University of LondonUnited Kingdom2017-12-212018-12-20
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.
211.Stephan OssowskiCenter For Genomic Regulation (CRG)Spain2017-07-132018-07-12
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.
212.Pier Paolo PandolfiBeth Israel Deaconess Medical CenterUnited States2017-08-172018-08-17
Cancer is a complex disease that is caused by mutations in DNA and manifests differently for each patient affected by this disease. Although our compendium of cancer-related mutations within proteins encoded in DNA is extensively annotated for common cancer subtypes, there is still an unmet need to understand the “non-coding,” yet functional, portion of DNA, which comprises around 70-80% of the genome. Using the controlled whole genome sequencing data from the International Cancer Genome Consortium, we seek to identify mutations within these non-coding regions that may help predict patient survival and outcome. We will then assess these so-called “biomarkers” for their potential as possible targets for novel therapies against this prevalent disease.
213.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.
214.Ji Wan ParkHallym UniversitySouth Korea2017-12-012018-11-15
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.
215.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.
216.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.
217.Lorenzo PasqualiInstitut de Recerca Germans Trias i PujolSpain2018-01-182018-08-27
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.
218.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.
219.Joshua PayneETH ZürichSwitzerland2017-09-262018-09-26
Genes are turned on and off at different times and different places. Much of this control process occurs at the level of transcription – the process by which RNA (the intermediary between DNA and protein) is produced. This is largely implemented by transcription factors, which selectively bind DNA to recruit or block the molecular machinery of transcription. Such binding often occurs in response to environmental signals. This endows the cell with an ability to sense its surroundings, and to respond accordingly. How a gene responds to the binding of transcription factors is referred to as the gene's regulatory logic. The goal of this project is to use the ICGC data to infer regulatory logic for as many genes as possible in the human genome. To do so, I will exploit the variable gene expression levels induced by genetic perturbations in cancer.
220.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.
221.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.
222.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.
223.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.
224.Paivi PeltomakiUniversity of HelsinkiFinland2017-12-052018-12-04
Lynch syndrome is the most prevalent human cancer syndrome. Individuals with Lynch syndrome are predisposed to cancers of multiple organs, but the reasons why certain organs are particularly prone to cancer development are unknown. The tendency to acquire molecular changes may differ between different organs and is also likely to be different in individuals with inherited predisposition (Lynch syndrome) compared to individuals without such predisposition (sporadic cases). To test these hypotheses, we are generating comprehensive mutational profiles for established (colorectal & ovarian) and putative Lynch syndrome carcinomas (breast) in our laboratory and will compare the results with mutation data available for corresponding sporadic carcinomas to be retrieved from ICGC. This research is expected to shed light on the mechanisms of organ-specific cancer susceptibility and identify molecular targets for tailored treatment and cancer prevention.
225.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.
226.Aurel PerrenInstitute of Pathology, University of BernSwitzerland2018-04-162018-05-30
Pancreatic Neuroendocrine Tumors (PanNETs) are tumors of cells with the capacity to produce hormones. On a molecular level they are still incompletely understood. We want to better understand how these tumors evolve, as only a subset of them will ever become malignant. Epigenetic changes (changes in secondary modifications of the DNA) seem to be important in PanNET progression. We are analysing such epigenetic changes in an international series of patients and want to examine their effect. With access to ICGC data, we will design a new RNA expression method to correlate our epigenetic results with gene expression. Finally we will identify pathways that are involved in PanNET tumor progression. This study could open the door to new therapeutic strategies and will allow for better selection of patients for specific treatments.
227.Dmitri PervouchineSkolkovo Institute of Science and TechnologyRussian Federation2017-10-182018-09-24
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.
228.Gloria PetersenMayo ClinicUnited States2017-08-242018-08-23
Pancreatic Cancer will affect almost one in a 100 persons over their lifetime, yet very little is known about the molecular alterations driving this cancer. This project aims to use the data collected by ICGC to find genomic alterations that can be used for risk evaluation, early detection, targetted drug therapies, and prognostic indications. This data may then be used to guide experimental work or patient trials.
229.RADHAKRISHNA PILLAIRajiv Gandhi Centre for BiotechnologyIndia2017-12-052018-12-04
Breast cancer is caused by genetic mutations and loss of control of normal functions at the cellular level. The disease progresses in various stages such as stage I, stage II, stage III and stage IV. These stages denote the size of the tumor, the involvement of lymph nodes and spread of the disease (TNM staging). This study aims to understand how these stages differ at the gene level. What are the pathways that are altered in breast cancer when the tumor becomes incurable or spreads to other organs? The gene expression data on Breast Cancer (Caldas group) available at ICGC will be utilized for this study. An enriched list of genes and functions that decide why a small tumor eventually grows and spreads to other parts of the body will be analyzed and findings will be published for research purposes.
230.Christoph PlassGerman Cancer Research CenterGermany2018-01-222019-01-21
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.
231.Hong QuPeking UniversityChina2017-12-112018-12-10
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.
232.Gerald QuonUniversity of California, DavisUnited States2017-12-142018-12-13
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.
233.David RaleighUniversity of California, San FranciscoUnited States2017-07-192018-07-18
More children die from brain tumors than any other type of cancer, and the most common type of brain tumor in children is medulloblastoma. Like all cancers, medulloblastoma is caused by uncontrolled cell growth. Approximately one-third of medulloblastoma cancers arise when a particular signal that tells brain cells to grow, called Hedgehog, gets stuck in the “on” position. We are interested in uncovering exactly how Hedgehog signals tell medulloblastoma cells to grow. To do so, we are investigating how the Hedgehog pathway is activated, and how Hedgehog activation regulates the expression of other signals to influence cell growth. We hope to use ICGC controlled data to understanding how Hedgehog signals cause cancer, which may show us how to turn off these signals, and potentially, lead to new therapies for medulloblastoma.
234.Karthik RamanIndian Institute of Technology, MadrasIndia2017-08-312018-08-30
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.
235.Benjamin RaphaelPrinceton UniversityUnited States2018-01-262019-01-25
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.
236.Gunnar RatschEidgenoessische Technische Hochschule ZuerichSwitzerland2018-01-262019-01-25
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.
237.Kristin ReicheFraunhofer Institute for Cell Therapy and ImmunologyGermany2017-11-232018-11-22
Prostate cancer is the most prevalent cancer disease among men in the US and patients often face unnecessary surgeries, because current classification models are of poor discrimination accuracy. We aim at a better understanding of the molecular dysregulation in prostate cancer. One layer of gene-regulation which has been undervalued for years but now emerges to be also associated with prostate cancer is gene regulation mediated by a novel class of RNA molecules, the so called long non-protein coding RNAs. They do not code for amino acid sequences, but are functional on the RNA level. We aim at integrating their expression patterns into a computational model for prostate cancer prognosis. With the support of ICGC data we will receive additional evidence of the relevance of long non-protein coding RNAs that have been associated to prostate cancer.
238.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.
239.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.
240.Marc RemkeGerman Consortium for Translational Cancer Research (DKTK)Germany2018-01-082019-01-08
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.
241.Victor RenaultFondation Jean DAUSSET - CEPHFrance2018-01-222019-01-21
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.
242.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.
243.Davide RobbianiRockefeller UniversityUnited States2018-01-122019-01-11
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 these molecules 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).
244.Steven RobertsWashington State UniversityUnited States2018-05-222018-07-05
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.
245.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.
246.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.
247.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.
248.Sameek RoychowdhuryThe Ohio State UniversityUnited States2017-12-152018-12-14
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.
249.Nidhi SahniMD Anderson Cancer CenterUnited States2018-01-192019-01-18
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.
250.Chris SanderHarvard Medical SchoolUnited States2018-04-282018-06-12
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 (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.
251.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.
252.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.
253.Nikolaus SchultzSloan Kettering Institute for Cancer ResearchUnited States2017-08-232018-08-22
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.
254.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.
255.Benjamin Schuster-BoecklerUniversity of OxfordUnited Kingdom2017-11-302018-11-29
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.
256.Roland SchwarzMax Delbrück Center for Molecular MedicineGermany2017-11-022018-11-01
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.
257.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.
258.Vladimir SeplyarskiyInstitute for Information Transmission Problems, Russian Academy of SciencesRussian Federation2017-08-172018-08-16
Many cancer samples (18%) and about half of all cancer types have a high burden of mutations attributed to erroneous activity of normal cellular enzymes participating in antiviral protection. Mutations introduced by this protein or by other sources likely serve as a fuel for cancer development and in many cases are associated with bad prognosis. We intend to understand how mutational processes change with cancer progression and find genes responsible for these changes. Data available from the ICGC will increase the power of our analyses.
259.Tatiana SerebriyskayaMoscow Institute of Physics and Technology (State University)Russian Federation2018-04-272018-06-11
Our objective of research was a small group of patients suffering from rare type of cancer. The aims of research was to build individual models of patient tumor processes based on different types of data and to elucidate mechanisms of drug resistance. We would like to use ICGC Controlled Data to check our approach to modeling of processes in patient tumors. We’ve developed our approach based on our proprietary data and need to verify it.
260.Praveen SethupathyUniversity of North Carolina at Chapel Hill - Lineberger Comprehensive Cancer CenterUnited States2017-08-302018-08-29
Liver cancer is one of the most common and deadliest types of cancer. There exist many different types of liver and liver-related cancers. However, the molecular similarities and differences between the various different liver cancers is not well understood. Our study plans on studying liver and biliary tumors by analyzing the RNA, a carrier of genetic information, from these tumors to determine what genes are expressed in each tumor. We will group liver cancers based on the expression of thousands of their genes. With this information, we can identify tumors that may have similar causes or treatments. Using ICGC data, we can gain a much deeper and broader understanding of what makes liver and liver-related cancers similar and unique. This knowledge may be able to help classify different patients to determine most effective treatment options.
261.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.
262.Ruty ShaiSheba Medical CenterIsrael2017-12-212018-12-20
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.
263.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.
264.Tatsuhiro ShibataNational Cancer CenterJapan2017-10-242018-10-23
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 that are inherited) 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 for cancer immunotherapy.
265.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.
266.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.
267.Arend SidowStanford UniversityUnited States2017-10-262018-10-25
Some people have inherited alterations in genes that increase their risk of developing particular types of cancer. For these hereditary tumors, such as those caused by mutations in the BRCA genes, little is known about how these alterations drive tumor development. However, it appears that the genomic profiles of these cancers may be fundamentally different from most of their sporadic counterparts, requiring distinct approaches to their diagnosis, prognosis, and treatment. To investigate this topic, we plan to carry out an analysis of multiple ICGC datasets from BRCA tumors and compare them with data we are currently generating ourselves. Our questions aim to reveal similarities and differences between sporadic cancers and those due to inherited predisposition.
268.Israel SilvaA. C. Camargo Cancer CenterBrazil2018-05-212018-07-05
Certain viruses are detected in cancer. One of the, 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. We 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 lead to gastric cancer.
269.Israel SilvaA. C. Camargo Cancer CenterBrazil2018-05-212018-07-05
Certain viruses are detected in cancer. One of the, 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. We 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 lead to gastric cancer.
270.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.
271.Anders SkanderupGenome Institute of Singapore, A*STARSingapore2017-11-092018-11-08
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.
272.Sigrid SkanlandUniversity of OsloNorway2017-08-162018-08-15
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.
273.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.
274.Paul SpellmanOregon Health and Science UniversityUnited States2017-12-062018-12-05
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.
275.Timothy StarrUniversity of MinnesotaUnited States2018-01-192019-01-18
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.
276.Justin StebbingImperial College LondonUnited Kingdom2018-04-222018-06-06
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.
277.Lincoln Stein Ontario Institute for Cancer ResearchCanada2017-12-212018-11-01
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.
278.Nicolas StranskyBlueprint MedicinesUnited States2017-08-102018-08-09
Blueprint Medicines is committed to developing the most effective, targeted therapies for cancer patients. A prerequisite to this objective is to better define the molecular blueprint of individual tumors. Analysis and annotation of the data comprised by the ICGC is critical so that we can tailor therapies to the molecular blueprint that underlies the growth of cancers in individual patients.
279.Shuyang SunNinth People's Hospital, Shanghai Jiao Tong University School of MedicineChina2017-11-082018-11-07
Mucosal melanoma (MM), arising from the mucosal tissue of the head and neck region, is an uncommon type of melanoma (a type of skin cancer). Unlike Cutaneous Melanoma (CM) with an established risk factor of excessive sun exposure, the cause of MM is not well elucidated. Thus, a comprehensive understanding of the molecular genetics of MM is an urgent need, which might provide clues for its prevention and treatment. Our project has gained a relatively large set of MM samples for whole genome sequencing (WGS), and we would like to get the access to the ICGC Controlled Dataset from the WGS study which contains multiple melanoma subtypes, to better understand the unique genetic underlying of MM. The results of this project can provide important clues develop novel treatment strategies for MM.
280.Mikita SuyamaKyushu UniversityJapan2018-04-272018-06-11
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.
281.Eric Sweet-CorderoUniversity of California, San FranciscoUnited States2017-11-272018-11-26
Pediatric cancers are rare diseases and thus hard to study. Most cancers are caused by alterations to the genetic material in cells. While in some cases sequencing studies can identify the genetic alterations most likely causing a particular cancer, often times the specific cause is still unclear. Our goal is to identify new alterations that may be particularly relevant to pediatric cancer. One type of alteration is called a fusion, which is when a chromosome breaks and gets “pasted” abnormally to another gene. We are searching for such alterations in pediatric cancer samples. However, fusion events in pediatric cancer are not easy to find because often times gene sequencing can result in "false positive" identification of fusions that are actually artifacts. We plan to use data from DACO to search for these fusion events and determine if they are likely to be real using a variety of computational approaches.
282.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.
283.Keiichi TamaiMiyagi Cancer Center Research InstituteJapan2018-05-192018-07-03
Cholangiocarcinoma is the most common primary malignancy of the biliary tract and one of the most difficult intra-abdominal malignancies to treat. Surgical management is the only potentially curative treatment, but it is limited to the early stage of disease. Generally, cancer stem cell are believed to be a good target for therapy, and it is recognized that the dormant status of cancer stem cells accounts for their therapy-resistance. But little is known about the maintenance of dormant cancer stem cells. The ICGC controlled data will be used for the identification of critical molecules in cancer stem cells. We will first analyze the data in cholangiocarcinoma, and extend the scope of the types of cancer.
284.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.
285.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.
286.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.
287.Soo-Hwang TeoCancer Research MalaysiaMalaysia2017-10-032018-10-03
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.
288.Giuseppe TestaEuropean Institute of OncologyItaly2017-08-082018-08-07
This project investigates the molecular basis of one of the most aggressive types of ovarian cancer, namely high grade serous ovarian cancer (HGSOC), through the combined use of: i) accurate tracing of tumors’ cell of origin; ii) close-to-physiological cellular models of normal and gynecological tumor samples; iii) the integration of multilayered molecular data generated from normal and tumor samples; iv) drug screening to identify clinically relevant molecules to be used for therapy. The integration of our datasets with those deposited in the ICGC consortium will allow us to expand our observations on a larger cohort, thus granting a more general value to our findings and allowing us to identify disease-relevant targets to be rapidly translated to the clinical setting.
289.Haluk TezcanLexent BioUnited States2017-08-022018-08-01
Currently, response to cancer treatment is assessed with imaging studies, which have certain limitations with regards to optimizing treatment management. Imaging studies usually require 2-3 months to demonstrate informative changes, using standardized criteria like RECIST 1.2, and the tumor may need to be greater or equal to 30% of original size to be conclusive. A minimally invasive blood draw that could detect the presence or absence of a tumor and assess changes in tumor burden is expected to improve treatment management. We would like to use the ICGC data to identify a “tumor signal” and optimize the signal using computational manipulations before we study the tumor signal in prospectively collected blood samples from patients undergoing treatment for their cancer. The larger ICGC data set of tumor and normal tissue whole genome sequence data is expected to improve the quality of the tumor signal.
290.Michael TolstorukovMassachusetts General HospitalUnited States2017-08-232018-08-03
Human genomic DNA is packaged inside each cell with the help of special protein molecules and other factors. This packaging is not random, but is rather orchestrated to ensure that each gene's information is read out in the proper manner and at the right time. The DNA-packaging proteins are also encoded in the genome and it was discovered that some of them are mutated in bone and brainstem tumors, including paediatric tumors. When mutated, these proteins cannot perform their functions correctly, and this contributes to cancer development. We will analyze ICGC data to discover how the read-out of the genetic information from DNA is altered in cancer cells when the mutated genome-packaging proteins are produced. Thus, we aim to understand the interplay between mutations in the genomic DNA and its proper organization in the cell. Our results will help develop new therapeutic strategies for targeting cancers associated with genome-packaging breakdowns.
291.Stefania TommasiIRCCS Istituto Tumori "Giovanni Paolo II"Italy2018-05-202018-07-03
Recent progress in cancer immunology and immunotherapy research has been truly remarkable. However, to date, there is an unmet clinical need for the identification of patients who could have a good clinical outcome. The aim of our project is the comprehensive computational study of tumor-immune cell interactions in cutaneous melanoma skin cancer, lung adenocarcinoma, squamous cell carcinoma, and diffuse large B-cell lymphoma. We will explore the microenvironment of tumors in relation to its genomic background. In detail, bioinformatic analyses of ICGC controlled data will allow us to dissect the immune infiltration deeply, both quantitatively and qualitatively, in order to summarize the immune activity against tumors. The final goal is the definition of a class of patients who could benefit from immunotherapy.
292.David TorrentsBarcelona Supercomputing CenterSpain2017-08-042018-08-04
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.
293.David TorrentsBarcelona Supercomputing CenterSpain2017-10-172018-07-26
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.
294.Didier TronoEPFLSwitzerland2018-05-232019-05-22
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.
295.Tatsuhiko TsunodaRIKENJapan2017-07-272018-07-26
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.
296.Tamir TullerTel Aviv UniversityIsrael2017-11-212018-11-20
Personalized medicine based on genomic information will become the gold standard in cancer therapeutics. There is a wealth of undiscovered and/or non-understood information in the genome, the ‘dark-matter’ of the DNA, that drives cancer evolution. Current models only consider mutations that affect the amino-acid compositions of proteins, while crucial genomic information is ignored. We will utilize ICGC controlled data and clinical data, and integrate this data with models related to the evolution of genomes at the molecular levels and various processes that take place in cancerous cells to model various additional types of cancerous mutations. In the future, these models may be used for various objectives such as diagnosis and drug design.
297.Sevin TurcanHeidelberg University HospitalGermany2018-01-242019-01-23
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.
298.Cord UphoffLeibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbHGermany2017-08-072018-08-06
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.
299.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.
300.Guy Van CampUniversity of AntwerpBelgium2017-10-232018-10-22
A commonly used therapy for patients with pancreatic neuroendocrine tumors (PNETs) is everolimus. However, in patients receiving this therapy, prolonged survival is limited to only 11 months on average. After this, the tumor no longer responds to treatment, which is called resistance. To study resistance against everolimus treatment, we created resistant tumor cells in our lab which we characterized. Now, we would like to compare our findings to ICGC controlled patient data. This will guide us in the interpretation of our data and will help us to identify tumor resistance mechanisms relevant for patients. Once these resistance mechanisms have been elucidated, it will be easier to develop strategies to avoid and overcome everolimus-resistance in patients.
301.Antoine Van KampenAcademic Medical Center, University of AmsterdamNetherlands2018-01-082019-01-08
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.
302.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.
303.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.
304.Roel VerhaakJackson Laboratory for Genomic MedicineUnited States2018-01-052019-01-04
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.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.
306.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.
307.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.
308.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.
309.Junbai WangOslo University HospitalNorway2018-05-222018-07-05
We intend to design new computational models for detecting functional gene regulation in cancer by incorporating diverse information to the model, e.g. large publicly available datasets obtained under various cell growth conditions, the cancer genome atlas (ICGC tumor/normal matched genomes from cancers), and datasets of histone modification 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, and nucleosome occupancy information.
310.xiaosong wangUniversity of PittsburghUnited States2017-08-112018-08-10
Our project seeks to employ robust integrative genomic analyses to discover cancer-driving genetic mutations or abnormality and drug targets in epithelial tumors. This approach looks for cancer-causing genes based on the evidence from different levels of genomics data, inclusive of the order of human whole DNA nucleotides, gene copy number variations (CNVs), gene product synthesis level, and physical contact of proteins. We will integrate the multidimensional genomics data from International Cancer Genome Project. We will focus our research in breast and lung cancers; priority will be given to abnormal arrangement of genes and cancer-causing genes.
311.Jiguang WangHong Kong University of Science and TechnologyHong Kong2017-10-302018-10-30
Recent progression in cancer genome projects has uncovered the common mutations and potential drug targets of many cancers, but the way cancer cells evolve with and without therapy is still unclear. One major reason for treatment failure is that different groups of cancer cells accumulate distinctive mutations, and are constantly evolving and selected by therapy. In this project, we will aim to (1) interpret the heterogeneity within the tumor (2) understand how tumors change over time and predict the impact of heterogeneity on tumor progression and patient survival (3) disentangle the order in which mutations occur. The ICGC Controlled Data will be used to learn the patterns of tumor evolution across different cancer types, in order to address this challenge.
312.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.
313.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.
314.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.
315.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.
316.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.
317.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.
318.David WedgeUniversity of OxfordUnited Kingdom2017-09-282018-09-27
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.
319.Nils WeinholdSloan Kettering Institute for Cancer ResearchUnited States2017-08-212018-08-21
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.
320.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.
321.Wen WenSecond Military Medical UniversityChina2017-10-052018-10-05
The Liver Cancer Study Group of Japan report that almost half of patients with liver cancer have more than one lesion. In patients who have more than one lesion, each lesion may either have been independently formed or have come from another lesion. Discriminating these is important in deciding the therapeutic strategy for the patient. In this project we will use ICGC's genome sequencing data from patients with liver cancer who have more than one lesion to construct a reasonable method of distinguishing multi-lesions and helping with clinical decisions.
322.Christoph WierlingAlacris TheranosticsGermany2018-01-192019-01-18
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.
323.Daniel WilliamsonNewcastle UniversityUnited Kingdom2017-10-052018-10-04
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.
324.Gane WongUniversity of AlbertaCanada2017-08-232018-08-22
We are interested in the molecular mechanisms by which cancer cells evade chemotherapy and eventually become resistance 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.
325.Jason WongUniversity of New South WalesAustralia2017-09-202018-08-13
Only about 1% of the human genome directly codes for proteins. The remaining “non-coding” portion of genome (that don't make proteins) is poorly understood and it is only recently that scientists have begun exploring what these other parts of the genome do and why they are important in dictating how cells in our bodies work. Equipped with this emerging knowledge, this project aims to use the ICGC genome sequencing datasets to determine whether there are mutations outside of protein coding regions that can potentially cause cancer. The discovery of new cancer causing mutations will have the potential to provide new ways to determine patient treatment regimes and offer new targets for therapy.
326.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.
327.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.
328.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.
329.Odessa YabutUniversity of California, San FranciscoUnited States2017-10-302018-10-29
Today, the available therapies for medulloblastoma (MB) does not work for all types of MB. Our ultimate goal is to develop treatments that will be effective for the type of MB, which is based on the genetic mutation that cause tumors. We will begin by evaluating the molecular differences between different types of mutations that cause one type of MB, the sonic hedgehog (Shh) subgroup (MB-Shh). We will use ICGC data to characterize and compare affected genes of MB patients carrying different types of MB-Shh mutations. By identifying these differences, we hope that we can develop individualized MB treatments, specific to the genetic mutation causing the tumor, and improve patient outcome.
330.Chunhua YanNational Cancer InstituteUnited States2017-09-142018-09-14
Detection of changes in the genome of cancer cells has been greatly improving the efficacy of cancer therapy in recent years, which is exemplified by the application of breast cancer gene test (BRCA1/2) to guide the selection of treatment strategies. The large collection of genomic data in ICGC provides cancer researchers with a unique resource to identify more cancer genes and understand the genetic cause of cancer. We will use ICGC data to improve the accuracy of software in detecting genomic changes, thereby refining cancer classifications. The detailed description of cancer genomic changes will lead to more accurate diagnosis and better treatment selection.
331.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.
332.Patricio YankilevichIBioBAArgentina2018-04-272018-06-11
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.
333.Iwei YehUniversity of California, San FranciscoUnited States2017-09-152018-09-15
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.
334.Sung-Soo YoonSeoul National UniversitySouth Korea2017-10-052018-10-04
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.
335.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.
336.Yan ZhangThe Ohio State UniversityUnited States2017-08-162018-08-06
Structural variations (SVs) are genetic mutations that affect large regions of DNA in the genome (at least 50bp). Such changes on the genome have been known to cause numerous diseases, and also contribute the formation and invasion of cancer tissues. The Pancancer Analysis of Whole Genomes (PCAWG) study is generating a comprehensive dataset of various SV types in cancer samples. However, there is still a gap between the genetic mutations and their functional impact. Thus we propose to develop computational and statistical tools to make the linkage, and apply our tools to the SV set and sequencing data generated by PCAWG and ICGC. From our study, we expect to reveal the distribution of these genetic mutations on the genome, and infer their potentially functional impact. These computational predictions will be further studied experimentally.
337.Zemin ZhangPeking UniversityChina2017-10-312018-10-30
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.
338.Degui ZhiUniversity of Texas Health Science Center at HoustonUnited States2017-08-252018-08-24
Gene expression data (the way the instructions in our DNA become activated) has played an important role in understanding disease mechanisms in cancer. However, generating high-resolution gene expression data is costly. In this project, we use statistical models to predict gene expression from epigenetic features (the mechanisms that turn genes on and off). So far, most studies focused on epigenetic features in the gene body or near the gene body to explain gene expression regulation. We use epigenetic features that are a far distance from the target gene, which greatly improves gene expression prediction. Our study will open a new door in epigenetic, gene expression, and cancer research using both types of data.
339.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.
340.Bin ZhuNational Cancer InstituteUnited States2017-10-192018-10-18
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.
341.Jun ZhuIcahn School of Medicine at Mount SinaiUnited States2017-09-082018-09-07
Cancer cells gain growth and survival advantages by altering cells’ genetic and genomic composition. We will develop methods to model cancer cells by integrating diverse types of data generated in the ICGC, as well as in TCGA and other large cancer data sets. These models will allow us to identify key mutations and key regulators for each cancer type, which will lead to a better understanding of cancer biology and better treatment options for each individual cancer patient.
342.Aviad ZickHebrew University-Hadassah Medical CenterIsrael2017-10-192018-10-18
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.
343.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.
344.Lihua Zounorthwestern universityUnited States2018-01-182019-01-08
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.Wilbert ZwartNetherlands Cancer InstituteNetherlands2017-12-042018-12-03
Cancer can result from changes in chemical modifications of the DNA, called epigenetic labels. By studying the changes in the epigenetic labels of tumors from patients who did or did not develop cancer recurrence later in life, we can identify the epigenetic labels that are important in the development of the disease. One of the labels is acetylation which we found to be different in samples from patients with a genetic alteration in the gene ERG. The data from ICGC will be used to validate our findings, as within the EGAS00001002496 dataset, the same acetylation is assessed in samples with or without the alteration in the gene ERG.