DACO Approved Projects

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

Principal InvestigatorPrimary Affiliationsort iconCountryDate Approved for AccessValid UntilTitle of Project
1.Jakob PedersenAarhus UniversityDenmark2020-06-022021-06-01
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.
2.Paweł ZawadzkiAdam Mickiewicz University, Poznań, PolandPoland2020-01-312021-01-29
Currently, because commonly used chemotherapeutics, such as carboplatin and cisplatin are cheap drugs, treatment decisions are made based on average overall survival rates rather than personalized approached and there is no diagnostics performed prior to treatment decisions. Therefore, we believe that there is an urgent need to develop diagnostic biomarkers (genomic scars indicating defects in biological mechanisms), allowing to predict response to these platinum-based agents and test whether the recurrent tumor is resistant or sensitive. This method requires a deep understanding of the interplay between DNA repair elicited by the initial chemotherapeutic damage to DNA and also the mechanisms for the initial chemotherapeutic response and subsequent resistance development. Therefore, we propose to use computational genomics technologies in our research to analyse ICGC controlled data, in a way so that our findings could be used to predict drug response and be easily transferred to clinics in the future.
3.Louis VermeulenAMCNetherlands2020-07-302021-07-29
The time patients survive with cancer varies widely. The expected time a patient will survive with cancer, and whether a certain treatment will help to extend the life-expectancy of a cancer patient, is of great clinical interest. Cancers arise from changes, or mutations, in specific genes that result in uncontrolled cell growth. While this principle underlying the initiation of cancers is very well known, it is much harder to predict how a cancer will continue to grow or respond to therapy from the mutations in a cancer cell. A problem that arises is that cancer cells from the same tumor can differ significantly in the mutations they contain. We developed a method that measures the variability in changes in the genome between cells in the same tumor. In this Research Project will apply our method to the ICGC dataset to investigate whether this variability can explain survival times.
4.Marek PiatekArdigenPoland2020-08-042021-08-03
Cancer tissues are characterized by complex genetic and epigenetic structures (structures that do not involve alterations in the DNA sequence). In order to better understand the cancer intricacies, we would like to understand gene relationships within and in between each cancer tissues on various levels, including genome and transcriptome (set of all RNA molecules in one cell or a population of cells) from ICGC controlled data. Identification of such markers, together with the development of state-of-the-art Machine Learning methodologies would allow us to boost the understanding molecular landscape of cancer and potentially improve the patient stratification for various therapies.
5.Ilya MazoArgentys InformaticsUnited States2020-09-022021-09-01
In human genome, there are sequences of DNA that encode for proteins and sequences of DNA that were considered "junk DNA", as its functions were not well known yet. Retroelements, which are the majority of this "junk DNA", are elements that can move around different locations in the human genome. We now know that by moving around in the genome, some of these retroelements can affect individuals by disrupting or regulating other essential DNA sequences. In several types of cancers, such events have been shown to take place at much higher rates. Our goal for this project is to identify and measure the activity of retroelements in different types of cancer. In order to do so, we will apply computational tools to the ICGC cancer data . We hope that our research project will lead to the development of novel cancer diagnostics approaches.
6.Kenneth BuetowArizona State UniversityUnited States2019-12-172020-12-15
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.
7.Hieu NimAustralian Regenerative Medicine InstituteAustralia2020-07-142021-07-13
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.
8.David TorrentsBarcelona Supercomputing CenterSpain2020-06-232021-06-22
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.
9.David TorrentsBarcelona Supercomputing CenterSpain2019-11-122020-11-10
This is a research project to determine the impact of how genomic analysis can be used for making oncology decisions and personalize treatments. The project aims to identify the specific genomic variants in a known location on a chromosome that can provide an increased knowledge of the genes that could be involved in the development and progression of the disease. By using dataset of tumour-normal sample pairs from chronic lymphocytic leukaemia (type of blood and bone marrow cancer) and medulloblastoma (cancerous brain tumor) (Alioto et al 2015), we will be able to calibrate the different programs by means of the comparison between their results, and the results verified in the ICGC dataset. That way will be possible to achieve the highest specificity and sensitivity for detecting the alterations that occur along the life in an individual. All this work will allow us to further our knowledge on the variants effects.
10.Claude ChelalaBarts Cancer InstituteUnited Kingdom2019-12-032020-12-01
Cancer is a complex malignancy involving alterations not only to the tumor cell but also the surrounding environment. We propose to use ICGC data from breast, ovarian, prostate, pancreatic and skin cancer to conduct comprehensive assessments of cancer samples and their “normal” counterparts. These data will be analyzed alongside those generated from in-house, and other publicly available, projects. Analyzing tumors both in isolation and with their matched tissues will enhance novel ideas and will help to rank candidate genes for experimental validation in our local cohorts and hypothesis testing. The ICGC dataset will be key to increasing the power to detect robust predictive alterations.
11.Jun WangBarts Cancer Institute, Queen Mary University of LondonUnited Kingdom2020-07-032021-07-02
A type of ovarian cancer known as high grade serous ovarian cancer accounts for over 70% of ovarian cancer deaths. It has the highest mortality and worst outcome of all cancers related to the female reproductive system. Current treatments have not changed in the last three decades. There is an urgent unmet need to further understand disease pathways, in order to identify novel biological molecules to monitor disease development and uncover potentially new drug targets. A unique set of genes known as long non-protein coding RNAs (lncRNAs) that function to switch on or off other genes represent an interesting set of targets to investigate. We have developed a robust analytic pipeline to identify known and novel lncRNAs, and we aim to test our pipeline using the ICGC Controlled Data and identify disease associated lncRNAs in ovarian cancer and other solid cancer types.
12.Aleksandar MilosavljevicBaylor College Of MedicineUnited States2020-02-182021-02-16
One promising therapeutic target in Pancreatic Ductal Adenocarcinoma (PDAC) is Mesothelin (MSLN), a cell surface protein that when targeted in model systems decreases cancer growth. However, multiple clinical trials have failed to show increases in disease free survival. In order to improve efficacy, a better understanding of MSLN’s function in human tumors is needed. We use a new method to look at MSLN in cancer cells in human tumors rather than across all cells present in the tumor to identify possible therapeutic targets to use in combination therapy. ICGC data will be used to look at cancer cell MSLN levels and validate interactions we have seen in the TCGA PAAD cohort.
13.Michael ScheurerBaylor College Of MedicineUnited States2020-07-032021-07-02
Genetic mutations that an individual acquires over a lifetime are termed somatic mutations as opposed to mutations that an individual is born with (i.e., germline mutations). Moreover, different mutational processes generate different combinations of somatic mutation types (e.g., point mutations and large scale mutations), termed ‘signatures’. To date, twenty one validated mutational signatures were identified in 30 different types of cancer; predominantly in adults. In these proposed studies, we aim to investigate the role of mutational signatures in development of different types of childhood cancers in comparison with adults. In addition, we seek to determine whether certain inherited genetic variation may predispose an individual to acquire these mutational signatures. To this end, we propose to utilize raw genetic data from ICGC. Our studies will be large collaborative studies including data/samples from a number of large institutions and/or consortia. Therefore ICGC data will be pooled with data from different sources.
14.Ryan MorinBC Cancer, Part Of The Provincial Health Services AuthorityCanada2020-08-042021-08-03
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.
15.Steven JonesBC Cancer, Part Of The Provincial Health Services AuthorityCanada2020-04-162021-04-15
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.
16.Steven JonesBC Cancer, Part Of The Provincial Health Services AuthorityCanada2020-09-112020-10-06
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, expand the knowledge-base of the classifiers, and develop automated approaches for determining important predictive genes and pathways for each sample classified by this model. The classifiers can then be used to characterize rare/unknown/metastatic cancers and uncover underlying biology of each category.
17.Sharon GorskiBC Cancer, Part Of The Provincial Health Services AuthorityCanada2019-12-192020-12-17
The Personalized OncoGenomics program (POG) at BC Cancer is a clinical research initiative that uses the information embedded in the DNA of a patient’s tumor to identify its vulnerabilities for effective treatments. Neuroendocrine neoplasms (NENs) are a group of rare tumors that is understudied and consequently has only a few therapeutic options available. To date, POG has enrolled 26 patients with neuroendocrine neoplasms. The Gorski group has previously reported the molecular alterations identified in POG patients with pancreatic NENs. This project aims to use the information learned from the 26 POG NEN patients and their diseases to boost our understanding of this rare disease as a whole and potentially identify novel therapeutic avenues for further experimentations. The ICGC Controlled Data will be used to compare with the 26 POG NENs and as an independent cohort for select result validations.
18.Xiaomin YingBeijing Institute of Basic Medical SciencesChina2020-06-182021-06-17
Researchers have demonstrated that bacteria in the human gut could promote the development of colorectal cancer. However, the mechanism of the interaction between colorectal cancer and bacteria is still unclear. Recently, researchers suggested that antibiotic therapy administered concurrently to immunotherapy should be considered carefully to avoid worse overall survival in patients with cancer, which means gut bacterial could promote or inhibit the specific immune response of the host against tumors. Therefore, in this study, our aim is to use a bioinformatics approach to analyze the relationship between gut bacteria and host immune activities. We will use the ICGC data, especially colorectal cancer data, to extract the non-human or bacterial information from human tumors to determine the bacterial composition and then seek the possible correlation between bacteria and host immune characteristics inferred from genome data. If a specific relationship is discovered, we will conduct mouse experiments for validation.
19.Jesper AndersenBiotech Research and Innovation Centre (BRIC), University of CopenhagenDenmark2020-07-082021-07-07
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.
20.Ravshan AtaullakhanovBostonGeneUnited States2020-03-242021-03-23
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 immuno- and targeted therapies. The data from ICGC will be processed through advanced series of computer programs resulting in an array of advanced molecular and immunological data and will let us comprehensively analyze predictive and prognostic factors of response to immunological and targeted therapies in an extensive set of cancer types.
21.Rameen BeroukhimBroad InstituteUnited States2020-07-082021-07-07
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.
22.Eric BanksBroad Institute of MIT and HArvardUnited States2020-07-092021-07-08
In this application, we propose to use the ICGC dataset to develop novel methods for the analysis of cancer genomes as part of our suite of open-source genomics software (see https://gatk.broadinstitute.org/hc/en-us). The basis of our project revolves around comparing normal samples to that of tumor samples to facilitate the discovery and characterization of various cancers. We will use the tumor and normal sequencing data to develop, evaluate, and improve computational tools for discovering both small and large-scale mutational events throughout the tumor genome.
23.David TorrentsBSC-IRB Research Programme in Computational BiologySpain2020-07-142021-07-13
This is a research project to determine the impact of how genomic analysis can be used for making oncology decisions and personalize treatments. The project aims to identify mutations that can affect a gene involved in the development and progression of the disease. By using the raw data provided by ICGC controlled data, we will be able to calibrate the different programs (such as Mutect2, pindel, strelka, muse, etc.) by means of the comparison between its results, and the results verified in the ICGC controlled data. That way, it would be able to achieve the highest specificity and sensitivity for detecting the alterations of an individual. All this work will allow us to further our knowledge on the variant effects.
24.Jehoshua BruckCalifornia Institute of Technology (Caltech)United States2020-07-302021-07-29
Instabilities in repeat regions in DNA have been identified in cancer patients. Given a large amount of repeat regions in human DNA, our study aims at finding specific repeat regions which can be attributed to different kinds of cancer based on variations in the number of repeats and mutations. In this regard, we will use DNA data for cancer patients provided by ICGC by extracting tandem repeat (TR) regions from their DNA and estimating the mutation rate there. TR regions are periodic sequences in DNA. For example, ACACACAC is a TR region with “AC” being repeated. These regions constitute 3% of the human genome. The mutation rates in these regions are particularly high and their estimates can be used to differentiate between healthy and cancerous people and also to study the variation among different cancers. This study can be useful in early cancer detection at a minimal computation cost.
25.Harry CliffordCambridge Cancer GenomicsUnited Kingdom2020-03-032021-03-02
Data-sets like ICGC's Precision Panc were developed to help pancreatic cancer patients, and their doctors, to find and understand accurate and reliable information about that specific patient’s cancer that could help them to decide what the most suitable type of treatment is. To do this, extremely accurate analyses must be applied to understand the exact genetic differences underlying each individual's tumour. By using the latest tools and analytical approaches, we at Cambridge Cancer Genomics aim to unearth novel clinical insights and understanding from this vast amount of consolidated data across many patients.
26.Richard MaraisCancer Research UK, Manchester InstituteUnited Kingdom2020-05-072021-05-06
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.
27.Robert BristowCancer Research UK, Manchester InstituteUnited Kingdom2019-11-192020-11-13
Prostate Cancer is the most common type of cancer among British men. Good management of the disease has led to increased survival rates. However, discrepancies in the treatment of men with high-risk disease could still impact their life expectancy and quality of life. This project searches for genetic differences between men with high-risk cancers that do or do not respond to treatment using machine learning techniques that allow for comparison of different types of genetic information for each man. These patterns within ICGC controlled data enable stratification of patients in future studies that would involve intensification/de-intensification of therapy in those patients whose outcome is deemed poor or good. The information could lead to future clinical trials using different treatment techniques to improve the response of treatment for patients that have poor prognosis.
28.Russell SchwartzCarnegie Mellon UniversityUnited States2019-11-272020-11-22
Cancer normally involves defects in the process of cell replication that result in once-healthy cells rapidly accumulating mutations over successive generations. The nature of this defective replication differs from patient-to-patient in ways that can have important implications for an individual’s prognosis and treatment options. The goal of this project is to develop computer algorithms to help us better characterize the mutation processes active in different cancers. We will apply the resulting programs to the ICGC tumor genomic data to identify how these processes vary for different patients and cancer types and improve our understanding of how they combine to influence the progress of the disease in individual patients.
29.Thomas LaFramboiseCase Western Reserve UniversityUnited States2020-01-142021-01-12
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.
30.Vinay VaradanCase Western Reserve UniversityUnited States2020-02-172021-02-05
This project is geared towards addressing an urgent and unmet need within the biomedical research community to develop reliable mathematical models that can decipher the biologic significance of genomic aberrations in individual tumors. Additionally, a central and daunting question in the field is to determine the functional role of the majority of genetic alterations associated with complex diseases, including cancer, whose predicted significance is currently unknown. Our long-term objective is to develop robust and biologically validated models and algorithms that can integrate tumor profiles to infer activities of biological processes in patient tumors. Using the ICGC datasets, we will fully develop, optimize and validate our mathematical models, with the ultimate goal to guide effective therapies in future cancer patients.
31.Tae-Min KimCatholic University College of MedicineSouth Korea2020-07-162021-07-15
We recently proposed some computational methods to identify genetic mutation patterns (also called mutational signatures) in cancer genomes. These methods may reveal the chronological order of the accumulation of mutations in cancer genomes and estimates how much each mutation contributes to the development of the cancer. However, it is still largely unknown how much the mutational patterns vary across the genomes of cancer patients. In this proposal, we plan to discover variations in mutational signatures by applying computational methods to analyze patterns of mutations available in the ICGC controlled data. The goal of our project is to construct an elaborate landscape of mutation signatures and to further link these specific patterns of mutation with biological phenomenon in the body. The expected outcome of the study will serve as a valuable resource for basic cancer research to discover novel markers of cancer for potential clinical utilities.
32.Hisashi Tanakacedars-sinai medical centerUnited States2020-09-212020-11-03
Human epidermal growth factor receptor 2 (HER2) is a critical factor for cancer development in 15-20% of breast tumors. HER2 is produced from ERBB2 gene, and in normal cells ERBB2 gene has two copies. In tumors, ERBB2 gene has multiple copies and produces a lot of HER2, which contributes to breast cancer. How ERBB2 gene gains multiple copy is an important topic of research. In our research, we have come up with a model of this process based on experimental data. We have theories on the critical sites of mutation and would like to confirm our hypothesis via the HER2 specific breast tumor data of the ICGC. We will determine if these specific mutations occur in real-world data.
33.Sarka PospisilovaCEITEC Masaryk UniversityCzech Republic2020-09-042020-10-19
The aim of the study is to use the requested dataset to optimize our pipeline for investigating mutation patterns in chronic lymphocytic leukemia (CLL). We will apply a broad range of methods in an attempt to identify distinct CLL subgroups. We will leverage observations obtained in the requested ICGC dataset to comprehensively explore a dataset that has been generated at our institute. We plan to develop an algorithm for better classification of leukemia patients with a goal of effective personalized care.
34.Christoph BockCeMM Research Center for Molecular Medicine of the Austrian Academy of SciencesAustria2019-12-092020-11-27
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.
35.Benjamin LehnerCenter For Genomic Regulation (CRG)Spain2020-07-132021-07-12
Cancer arises from changes to genes that control the way cells grow and divide. Major risk factors that may contribute to cancer progression are environmental hazards and hereditary genetic mutations. Hereditary genetic mutations that exhibit highly increased risk of cancer are observed in ~ 5 to 10 % of cancers. Our project aims to integrate ICGC data with other data sets in order to help understand how these mutations contribute to tumor progression (e.g., early onset of cancer). We will also investigate how much the patients' mutation frequencies differ from those of healthy people without cancer in order to identify cancer-associated genes.
36.Donate WeghornCenter For Genomic Regulation (CRG)Spain2020-08-182021-08-17
Cancer is a disease caused by changes in the DNA in some cells of the human body. By looking at these changes, we can try to find which regions of the DNA are especially important for cancer to develop. We can also get an impression of the factors that cause the DNA changes in the first place. The ICGC database contains tables with all the DNA changes of many tumors from patients around the world and therefore provides the resource for such analyses. Ideally, the results will then be used to develop new cancer therapies.
37.Murali BashyamCentre for DNA Fingerprinting and Diagnostics (CDFD), Hyderabad, INDIAIndia2020-05-072021-05-06
The SWI-SNF complex gene is composed of several proteins that help in the development of embryos. Research in the past decade revealed frequent aberrations in these proteins during cancer development. A detailed understanding of the role of these proteins in cancer development will help in designing drugs for treating cancer. Our analysis in a small number of cancer patients revealed interesting observations regarding the role of SWI-SNF proteins in cancer. In this proposal using ICGC controlled data, we aim to identify the frequency of aberrations in SWI-SNF complex genes in head & neck, colorectal and esophageal cancers. In addition, we will also identify genetic aberrations in other known cancer genes and their association with aberrations in SWI-SNF complex genes.
38.Francesca DemichelisCentre for Integrative Biology (CIBIO) - Universita degli studi di Trento - ItalyItaly2020-06-172021-06-16
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).
39.Alessandro RomanelCentre for Integrative Biology (CIBIO) - Universita degli studi di Trento - ItalyItaly2020-04-212021-04-16
Over the past 20 years, numerous inherited DNA alterations have been linked to an increased risk of developing prostate and breast cancer, two common cancers that together cause over 800,000 deaths worldwide annually. In addition large genomics studies have precisely defined the broad landscape of acquired DNA alterations characterizing these two common cancers. The goal of our research is to use mathematical and computational approaches based on omics data (including ICGC controlled data) to explore the links between inherited and acquired DNA alterations. The ultimate goal of this project is to identify genetic biomarkers able to stratify patients with potential aggressive cancer progression.
40.Ivo GutCentro Nacional de Analisis Genomico (CNAG)Spain2020-08-182021-08-17
The ICGC data sets with the tumor-normal sample pairs is, to our knowledge, one of the most comprehensive datasets to assess the performance of the programs used to call non-inherited mutations (somatic mutations) to date. We would like to access the ICGC Controlled Data to download the sequenced reads and predicted variants of the chronic lymphocytic leukaemia (a type of blood cancer) and medulloblastoma (a cancerous brain tumor) control-tumor samples. These data sets are going to be used as a gold standard to benchmark all the steps of our variant calling workflows. We are interested in finding possible caveats and also ways to improve the quality of our pipelines, specially by identifying common technical patterns in false positive and false negative variants.
41.L. Frank HuangCincinnati Children's HospitalUnited States2020-02-202021-02-19
Medulloblastoma is one of the most common brain tumors in children with around 500 cases diagnosed in the United States each year, approximately 1 in 5 tumors. With current treatments, such as multi-agent chemotherapy and radiotherapy, survivors often face major long-term side effects, life-long learning disabilities, and poor quality-of-life. This study will focus on using computational approaches to analyze the ICGC Controlled data by comparing the genomic data of medulloblastoma patients to normal. In this way, we aim to identify the susceptive genes or novel targets that lead to the tumor formation of medulloblastoma. These targets could be very helpful to identify therapeutic drugs for medulloblastoma.
42.Benoit HedanCNRSFrance2019-11-142020-11-07
The "Canine genetics" team led by Catherine ANDRE has been working during the last 20 years on human/dog homologous genetic diseases and especially cancers. Canine cancers with similar to human subtypes are frequent in dogs but rare and not well known in human. The final objectives are to identify the genetic bases of these cancers and to propose therapeutic targets, clinical trials "first in dogs" for veterinary and human medicine benefits. We identified specific alterations in canine melanomas and sarcomas. Our specific aims are to test if these alterations are also involved in some human cancers, even in rare cases. In this context, the ICGC controlled data will be used to screen for these potential rare alterations in human cancers, but known to be important in cancer developpement based on canine cancer data.
43.Dimitris AnastassiouColumbia UniversityUnited States2020-08-182021-08-17
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.
44.Raul RabadanColumbia UniversityUnited States2020-05-112021-05-09
Pancreatic cancer will become the second leading cause of cancer mortality within the next decade if outcomes do not improve. One reason for the high mortality rate is the fact that this cancer type is diagnosed typically in late stages when few treatments are available. Another reason stems from our current inability to screen high-risk individuals; it is therefore imperative to find novel markers of pancreatic cancer risk. Based on preliminary findings that implicate the immune system and pathogenic infections in this disease, we propose an in-depth study of potential associations with viruses, bacteria and features of our immune system in order to address the current lack of known risk factors of pancreatic cancer. The ICGC dataset is an invaluable resource to this end; we will use the genetic data (raw and processed files) from cancer samples in order to discover new inherited and acquired risk factors for pancreatic cancer.
45.Andrew ConeryConstellation PharmaceuticalsUnited States2020-04-272021-04-26
Constellation Pharmaceuticals is committed to delivering novel targeted cancer therapeutics that maximize benefit to patients and minimize treatment-related side effects. We are particularly interested in identifying targets that change the identity of cells by changing how the cell's genetic blueprint is interpreted. In our continuing search for new cancer targets, we have recognized that the most promising targets are those that are altered in many cancer patients. The goal of this project is to identify which of these "cancer drivers" might serve as targets for novel therapeutics using standard computational approaches. To understand how cancer drivers function requires information at the level of the individual patient (the ICGC Controlled Data) so that we can understand how cancer drivers act together, and to understand how cancer drivers are related to patient prognosis and response to therapy. We hope that the ICGC Controlled data will generate new targets for therapeutic intervention.
46.Bahram KermaniCrystal Genetics, Inc.United States2020-02-102021-02-02
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.
47.Andrew AguirreDana Farber Cancer InstituteUnited States2020-08-172021-08-16
Pancreatic cancer is an aggressive cancer with a median 5 year survival rate of less than 9%. By applying computational methods to the ICGC controlled data, we seek to separate the disease into different classes based on patient-specific markers and clinical outcome. This will be performed using patient data, such as survival time when available, and the genetic and expression differences in genes that are commonly altered in pancreatic cancer. Once identified, these classes can be used to select patients for more personalized therapies with the hope that we may one day improve treatment options for pancreatic cancer patients.
48.Eliezer Van AllenDana-Farber Cancer InstituteUnited States2020-05-222021-05-21
Previous studies in cancer genomics have focused mainly on mutations that directly change the structure of proteins. These studies have substantially contributed to our understanding of how mutations affect cancer development. Most mutations, however, fall between genes, i.e., they do not directly influence the structure of proteins. Thus far, systematic analyses of these mutations have been rare, potentially because most tools in cancer genomics search for mutations that directly affect protein structures. In a previous project, we developed statistical tools to characterize important mutations that change the protein structure. Here, we will generalize these tools towards mutations between proteins and apply this tool the ICGC & PCAWG datasets. Potential applications of our project include to identify genes not known to be involved in cancer, explain the underlying biological mechanisms of cancer, identify new starting points for cancer drug development, and expand the range of mutations for cancer diagnostics.
49.Rinath JeselsohnDana-Farber Cancer InstituteUnited States2019-12-202020-12-18
In our preliminary work with pre-clinical models, we found that there are differences between breast cancers that are treatment responsive versus non-responsive that are not due to mutations in the genome , but rather driven by differences in the expression of certain genes. In the proposed work we plan to study the mutational burden (total number of significant mutations) in the DNA regions, using ICGC data, that are important for the regulation of the expression of key genes in breast cancer in order to study if such differences are a mechanism of resistance to treatment and tumor progression
50.Claudio SetteDepartment of Neuroscience, Section of Human Anatomy, Catholic University of the Sacred Heart, 00168 Rome, ItalyItaly2020-05-132021-05-12
Medulloblastoma (MB) is the most common malignant brain tumor in children with incidence before the age of five. If combined therapeutic approaches, which include surgical resection as well as radio- and chemotherapy, partially improve overall survival rates of MB patients, they are extremely aggressive and devastating for these young patients. To date, developing novel therapies for MB patients is a clinical priority. In light of this, our research project consists in identifying new molecules that are preferentially expressed in MB patients and able to confer MB tumor cells the capacity to survival and to escape standard chemotherapies. We will use computational approaches to analyze the ICGC MB patient data to learn more about MB genetic molecular composition, thus to develop novel therapeutic approaches and to ameliorate the diagnosis and the treatment of MB.
51.Andrew AllenDuke UniversityUnited States2020-08-242021-08-23
Only 1-2 percent of the genome codes for amino acids comprising proteins--complex molecules that are required for the structure, function, and regulation of the body's tissues and organs. The function of the other 99-98 percent non-coding part of the genome is less well mapped out, but within are sequences, called regulatory sequences, that regulate how much of any protein is available in any given cell. We aim to identify mutations in gene regulatory sequences that influence cancer cell growth. Our strategy is to intersect non-coding mutations that are thought to affect the development of cancer with previously identified regulatory sequences that control cancer cell growth. By applying computational tools to the ICGC controlled data, we aim at identifying driver mutations that disrupt regulatory elements influencing cancer cell growth. This study will further our understanding of cancer mutations in noncoding sequence.
52.Sven NahnsenEberhard-Karls-University TübingenGermany2020-01-142020-12-16
The routine research of cancer data requires large investments in infrastructure for analysis. Despite the benefits of cloud providers hosting ICGC data, there are still technical difficulties in using these infrastructures to analyze medical data for privacy reasons. One big issue is to ensure that individual patient data is kept private at all times. Our distributed computing approach based on standardized and portable analysis pipelines will be able to fulfill these requirements, as we can calculate important statistics on the 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.
53.Ramu AnandakrishnanEdward Via College of Osteopathic MedicineUnited States2020-09-122020-10-27
Cancer is known to result primarily from genetic defects. Yet, despite decades of research and the availability of extensive genomic data, the specific cause for individual instances of cancer can not generally be determined. One reason is that current computational methods focus on identifying individual "cancer genes", while cancer results from a combination of multiple genetic defects (multi-hit combinations). We are developing an algorithm for identifying multi-hit combinations instead of cancer genes. Information from ICGC controlled data will be used to differentiate between cancer-causing and non-cancer causing mutations. The multi-hit combinations identified by this project are likely to better explain the cause of cancer and suggest new ways to view, diagnose and treat cancers.
54.Gunnar RatschEidgenoessische Technische Hochschule ZuerichSwitzerland2020-01-302021-01-23
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.
55.Jonathon CohenEmory UniversityUnited States2020-02-202021-02-19
It has been recently reported that patients with Chronic Lymphocytic Leukemia (CLL) whose disease transforms to an aggressive lymphoma do not respond well to the treatments developed for CLL. This project aims to use ICGC-controlled data to find differences in how genes are turned on or off between CLL cells that have transformed versus ones that have not transformed. This will allow us to understand why transformed CLL does not respond to currently available treatments. With this information, we intend to develop novel therapies that can more effectively treat transformed CLL.
56.Henk-Jan HamENPICOM B.V.Netherlands2019-12-172020-12-15
Cancer is caused by mutations, which are changes to a person's normal DNA. Part of the DNA encodes proteins, and mutations in these areas of the DNA can lead to differences in proteins expressed by the tumour. These altered proteins can be recognized by the immune system as so-called 'neo-antigens', and can ignite an immune response that could potentially lead to the irradication of the patient's tumour. This project uses ICGC controlled data to set up a prediction algorithm to identify the neo-antigens that are present in a cancer patient, based on DNA sequences from both healthy and tumour tissues. Insights from this project will contribute to the development of new interventions for fighting cancer.
57.Istvan CsabaiEotvos Lorand University BudapestHungary2020-02-112021-02-09
There are biological processes that are present in cancer but absent in normal cells, or the other way around, absent in cancer but present in normal cells. DNA repair is one such biological process. It is present in all normal cells to ensure the faithful, error free propagation of the genetic material (DNA) from mother cell to daughter cell, but it is often missing or damaged in cancer cells. There are several DNA repair pathways and the lack of one of those, homologous recombination, has been successfully exploited clinically by the therapeutic application inhibitor drugs. Cancer cells lacking homologous recombination cannot repair the severe DNA damage induced by inhibitors, therefore the cancer cells die, while the normal cells possess this compensatory mechanism and survive. Using ICGC Controlled Data, we aim to develop a method to diagnose prostate cancer in patients who lack homologous recombination.
58.Bart DeplanckeEPFLSwitzerland2020-03-122021-03-09
We are using a novel approach to study how genetic variation in regions outside genes affects cancer susceptibility and outcome. As certain types of cancer are particularly enriched for these types of mutations, we want to exploit the ICGC Controlled data to explore to what extent our method can detect genetic variation linked to cancer prognosis. The utility of these data are twofold: 1) it would allow us to investigate what variants are specific for a particular cancer and 2) we would be able to study the effect of these variants on cancer patients.
59.Sonja BuschowErasmus University Medical CenterNetherlands2020-07-032021-07-02
Pancreatic cancer (PC) is a complex disease where the immune system holds the potential to combat the tumor. Previously, using the ICGC controlled data, others have identified a tumor subtype in PC patients that is associated with better chances of responding to immunotherapy. We are interested in why these patients have this presumed favorable profile, and suspect that subtypes of a protein called the Human Leukocyte Antigen (HLA) play a crucial role in this. We want to apply computational tools to the ICGC controlled data to identify patients with this favorable tumor subtype based on their genetic data, and investigate whether certain HLA-types and other cancer-related proteins are more frequently present in this cohort.
60.Kai YeFaculty of Electronic and Information Engineering, Xi'an Jiaotong UniversityChina2019-12-112020-12-09
The identification of DNA mutations causing various forms of cancer would shed light on disease progression and provide tremendous help in identifying personalized treatment strategies. In this study, we will develop a new methodology to discover various mutations in highly repetitive regions of the genome. This, along with other available tools on the market would provide us with a comprehensive catalog of DNA mutations. The ICGC controlled data includes numerous tumor DNA sequences in both protein coding and non-coding regions. We will apply our new methodology to all ICGC data and search for novel DNA mutations that are potentially vital for disease diagnose or treatment selection.
61.Noah BerlowFirst Ascent Biomedical CorpUnited States2020-03-232021-03-22
The goal of this project is to better understand the genetic relationships that dictate whether a drug will work in a patient. In order to study our ability to predict genetic relationships that explain when a drug will work, we are using a public database of drug data and genetic data to use artificial intelligence analysis to find genetic relationships that predict drug response. The non-ICGC dataset will be used to build predictive models which will be validated using the clinical data in the ICGC Controlled Dataset We will compare the ICGC Controlled Data clinical results with the predicted effect of the drug in the patient clinical and how predictive the models we designed were.
62.Wigard KloostermanFrame TherapeuticsNetherlands2020-06-092021-06-08
Differences between the DNA of a patient’s healthy cells and tumor cells can give rise to proteins which are cancer-specific. These proteins, known as neoantigens, can allow the immune system to distinguish between normal tissue and cancer cells. Therapeutically vaccinating a patient against these novel proteins can help the immune system mount a stronger response against the tumor. This project aims to apply computational methods on the ICGC Controlled Data to identify common changes in the DNA of cancer which give rise to the same neoantigens in different patients. These shared neoantigens can be used for off-the-shelf vaccines which provide rapid and effective treatment.
63.Peter Van LooFrancis Crick InstituteUnited Kingdom2020-04-282021-03-28
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.
64.Kristin ReicheFraunhofer Institute for Cell Therapy and ImmunologyGermany2020-03-242021-03-23
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 detected biomarkers that have been associated to prostate cancer by us.
65.Andrew HsiehFred Hutchinson Cancer Research CenterUnited States2019-12-172020-12-15
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.
66.William GradyFred Hutchinson Cancer Research CenterUnited States2020-07-202021-07-19
There is a substantial unmet need to advance our understanding of Esophageal adenocarcinoma (EAC) (i.e. a type of esophagus cancer) and to make progressions in our ability to treat it. We are particularly interested in DNA methylation, which is a process where certain molecules are added to a DNA segment and change its regular activity. DNA methylation changes have been reported playing important roles in EAC development. In this project, we propose to perform mathematical analyses on DNA methylation data of normal and EAC patient samples to identify different EAC subtypes as well as subtype-specific DNA methylation alterations. In the experimental part of our study, we aim to validate DNA methylation patterns and mechanisms that may lead to cancer, as well as their clinical relevance to treatment responses. We will apply computational tools to the ICGC Controlled Data to confirm the potential therapeutic targets and markers found in our studies.
67.Ming YiFrederick National Laboratory for Cancer ResearchUnited States2019-11-252020-11-23
Cancer is one of the most complex diseases because the same type of cancer can be caused by different genes on different genetic mutations. RAS family genes (a family of genes that make proteins involved in cell signaling pathways that control cell growth and cell death) were found to be critical driver genes (genes whose mutations directly cause cancer growth) for onset of cancer. The RAS pathway is a set of genes work closely together with RAS family genes. Our research goal is to use bioinformatics (computational tools) and statistical methods to study the driver gene dataset from ICGC Controlled Data that we try to access to examine the roles of RAS pathway genes for their potentials as driver genes and characterize their relations as driver vs other genes within RAS pathway. Such efforts would help us to understand the refined underlying mechanisms and develop corresponding diagnosis and treatment strategies.
68.Chris GoodnowGarvan Institute of Medical ResearchAustralia2020-03-242021-03-23
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.
69.Oliver ZillGenentechUnited States2020-02-182021-02-09
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.
70.Jonathan GoekeGenome Institute of SingaporeSingapore2020-01-282021-01-21
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.
71.Jianjun LiuGenome Institute of Singapore, A*STARSingapore2020-04-092021-04-08
Nasopharyngeal carcinoma (NPC) is the cancer that occurs in the nasopharynx, which is located behind the nose and above the back of the throat. This cancer occurs very frequently in Southeast Asia. In our previous study, we have discovered that a high proportion of NPC patients from southern China carry specific Epstein-Barr virus (EBV) strains/variants. In this study, we sought to investigate the distribution of these high-risk EBV strains/variants in NPC patients from other geographical regions as well as patients with other diseases. To approach this issue, those raw reads collected from ICGC Controlled Data will be mapped to EBV reference genome sequence in order to identify the potential virus variants. We believe that this series of studies will ultimately shed light on early diagnosis and treatment of EBV associated cancers through identifying these high-risk individuals.
72.Shaoping LingGenome WisdomChina2020-08-052021-08-04
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. We will use controlled access ICGC data to develop an all-round ultra-fast Genomic Variant Caller that can efficiently and accurately detect mutations in major types of cancers, and apply these tools to the ICGC data to look for new mutations that are important for tumor development.
73.Benedikt BrorsGerman Cancer Research CenterGermany2020-04-062021-04-05
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.
74.Stefan PfisterGerman Cancer Research CenterGermany2020-07-142021-07-13
Brain tumors are among the most common tumors in children and are associated with high fatality. Current therapeutic approaches focus primarily on surgery and chemotherapy, which result in lower standard of life due to subsequent cognitive and behavioral disabilities, even in the case of successful treatment. Using computational methods, our project will investigate tumour sample data from the ICGC Controlled Data and compare it to developing human brain. Cell types in the brain which display similarities to tumor cells will be further investigated in terms of their role in tumor development.
75.Marc RemkeGerman Consortium for Translational Cancer Research (DKTK)Germany2020-02-112021-02-09
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.
76.Kathleen MarchalGhent UniversityBelgium2020-01-212021-01-08
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.
77.Jinfeng LiuGilead SciencesUnited States2019-12-042020-12-02
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.
78.Jinfeng LiuGilead SciencesUnited States2019-12-042020-12-02
Diffuse Large B-Cell Lymphoma (DLBCL) is a complex cancer. As one of the most effective treatment options, genetically engineered T cells (CAR-T) can recognize and destroy specific cancer cells with very high efficiency. At molecular level, DLBCL patients are different from each other, which causes different clinical response to CAR-T therapy. To explain such differences, we hypothesized that DLBCL tumors can be either “hot” or “cold”. “Hot” tumors usually have high level of immune activity and show good clinical response to treatment, whereas “cold” tumors have exhausted immune activity and show poor clinical response. The ICGC controlled data set (EGAD00001003600) was derived from DLBCL patients and provides a global measure of gene activities. This data will allow us to identify "hot" or "cold" subgroups with different tumor characteristics, and such knowledge may help us match types of tumors with the corresponding optimal CAR-T therapies.
79.Sergey NikolaevGustave RoussyFrance2019-11-132020-11-11
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.
80.Marc DelogerGustave RoussyFrance2020-04-022021-03-31
Based on laboratory experiments, we predicted that certain variations in a patient DNA could change the way the patient responds to cancer treatment. We will use the PCAWG whole genome datasets available as part of ICGC Controlled Data to verify the effect of these DNA changes in different tumors. This analysis is performed in three stages: first, we obtain the list of genetic variant found in the genome of cancer patients. Second, we select those variants meeting specific criteria in trmes of location in the genome and frequency in the population, third we use statistical methods to find whether those variants could be associated to a different patient survival or response to treatment. If we find variants with such effects, this could lead to improved personalized cancer treatments.
81.Xihao HuGV20 TherapeuticsUnited States2020-07-132021-07-12
The interactions between tumor and the host immune system is critical for finding markers to predict drug efficacy, reducing drug resistance, and developing new therapies. The environment around a tumor (known as "tumor microenvironment") plays a vital role in predicting the response of cancer immune therapy. We seek to use the ICGC Controlled Data to apply the latest tumor micro-environment analysis tools to estimate the composition of immune cells inside tumors and understand tumor-immune interactions in cancers.
82.Liang WangH. Lee Moffitt Cancer Center and Research Institute, Inc.United States2020-07-162021-07-15
The human genome contains many repetitive DNA sequences. These sequences are believed to play an important role in the exchange of genetic materials and hence structural changes of genes. Additionally, the number of copies of a gene may play a crucial role in the clinical outcomes of a patient. Previously, we found a significant association between the number of gene copies in a specific gene region and the total number of the repetitive sequences in the genome of two independent groups of prostate cancer patients.. The observed association is highly consistent in the two patients’ groups. However, the previous analysis was performed using circulating cell-free DNAs in blood, which are DNAs that are released into blood stream after cells die. In our new project, we aim to confirm this association by applying our computational methods to the ICGC controlled data to analyze DNA sequences from tumor tissues.
83.Ji Wan ParkHallym UniversitySouth Korea2019-12-172020-12-13
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.
84.Jin-Wu NamHanyang UniversitySouth Korea2020-01-282021-01-21
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.
85.Peter ParkHarvard Medical SchoolUnited States2020-03-172021-03-16
This is an international research project including researchers of the International Cancer Genome Consortium (ICGC) and its regional components, 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 perform uniform computational processing of the whole genome sequencing data from tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are caused by different data analysis methods. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes project and by using ICGC Controlled Data, 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.
86.Peter ParkHarvard Medical SchoolUnited States2020-03-032021-03-02
Cancer cells acquire genetic alterations in the course of their development. In contrast to healthy cells, the cancer cells often increase the copies of important genes that can accelerate their growth, or decrease the copies of some genes working as a brake to their cellular proliferation. In this study using the ICGC Controlled Data, the researchers explore the mechanisms by which the cancer cells could increase or decrease the copy numbers of important genes, by analyzing massive datasets of cancer genome sequences.
87.Itamar SimonHebrew University of JerusalemIsrael2020-07-162021-07-15
Cancer is a disease that stems from the accumulation of many mutations in the DNA. Interestingly, the mutations' distribution is not even and there are certain areas of DNA that gain more mutations than others. We are planning to use ICGC data that contains information about the location of all mutations in several thousand tumors, to study the rules that govern the spatial distribution of the mutations. Better understanding of mutational distribution will allow us to identify tumors that deviate from the regular pattern. The identification of such tumors is extremely important since such tumors require a special treatment which will be guided by our analysis.
88.Kwok Wing TSUIHong Kong Bioinformatics Centre, The Chinese University of Hong KongChina2020-08-242020-10-07
There are hundreds of different cancer types depending on the location, origin and genomic change during the treatment.The underlying biology of cancers remains unclear. Studying cancers systematically may shed light on the diagnosis and potential therapies. Here we would analyze data from patients with primary tumors in different sites of the body, including prostate, bladder, colon, lung, head and neck, ovarian and liver. We will use the ICGC Controlled Data for comparing different tumor types in term of mutations and genes in order to uncover the underlying biology of cancers.
89.Sara CooperHudsonAlpha Institute for BiotechnologyUnited States2020-01-232021-01-21
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.
90.Ramon ParsonsIcahn School of Medicine at Mount SinaiUnited States2020-07-302021-07-29
The development, progression of cancer and response to drugs are known to be driven by a combination of genetic and non-genetic modifications in cancer cell chromosomes and the evolution of cells within the tumor. Using molecular and clinical data from ICGC, we are interested to investigate specific molecular patterns as well as micro-organisms composition (presence of microorganisms genomes in tumor samples) in several types of cancer; including to study the genomic landscape of triple negative breast cancer (the subtype of breast cancer less understood and druggable so far) and to decipher the p53 network and how it maintains expression of other tumor suppressor genes in colon cancers. The lab will concentrate mainly these studies on seven cancers but we will extend our studies to other cancers in specific projects to investigate if we find the same patterns.
91.Kuan-lin HuangIcahn School of Medicine at Mount SinaiUnited States2020-04-142021-04-13
Genetic predisposition factors have been identified in patients across many cancer types. However, not all individuals sharing the same genetic factor develop the same disease, and in cases where they do, they frequently develop cancer at different ages and show different types of symptoms. The difference across patients is even observed within patients carrying very strong genetic factors. The molecules that modify the effect of cancer susceptibility need to be identified. Using the ICGC controlled data, we will develop computational methods that integrate many types of data to find these factors. The results will provide an effective model for the investigation of modifiers in many cancer types. The resulting findings will not only refine cancer diagnosis and screening but also contribute to the development of prevention strategies tailored to individuals carrying a diverse spectrum of inherited genetic factors.
92.Lucile CouronneImagine InstituteFrance2020-05-112021-05-10
Our research goals are to identify new mechanisms of cancer development and to identify novel gene targets for cancer treatment, in a particular rare subtype of lymphoma mainly affecting the nasal cavity and characterized by a poor prognosis. From previous works, we have identified several candidate genes that are recurrently mutated in this disease. This application requests approval to analyze data from ICGC to validate our previous findings. In particular, we want to confirm the recurrence of alterations in the candidate genes and to determine their consequences on diverse biological functions. This international collaborative research project will involve French and Japanese teams. Future results may reveal novel processes critical to development of this rare lymphoma and identify new therapeutic targets that may guide the production of more effective therapies.
93.Jason FuntImmuneering CorporationUnited States2020-05-042021-05-03
Cancer is a complex disease in which multiple genetic changes drive disease onset and progression. By studying the genetic profile of tumors, we intend to better characterize the similarities and differences between patients to identify the best treatments matched to specific types of disease. ICGC Controlled Data will be evaluated to detect mutations and associate them to known cancer-causing mechanisms, such as exposure to ultraviolet radiation or smoking. This work will enable us to more efficiently match the right drugs to the right patients and improve clinical outcomes to restore quality of life and transform cancer into a treatable, chronic disease.
94.Robert BrownImperial College LondonUnited Kingdom2020-06-112021-06-10
At present we don't understand why ovarian cancer patients responds poorly to immune therapies, despite their tumour cells being recognised by the immune system. One possibility is that there is inactivation of genes that regulate immune responses during tumour development or following chemotherapy. We have observed that marks in DNA (DNA methylation) involved in switching genes off and on are altered in immune genes in ovarian cancer. Using the ICGC controlled data we now aim to examine if these marks correlate with how much of these genes are being made in the tumour, as well as clinical features, such as numbers of DNA mutations or patient survival. Since there are new therapies (DNA demethylating agents) which target DNA methylation and can hence switch genes back on again, these analyses may provide support for preclinical studies and clinical trials of such therapies to improve ovarian cancer patients’ responses to immune therapies.
95.Debnath PalIndian Institute of ScienceIndia2020-01-282021-01-26
Oral cancers are 30% of all cancers in India and predominantly occur due to excessive use of chewing-tobacco. Cancer tissue contains different cell types of which cancer stem cells are important due to their role in cancer initiation, recurrence and drug resistance. These stem cells are unique due to their ability to form other cell types as well as to produce more of its own kind. Using the ICGC controlled data, we will study the functional changes in the genes of cancer stem cells especially using samples sourced from Indian subcontinent. This study aims at identifying important gene mutations which will give insight into the disease pathology. Our future plan is to extend the study to other cancers.
96.Hamim ZafarIndian Institute of Technology, KanpurIndia2020-02-202021-02-12
Cancer is one of the most complex diseases involving abnormal cell growth with the potential to invade or spread to distant parts of the body. Oral cancer involves malignant growth of the lining of the lips, mouth, or upper throat. It constitutes ~2-4% of all cancers worldwide, while in India, it is the 2nd most commonly diagnosed cancer and accounting for 45% of all cancers. In our study, we aim to explore the heterogeneous cell populations in oral cancer by studying the genetic mutations. We will use ICGC controlled datasets for studying the mutations. This will help in understanding the biological processes involved in different stages of oral cancer and will help in guiding clinical decisions for personalized therapy of future oral cancer patients.
97.Karthik RamanIndian Institute of Technology, MadrasIndia2020-09-032021-09-02
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.
98.Marc-Henri SternInstitut CurieFrance2019-12-182020-11-27
Cancer is often linked with acquired abnormalities of the tumor genome, such as mutations, gains and losses of parts, and other aberrant structures. Some tumors are characterized by an increased rate for such abnormalities, a process named genomic instability. Our research project is devoted to unravelling the origins of genomic instabilities in cancers. Our approach consists in the systematic analysis of cancer genome architecture with relation to the genes altered in various types of cancer. By analyzing ICGC controlled data, we aim at deciphering associations and functional links between gene alterations and the genomic instability patterns. Taking into consideration genomic instability could improve tumor molecular classifications, prognosis and prediction of response to treatment.
99.Fabien ReyalInstitut CurieFrance2020-02-252021-02-24
The goal of our research project is to develop and evaluate a new computational tool to unravel cancer evolution. This new tool, CloneSig, is the first one to jointly leverage two information that are currently analysed separately: (i) existence of subpopulations in the tumor, as captured by the variability in observed prevalences of mutations within a tumor, and (ii) mutational processes, as revealed by the distribution of the types of mutation and their immediate sequence patterns. We plan to apply CloneSig to the ICGC datasets, to provide the community a better description of the mutational forces at the genomic level that shape the apparition and progression of cancer. Additionnally, those results will allow to compare CloneSig to previously published results, obtained by other computational approaches, and further validate the new findings it may uncover.
100.Philippe HupéInstitut CurieFrance2020-08-272021-08-27
The EUropean-CANadian Cancer network (EUCANCan) project aims to enable Personalized Medicine in Oncology by promoting standards to analyze DNA sequencing and clinical data. The ICGC Controlled Data will be analyzed using different biostatistical and bioinformatics algorithms. We will compare the ability of these different algorithms to detect DNA mutations and modifications in the tumour genomes using golden datasets of which experimentally validated data are available. The objective is to improve the performance of current algorithms to reach a better efficiency and reliability such that the results can support the clinicians to decide what treatment is the most appropriate for each patient individually. This work will be performed in collaboration with all the researchers of the EUCANCan project.
101.Josep M LlovetInstitut D'Investigacions Biomediques August Pi i Sunyer (IDIBAPS)Spain2020-07-152021-07-14
Hepatocellular carcinoma (HCC) accounts for the majority of primary liver cancers (90%), and is one of the few cancer types characterized by a rising incidence and mortality as well as limited treatment options, thus representing a major public health issue worldwide. Recent studies using immune checkpoint inhibitors, which are treatment drugs that influence the immune system's ability to respond to cancer, have elicited remarkable responses in approximately 20% of the HCC patients. Trials in the field offer genuine hope that immunology-focused research may hold the key to better treatments for many more patients. We aim at understanding how the immune system of patients responds differently to the cancer and subsequently impacts the development or progression of the disease. To achieve this, we plan to use computational tools to conduct genomic analyses incorporating the ICGC controlled data.
102.Tuyen HoangInstitute for Clinical and Translational Science, University of California in IrvineUnited States2020-09-162020-10-30
In a previous genetic study of gastric cancer, we found that recently immigrant Asian patients had better survival than Caucasian Americans. To search for genes that could explain better survival among Asians, we propose to use open-access genomics data to compare genetic profiles among three groups: Asians in Asia, Asian Americans, and Caucasian Americans. We have already obtained the genetic data of Asian Americans and Caucasian Americans from our previous genetic study. To obtain the genetic data of Asians in Asia, we would like to request access to two open-access genomics datasets from a China project and a Japan project, available at the ICGC and combine these data to our existing American genetic data. Each subject will be flagged with or without a mutated gene. Frequencies of mutated genes will be compared between two ethnic groups using statistical analysis. This project will in no way re-identify research participants.
103.Marat KazanovInstitute for Information Transmission Problems, Russian Academy of SciencesRussian Federation2020-07-212021-07-20
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.
104.Nuria Lopez-BigasInstitute for Research in Biomedicine (IRB Barcelona)Spain2020-01-142021-01-12
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.
105.Fran SupekInstitute for Research in Biomedicine (IRB Barcelona)Spain2020-07-302021-07-29
DNA in human cells needs to be repaired as cells age and divide. Failures to do so may result in cancers and possibly in other age-related pathologies. Our interests lie in peforming large-scale statistical analyses using genomic data, including ICGC controlled data, to learn about the DNA repair mechanisms that safe-guard the information stored in the DNA. We are also interested in learning how commonly failures in DNA integrity affect different individuals and the consequences this has on cancer risk and the mechanisms enabling tumor formation, as well as implications for therapy of tumors.
106.Piotr ZielenkiewiczInstitute of Biochemistry and Biophysics, PANPoland2020-08-172020-10-01
There are many factors that influence cancer development. Increasingly, there is evidence that an important cellular component called the mitochondria becomes important in cancer development when the mitochondria’s function is affected by the presence of certain bacteria and viruses. The mitochondria is distinct from most other cell components in that it has its own distinct DNA. This project aims to investigate whether the DNA of specific bacteria and viruses can be detected in the DNA of the mitochondria. The ICGC data we are requesting contains useful data on mitochondrial DNA that would allow us to study the patterns of specific bacterial and viral DNA associated with mitochondrial DNA in various cancer tissues.
107.Takashi AngataInstitute of Biological Chemistry, Academia SinicaTaiwan2020-06-022021-06-01
Cell surface is decorated by glycans, which are made of a few to dozens of various sugars as building blocks. The glycans on cancer cells are often different from those on normal cells. Some types of cancer cells escape destruction by killer cells by engaging Siglecs (a family of glycan-recognition proteins) on killer cells. B cell chronic lymphocytic leukemia (B-CLL) is caused by the accumulation of B cells (a type of white blood cells) that resist cell death. We found Siglec-7, a Siglec expressed on killer cells, binds strongly to B-CLL cells. The binding between Siglec-7 on killer cells and its ligand (binding partner) on B-CLL cells may help B-CLL cells escape the destruction by killer cells. We aim to understand how the Siglec-7 ligand is increased on B-CLL cells, by analyzing the expression patterns of glycan-related genes, and test whether such information is useful for patient prognosis.
108.Bissan Al-LazikaniInstitute of Cancer Research, UKUnited Kingdom2020-03-312021-03-30
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.
109.Richard HoulstonInstitute of Cancer Research, UKUnited Kingdom2020-02-112021-02-07
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.
110.Colin SempleInstitute of Genetics and Molecular Medicine, University of EdinburghUnited Kingdom2020-03-172021-03-16
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.
111.Jose Luis Bello LopezInstituto de Investigacions Sanitarias de Santiago de CompostelaSpain2020-04-232021-04-22
Lymphoid tumors are frequent blood cancer characterized by a diversity of clinical manifestations and treatments. It has been known for years that lymphoid tumor cells harbour genetic alterations which have prognostic impact. Nevertheless, recent studies of lymphoid tumor genomes have enabled the detection of a myriad of mutated genes and the inference of various common mutational patterns, reinforcing the idea that lymphoid tumor are genomically complex diseases. In this project, we will integrate several layers of biological complexity in order to identify new genes implicated in lymphoid tumor biology and to study their association with clinical events. We will use the ICGC database as our initial genomic data input to characterize lymphoid tumor genomes. Further experimental studies will be formulated on the basis of the results that we may obtain.
112.Jiri ZavadilInternational Agency for Research on Cancer (IARC)France2020-03-242021-03-23
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.
113.roberto puzoneIRCCS Ospedale Policlinico San MartinoItaly2019-11-192020-11-17
Most cancer are heterogeneous genetic disease in which many genes are involved, and some inherited gene mutations are already known to be associated with different therapies, cancer progression and prognosis. Our study investigates into a potential increase of the known specific risk associated with some inherited point mutations (SNV), in cancers which have acquired mutations in genes which are known to strongly promote specific cancer development (driver genes). Because these SNV are in totally different DNA positions than the driver genes no direct influence can be thought, thus the effect should involve the genes as a network (pathways). Using ICGC controlled data, we will focus on high incidence cancer such as lung, breast, ovarian, and colon cancer, to perform an investigation that will include comparison among the different cancers.
114.Roel VerhaakJackson Laboratory for Genomic MedicineUnited States2020-01-172021-01-07
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
115.Edison LiuJackson Laboratory for Genomic MedicineUnited States2020-08-042021-08-03
At The Jackson Laboratory, we are interested in understanding the precise genetic mechanisms that lead to tumor initiation and growth and to uncover the tumor intrinsic features that influence therapeutic response. We have recently described a collection of complex cancer genetic profiles characterized by hundreds of DNA segmental duplications, collectively known as tandem duplicator phenotypes (TDPs). Tumors that classify as TDP feature highly complex genomic structures that perturb the activity of a wide range of critical cancer genes, ultimately promoting tumor growth and tumor evolution. We will use the ICGC breast cancer dataset to expand our current analysis of human cancer genomes and to investigate the potential mechanisms leading to the observed TDP genomes and their consequences for tumor growth and therapeutic response.
116.Pedro CruzJanssen R&D, LLCUnited States2020-05-252021-05-24
Cancer is a complex disease, with numerous genetic alterations co-occurring and evolving within one single patient over one diagnosis. Some of these sporadic mutations confer the tumor enhanced proliferation capabilities and lead to poor prognosis. Many of these mutations are targets for therapeutics but many targets are yet to be identified. In analyzing the ICGC controlled access data, with a focus on gastrointestinal tumors, we intend to identify novel mutations, especially within regions of the DNA that vary between individuals. These regions, called variants, are complex and diverse. To achieve this goal, we plan to develop and publish novel machine learning approaches that explore the relationships between variants and their regulatory effect on genes.
117.Michael GormleyJanssen Research and DevelopmentUnited States2020-04-212021-04-20
Cancer is a complex disease in which multiple genetic changes drive disease onset and progression. By studying the genetic profile of tumors, we intend to better characterize the biological similarities and differences between patients to identify the best treatments matched to specific types of disease. ICGC Controlled Data will be evaluated to detect genomic mutations and associate these with biological signatures of known cancer causing mechanisms, such as exposure to ultraviolet radiation or smoking. This work will enable us to more efficiently match the right drugs to the right patients and improve clinical outcomes to restore quality of life and transform cancer into a treatable, chronic disease.
118.EKTA KHURANAJoan & Sanford I. Weill Medical College of Cornell UniversityUnited States2020-04-162021-04-15
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.
119.Sepp HochreiterJohannes Kepler University LinzAustria2020-03-242021-03-23
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.
120.Rachel KarchinJohns Hopkins UniversityUnited States2020-08-262021-08-25
Many DNA mutations implicated in cancer causation are found in a substantial fraction of patients. However there exist a very large number of mutations that are only seen in a few patients. We are developing a computational method to predict which of these rare mutations are involved in cancer origination and progression. ICGC Controlled Data will be used as a source of mutations in cancers of multiple types.
121.Jung Kyoon ChoiKAISTSouth Korea2020-03-312021-03-30
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.
122.Yilong LiKaleidoscope GenomicsUnited States2020-07-302021-07-29
There are two main classes of mutations. Most research focuses on point mutations, which are small alterations to the DNA sequence and they have been comprehensively characterized over the past decade. On the other hand, genomic rearrangements, where large chunks of DNA are deleted, duplicated or relocated in the genome, have received less attention due to their relative complexity. Our group recently developed novel algorithms for characterizing genomic rearrangements. The goal of this project is to apply our algorithms on ICGC Controlled Data to study the patterns and effects of genomic rearrangements in cancer, as well as how they relate to other forms of genomic alternations.
123.Jonas FrisenKarolinska InstitutetSweden2020-08-262021-08-25
Prostate cancer is one of the most common tumour types in the world. Most men can live with their prostate cancer for a long time and die of other causes, but for some men, the cancer is a lethal disease in which the tumour cells spread in the body. Analysing the ICGC controlled CPC-GENE prostate cancer cohort in which a large number of patients’ blood and tumours have been sequenced, we will determine if components of the immune system and infections are associated to tumour features by state-of-the-art computational methods. Knowledge retrieved from ICGC controlled data will aid in determining if components of the immune system and infections are associated to prostate cancer prognosis and their potential utility as drug targets.
124.Anita GrigoriadisKing's College LondonUnited Kingdom2020-07-302021-07-29
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.
125.Francesca CiccarelliKing's College LondonUnited Kingdom2020-01-162021-01-15
Mutation data can provide useful clinical information about cancers, but it remains challenging to identify which mutations drive tumour progression in individual patients ("driver mutations"). Our lab is continuing to develop a machine learning tool for this purpose that we have already demonstrated on oesophageal cancer data. The new release will include a feature to incorporate data from new samples as they become available, which is particularly relevant to clinical settings where new data often arrive sporadically. In order to develop this feature, we require ICGC samples to add to the cohort we are currently using, which is from The Cancer Genome Atlas. Specifically, we are focusing on gastro-intestinal cancers, and plan to make use of the available colorectal cancer samples in ICGC. We will assess how the inclusion of these additional samples improves the ability of our tool to correctly identify driver mutations in individual patients.
126.Eugene Makeyevking's college university of londonUnited Kingdom2020-09-092021-09-08
Cancer progression often involves large-scale changes in the genetic makeup of human cells. By applying computational methods to the ICGC Controlled Data, we hope to find out how especially prevalent mutations in cancer cells affect function of corresponding genes and to understand consequences of these changes for cancer biology. We hope that this research will uncover new fundamental mechanisms underlying formation of aggressive tumours and eventually lead to new ways to diagnose and treat cancer.
127.Francesca CiccarelliKing's college University, LondonUnited Kingdom2020-04-142021-04-13
Oesopheageal cancer is a disease in which cells of the oesophagus grow at an uncontrolled rate. It can present in two main types: squamous cell carcinoma and adenocarcinoma. The incidence of the latter in the UK has dramatically increased in the last decade. Few therapies have proven beneficial in improving patient survival diagnosed with oesophageal adenocarcinoma. One of the main reasons is the high genetic difference that exists among oesophageal adenocarcinomas from different patients. In this context, we aim to identify which genes cause the initiation and progression of the tumour at single-patient level in order to investigate what causes the tumour to develop. ICGC controlled data will be used to investigate how the tumour from its initial site colonises distant organs of the body.
128.Francesca CiccarelliKing's college University, LondonUnited Kingdom2020-09-012021-09-01
Identification of genomic alterations that sustain cancer and understanding their impact on cancer progression is one of the key goals of cancer biology. A list of known genes involved in cancer is still lacking to completely explain the disease in most of the samples. To overcome that limitation, we developed sysSVM (system-level Support Vector Machine), an algorithm that predicts cancer genes in individual patients. The main idea behind the algorithm is that known cancer genes have properties that distinguish them from the rest of human genes. Therefore, these properties are learned by the algorithm in order to detect novel cancer genes. We will use ICGC data to assemble an independent cohort to confirm the predictions obtained on the initial pan-cancer cohort. Confirmed cancer genes will be used to develop, apply and compare models of cancer progressions to interrogate how alterations in cancer genes modify cancer progression.
129.Nathan LackKoc UniversityTurkey2019-11-122020-11-10
Prostate cancer is an extremely common disease that affects an estimated one out of every seven North American men in their lifetime. It has been demonstrated in simple experimental models that the growth of prostate cancer can cause DNA damage at specific genetic locations. To test if these same mutations arise in patient tumours, this project will use ICGC controlled data to characterize the type and frequency of mutations that happen near certain genetic elements from clinical data.
130.Christine DesmedtKU LEUVENBelgium2019-12-092020-12-07
Increased body mass index (BMI) has been recognized as a risk factor for developing breast cancer (BC) and has also been associated with adverse survival. Here we aim to use the ICGC Controlled Data to investigate the associations between the individual'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 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. In other words, this project aims to use ICGC controlled data to find differences in genetic components and nongenetic influences between lean and obese individuals in order to better understand the biology of BC developed in overweight individuals.
131.Seishi OgawaKyoto UniversityJapan2020-08-222020-10-06
The dynamics of clonal evolution from non-malignant mammary epithelial cells to invasive breast cancers are poorly understood. In this study, we will analyze the genetic alterations of non-invasive breast cancers and precancerous lesions by analyzing DNA sequences. To investigate the alterations correlated with invasiveness, we would like to analyze the ICGC sequencing data of invasive breast cancer cohort and compare them to our non-invasive cohort data.
132.Seishi OgawaKyoto UniversityJapan2020-04-162021-04-13
Lymphoma is a cancer of certain types of white blood cells called lymphocytes, and primary central nervous system lymphoma (PCNSL) is a rare lymphoma that is confined to the brain at the time of diagnosis. Approximately 95% of PCNSLs are pathologically classified as diffuse large B-cell lymphomas (DLBCLs), which is the most common lymphoma type of B cells and usually occurs at lymph nodes. Outcome of patients with PCNSL is often poor, although it has improved substantially over the past two decades. Genetic causes of PCNSL and the genetic differences between PCNSL and DLBCL in lymph nodes remains poorly understood. In this study, we will analyze the ICGC dataset of DLBCLs in lymph nodes and compare it to our dataset of PCNSLs in terms of the number or type of somatic mutations and other genetic changes.
133.Atsushi TakaiKyoto University HospitalJapan2020-02-252021-02-23
The characteristics of genetic alterations in cancer genome are associated with the background diseases. However, the carcinogenic mechanisms of the cases without representative background-associated alterations are still unclear. To reveal the background-specific carcinogenic mechanisms and to identify a new therapeutic target of cancers in gastrointestinal tract and liver, we plan to analyze genetic features of human cancer specimens by using both clinically obtained specimens in our institute and ICGC controlled data.
134.Mikita SuyamaKyushu UniversityJapan2020-06-022021-06-01
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.
135.Swee Seong WongLifeomicUnited States2020-03-312021-03-30
In this study, we will use ICGC controlled data to study a type of mutation called "transposable elements" which can change their own positions by "jumping" to different points in a cell’s genome. In collaboration with Dr. Milan Radovich of Indiana University, we will study how these elements insert themselves into normal DNA in order to discover how these alterations and other genetic variants could lead to cancer development. We will use ICGC Controlled Data to develop new computer algorithms and "machine learning" approaches to this data analysis, which may help us to predict disease characteristics and patient outcomes. This will be valuable in translating research into practical clinical use.
136.Manish GalaMassachusetts General HospitalUnited States2020-03-032021-03-02
Tumor genome projects have primarily focused on the mutations found uniquely in the tumor. In this proposal, we seek to underline more of the inherited predisposition to cancers derived from normal tissue genome sequencing available from ICGC Controlled data, as well as how these inherited genetic changes influence how tumors develop their mutations as demonstrated from intermediate files from the ICGC Controlled Data.
137.Tyler JacksMassachusetts Institute of TechnologyUnited States2020-08-252021-08-25
Immunotherapy works by enhancing the immune system’s ability to identify and destroy cancer cells. Cytotoxic (‘cell-killing’) immune cells accomplish this by recognizing tumor-specific molecules, called neoantigens, that are presented on the surface of tumor cells. With this in mind, a major goal of immunotherapy research is to expand our ability to identify patient neoantigens, for the purpose of improving immunotherapies and better understanding how they work. In our project, we will use a newly constructed computational program and ICGC’s genomic sequencing data of cancer patients to computationally evaluate neoantigens in patients across various cancer types. This will enable us to potentially uncover novel classes of neoantigens, which will inform downstream functional efforts to study these neoantigens in preclinical models and test immunotherapies designed to target them.
138.Roland SchwarzMax Delbrück Center for Molecular MedicineGermany2020-09-152020-10-28
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
139.Altuna AkalinMax Delbrück Center for Molecular MedicineGermany2020-02-042021-01-30
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 different types of genomic data from ICGC to measure the alterations on regulatory regions and the activity of genes and regulatory regions.
140.Zlatko TrajanoskiMedical University InnsbruckAustria2020-04-272021-04-26
In our previous work we have shown that specific mutation profiles are determining the composition of the immune cells that infiltrated into tumors, and that these immune profiles control the growth of the tumor and likely their responses to therapies. In this project we will use ICGC controlled data to estimate the composition of the immune cells that infiltrated into tumors and analyze which immune cells types can predict response to therapy. Additionally, we will determine the antigens that are presented by the tumors and seen by the immune cells. These antigens are potential candidates for therapeutic cancer vaccines.
141.Omar Abdel-WahabMemorial Sloan Kettering Cancer CenterUnited States2020-09-202020-11-03
Pancreatic cancer has recently been identified to be composed of distinct subtypes based on gene expression. The proposed research will use ICGC Controlled Data to help evaluate to the extent to which these changes are caused by a protein that it is mutated in 60% of pancreatic cancer patients, called p53, with the hope that this will identify previously unknown and fundamental biological aspects of this highly fatal disease. This knowledge will provide the foundation for future research that will lead to the development of more effective approaches to treat pancreatic cancer.
142.Sohrab ShahMemorial Sloan Kettering Cancer CenterUnited States2020-09-152020-10-29
The International Cancer Genome Consortium aims to identify patterns of variation in the tumor 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 tumor biology, and translate this information into new diagnostic tests, prognostic tools, and therapies.
143.Nadeem RiazMemorial Sloan Kettering Cancer CenterUnited States2020-01-212021-01-16
Cancer patients have different mutation patterns in their tumors. Some of these mutations relate to the ability of the tumor cells to repair their damaged DNA. When patients receive radiation therapy, the radiation will damage tumor cell DNA and kill the tumor cells if there are enough DNA damages. To cure cancer or to stop further tumor growth, we want to use radiation therapy to damage tumor DNA effectively. In this study, we are studying if some mutation patterns in tumor cells will make radiation therapy more effective. It will both benefit tumor treatment with radiation therapy and improve patients' life quality when growing tumor cells need to be stopped by radiation treatment.
144.Daniel HigginsonMemorial Sloan Kettering Cancer CenterUnited States2020-05-252021-05-24
Many cancers are caused by defects in the way cells repair DNA damage. Defective DNA repair can lead to small changes in the DNA sequence, such as loss of small pieces of DNA (called deletions). The pattern of deletions in DNA can provide insight into how cancers are defective in DNA repair. We have discovered an association between throat cancers caused by the human papillomavirus (HPV) and a specific type of DNA deletion. In this study, we aim to further characterize this association and also test for new associations. To perform these analyses, we will analyze ICGC controlled data to identify HPV sequences and mutations in inherited genes that repair DNA. Then, we will compare the characteristics of deletions in cancers with those mutations. This effort may be of benefit to help better identify cancers with DNA repair defects, which are more sensitive to DNA damaging chemotherapies.
145.Benjamin GreenbaumMemorial Sloan Kettering Cancer CenterUnited States2020-08-052021-08-04
IIn this application, we propose to use the ICGC controlled data to test novel mathematical methods, developed in our lab, for the analysis repetitive elements, also known as “repeats”, in cancer genome. Repeats are large repetitive sections of the human genome that are missed in conventional analysis of DNA, but can be discovered when analysing the complete DNA sequence of an organism. Our lab has previously shown techniques to define immunological states in cancer using repeats. Likewise, recent analyses of ICGC controlled datasets have shown the movement of repeats to be associated with variations in the DNA of individuals. We will use the tumor and normal sequencing data from the ICGC controlled data to develop, evaluate, and improve computational tools for assessing the role of these regions in generating variations in tumors. In doing so, we hope to better characterize their role in the development of cancer tumorigenesis.
146.Ronglai ShenMemorial Sloan-Kettering Cancer CenterUnited States2020-01-092021-01-08
Increasingly cancers are being evaluated by modern laboratory tests that identify mutations that have occurred in the DNA that have led to the cancer. However, we have very little knowledge about the influences of the vast majority of these mutations that tend to be unique to an individual patient. This project will use sophisticated statistical tools to extract information about these “rare” mutations from the ICGC data with a view to accurately identifying the organ in the body in which the tumor occurred, an important clinical challenge for cancers where the primary site is unknown or when the cancer is detected in a blood screening test.
147.Pawel ZawadzkiMNM DIAGNOSTICS SP. Z O.O.Poland2020-07-152021-07-14
It is believed that the molecular mechanism associated with treatment response in cancer patients is associated with their genetic predisposition. Nevertheless, analyzing these genomic changes and understanding their biological meaning constitute one of the biggest challenges. Therefore, the main purpose of this project is to identify all genomic features that may have clinical significance, serving as markers of drug response in ovarian, breast, pancreatic and prostate cancer samples obtained from the International Cancer Genome Consortium. The analyzed data will be used to build meaningful clinical tool for assessing genomic changes, thus providing a distinction between sensitive and resistant tumors. Consistently, this tool will contribute to the development of personalised medicine, improving patient treatment and stratification.
148.Zhibin HuNanjing Medical UniversityChina2020-07-022021-06-29
At present, global morbidity and mortality of cancer are rising. The increased burden of cancer has become public health issues. Identifying genome alterations of cancer is essential for comprehending the initiation, processes and the differences in treatment outcomes. In view of the unique tumor genomic characteristics of Chinese population compared with other populations, such as European and Southeast Asian, systematic clarification of the similarities and differences between various populations is conducive to promoting the development of precision medicine and reducing the burden of public health. ICGC data will help us elucidate the population-specific alteration of cancer and provide new evidence on the translation of precision medicine.
149.Stephen BenzNantOmics, LLCUnited States2020-08-222020-10-06
Each gene in the genome interacts with other genes in two ways: a gene’s activity is regulated by others, and the biological activity of a gene in a cell may require the coordination of other gene’s. In tumors, mutations will aberrantly turn genes on or off, resulting in the abnormal behaviors of tumor cells. Using ICGC controlled data, we will map each tumor’s mutations onto the set of genes, and then the corresponding interactions, in an attempt to explain tumor cell’s behavior in terms of how these regulatory interactions have been modified. We will provide an overview of what sets of interactions are most often modified together, in order to better understand what biological processes must be co-regulated in cancer.
150.Tatsuhiro ShibataNational Cancer CenterJapan2020-08-182021-08-17
Recent studies reported that the total number of mutations was associated with clinical response to immune checkpoint inhibitors in melanoma (skin care), lung cancer and others. The non-self antigens ("from the external environment") produced by somatic mutations (acquired genetic alterations) are called neo-antigen. Therefore, the landscape of neo-antigen in individual patient is expected to contribute to the personalized immunotherapies. However, several previous studies reported more complex association between neo-antigen and anti-tumor immune responses. To better understand the biological significance of neo-antigens in immunological features of tumor, we will perform comprehensive analyses using various sequencing techniques and ICGC data. We will identify neo-antigen, and then investigate the critical factors that contribute to differences in immune system within each tumor type. Through this study, we attempt to uncover clinically useful biomarkers (indicators we use to examine biological processes) for cancer immunotherapy.
151.Mamoru KatoNational Cancer CenterJapan2020-08-182021-08-17
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
152.Keisuke KataokaNational Cancer CenterJapan2020-08-262021-08-25
Recent scientific advances have enabled us to systematically identify tumor genomic abnormalities. However, detection of genomic abnormalities still remains challenging. Therefore, this study aims to comprehensively characterize the entire landscape of genomic abnormalities in a variety of cancers using our newly created pipelines. We would compare the characteristics of these genomic abnormalities both within and across cancer types, and we attempt to identify the critical abnormalities in tumor genomes. Clearly describing the genomic abnormalities and identifying the novel abnormalities would be of great significance for achieving Genomics-Driven Precision Medicine. To perform this research, we are planning to run our newly developed pipelines on the data deposited by ICGC.
153.Keisuke KataokaNational Cancer CenterJapan2020-07-102021-07-09
Our research goals are to identify new mechanisms of cancer development and to identify novel gene targets for cancer treatment, in a particular rare subtype of lymphoma mainly affecting the nasal cavity and characterized by a poor prognosis. From previous works, we have identified several candidate genes that are recurrently mutated in this disease. This application requests approval to analyze data from ICGC to validate our previous findings. In particular, we want to confirm the recurrence of alterations in the candidate genes and to determine their consequences on diverse biological functions. This international Collaborative Research project will involve French and Japanese teams. Future results may reveal novel processes critical to development of this rare lymphoma and identify new therapeutic targets that may guide the production of more effective therapies.
154.Tatsuhiro ShibataNational Cancer Center JapanJapan2020-07-022021-07-01
Structure variants (SVs) are large-scale alternations in the genome, which were caused by deletion, insertion, duplication, and translocation of the chromosomal fragments. We have established a highly efficient and unique bioinformatics pipeline to detect SVs from cancer genome sequencing data and applied it to the Japanese cancer cohort. Thereafter, we also identified characteristic combinations and patterns of SVs in our samples. Here, to further investigate any association between these patterns and the epidemiological or ethnic backgrounds, we propose to analyze sequencing data of cancers of distinct ethnic groups deposited in the ICGC database. We will reanalyze the data by our analytical pipeline and would like to compare the compositions of the patterns with those identified in our cohort.
155.Yuichi ShiraishiNational Cancer Center JapanJapan2020-08-042021-08-03
Recent advances in scientific technology have enabled us to identify genomic abnormalities, including genomic mutations, gene expression and epigenetic alterations that affect the gene regulation without altering the sequence itself. However, the comprehensive analysis of genomic, gene expression and epigenetic abnormalities still remains challenging. In this project, we plan to use the controlled data from ICGC to develop a novel approach for comprehensively detecting the alterations on the genome and reveal their relationships in a variety of cancers. Our newly created pipelines will contribute to the characterization of tumor genomic and epigenetic abnormalities.
156.Bin Tean TehNational Cancer Centre SingaporeSingapore2020-09-112021-09-10
Cholangiocarcinoma (CCA) is the second most frequent liver malignancy. In some Asian regions, herbal plants are commonly used in traditional medicine. Previous studies have shown that that CCA could be caused by herbal plant abuse. However, its effects and pathogenic mechanisms are still not clear for CCA. To further investigate this, we will apply computational tools to the ICGC controlled data to compare CCA samples with herbal drug history and those without that. Our goal is to deepen our understanding of CCA tumor development, as well as a wide variety of cancers with a similar mechanism of tumor development.
157.Ludmila Prokunina-OlssonNational Cancer InstituteUnited States2020-04-232021-04-22
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.
158.Javed KhanNational Cancer InstituteUnited States2020-09-042020-10-19
Pediatric malignancies are a leading cause of mortality in children. Improvements in understanding of biology and advancements in therapies have driven the over all survival rate close to 80% today. Despite these successes, survival remains dismal for patients whose cancer comes back and those with certain tumor types. Additionally, traditional therapies come with a life altering side effects, including neurocognitive loss, developmental issues, and secondary cancers. This highlights the need for therapies specifically targeting the tumor. An emerging cancer treatment is cellular immunotherapy, where a patient's own immune system is strategically altered to kill the tumor while sparing healthy organs. We will use the ICGC Controlled Data to identify tumor specific targets and understand ways the tumor would evade the immune system. We also will use the data to understand the pre-existing immune cells in the tumor. This knowledge will be integrated to develop novel approaches to cure children with
159.SOHYOUNG KIMNational Cancer InstituteUnited States2020-05-132021-05-12
DNA contains a coding region, which are instructions to make proteins, and a non-coding region, which plays essential roles in regulating normal cell functioning. Malfunction of those non-coding regions is critical in the initiation and progression of prostate cancer (PC). However, how the non-coding regions affect PC initiation and progression remains mostly unknown. Our current study aims to uncover how those non-coding regions are altered in aggressive PC patients compared to their healthy tissue. We will evaluate the discovered association in prostate cancers using independent patient cohorts from ICGC. The identified non-coding regions that are malfunctioning in aggressive PC will help us to develop markers predictive of potential patient clinical outcomes.
160.Andre NussenzweigNational Cancer InstituteUnited States2020-07-072021-07-06
The human genome is subject to a variety of assaults from both internal and external influences that can result in a diverse set of alterations and changes to the genome that is collectively referred to as structural variation. These variations are typically a hallmark feature of cancer genomes and usually arise when normal biological processes go awry. The goal of this project is to understand the mechanistic underpinnings that portend the formation of structural variation. We have recently developed a novel method called END-Seq that allows us to survey these structural variations in cancers. We will deploy this technique in conjunction with analysis of ICGC cancer data sets to extract features surrounding structural variations that will help inform the biology behind the changes in the genome that lead to a spectrum of cancer pathologies.
161.Dmitry GordeninNational Institute of Environmental Health Sciences, NIHUnited States2020-01-302021-01-27
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.
162.David SturgillNational Institute of Health (NIH), Bethesda, Maryland, USAUnited States2020-01-212021-01-19
Although the cells in the body carry essentially the same genome, they use this same set of instructions to carry out very different functions. This is made possible by modulating how these instructions are used, and how the genes are deployed. The major route for this process is by alternative processing of gene transcripts (mRNA), which can lead to a multiple possible proteins. Defects in mRNA processing are characteristic of many cancers. Whether they are cause or consequence of disease is often unknown. Sequencing data from a diverse set of tumors, such as ICGC Controlled Data, will allow us to dissect this phenomenon and better understand such aberrant RNA processing programs.
163.SHU JUI HSUNational Taiwan UniversityTaiwan2020-08-312021-01-20
In order to evaluate the performance of current mutation discovery methods for clinical cancer samples, it is crucial to have an ICGC Controlled Data in the European Genome-phenome Archive to serve as the benchmark dataset. As different methods may heavily affect the overall mutation results, publicly available data can be used to compare and contrast. We will use identical data with published results to test both the false positive/negative detection rate in several major tumor mutation discovery tools. Then, we may further adjust our analytic strategy accordingly, which would be applied to our patients.
164.Cathal SeoigheNational University of Ireland, GalwayIreland2020-02-112021-02-09
Somatic mutations (mutations that are acquired during a person’s lifetime) are frequently the basis for the development of cancer. Some somatic variants can be present at a low frequency and are hard to distinguish from errors incorporated during sample processing and the following analysis. We propose to use the ICGC Controlled Data to develop and test statistical models to extract information about the rates and patterns of somatic mutations efficiently from cancer genomics data. Because the burden of somatic mutations can inform cancer prognosis and help to predict the extent to which a patient will benefit from certain treatments, these tools have the potential to be of benefit to cancer patients.
165.Steven RozenNational University of SingaporeSingapore2020-08-042021-08-03
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.
166.Steven RozenNational University of SingaporeSingapore2020-08-042021-08-03
Cancer is caused by alteration (mutations) to a cell's genome. Recent advances in computational methods enable the identification of genetic alteration patterns or mutation signatures in individual cancer genomes. This project aims to identify these mutation signatures operative in cancer and begin to experimentally link identified signatures to a specific biochemical or biological process. This will improve our knowledge of specific process contribution to cancer development and allow for prioritization of regulatory and educational efforts to reduce or eliminate people's exposure to carcinogens. We will examine the somatic mutations (detected by ICGC) of each tumour in the database to understand the tumour's mutational signature and link this to possible exogenous mutagenic exposures or other somatic or germ-line genetic variants.
167.Jana SponarovaNEBION AGSwitzerland2020-07-092021-07-08
Cancer is the second leading cause of death globally. The immune system plays a key role in cancer prevention, development, and defense. Variations in genetics can affect how an individual’s immune system will respond to cancer. We would like to use ICGC Controlled access data to explore how these genetic variations will impact the immune response of specific cancers and whether these variations might influence the effectiveness of cancer treatment. We will apply computational tools to explore correlations in genetic variations and their effects on cancer development, survival, and treatment responses. Those variations, in immune receptor genes particularly, where we find significant correlation will be further explored in future research.
168.Marcin ImielinskiNew York Genome CenterUnited States2020-06-292021-06-28
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
169.Daniel WilliamsonNewcastle UniversityUnited Kingdom2020-07-082021-07-07
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.
170.Lihua Zounorthwestern universityUnited States2019-12-022020-11-30
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.
171.Zhe Jinorthwestern universityUnited States2020-08-272021-08-26
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.
172.Gilad EvronyNYU Langone HealthUnited States2020-02-062021-02-04
Mutations occur in tumors at different timepoints: some occur early just as the tumor is starting, while others occur later. Early mutations would be expected to be present in most or all tumor cells, while later-occurring mutations would be expected to be present in fewer tumor cells. In this project, we will analyze the levels of of mutation in ICGC controlled data to understand how tumors develops. This may help us understand the initial processes that create tumors and the way they subsequently grow.
173.Jon CokerOmniTier Inc.United States2020-02-112021-02-09
Dr. Christina Yung and OmniTier Inc scientists will collaborate on a new method for identifying variations in human DNA from raw sequencer data using speed-optimized, memory-tier server architectures, which use multiple types of memory. The project's goal is to demonstrate improved variant calling while lowering the time and cost of the analysis. These advantages accrue from a novel variant calling system, CompStor Novos. ICGC Controlled Data will be used to compare the variant-calling accuracy and clock time performance of the CompStor Novos method to those of the current ICGC-ARGO methods developed under the supervision of Dr. Christina Yung. Accuracy will be measured by comparison to a truth set and to analysis of the same ICGC Controlled Data using alternative methods. Run times will be compared.
174.Lincoln Stein Ontario Institute for Cancer ResearchCanada2020-09-052020-10-20
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.
175.Paul SpellmanOregon Health and Science UniversityUnited States2020-02-252021-02-19
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.
176.Junbai WangOslo University HospitalNorway2020-03-312021-03-30
We intend to design new computational models for detecting gene regulation in cancer by incorporating diverse information to the model, e.g. large publicly available datasets obtained under various conditions, the cancer genome atlas (ICGC tumor/normal matched genomes from cancers), and datasets of other modifications on gene regulation. The project aims to extend our current BayesPI program that includes protein-DNA interaction data, to an improved application that includes chromosomal interaction, DNA sequence variation, epigenetic data (heritable chemical marks on specific parts of DNA that control the gene regulation without affecting the underlying genetic sequence – A, C, T, G), and nucleosome occupancy (the fraction of DNA sequences is occupied by any histone proteins) information.
177.Yu LiuPediatric Translational Medicine Institute, Shanghai Children's Medical Center, Shanghai Jiaotong University School of MedicineChina2020-08-272020-10-10
Some regions of the human genome contain code that the cell uses to produce proteins and other regions do not. Genetic variations in these "noncoding regions" could cause tumors by activating genes that are involved in cancer. We developed a computational pipeline to discover new variations of this type. We plan to use ICGC data to improve our pipeline, which should help to expand our insights into the human genome and the molecular mechanisms underlying cancer.
178.Sherene LoiPeter MacCallum Cancer CentreAustralia2020-04-282021-04-27
Breast cancer arising in young women (aged 40 yrs or less at diagnosis) is rare and associated with a particularly poor outcome relative to older age groups, particularly those breast cancers where the estrogen hormone supports tumour growth. Using ICGC controlled data, we aim to investigate how mutations, groups of activated or inactivated genes and the influence of the immune system may explain their aggressive disease course, as well as provide potential treatment implications.
179.Arcadi NavarroPompeu Fabra UniversitySpain2020-08-142020-09-27
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.
180.Olga TroyanskayaPrinceton UniversityUnited States2020-09-202020-11-04
Uncontrolled growth of cancer arises through mutations that alter genes (<2% of the genome) as well as mutations that alter the part of the genome that regulates the timing and location of gene activity (>98% of the genome). While scientists have long used a “genetic code” to understand how mutations in genes change gene activity, there was no way to systematically and comprehensively explain how mutations in the regulatory portion of the genome impact biology. Our group has developed such a code that uses artificial intelligence algorithms trained on vast amounts of public “big data”. We aim to apply this framework to decode the millions of regulatory mutations in the ICGC whole cancer genome collection to learn how these mutations and the genes linked to them are dysregulated in order to arrive at a more comprehensive understanding of specific cancer processes and expose opportunities for therapy development.
181.Bernard FoxProvidence Cancer Center, Earle A. Chiles Research InstituteUnited States2020-09-152020-10-01
Comparison of matched normal and cancer genomes has given insights to genomic variations which we propose to examine for correlations with protein, gene expression, and clinical data in our cancer immunotherapy research participants using both genomes for the cancers they are being treated for, and across tumor tissue type cancer genomes (using ICGC controlled data).
182.Juliet FrenchQIMR Berghofer Medical Research InstituteAustralia2020-07-232021-07-22
Genetic mutations are important contributors to the development of cancer. Here, using bioinformatics approaches, we study genetics of cancer such as mutations in human DNA sequences that may be responsible for cancer progression. Bioinformatics analysis of ICGC datasets can be used to adjust the hypotheses and design the experiments. For example, we attempt to identify non-coding genomic regions (such as regulatory elements and lncRNAs) which are highly somatically mutated (mutations that are not inherited) and then study their biological implications.
183.Trevor GrahamQueen Mary, University of LondonUnited Kingdom2020-04-302021-04-29
Cancers change over time. Understanding how cancers change is important for determining a patient's prognosis and to improve the effectiveness of treatment. It is not possible to directly watch a cancer change, and so instead we have to infer the changes using the patterns of mutations across the cancer genome. These mutational patterns in cancer are analogous to the rings inside a tree trunk that are a 'secret diary' of how the tree grew. We have developed mathematical tools to read the patterns and work out how cancers grow and change, and we apply these tools to study the ICGC controlled data across cancer types. We will make specific use of the ICGC Controlled Data by examining the relationship between the mutational patterns and exposures a participant has had, such as chemotherapy, and the histological features of their tumour.
184.Shivashankar NagarajQueensland University of TechnologyAustralia2020-06-222021-06-21
Cancer is a genetic disease and it can be better treated if we understand which genes help cancer to spread from one organ to another. The goal of this study is to use machine learning algorithms to identify cancer-specific genes. Genes that are active in a particular cancer are valuable targets in cancer treatments as they can be targeted with minimum side effects. Also, it is unclear what happens to cancer-specific genes when cancers spread to different organs in the human body. Therefore, we want to evaluate the effect of spreading on genes when compared to the site of origin of cancer using the ICGC controlled data. Importantly, the model isn’t capable of reidentifying patients because only gene expression values are used. The results (e.g. cancer- specific genes) and the algorithm will be made available to the general and research community.
185.Zahra JalaliRafsanjan University of Medical SciencesIran2020-01-032020-12-20
A challenging obstacle in cancer treatment is the high level of changes that occur in cancer cell DNA. Ultra Conserved Regions (UCRs) are the most stable regions of human genome during development; however, it is not understood whether they remain stable in cancer cells or harbor changes during cancer progression. We aim to investigate UCRs’ genetic changes in cancer. The UCR sequences from ICGC will be analysed to identify the mutations in their DNA sequences. Next, it will be investigated whether their mutation rate is different from the sequences of DNA in the genome located beside UCRs. In addition, our proposed study aims to answer whether the cancer cell genetic alteration in UCRs might have affected cancer clinical features and outcome.
186.Toshiro OhsumiRepare Therapeutics USAUnited States2020-08-172020-10-01
We are looking to develop a drug (used in certain cancer therapies) that disrupts proteins in the body called ataxia telangiectasia and rad3 related protein (ATR). A great challenge of drug development is finding an ideal patient population for the given drug. One way to optimize the patient population is through using patterns of variation in patients’ DNA called biomarkers. The primary goal of this project is using computational analyses of large quantities of genomic data (more specifically, a technique known as Whole Genome Sequencing, which allows researchers to look at the complete genome as opposed to parts of it) to find novel biomarkers for patient enrollment. For this project, we request access to ICGC data because it is a large and diverse genomic data set, which we can use to attempt to find new biomarkers.
187.David SzutsResearch Centre for Natural SciencesHungary2020-09-212020-11-05
Cancer is a disease of the genome, caused by the accumulation of permanent changes in DNA structure leading to the cancerous transformation of normal human cells. In the case of skin cancers, the cause of these changes is the effect of UV radiation from sunlight. This effect can be even shown a long time after the exposure to the harming effect, only by looking at the types and numbers of these DNA changes. During our project we analysed the mechanistic generation of these patterns by treating cell cultures lacking certain genes with UV among other DNA damaging agents. We are applying for this ICGC Controlled Dataset because we would like to show that our findings about the connection between these patterns and the examined mutations also hold true in real cancer samples, and the number of patient samples and identifiable genetic changes make it possible to draw statistically sound conclusions.
188.Ulrich SchuellerResearch Institute Children's Cancer Center HamburgGermany2020-02-272021-02-25
here are currently only relatively few parameters that can be used to estimate the progonsis of children with brain tumors. We want to use own and previously published molecular data from ICGC for the characterization of non-tumor cells that infiltrate the tumor tissue and might influence tumor growth and patients‘ prognosis. To this end, we will use bioinformatic and biostatistical methods and algorithms that will hopefully enable us to define clinically relevant tumor subgroups
189.Nada JabadoResearch Institute McGill University Health CentreCanada2020-05-072021-05-06
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.
190.Joachim WeischenfeldtRigshospitaletDenmark2020-03-312021-03-30
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.
191.Davide RobbianiRockefeller UniversityUnited States2020-01-072021-01-03
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.
192.Davide RobbianiRockefeller UniversityUnited States2019-11-122020-11-10
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).
193.Wenyan MiaoRome TherapeuticsUnited States2020-07-162021-07-15
n this application, we propose to use the ICGC Controlled Data to improve upon existing methods for discovering mutations in cancer genomes. Of particular interest to us are extremely repetitive DNA sequences that can move around the genome and cause mutations. They have been shown to be associated with certain cancer types. The basis of our project revolves around using computational tools to compare normal samples to that of the corresponding tumor sample from the same patient in order to aid in the discovery of such mutations. We will therefore use both the tumor and normal sequencing data to evaluate existing computational methods and then to improve upon them
194.Georg FuellenRostock University Medical CenterGermany2019-12-092020-12-05
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.
195.Qianqian ZhuRoswell Park Comprehensive Cancer CenterUnited States2020-08-122021-08-11
Majority of ovarian cancer cases do not carry mutations in genes that are known to cause cancer. To identify new genes that can contribute to ovarian cancer development, we investigated gene mutations in ovarian cancer patients with family history and some new highly mutated genes in this group of patients. We will further investigate the contribution of these newly found genes to ovarian cancer development by applying computational methods to the ICGC Controlled Data. We will evaluate whether these genes are also highly mutated in ICGC ovarian cancer cases and whether the pattern is specific to ovarian cancer. In addition, we plan to address the questions of whether mutations in these new genes will affect patients’ age of developing ovarian cancer and the aggressiveness of the cancer, as well as how known cancer genes could modify the effect of these new genes.
196.Lara MangraviteSage BionetworksUnited States2020-02-112021-02-07
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.
197.Erik Larsson LekholmSahlgrenska Academy at University of GothenburgSweden2020-08-052021-08-05
We are currently pursuing a number of projects in cancer genomics, where we try to pinpoint important molecular changes in tumors that are contributing to the disease. This includes development of a new computational tool for identifying changes in the DNA inside mitochondria, which are the "powerhouses" of the cell. Changes in mitochondrial function are known to contribute to cancer. Access to ICGC controlled data will enable us to search for changes in mitochondrial DNA inside of human tumors. This will be helpful when establishing our methodology and may also reveal potential diagnostic uses of changes in mitochondrial DNA.
198.Roland GeisbergerSCRI-LIMCR, Salzburg, AustriaAustria2020-02-042021-01-27
Chronic lymphocytic leukemia (CLL), the most frequent leukemia in Western countries, is characterized by a highly variable clinical course. Despite promising initial treatment response in many cases, development of treatment resistance remains a major clinical challenge. This project aims to identify genes and molecules associated with treatment response and tolerability with a modern targeted cancer therapy. By analysis of the expressed genome and comparison of our data with publicly available genomic databases of tumor patient samples, such as TCGA and ICGC, we will identify features in both, the tumor cells and the host immune System that will be instrumental to uncover novel pathways responsible for response to and tolerance of the therapeutic regimens and allow for the establishment of therapeutic targets as well as prognostic tests predictive for response or side-effect profile, feeding into a personalized medicine approach.
199.Sung-Soo YoonSeoul National UniversitySouth Korea2020-01-092021-01-07
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.
200.Jong-Seo KimSeoul National UniversitySouth Korea2020-03-242021-03-23
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.
201.Ji Hyun ChangSeoul National University HospitalSouth Korea2020-01-032021-01-03
Radiotherapy is one of the main modality for cancer treatment. Although radiation eliminates cancer cells, it can induce tumors as a severe treatment-related complication. In this study, we will identify the genetic changes associated with radiation exposure. This would provide us with a comprehensive understanding of radiation-induced tumorigenesis. For that, we need both samples of radiation-induced and sporadic tumors. In this respect, the ICGC controlled data including DNA sequences of radiation-induced and sporadic soft tissue tumors will be invaluable resources. We will utilize the ICGC dataset to identify genetic changes in our own samples. This will help us uncover the important DNA mutations and potentially will highlight novel therapeutic targets for radiation-induced tumors.
202.Yu HuangShanghai Institute of Materia Medica, Chinese Academy of SciencesChina2020-04-072021-04-06
Knowing DNA for both tumor and normal samples is a significant leap towards understanding the cancer genetic landscape. However, contamination by adjacent normal cells and the presence of different kinds of tumor cells confounds the characterization of tumor mutational landscape. Based on our existing computational method Accurity, we (Yu Huang and Linghua Meng group at SIMM, CAS) aim to infer potential cancer driver genes (a cancer driver gene is defined as one whose mutations increase net cell growth under the specific microenvironmental conditions that exist in the cell in vivo) from the ICGC data and experimentally validate those findings.
203.Ying GaoShanghai Institutes of Biological SciencesChina2020-03-242021-03-23
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.
204.Xue-Song LiuShanghaiTech UniversityChina2020-09-062020-10-20
Cancer therapies that enhance human body`s immune response, so called immunotherapy, are transforming the treatment of cancer. However, only a fraction of patients show response to immunotherapy, and there is an unmet need for markers that will identify patients more likely to respond to immunotherapy. Our research goals are to identify novel features and patterns of DNA alteration in cancer, and use these features as marker for cancer immunotherapy response prediction. We will develop novel analysis methods to analyze ICGC controlled data, to identify novel signatures and patterns of DNA alteration. This study will help to determine which cancer patients will respond best to different types of therapy
205.Ruty ShaiSheba Medical CenterIsrael2020-09-092020-10-20
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.
206.Amit TiroshSheba Medical CenterIsrael2020-03-312021-03-30
DNA (a material which carries hereditary information) and RNA (a molecule acting as a messenger carrying instructions from DNA for the production of proteins) are made up of 3 billion genetic "letters" in a particular order. Tumors are clusters of cells that arise from uncontrolled growth, usually due to a problem in the order of these letters. Understanding the genetic order of tumor samples, and the use computational tools developed at our lab to identify cells surrounding the tumors, may help us identify tumors with high tendency to progress. In the clinical practice, we may use diseased tissue samples and genetic analysis techniques to better manage the treatment of our patients. The robust genetic and clinical data available at the ICGC is pivotal in our ability to validate our initial findings with a sufficient statistical power, and move forward with our tendency to improve the clinical care of our patients.
207.Zhenyu XuSophia GeneticsSwitzerland2020-01-212021-01-13
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.
208.Nuria MalatsSpanish National Cancer Research Centre (CNIO)Spain2020-09-092021-09-08
Pancreatic cancer will become the second leading cause of cancer mortality within the next decade if outcomes do not improve. One reason for the high mortality rate is the fact that this cancer type is diagnosed typically in late stages when few treatments are available. Another reason stems from our current inability to screen high-risk individuals; it is therefore imperative to find novel markers of pancreatic cancer risk. Based on preliminary findings that implicate the immune system and pathogenic infections in this disease, we propose an in-depth study of potential associations with viruses, bacteria and features of our immune system in order to address the current lack of known risk factors of pancreatic cancer. The ICGC dataset is an invaluable resource to this end; we will use the genetic data (raw and processed files) from cancer samples in order to discover new inherited and acquired risk factors for pancreatic cancer.
209.Jinghui ZhangSt. Jude Children's Research HospitalUnited States2020-08-262021-08-25
Cancer chemotherapy treatments can cause changes (mutations) in DNA, which may lead cancer to become resistant to treatment. We recently found that chemotherapies used to treat childhood leukemia may cause these types of mutations, as determined by analyzing the "fingerprint" (signatures) of mutations in treated leukemia samples. However, leukemia patients receive many types of treatment, and it's unclear which chemotherapies are the culprit. Using ICGC controlled data, we will test whether the mutation fingerprints observed in childhood leukemia are also found in adult cancers, which may help narrow down which chemotherapies are causing these mutations in leukemia, and potentially determine whether the same thing also happens in adult cancers. Additionally, we identified deletions in DNA sequences upstream of adult breast cancer related genes in pediatric cancer patients. We wish to explore whether ICGC controlled data also contains such deletions. This may help us improve detection of breast cancer susceptible individuals.
210.Arash AlizadehStanford UniversityUnited States2020-09-212020-09-24
The availability of diverse genomic data collected from specific individuals with cancer allows a unique opportunity made possible through the ICGC. We aim to study this data to develop approaches that help target therapies to tumor subtypes.
211.Michael SnyderStanford UniversityUnited States2020-08-302020-10-14
Colorectal cancer is a major worldwide health concern. This project seeks to develop a precancer atlas of colorectal cancer using familial adenomatous polyposis (FAP) as a disease model and to analyze the evolution of tumors from a precancerous to a fully malignant state using “multi-omics” techniques. These techniques extend analysis beyond the DNA level by analyzing all aspects of genetic information. Our research is focused on the development of analytical tools to integrate cancer genomic datasets at a multi-omics level. We request access to individual-level data available through the ICGC for integration and validation purposes. These analyses will result in a more comprehensive molecular map of cancer, that may ultimately inform novel therapeutic strategies.
212.Marieke CoenenStichting Katholieke Universiteit, doing business as Radboud university medical center Nijmegen, the NetherlandsNetherlands2020-08-192021-08-18
Children who are diagnosed with medulloblastoma (a type of brain tumor) are often treated with surgery, radiotherapy and chemotherapy. Common side effects of these treatments are hearing loss and renal damage. However, every child reacts different to the given therapy, especially when we look at side effects. In this study, we will apply computational tools to the ICGC Controlled Data to investigate how differences in genetic make-up between children plays a role in how a child reacts to chemotherapy. As we are investigating the whole genetic code, we need a large group of patients to detect differences. We will focus on the side effects hearing loss and kidney damage, with the ultimate goal to identify risk genes which enables us to individualize treatment upfront and prevent severe chemotherapy side effects in the future.
213.George BovaTampere UniversityFinland2020-07-142021-07-13
The Tampere University Faculty of Medicine and Health Technology, in Tampere Finland 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 Tampere University, 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.
214.Dongwan HongThe catholic university of KoreaSouth Korea2020-05-262021-05-25
Ovarian cancer has a poor prognosis and accounts for 20% of gynecologic cancers. However, the causes of rapid spread and recurrence of cancers are not well identified. One of the reasons why cancer cells abnormally differentiate and spread rapidly is due to the dysregulation of certain genes. The functions of these genes are disrupted by mutations and changes in the structure of the DNA. When numerous of these mutations occur simultaneously, they can have a greater impact on cancer prognosis than when they act individually. Using computational methods, we will investigate the relationship and characteristics of these mutations in ovarian cancer. Therefore, we would like to understand the ovarian cancer genome and define the order of genomic mutations using ICGC Controlled Data.
215.John PearsonThe Council of the Queensland Institute of Medical ResearchAustralia2020-02-042021-02-02
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 computational models, animal models and patient cohorts. We will also search ICGC data for common patterns in cancer. 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.
216.Peter Van LooThe Francis Crick InstituteUnited Kingdom2020-03-262021-03-25
We want to understand how certain changes to our DNA may contribute to cancer development. In particular, we focus on a specific type of mutation called copy number alterations. Usually, we have two copies of each chromosome, one from our mother and one from our father, but when parts of a chromosome are lost or copied multiple times, we call this a copy number alteration. When a cell develops into a cancer cell, it accumulates many DNA changes including copy number alterations which will lead to a rewiring of its inner workings. Computational methods have been developed to link a copy number alterations to the processes that caused it. Now, we want to use the ICGC Controlled Data to chart the different mutational processes that cause copy number alterations and thereby drive cancer development.
217.Adam ShlienThe Hospital for Sick ChildrenCanada2020-01-302021-01-28
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.
218.Adam ShlienThe Hospital for Sick ChildrenCanada2020-01-282021-01-26
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.
219.Michael TaylorThe Hospital for Sick ChildrenCanada2020-03-242021-03-23
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.
220.Seema MitalThe Hospital for Sick ChildrenCanada2020-03-312021-03-30
Congenital heart disease (CHD) affects 1 in 100 births and is a leading cause of deaths in newborns. Currently available genetic tests find a gene defect in 20% of cases, but we haven't yet identified all of the genetic causes of the disease. In order to find what gene defects cause CHD, we will sequence the entire genomes of over 1600 children and adults with the disease, and search for gene mutations that are the most likely causes. Moreover, with these data we will build tools that predict why some children have more severe disease than others, which will help determine how to follow-up children and what treatments to offer based on their genotype. The ICGC Controlled Data will be used to compare if the burden of the new gene defects we find in our patients with CHD is higher than that in ICGC patients who do not have CHD.
221.Ryan YuenThe Hospital for Sick ChildrenCanada2020-08-192021-08-18
The study aims to study the role of genetic change in various human diseases, and to understand the molecular mechanisms underlying its functions. We will compare genetic data from normal samples to tumor samples to identify significant genetic mutations that could be associated with diseases. Computational tools will be applied to ICGC controlled data to classify these genetic changes. Given the wealth of information from ICGC controlled data on genetic and clinical outcomes, we will analyze and establish any possible correlations between the genetic changes and any of studied outcomes
222.Andrea SottorivaThe Institute of Cancer ResearchUnited Kingdom2019-11-142020-11-11
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.
223.Xiaohua WuThe Scripps Research InstituteUnited States2020-02-032021-02-01
DNA damages, if not properly repaired, will lead to genome instability and possibly cancer. Cells use many ways of repair to safeguards the genome and defective DNA repair genes could cause the accumulation of mutations in cancer genomes. Our goal is to understand the roles of these DNA repair genes and to develop new strategies for cancer prevention and therapy. We will analyze ICGC controlled data of cancer genomes using bioinformatic tools to find out if certain repair gene defects are associated with specific mutations and rearrangement of DNA often found in cancer. This could provide additional proof to other experiments that directly demonstrate the importance of these repair genes in protecting the genome.
224.Martin TaylorThe University of EdinburghUnited Kingdom2020-01-282021-01-23
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.
225.Andrew SharrocksThe University of ManchesterUnited Kingdom2020-05-192021-05-18
Oesophageal cancer is a deadly disease and survival rates remain low. This is partly because we do not know the molecular events that cause and sustain this disease, and hence we lack appropriate therapeutic targets. Oesophageal cancer progresses through several defined stages, with each one specifying a poorer outcome. We aim to define the key processes that operate to create and sustain this disease and drive its progression. We have adopted an experimental system in the lab that identifies active areas of the genome in a cancer cell line model. We now wish to verify whether these are of relevance to the patient, by interrogating the data generated by the ICGC on patient samples to determine whether the molecular events we observe in our test systems are the same as found is cancer cells in patients.
226.Sean GrimmondThe University of MelbourneAustralia2020-03-172021-03-16
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.
227.Christopher HovensThe University of MelbourneAustralia2020-04-212021-04-20
Prostate cancer is now the most commonly diagnosed cancer in the Western world but only 10% of men with it, will die from it. Our current ability to discriminate between cancers which are harmless and those that are life threatening is poor. This project will examine the genetic make up of cancer clones that are present in high risk prostate cancer and define and trace the spread of those cancers that break away from the prostate and lodge in distant sites, causing death. We utilize ICGC genomic datasets that have detailed genetic information on common cancer types that have spread around the body away from the primary organ where they first arose, this includes, prostate, breast and colorectal cancers.
228.Haris VikaloThe University of Texas at AustinUnited States2020-07-202021-07-19
Tumor samples typically contain a mixture of normal cells and one or more populations of cancerous cells. Using ICGC Controlled Data, in this project we will develop accurate and computationally efficient methods and software for reconstruction of tumor genomes and discovery of ancestral relationships between mutations present in tumor cells. The developed algorithms will help reveal highly valuable information about molecular signatures of cancer and point towards specific therapeutic treatments.
229.Andrew FutrealThe University of Texas M.D. Anderson Cancer CenterUnited States2020-02-182021-02-16
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.
230.Ken ChenThe University of Texas MD Anderson Cancer CenterUnited States2020-04-072021-04-06
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.
231.Han LiangThe University of Texas MD Anderson Cancer CenterUnited States2020-09-042020-10-19
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.
232.Yiwen ChenThe University of Texas MD Anderson Cancer CenterUnited States2020-02-112021-02-09
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.
233.Dadi JiangThe University of Texas MD Anderson Cancer CenterUnited States2020-07-152021-07-14
A protein called X-box binding protein 1 (XBP1) is activated as a pro-survival mechanism in response to stresses encountered in growing tumors and its higher activity results in worse survival of cancer patients. We have previously shown that blocking the function of this protein results in decreased tumor growth. Therefore, blocking XBP1 activation is a promising option for treating cancer patients. In the proposed study, we will determine the role of XBP1 in promoting the aggressiveness of multiple types of cancer more accurately. The measured levels of XBP1 proteins can help us characterize its multiple roles and significance in many cancer types. By using ICGC data, we hope to be able to quantify XBP1 protein levels that would allow us to elucidate the role of XBP1 in promoting cancer. This strategy has the potential to impact cancer treatment through the identification and validation of new treatment options.
234.Zbyszek OtwinowskiThe University of Texas Southwestern Medical CenterUnited States2020-01-072021-01-05
There are times when our bodies are unable to keep their DNA healthy. DNA damage causes mutations, which may contribute to cancer development, and these mutations are also a sign that the cancer is growing quickly. Much scientific effort is invested into identifying the features of such mutations because recognizing them helps in selecting the best cancer treatments. However, these mutations can be found only if they are frequent enough and they become frequent only after they have multiplied via the cancer. We intend to find them earlier, before they can be enhanced by cancer growth. To this end, we plan to use the ICGC controlled data obtained from cancer patients to train various computer programs to recognize whether a mutation is related to cancer or whether it is benign, with the program ultimately counting mutations that might be dangerous so that cancer can be detected earlier.
235.Kung AhnTheragen BioSouth Korea2020-06-052021-06-02
We aim to control a quality of cancer data generation and the design required tumor of cancer data. For example, in a case of including misinformation about samples (e.g., labeling error between normal and tumor samples, the origin of tumor), which influences crucially to analyze, we develop deep-learning model based on using ICGC data about cancer. We generate a classification that can distinguish the type of samples and the origin of tumor including Xgboost deep-learning method.
236.Mulin Jun LiTianjin medical universityChina2020-07-142021-07-13
Evidences indicate that structure variants (SVs) can affect gene expression (gene products) by disrupting Topological Associated Domains (TADs) of nuclear chromatin. To inspect whether cancer recurrent SVs could reorganize the gene expression pattern through affecting TAD configuration, we are going to develop a computational framework based on pan-cancer SV events. Using the ICGC controlled dataset, we will detect hotspot of recurrent SVs and then associate them with gene expression changes within same TAD. We believe that this methodology will facilitate the interpretation of biological function of SVs in the development of human cancers.
237.Tong MengTongji UniversityChina2019-12-032020-12-01
Chordoma is a rare bone malignancy that can occur at any level of the spine and skull base. Treatment dilemma leads to a high rate of local relapse and distant metastases. Using ICGC controlled data, we want to compare the chromosome conformation between chordoma cells and normal human cells. Due to the rarity of chordoma, we need more genetic data to increase annotation and accuracy. We will use our findings to explain the molecular mechanism of chordoma and hope to find new accurate target for molecular targeted therapy.
238.Yilong LiTotientUnited States2020-02-042021-02-02
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.
239.Martin LoewerTRONGermany2020-04-212021-04-20
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.
240.Ana GrossoUCIBIO, Faculdade de Ciências e Tecnologia, Universidade Nova de LisboaPortugal2020-06-162021-06-15
Cancer is a complex disease characterized by extensive genetic diversity. Significant heterogeneity is also frequently observed even within a single tumor of a patient (named Intra-Tumor Heterogeneity, or ITH). This intra-tumor diversity has been detected in almost all cancer types and affects tumor development and clinical outcome, ITH being one of the major causes for drug resistance and tumor relapse. Despite advances in cancer genomics and drug development, we still miss the mechanisms underlying ITH, which could lead to more effective treatment approaches. ICGC Controlled Data will be essential for us to estimate the heterogeneity of each patient's tumor, and correlate with genetic and clinical characteristics of that patient. By doing this with many different patients and different tumor types available in ICGC, we hope to uncover robust mechanisms driving ITH, which can hopefully suggest possible treatments to prevent cancer progression.
241.Giuseppe GasparreUniversità di Bologna, Dipartimento di Scienze Mediche e ChirurgicheItaly2020-02-182021-02-16
Hepatocellular carcinoma (HCC) is the most common primary liver cancer and represents the second cause of cancer related death worldwide. In the majority of cases HCC occurs in people with chronic liver diseases, such as cirrhosis caused by hepatitis B or hepatitis C infection. It is an aggressive tumor, with high reccurence rates even if diagnosed at early stage, when the tumor is small. Even though there are different treatments available including chemotherapy and different types of surgery, more than half of the patients will not survive after five years from diagnosis. The explanation to why some patients will develop HCC while others do not, despite having common risk factors, still remains to be discovered. Therefore, in our project we aim to analyze variant found in the mitochondrial genome by accessing ICGC Controlled Data and to identify the variants that could offer insights on the development of this disease.
242.Alessandro WeiszUniversita' degli Studi di SalernoItaly2019-11-182020-11-16
Many molecules encoded in the human genome emerged recently as a major source of information on the clinical behaviour of cancers and as biomarkers of these diseases. The main objective of our research is to search for differences in expression and activity of a subgroup such molecules in many cancers, and between cancerous and normal tissues, aiming at identifying their possible involvement in the processes that lead to appearence and progression of tumors. Linking these genetic data to clinical information relative to disease outcome available in ICGC, we will investigate the involvement of specific cellular components in the carcinogenic process and will search for new tumor markers to help early diagnosis and to identify patient-specific characteristics that can be used to improve treatment of these diseases.
243.Mario CaceresUniversitat Autònoma de BarcelonaSpain2019-12-022020-11-30
Inversions are one type of genetic variants that affect a large fraction of the human genome and that have been implicated in functional differences between individuals. Nevertheless, they have been poorly studied due to technical challenges in their detection, which has precluded determining their role in disease susceptibility. In addition, it has been recently shown that many inversions have appeared independently multiple times in different individuals and their effects have been largely missed by current studies. Therefore, this project aims to carry out a complete analysis of the functional effects of human inversions, including their association with different common complex diseases and other health related traits. In particular, by using the available ICGC Controlled Data corresponding to sequence information from different types of cancer, we will be able to check the role of inversions on the genetic predisposition to the disease, which can result in potential significant social benefits.
244.Pierre-Etienne JacquesUniversité de SherbrookeCanada2020-07-142021-07-13
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.
245.Jonathan BondUniversity College DublinIreland2020-09-182020-11-02
The best way to improve treatments is to try to understand the reasons why they sometimes don’t work. This is not straightforward, as the ‘internal wiring’ of a leukemia cell is very complicated, and the leukemia cell is very good at ‘rewiring’ itself to escape being killed by the therapies we currently give. We use a scientific approach called ‘Systems Biology’ to try and better understand how this happens. This approach involves making computer models of the gene and protein networks that keep a leukemia cell alive. We wish to use ICGC controlled data to see whether some of the mutations found in leukemia cells might interact with each other. We think that this analysis will help to identify hidden ‘Achilles’ heels’ in the leukemia cells, which might help us find more precise and effective cures for children with blood cancers.
246.Steven GallingerUniversity Health NetworkCanada2020-07-282021-07-27
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.
247.Trevor PughUniversity Health NetworkCanada2020-05-192021-05-18
Traditionally, many of the most significant mutations in the genome occur in the “coding regions” (genes that write proteins). However, increasingly, studies are showing that mutations beyond these regions can be significant. We propose to investigate and characterize these regions by using computational approaches to analyze the ICGC data. We hypothesize that mutations in DNA beyond the coding region can lead to changes that promote breast cancer development. We will then use various methods to verify the effects of our observations.
248.Michael FraserUniversity Health NetworkCanada2020-05-142021-05-14
Prostate cancer is the most commonly diagnosed cancer in North American and European men. While most men can be cured with surgery or radiation, about 30% of men will have their cancer return. These men are at high risk for spread of their cancer outside of the prostate (’metastasis’), which is a life-threatening condition. Unfortunately, doctors currently do not have the tools required to accurately determine whether an individual man is likely to have their cancer return after treatment. Our research team will use ICGC Controlled Data to identify genetic mutations that influence whether a man is likely to have his cancer return. We will also use these data to determine how mutations of different types can interact with one another. Finally, we will use these data to help develop new tests that doctors can use to increase the number of men who are cured of their prostate cancer.
249.Edwin CuppenUniversity Medical Center, Utrecht, The NetherlandsNetherlands2020-09-032021-09-02
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.
250.Jimmy BreenUniversity of AdelaideAustralia2020-07-092021-07-08
Acute Lymphoblastic Leukaemia (ALL) is a disorder of the blood that is driven by genetic changes in a cell. These changes can be detected after DNA genetic information is copied into a messenger RNA (mRNA), a molecule that carries information to make proteins in the body. This abnormal mRNA can be identified in ALL patients and can drive the blood disorder, so computational analyses aim to quickly identify these mutations. In this project, we will use known genetic changes identified in ICGC Controlled Data to identify ALL causing mutations in Australian patients. By accumulating known ALL-causing mutations, we aim to develop new diagnostic tools that reduce the computational burden and diagnosis time from days to minutes.
251.Ryan MillerUniversity of Alabama at BirminghamUnited States2020-08-172021-08-16
Genes enclose DNA instructions for producing molecules. A gene is “expressed” when a molecule is made in our body. Human tumors are known to produce too much or too little of certain molecules due to mutations in genes. We will investigate whether our model of diffuse intrinsic pontine glioma (DIPG), a devastating pediatric brain tumor with no cure, resembles the real human disease. We will apply computational tools to the ICGC controlled dataset to validate whether our model expresses the same genes as the human tumors. If our model expresses too much or too little of the same cancer-related genes as the human tumors do, we can use our model to test drugs that might return those genes to their normal levels. Ultimately, we hope to use our model to find new drugs that might work to treat DIPG.
252.Guy Van CampUniversity of AntwerpBelgium2020-07-142021-07-13
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.
253.Mauro CivesUniversity of Bari, Department of Biomedical Sciences and Human OncologyItaly2020-07-312021-07-30
Tumor-specific small peptides (tumor neoantigens) are capable of eliciting an anti-tumor immune response. It is currently unknown if rare tumors of the pancreas named pancreatic neuroendocrine neoplasms express such neoantigens. In this project, we aim at investigating the presence and recurrence of neoantigens in order to i) provide a molecular/immunological classification of pancreatic neuroendocrine neoplasms ii) identify targets for new therepeutic strategies. ICGC Controlled Data will be used for detection of tumor mutations and subsequent computational prediction of neoantigen presence/type. The results of such analysis will be validated by using an independent cohort of pancreatic neuroendocrine neoplasms to be subjected to mutation and neoantigen identification. Finally, results will be validated in vitro.
254.Ola MyklebostUniversity of Bergen, NorwayNorway2020-05-072021-05-06
The Norwegian Sarcoma Consortium (NoSarC.no) was initiated as part of the Norwegian Cancer Genomics Consortium (NCGC, cancergenomics.no), and has now collected samples from most Norwegian sarcoma patients over 3 years. More than 300 tumour/normal sample pairs are being exome sequenced (all genes), and these data are analyzed for biolgical and cancer mechanistic insight, with the aim to find new treatments. As a partner in the ICGC Bone cancer Group, we know the value of international sharing of samples and data. This is essential for sarcomas, as the cancers are rare and there are around 80 subtypes. Thus the ability to validate our findings in independent patient cohorts is very important.
255.Gianmarco ContinoUniversity of Birmingham, UKUnited Kingdom2020-05-052021-05-04
This project aim is to investigate specific regions of human DNA that are known to be commonly affected by large-scale mutations (i.e. mutations involving rearrangements of long segments of DNA). These regions, also known as common fragile sites, are affected in many cancers with different patterns. This project aim is to investigate the mutations across common fragile sites in the ICGC Controlled Data using bioinformatic tools. We expect that this analysis may provide information on cancer-specific mechanisms of mutation.
256.Edwin WangUniversity of CalgaryCanada2019-12-202020-12-18
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.
257.Anthony WangUniversity of California Los AngelesUnited States2020-09-102021-09-09
Using advanced machine learning techniques, we aim to analyze ICGC controlled data to estimate susceptibility to immunotherapy in select forms of pediatric brain tumors. We will use these data to predict targets for cancer vaccines in these same tumor types. Accessibility patterns learned from even a single tumor type may help us to discriminate susceptibility and understand response to immunotherapy across many cancers. Thus, we will also test whether this technique is adaptable to other pediatric brain tumors, and whether these analyses can predict targets and response to immunotherapy in these other forms of pediatric brain tumors.
258.Paul BoutrosUniversity of California Los AngelesUnited States2020-08-052021-08-04
Cancers arise when DNA mutations occur in healthy cells. These mutations can occur by random chance, or be influenced by hereditary features or environmental influences of different types. We study the characteristics of these mutations -- when they occur, where they occur and the patterns of which mutations tend to happen together. The goal of our research using ICGC Controlled Data is to understand how features of the patient like sex, age and ancestry influence the to final tumour that is ultimately diagnosed by modeling the mutational and evolutionary processes that occur. This will involve both creating new computational methods and applying them to understand clinical outcomes. Ultimately this work will help patients, caregivers and clinicians make better decisions for the management of their unique tumours.
259.Angela BrooksUniversity of California Santa CruzUnited States2020-07-022021-07-01
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.
260.David HausslerUniversity of California Santa CruzUnited States2019-11-192020-11-17
The focal point of this project is comparing gene-level expression estimates of an individual pediatric patient's tumor to the gene-level expression estimates of thousands of pediatric and adult tumors, called the research compendium. The gene-level expression estimates for some of the ICGC controlled access datasets will be calculated and included in the research compendium. This comparison is called a pan-cancer analysis, because the individual's data is being compared to data from many different cancer types. Although pediatric cancer is rare, by comparing each case to the research compendium, similarities can be spotted. This approach could be an efficient method of determining which cancer fighting treatments developed for adult tumors might be good candidates in specific pediatric cancers.
261.Melissa ClineUniversity of California Santa CruzUnited States2020-05-272021-05-26
Genetic testing can prevent cancer, but is hampered by rare genetic Variants of Uncertain Significance. These variants, called VUS, are variations of genes with unknown functions. We believe that the patterns of mutations in tumors can serve as a line of evidence to interpret VUS in the genes involved in DNA repair. By applying computational methods to the ICGC Controlled Data, we will evaluate DNA repair genes to discover whether samples contain known damaging variants and show patterns of mutations in inherited and tumor-related DNA. If successful, then we will look for samples that have (1) patterns of tumor-related mutations that suggest deficiencies in DNA repair, (2) damaging tumor-related mutations within the DNA repair genes, and (3) VUS in the inherited DNA. These three lines of evidence together may suggest that the VUS are damaging.
262.Charles LangleyUniversity of California, DavisUnited States2020-09-082021-09-07
Chromosomes are molecules that contain all genetic information of organisms (i.e. DNA sequences). The centromeric region (the central region of a chromosome) plays an essential role in preserving genetic information when cells are dividing and multiplying in the body. Centromeric regions are difficult to study because they contain highly repetitive sequences of DNA and are highly packed in a dense structure called “chromatin”. Errors in the transmission of genetic information and the amount of chromatin in chromosomes have been related to disease such as cancer. The initial aim of this project is to analyze the association between variations of DNA sequences in centromeric regions and the development of tumours in humans. The second aim involves contrasting the frequencies of clearly classified centromeric region DNA sequences in cancer patients with those in the general population. We hope to achieve these aims by applying computational methods to the ICGC controlled data.
263.Jeremy ChienUniversity of California, DavisUnited States2020-07-162021-07-15
In the human genome, the TP53 gene produces a protein called p53. This protein has the property to suppress tumorous cells from dividing, thereby suppressing tumor growth. However, mutations in the TP53 result in the loss of the ability of p53 protein to suppress tumors. Specific mutations in TP53 can also convert it into an oncogene, meaning that it could gain the ability to transform normal cells into tumorous cells. An oncogene can cause treatment resistance, cancer progression, and spreading of cancer to distant organ sites. Therefore, these properties of mutated p53 require better understanding so that they can be targeted for cancer suppression and effective therapies. We will investigate the combined contribution of inherited DNA sequence changes and tumorous mutations in TP53 in cancer development. We will apply computation tools to the ICGC Controlled Data to determine which inherited DNA sequences and mutations are associated with aggressive cancer features.
264.Olivier CinquinUniversity of California, IrvineUnited States2020-04-162021-04-15
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.
265.Ludmil AlexandrovUniversity of California, San DiegoUnited States2019-12-202020-12-10
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
266.Lukas ChavezUniversity of California, San DiegoUnited States2020-04-142021-04-13
We have previously analyzed genetic mutations in 961 tumours from children, adolescents, and young adults, comprising 24 distinct types of cancer. We have now developed new computational tools that are more sensitive to discover genetic mutations, such as complex structural variants of DNA. By using our improved tools, we now aim to analyze the ICGC Controlled Data focused on the discovery of previously overlooked structural variants and other genomic rearrangements. To evaluate the functional consequences of these structural variants, we aim to identify oncogenes or tumor- suppressor genes whose expression are potentially altered by genomic rearrangements.
267.Iwei YehUniversity of California, San FranciscoUnited States2020-07-302021-07-29
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.
268.Matthias HebrokUniversity of California, San FranciscoUnited States2020-08-222020-10-06
Ceramide, a component of cell membranes, and its breakdown products are involved in mediating critical responses in pancreatic cancer cells. Ceramide is a crucial regulator of cancer cell death and a component of cancer-cell derived exosomes. Exosomes, which are secreted by cells, are carriers of cellular cargo, such as protein or DNA, that play various roles in the formation of pancreatic cancer. The cellular processes regulating the content of exosomes made by pancreatic cancer cells are unknown. Our research addresses critical questions related to the function of ceramide and ceramide-mediated exosome production during progression and chemotherapeutic response of pancreatic cancer. The ICGC Controlled Data will be used to study the cellular pathways altered in pancreatic cancer patients with high and low expression of ceramide. From these studies, we will gain insight into the role of ceramide production during the progression of pancreatic cancer and chemotherapeutic response of pancreatic cancer patients.
269.Bjoern SchwerUniversity of California, San FranciscoUnited States2020-01-142021-01-12
Brain tumors are a major cause of cancer-related mortality in children. Treatment of brain tumors in children often requires high dose multi-modal chemotherapy and radiotherapy that come with significant and long-term consequences, such as severe mental and physical disabilities, even when a cure is obtained. We aim to identify the cellular and genetic origin of medulloblastoma, a brain tumor of the hindbrain, and other brain cancers of the nervous system. We are planning to use the ICGC Controlled Data to understand the genetic changes that occur in the tumor compared to healthy brain tissue. We will then use this information to model tumor development experimentally in order to identify the genetic drivers of this cancer. Our long term goal is to develop better and targeted therapeutics for pediatric brain cancers.
270.Yutaka HashimotoUniversity of California, San FranciscoUnited States2020-01-162021-01-14
Particular environmental exposures, such as from touching/intaking heavy metals or from long-term drug treatments, are known to cause a malignant transformation in human cells, including prostate cancer cells. However, the causes of these transformations have not been unveiled. The IGCG data sets are enormous and contain robust human RNA (commonly a messenger form of DNA) data that can allow us to find a new hypothesis and translate basic scientific data into clinical environments. In this project, we would like to be clear about how these environmental factors affect human cells, both cancerous and non-cancerous, in terms of RNA biology, and then try to bring the results into the clinical field by comparing them with IGCG patients' data. We expect that our study will identify the mechanism of drug-resistance and metal-toxicity cancer initiation.
271.Jingjing LiUniversity of California, San FranciscoUnited States2019-12-192020-12-17
Earlier studies in prostate cancer genomics have identified many risk alleles that contribute to cancer susceptibility. However, most of the identified risk alleles fall in the regions not encode for proteins (non-coding regions), and thus we still do not understand how they could contribute to tumor growth, and why prostate cancer display a strong prevalence among African Americans compared with many other populations. The proposed study is to model the mutational effects leveraging abundant ICGC cancer genomes, and determine how likely these mutations could affect gene expression. By identifying a subset of consequential mutations, we will directly assess their contribution to the disparity of population prevalence. The proposed study will help identify mutations causing cancer, and develop personalized prognostic and intervention strategies for prostate cancer.
272.serena nik-zainalUniversity Of CambridgeUnited Kingdom2020-07-302021-07-29
We have created a comprehensive reference database of mutation patterns that are present in human cancers which are called mutational signatures. This database is like an encyclopaedia of mutation information and is called Signal. Signal holds all the information on mutation patterns that are present in cancers, and that have been generated artificially in a laboratory by either treating cells with elements in the environment that are known to cause cancer, or by making genetic changes to the cells. ICGC Controlled Data is also used in this database in order to understand mutation patterns. This project generates a large amount of useful data and all that data can be explored by anyone who is interested to learn about mutation patterns.
273.Shamith SamarajiwaUniversity Of CambridgeUnited Kingdom2020-08-232020-10-07
Burkitt Lymphoma (BL) is an extremely aggressive form of non-Hodgkin Lymphoma. In the developed world it is potentially curable in children and younger adults. However, this requires high intensity chemotherapy, which cannot be tolerated by older patients and is not possible in third world countries, where childhood BL is especially common. These patients are currently treated palliatively. New therapy, targeting the molecular basis of the disease, is needed. Recent studies have revealed that approximately one third of BL patients have a mutation in the DDX3X gene. This study will determine which cell functions are controlled by DDX3X, how its mutation could corrupt this process and whether drugs that specifically target DDX3X might be beneficial for the treatment of BL. We will use ICGC Controlled Data to investigate the effect of DDX3X mutation in lymphomas and other cancers.
274.Carlos CaldasUniversity Of CambridgeUnited Kingdom2020-06-292021-06-29
Breast cancer is a heterogeneous disease, and our prior research using the previous generation of technology has found that breast cancer tumours can be split into 11 distinct biological subtypes. The Personalised Breast Cancer Program (PBCP) is a new Cambridge (UK)-based initiative to discover the mutations that are present in breast cancer patients’ tumours, using new technology that gives more detailed results. We are using this detailed data to develop new methods that find patterns of mutations that might drive the development of the cancers belonging to the 11 subtypes. These patterns might be utilised to, for example, predict patients’ risk of their cancer coming back. To test these new methods, we will use ICGC controlled data, and compare these results to those for patients treated in Cambridge.
275.Michael ImbeaultUniversity Of CambridgeUnited Kingdom2020-07-272021-07-26
It is vital that our cells orchestrate which genes are active, at what levels, and at what time. In many cases, dysregulation of this process can lead to disease, including cancer. We will apply computational tools to the ICGC Controlled Data to identify such disruptions through mutation in cancer patients. In particular, we are focussing on regions of the genome which do not instruct cells to produce proteins, but are control regions that can influence nearby gene levels - we will quantify mutations at precise locations we know are important for the binding of proteins that determine if a control region is active or not. This will broaden our understanding of cancer initiation and progression, potentially identifying previously undiscovered targets for therapeutic intervention. This analysis will offer a greater insight into the evolutionary processes which govern these control regions of the genome and how they participate in normal cell function.
276.Florian MarkowetzUniversity of Cambridge Cancer Research UK Cambridge InstituteUnited Kingdom2020-01-312021-01-29
In our research group we want to understand how certain changes to our DNA may contribute to cancer development. In particular, we focus on a specific type of mutation called copy number aberration. Usually, we have two copies of each chromosome, one from our mother and one from our father, but when parts of a chromosome are lost or copied multiple times, we call this a copy number aberration. When a cell develops into a cancer cell, it accumulates many DNA changes including copy number aberrations which will lead to a rewiring of its inner workings. We have developed a method to link a copy number aberration to the process that caused it. In total, we identified 17 of such processes which we call signatures. Now, we want to use the ICGC Controlled Data to prove that our signatures meaningfully represent processes driving cancer development.
277.Lixing YangUniversity of ChicagoUnited States2020-05-212021-05-20
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.
278.Axel HillmerUniversity of Cologne, Institute of PathologyGermany2020-04-272021-04-26
Cancer cells can lose their Y chromosome – what does that mean? Point mutations and other chromosomal changes including gain and loss of chromosomes are hallmarks of cancer. Cancer cells of some tumor types tend to lose the Y chromosome. This seems to be in particular frequent in esophageal cancers. It is unknown why the loss of chromosome Y is more often found in some cancer types than others. One type of esophageal cancer is nine-fold more often found in males compared to females. In our project, we aim at understanding the molecular consequences of chromosome Y loss and want to test for its connection with clinical parameters like tumor stage. We use the ICGC data to find out which other chromosomal changes are typical for esophageal cancer. With our project, we plan to explore whether the loss of chromosome Y plays a role in the development of esophageal cancer.
279.Rajeev VibhakarUniversity of Colorado Denver/ Anschutz Medical CampusUnited States2020-04-232021-04-13
Medulloblastoma (MB) is the most common malignant brain tumor in children, accounting for 20 to 25 percent of pediatric brain tumors and there is no targeted therapy identified for most of medulloblastoma types. We are working to determine the role of a tumor suppressor in MB. The tumor suppressor can be rescued by various clinical drugs. We will use the data in ICGC to analyze the mutation of this tumor suppressor in MB. Then, we will test its function in animal. In order to determine the classification of the tumor, we will perform experiment using the tumor from the animal and compared the results with the data in ICGC.
280.Ryan LayerUniversity of Colorado, BoulderUnited States2020-02-202021-02-18
Understanding the full spectrum of genetic mutations in cancer is immensely valuable, yet a large component that is frequently ignored is “structural variants”, a type of mutation. It is often difficult to distinguish if structural variant mutations are unique to the tumor or a harmless variant inherited from a parent. We hope to answer this question by searching through ICGC data of healthy and tumor genomes, and looking for patterns of recognition that may help identify the significance of the variant. For example, if an unknown variant is found in healthy tissue samples, it is likely harmless, while a variant that is common in many late-stage tumor samples is likely to be significant.
281.Benjamin EvansUniversity of East AngliaUnited Kingdom2020-03-262021-03-25
Antibiotic resistance is recognised internationally as one of the most urgent challenges in healthcare. The overuse and misuse of antibiotics has resulted in bacteria becoming resistant to antibiotics such that an increasing number of infections with these bacteria are becoming very difficult to treat. Understanding how bacteria become resistant to antibiotics and how they spread is crucial for developing strategies to counter this threat. There is growing evidence that drugs other than antibiotics, such as cancer chemotherapy drugs, are causing bacteria to become resistant to both the chemotherapy drugs and simultaneously to antibiotics. This suggests that cancer chemotherapy is contributing to the rise in antibiotic resistance. Using ICGC Controlled Data, this project will identify how common it is to find chemotherapy-resistant bacteria in cancer patients, and the degree to which chemotherapy drugs can cause antibiotic resistance in bacteria.
282.Vladimir TeifUniversity of EssexUnited Kingdom2020-05-222021-05-21
Chronic lymphocytic leukaemia (CLL) is a blood cancer which results from the production of white blood cells which are not fully developed and do not work properly. Therapeutic choices for treating CLL have vastly improved during the last decade, but understanding of the mechanisms that determine the diseases state is still limited. We are performing comparison of genome organisation in white blood cells from blood of CLL patients and healthy donors. This has revealed CLL-specific changes at many regulatory regions of the genome. We have profiled a number of regions in the genome of interest to our research in CLL and matched healthy controls. We will combine these in the integrative analysis with the ICGC Controlled Data for CLL patients. The ICGC Controlled Data and our complementary datasets refer to different patients, thus there is no risk of patient re-identification.
283.Stefano VoliniaUniversity of FerraraItaly2020-04-302021-04-29
A large fraction of human genes do not generate proteins but instead generate intermediate ribonucleic acids which help to regulate the activity of our cells. A novel class of these intermediate ribonucleic acids is made of circular and closed molecules, as opposed to the standard linear ones. Drug resistance is a major problem in cancer therapy and it is possible that these novel class of molecules play a significant role in determining the effectiveness of cancer treatments. Although identifying them would represent a major success, it is still mostly unknown their interplay with mutated genes in cancer cells. Therefore, we will use ICGC data to study the relationships between cancer mutations and circular ribonucleic acids and to determine their impact on the effectiveness of treatment.
284.Thomas DrakeUniversity of GlasgowUnited Kingdom2020-09-072020-10-21
Immunotherapy has become an important mainstay in the treatment of solid tumours. Trials in liver cancer have been promising, but do not work for all patients. How many and which immune cells are present is an important factor in predicting responses to immunotherapy. This project aims to characterise the immune cells present in liver cancers and to identify genes which may be driving this. We will use the data from patients in the ICGC and test our hypotheses generated from this data in the laboratory in order to develop better immunotherapies.
285.Magnus LindhUniversity of GothenburgSweden2020-04-232021-04-22
Cancer is a major health issue in the whole world. Some cancers are caused by viruses, for example liver cancer caused by hepatitis B virus and cervical cancer from human papilloma virus. Tumors cells are altered and instead of having a controlled proliferation (life span and death) they continue to grow. Viruses can have a direct effect on these functions in the cell. ICGC Controlled Data will be essential for the project to study viruses, tumors and abnormal variants of the nucleic acid RNA. RNA is essential for production of proteins in cells, but can also play a role in cancer. We want to know how certain strange looking RNAs are involved in cancer development and also if they are linked to viruses, this can be achieved from computational analyses of the ICGC data. Results may be of future use in cancer diagnostics and treatment.
286.Lauri AaltonenUniversity of HelsinkiFinland2020-06-022021-06-01
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.
287.Sampsa HautaniemiUniversity of HelsinkiFinland2020-06-022021-06-01
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.
288.Audrey FuUniversity of IdahoUnited States2019-12-182020-12-16
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 to infer networks of genes, where directed edges indicate how genes regulate one another; such networks are called gene regulatory networks. These networks derived from patients will tell us which genes are potentially key to disease or treatment. Combining ICGC controlled data with data from the GTEx consortium (health individuals) and the TCGA consortium (cancer patients) will be helpful for us to examine the effectiveness of the methods we are developing and comparing. We will apply these methods to genomic and clinical data of individuals, in particular patients of breast and ovarian cancers.
289.Michael GreenUniversity of Massachusetts Medical SchoolUnited States2020-08-192020-10-02
Every year, nearly 30,000 Americans die from liver cancer (also called hepatocellular carcinoma or HCC), underscoring the need for new and effective treatments. We have discovered that a protein called EZH2 is required for growth of HCC tumors, and that inhibiting EZH2 blocks HCC tumor growth, suggesting EZH2 inhibition may be a new therapeutic approach for treatment of HCC. We have found that EZH2 promotes growth of HCC cells cultured in the lab by switching off several of its target genes. To determine if this mechanism also occurs in human tumors, we will analyze clinical data from the ICGC to determine whether EZH2 and its target genes are turned off or on in HCC patient samples. The results of this project will support our hypothesis that EZH2 promotes HCC by turning off its target genes, and help evaluate EZH2 inhibition as a new therapeutic approach for HCC treatment.
290.Shakuntala BaichooUniversity of MauritiusMauritius2020-09-122020-10-27
Breast, Ovarian, Prostate, Colorectal and Lung cancers are very common on the African continent, including Mauritius. This team of researchers aim at applying computational knowledge to the genomic and proteomic study of these common cancers. Unfortunately the ongoing therapies are often not adapted to the varied ethnic background of the African population, thus leading to a high rate of deaths related to cancer. This project will help build tools and expertise using worldwide cancer genomics data, while also advancing our genomic understanding of the chosen cancers from a multi-omics perspective. This application requests approval to access "ICGC controlled data" in order to build a preliminary data bank for our analyses and tool development.
291.Arul ChinnaiyanUniversity of MichiganUnited States2020-04-232021-04-22
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.
292.Rendong YangUniversity of MinnesotaUnited States2020-07-302021-07-29
Cancer can be caused by abnormal gene expression and one of such abnormality is called alternative splicing. Recent developed human genome sequencing technology has provided a way to detect cancer associated alternative splicing events. We would like to use the ICGC controlled sequencing data to detect abnormal alternative splicing in human cancer. These events may serve as novel therapeutic targets to control cancer progression and metastasis.
293.Mira HanUniversity of Nevada, Las VegasUnited States2020-02-252021-02-23
Cancers of Unknown Primary (CUPs) are cancers that are found at a metastatic stage, but without a primary site. Knowing the primary site for CUPs is important, because it can direct the therapies in cases where there are primary site-specific therapies available. This project aims to find molecular markers that are useful for tissue identification, and to develop a classifier that enables primary site prediction. Data from ICGC will be used to train the classifier and a subset will be used as an independent dataset to validate the approach. Using the markers and the pipeline developed, we will be able to better predict primary sites for CUPs.
294.Jen Jen YehUniversity of North Carolina at Chapel HillUnited States2020-05-212021-05-20
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers. There are two subtypes of PDAC; one is associated with worse outcomes. Microbes present inside tumors, which form a “tumor microbiome”, have recently been shown to play important roles in cancer. When human tissue is prepared for analysis, all DNA is extracted, including DNA from any microbes present. Normally, this microbial DNA is filtered out before most analyses. We plan to use the microbial DNA component of unfiltered ICGC data to identify what microbe species are present in pancreatic tumors and in what abundances. Then, we plan to compare the microbes between the two subtypes of PDAC. Discovering differences in the tumor microbiome between subtypes may partly explain why one is associated with worse outcomes and information about the tumor microbiome may be used in the future to develop cancer therapies targeting the microbiome.
295.Xose PuenteUniversity of OviedoSpain2020-08-062021-08-05
Recent advances in cancer genomics have improved our ability to identify the genomic mutations present in tumor cells. The presence of a specific mutation in a tumor can be used for diagnosis and to decide the best treatment for a particular patient, sometimes with drugs aimed at counteracting the effect that that particular mutation exerts on the cell. However, our understanding of the human genome is still limited, and it is difficult to predict what effect will have a particular mutation. In some cases, current algorithms fail to correctly predict the effect of a mutation, leading scientists to miss some important mutations. Our study will use ICGC Controlled data to integrate genomic data (genomic mutations) with functional data generated by ICGC to identify these mutations that are frequently missed by current approaches.
296.David WedgeUniversity of OxfordUnited Kingdom2020-08-242020-10-07
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.
297.Benjamin Schuster-BoecklerUniversity of OxfordUnited Kingdom2019-12-172020-12-15
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.
298.Robert VonderheideUniversity of PennsylvaniaUnited States2020-04-162021-04-15
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.
299.Rebecca WattersUniversity of PittsburghUnited States2019-12-172020-12-15
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.
300.Xiaosong WangUniversity of PittsburghUnited States2020-09-182020-11-02
This proposal seeks to employ a robust integrative genomics approach to discover cancer causing genetic changes and drug target genes in breast tumors. This approach looks for candidate cancer causing genes and genetic changes using evidence from different levels of genomics data using the ICGC Controlled Data. We will integrate multidimensional genomics data inclusive of determining the order of the four chemical building blocks in DNA, abnormal changes in the organized structure of DNA, amount of functional gene product, and physical interactions of proteins. We will focus our discovery on breast cancer and a priority will be given to recurrent structural or numerical chromosome changes or abnormally regulated tumor causing genes.
301.Eduardo ReisUniversity of Sao PauloBrazil2020-05-212021-05-20
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.
302.Dongmei AiUniversity Of Science And Technology BeijingChina2020-07-022021-07-01
Numerous studies have shown that viruses and bacteria are closely related to cancer, and research on the genomic data of cancer samples is helpful to further explore the mechanism of action between microorganisms and cancer. ICGC integrates different types of data from a variety of cancers, allowing us to analyze the association between cancer and microbes from multiple perspectives. In this project, we will develop corresponding pipelines for different types of data and find key microorganisms based on the above pipelines.
303.Kun QuUniversity of Science and Technology of ChinaChina2020-05-202021-05-19
Most cancers are caused by the accumulation of key genetic mutations. However, effective screening of cancer patients based on gene mutations remains a challenge due to the large heterogeneity between individuals. In this project, our goal is to develop cancer prediction models based on genetic mutations and deep learning algorithms to screen high-risk populations. We will use the gene information of breast cancer patients from EGA (European-Genome Phenome Archive, controlled by ICGC) and normal samples from the HapMap project to test whether our model can accurately predict the cancer status of each individual. We will create a public interactive website for all other researchers to predict the cancer risk of their samples based on genetic data. Any information made public on our website will respect the ICGC's confidentiality terms found in the Data Access Agreement.
304.George BlanckUniversity of South FloridaUnited States2020-05-262021-05-25
ICGC controlled data, which are basically DNA sequences of tumors, represent the raw material from which cancer mutations are obtained. However, over the last several years, unexpectedly, it has become apparent that the genetic composition of immune cells in the tumor can also be found in these DNA sequences. When the DNA of the tumor is extracted and analyzed, the genetic composition of these immune cells are retained. Thus, this project is designed to provide a more accurate distinction between patients with regard to the immune status of their tumors. Eventually, such distinctions may be useful in determining which patient will, and will not respond to certain therapies, especially immunotherapies. As for methodology, we are able to obtain these immune cell DNA sequences because of an original software, which has been made freely available to the scientific community.
305.Leng HanUniversity of Texas Health Science Center at HoustonUnited States2020-01-092020-12-01
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.
306.Todd AguileraUniversity of Texas Southwestern Medical CenterUnited States2020-08-302020-10-14
Prediction and assignment of an experimental combination of cancer therapies to a group of patients well suited is a challenging and expensive task. New ways to make predictions and test clinically while being cost effective is critical. To generate hypotheses to address this problem we intended to use the available ICGC data to evaluate the genetics of cancers from patients that had differential survival. We seek to identify genetic characteristics that could be prognostic and potentially predictive of the optimal therapeutic intervention. The ICGC data will be used as a foundation for laboratory experiments on tissue in the lab and in animal models to verify computational findings from the database. We expect the outcomes of the current proposal will accelerate the development of new hypotheses that can make precision medicine based predictions that can be tested translationally in clinical trials.
307.Mandeep KaurUniversity of The WitwatersrandSouth Africa2020-03-052021-03-05
Cancer represents one of the most complex diseases, due to the inherent abilities to move between tissues and evade treatment such as chemotherapy. Our use of the ICGC Controlled Data is aimed at identifying the characteristics of breast cancer cells that give them the ability to move between tissues and evade therapy. The PCAWG study included under ICGC Controlled Data provides a large variety of patient sequence data, which grants us a lot of potential to perform further analyses. This identification of characteristics in this data will help us to understand the possible root causes of cancer progression, opening up new treatment avenues for further drug development in cancer therapy.
308.Yutaka SuzukiUniversity of TokyoJapan2020-02-062021-02-05
We are trying to make use of a new type of instrument to determine DNA sequences. This new analytical platform enables us to analyze a longer DNA fragments, thus, have substantial advantages over previous instruments by which only the fragmented DNA could be analyzed. We believe the application of this new instrument for analyzing cancer genomes would bring better understandings of cancerous genomic mutations. We have now collected the results of the initial analysis, but we would like to compare the results with those of illumina sequencers, in order to validate the accuracy and the precision recall rates. For the latter datasets, one of the richest data resource is that of the ICGC data archive. We wish to analyze the data in a pan-cancer manner. We would like to also compare the results depending on the type of patients.
309.Alberto BardelliUniversity of TurinItaly2020-06-162021-06-15
Therapy for ovarian and breast cancer has made significant steps forward since drugs targeting DNA damage repair (DDR) proteins have been approved. Around 2% of colorectal cancers (CRCs) bears genetic aberrations in DDR genes, thus conferring a potential vulnerability that could be exploited to treat CRCs with reduced therapeutic options. With this project, we aim at understanding whether drugs targeting DDR proteins could be successfully used to treat colorectal cancer carrying alterations associated to primary and acquired resistance to commonly used targeted agents. We will use ICGC Controlled Data to develop computer tools to identify specific genomic alterations occurring in CRC preclinical models we have set up in our lab. Our study might unveil novel therapeutic strategies to treat CRCs refractory to conventional therapies.
310.Moses EkpenyongUniversity of UyoNigeria2020-03-022021-03-01
Access to cancer datasets is critical for advancing research. This research proposes a learning model that can efficiently classify cancer diseases in patients, using the ICGC controlled Data–donated by subjects around the world. The research shall not only enable the identification of possible markers of cancer and the effect of cancer disease on metabolism; but also provide accurate predictions of cancer diseases, as well as variation in gene mutations of cancer risk patients.
311.Florian ErhardUniversity of WuerzburgGermany2020-06-232021-06-22
The success of cancer immunotherapy has shown that the immune system is in principle able to distinguish cancerous from normal cells. This recognition mechanism is mediated by small biomolecules called peptides that are constantly presented at the surface of most cells. Specific immune cells are able to identify specific peptides that a healthy cell would never present. This includes tumor specific peptides that represent ideal targets for vaccination or other immunotherapeutic strategies. Using a combined approach of advanced experiments and computational analyses we have recently discovered a new class of potential targets by analyzing a limited group of patient samples. The much larger patient cohorts from ICGC/TCGA will enable us to assess the recurrence and tumor specificity of these targets and to select promising candidates for further investigation.
312.Michael BaudisUniversity of ZurichSwitzerland2020-02-042021-01-29
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.
313.Richard ChahwanUniversity of ZurichSwitzerland2020-04-062021-04-05
B cells are a critical cell type of the immune system which can become cancerous. Non-Hodgkin lymphomas (a common form of this cancer type) can often be caused by viral infections, even though ironically, one of the core functions of B cells is to neutralize viral infections. Our work aims to understand how and why viral infections cause B cell cancers. Namely, we aim to elucidate why viral infections affect some patients disproportionately more. We propose to do this by analyzing the ICGC controlled data for mutation evidence, which we can use to better understand tumor development in patients. This would hopefully allow us to better understand the different factors in individual immune responses. Overall, we aim to virus-immune cell interactions, and the mutations they cause to better understand the process of viral infections and the formation of tumors.
314.Joakim CronaUppsala UniversitySweden2020-09-032021-09-02
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.
315.Claes WadeliusUppsala UniversitySweden2020-02-112021-02-09
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.
316.Karin Forsberg NilssonUppsala UniversitySweden2020-05-202021-05-18
The human genome consists of >3 billion letters. Genes, the blueprint for the proteins made in our bodies, comprise only ~1% of the genome. However, regulatory regions that determine when, and how much, of a protein should made, probably make up ~5%. So far, it has been hard to predict which mutations outside genes that cause disease. We now have a novel method to do this. By comparing the genomes of >200 mammals we can find the important elements that govern the regulation of genes. Now we can look specifically in these functional regions and identify genes regulated by novel mutations. By using the ICGC controlled data we will be able to identify new cancer genes where the regulation is disrupted, both within and across cancer types. In the future, this may lead to a better understanding of the disease mechanism and thereby open up for better treatment options.
317.Maria AbadVall d'Hebron Institute of OncologySpain2020-04-022021-04-01
Our project aims at identifying novel small proteins (microproteins) that are secreted in small vesicles by pancreatic tumours into the bloodstream of patients to promote tumour growth and metastasis. In other words, this project is about discovering the messengers that pancreatic cancer uses to grow and colonize new organs, and how they act. Ultimately, this can lead to the establishment of new diagnosis and therapy strategies targeting them. The ICGC Data will be used to identify the repertoire of secreted micropeptides by looking at the regions of the genome that are dysregulated in pancreatic cancer. It will also allow us to analyse their mutations in pancreatic cancer to gain insight into their function.
318.Xingyi GuoVanderbilt University Medical CenterUnited States2020-02-182021-02-16
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.
319.Yongsoo KimVU university medical centerNetherlands2020-09-182020-11-02
Tumors consist of diver cell types, including cancer cells with heterogeneous genomic profiles and also surrounding normal cells, often termed intra-tumor heterogeneity. Characterizing cellular composition may lead to a novel biomarker for treatments such as immunotherapy. However, the standard sequencing techniques measure averaged genome and transcriptome profiles of a large collection of cells. We aim at developing statistical machine-learning methods that can deconvolute the averaged genomic/transcriptomic profiles to address intra-tumor heterogeneity. A large number of sequencing data in ICGC controlled data is essential to establish the methods.
320.Steven RobertsWashington State UniversityUnited States2020-06-232021-06-22
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.
321.Li DingWashington University in St. LouisUnited States2020-09-142020-10-14
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.
322.John EdwardsWashington University in St. LouisUnited States2020-04-212021-04-20
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.
323.Malachi GriffithWashington University in St. LouisUnited States2020-02-042021-01-28
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.
324.Charles KaufmanWashington University in St. LouisUnited States2020-01-032021-01-01
Cancers are caused by changes in DNA, or mutations, that cause cells to grow too much and spread throughout the body. Melanoma is a type of skin cancer that has many of these mutations, yet we do not know how many of them lead to this cancer. In this project, we will use ICGC controlled data to look for specific kinds of mutation that change how a gene is turned on or off and how this leads to melanoma. Understanding this may lead to earlier cancer detection or better treatments.
325.Nikolay IvanovWeill Cornell Medical CollegeUnited States2020-02-242021-02-22
Childhood brain cancer is a devastating disease that is still poorly understood. Glioblastoma is a type of brain cancer, in which a specific type of cells in the brain (called astrocytes), become cancerous. In our study, we will study how the DNA in glioblastoma cells differs between different groups of patients. We will also study how these differences in DNA affect gene expression. This will allow us to gain a greater understanding of how pediatric glioblastoma develops, how it differs between different groups of patients, and how childhood glioblastoma differs from adult glioblastoma. We will use the ICGC Controlled Data in our study to study glioblastoma gene expression.
326.Liran ShlushWeizmann Institute of Science, IsraelIsrael2020-03-312021-03-30
Acute Myeloid Leukemia (AML) is a type of blood cancer which has been associated with various genetic changes. This project aims to identify previously unknown genetic variations that occur in the cell that drive AML progression. To achieve this, we require controlled ICGC data from the blood tissue of patients across cancer types. This would enable us to better characterize these important changes in AML and other myeloid disorders, potentially leading to improvements in early diagnosis and treatment.
327.Peter CampbellWellcome Trust Sanger InstituteUnited Kingdom2020-09-012021-08-31
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.
328.Mark GersteinYale UniversityUnited States2020-02-042021-02-02
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? By comparing ICGC Controlled Data with 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. We 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.
329.Sangwoo KimYonsei University College of MedicineSouth Korea2020-02-202021-02-18
Cancer is a complex disease with genetic mutations. While many mutations are present in the entire cancer cells, some mutations only exist in a small part and hard to detect. In this project, we will apply advanced bioinformatics and mathematical techniques to ICGC data to discover these rare mutations, to see their amounts and characteristics in multiple cancer types. The goal of our project is to draw a landscape of these rare mutations and find out genetic events in a late stage of cancer and how cancer evolves.