DACO Approved Projects

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

Principal Investigatorsort iconPrimary AffiliationCountryDate Approved for AccessValid UntilTitle of Project
1.Nada JabadoResearch Institute McGill University Health CentreCanada2019-03-262020-03-24
One in every 450 children will suffer from a cancer before the age of 15 years. High-grade astrocytomas (HGA) are a particularly lethal and disabling form of brain cancer, with barely 10% of children and young adults surviving 3 years after their diagnosis. We recently identified mutations in an important gene known as histone 3.3 in a significant fraction of children and young adults with this brain tumor. This histone gene is involved in regulating the development and growth of many body tissues, but particularly the brain. Blood tests for this gene will help for diagnosis. We are identifying genes(ICGC) which can be used for drug development, to improve immediate and long-term survival of children with HGA using genome analysis tools and animal models to reproduce the effects of these mutations. This will offer the real possibility of identifying promising treatment targets while already testing known drugs for their efficiency.
2.Edwin WangUniversity of CalgaryCanada2018-11-062019-11-05
Advanced technologies in producing genetic data from patients provide potentials in matching drugs for the treatment and prevention of cancer. This project will use genetic data from tumors available from the ICGC to develop tools that will help clinicians to (1) apply the 'right' drugs to the 'right' cancer patients and (2) detect tumors in early stages so that cancer patients could be better managed.
3.Lauri AaltonenUniversity of HelsinkiFinland2019-05-022020-05-02
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.
4.Todd AguileraUniversity of Texas Southwestern Medical CenterUnited States2018-10-182019-10-17
Prediction and assignment of an experimental combination of cancer therapies to a group of patients well suited is a challenging and expensive task. New ways to make predictions and test clinically while being cost effective is critical. To generate hypotheses to address this problem we intended to use the available ICGC data to evaluate the genetics of cancers from patients that had differential survival. We seek to identify genetic characteristics that could be prognostic and potentially predictive of the optimal therapeutic intervention. The ICGC data will be used as a foundation for laboratory experiments on tissue in the lab and in animal models to verify computational findings from the database. We expect the outcomes of the current proposal will accelerate the development of new hypotheses that can make precision medicine based predictions that can be tested translationally in clinical trials.
5.Altuna AkalinMax Delbrück Center for Molecular MedicineGermany2019-02-132020-02-13
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.
6.Bissan Al-LazikaniInstitute of Cancer Research, UKUnited Kingdom2019-03-222020-03-20
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.
7.Ludmil AlexandrovUniversity of California, San DiegoUnited States2019-05-162019-06-28
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.
8.Arash AlizadehStanford UniversityUnited States2019-04-012019-09-10
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.
9.Ramu AnandakrishnanEdward Via College of Osteopathic MedicineUnited States2018-11-272019-11-21
Cancer is known to result primarily from genetic defects. Yet, despite decades of research and the availability of extensive genomic data, the specific cause for individual instances of cancer can not generally be determined. One reason is that current computational methods focus on identifying individual "cancer genes", while cancer results from a combination of multiple genetic defects (multi-hit combinations). We are developing an algorithm for identifying multi-hit combinations instead of cancer genes. Information from ICGC controlled data will be used to differentiate between cancer-causing and non-cancer causing mutations. The multi-hit combinations identified by this project are likely to better explain the cause of cancer and suggest new ways to view, diagnose and treat cancers.
10.Dimitris AnastassiouColumbia UniversityUnited States2019-06-102019-07-25
The goal of this research project is to identify cancer-associated genomic components present in multiple cancer types by computational mining of the ICGC data sets. We recently identified multiple sets of genomic features that act in nearly identical patterns across multiple cancer types, using a computational method that we developed, called the "attractor" method. We also proved that these features are prognostic of the outcomes of patients with breast cancer. The method is designed to point to the core of those genomic features, thus shedding light on the underlying biological mechanisms. We plan to analyze the ICGC cancer data sets using the attractor method to identify new cancer-associated features and improve the accuracy of existing ones, which will potentially improve the accuracy of diagnosis and prognosis in cancer.
11.Jesper AndersenBiotech Research and Innovation Centre (BRIC), University of CopenhagenDenmark2018-09-182019-09-17
Biliary tract cancer (CCA) is among one of the fastest growing cancers in the world. We have performed an analysis of the genomes of 142 samples of CCA, and detected commonly occuring genetic alterations as well as alterations unique to subsets of patients, which may have specific therapeutic implications. Applying advanced genetic methodologies together with translational research approaches, we will characterize the genetic landscape in CCA and compare to cancers in the upper gastrointestinal systme (pan-GI: liver, gallbladder, biliary tract and pancreatic). Unique and pan-GI alterations will be investigated for their implications on the causes of the malignancy. Increasing the size of our patient group (by including data from the ICGC) allows for further detailed analysis into the subsets of patients as well as novel affected groups. Furthermore, increasing our study to include patients in the upper pan-GI system allows us to understand the genetic variability and commonalities.
12.Martin ArmstrongUCB Biopharma SPRLBelgium2019-05-142020-05-14
The overall aim of this study is to identify new genetic predispositions to cancer, unravel the mechanisms behind this increased susceptibility and potentially discover new therapeutic targets. To this end, we would validate, in cancer, existing knowledge on genetic differences in the population and the functional consequences of these alterations. The ICGC data is instrumental for this analysis because it maps, for the same patient, both these genetic differences and the genes active in cancer tissue.
13.Ravshan AtaullakhanovBostonGeneUnited States2019-02-052020-02-05
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.
14.Shakuntala BaichooUniversity of MauritiusMauritius2018-11-202019-11-20
This team of researchers aim at applying computational knowledge to the genomic study of cancers relevant to the African continent. Breast, Ovarian and Prostate cancer are very common on the African continent, including Mauritius. Unfortunately the ongoing therapies are often not adapted to the varied ethnic background of the African population, thus leading to a high rate of deaths related to cancer. This project will help build tools and expertise using worldwide cancer genomics data. This application requests approval to access ICGC controlled data in order to build a preliminary data bank for our analyses and tool development.
15.Dafna Bar-SagiNew York University School of MedicineUnited States2018-12-072019-12-06
Pancreatic cancer is a devastating disease, with a dismal 6% 5-year survival rate and no existing treatment. The overall goal of our study is to understand how tumor cells evolve in the pancreas and to decipher how cells within and outside pancreatic tumors interact to promote and maintain tumor growth. We propose to apply informatics methods to pancreatic cancer patient level Controlled Data from the ICGC to infer how human pancreatic tumors have evolved. We will use these findings to inform creation of new pancreatic cancer cell models from normal mouse pancreatic cells. We will implant these cells into pancreata of immune-competent mice to study how these cells communicate with each other and with supporting cells in the pancreas. An improved understanding of pancreatic cancer in this context may reveal processes critical to tumor survival or immune evasion, potentially representing promising targets for therapeutic intervention.
16.Murali BashyamCentre for DNA Fingerprinting and Diagnostics (CDFD), Hyderabad, INDIAIndia2019-05-012020-04-25
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.
17.Oliver BatheUniversity of CalgaryCanada2019-04-042020-04-03
Cancer consists of a heterogeneous population of different cell types. In addition to cancer cells, there are other cell types. There is evidence that some non-cancer cells (called stromal cells) can affect the growth and function of cancer cells. Pancreatic cancer is known to contain many stromal cells, but the effects of these cells on the clinical behaviour of pancreatic cancer is not well understood, we will use the highly detailed molecular information collected by ICGC to explore the interactions of tumour cells and stromal cells.
18.Michael BaudisUniversity of ZurichSwitzerland2019-01-302020-01-29
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.
19.Daniel BauerChildren's Hospital BostonUnited States2019-05-102020-05-10
We are planning to perform a clinical trial using gene editing to change disease-associated genetic sequences in blood cells from patients with serious blood disorders. Using ICGC Controlled Data, we want to understand what types of changes around these genetic sequences may have been found in patients with cancer, so we can design the safest possible therapeutic gene editing strategy.
20.Jose Luis Bello LopezInstituto de Investigacions Sanitarias de Santiago de CompostelaSpain2019-04-152020-04-15
Chronic Lymphocytic Leukemia (CLL) is a frequent blood cancer that predominantly affects elderly individuals. It has been known for years that CLL cells harbour genetic alterations which have prognostic impact. Nevertheless, recent studies of CLL genomes have enabled the detection of a myriad of mutated genes and the inference of various common mutational patterns, reinforcing the idea that CLL is a genomically complex disease. In this project, we will integrate several layers of biological complexity in order to identify new genes implicated in CLL biology and to study their association with clinical events. We will use the ICGC CLL genome database as our initial genomic data input to characterize CLL genomes. Further experimental studies will be formulated on the basis of the results that we may obtain.
21.Stephen BenzNantOmics, LLCUnited States2018-10-262019-10-25
Each gene in the genome interacts with other genes in two ways: a gene’s activity is regulated by others, and the biological activity of a gene in a cell may require the coordination of other gene’s. In tumors, mutations will aberrantly turn genes on or off, resulting in the abnormal behaviors of tumor cells. Using ICGC controlled data, we will map each tumor’s mutations onto the set of genes, and then the corresponding interactions, in an attempt to explain tumor cell’s behavior in terms of how these regulatory interactions have been modified. We will provide an overview of what sets of interactions are most often modified together, in order to better understand what biological processes must be co-regulated in cancer.
22.Benjamin Bermancedars-sinai medical centerUnited States2019-06-032019-07-18
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
23.Rameen BeroukhimBroad InstituteUnited States2019-01-252020-01-24
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
24.Rameen BeroukhimBroad InstituteUnited States2019-01-242020-01-23
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.
25.Dieter BeuleMax-Delbrück-Centrum für Molekulare Medizin (MDC) Berlin-BuchGermany2018-11-092019-11-08
The detection of genomic alterations in cancer is of paramount importance for the diagnosis of cancer types in the clinic. These genomic alterations can help clinicians to choose the most optimal treatment for their particular patients, and therefore improve their quality of life through treatment and even their possibility of survival. We will use ICGC protected data to evaluate which computational methodology is the most optimal to identify these genomic alterations.
26.Andrew BiankinWolfson Wohl Cancer Research Institute, University of GlasgowUnited Kingdom2019-01-172020-01-16
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
27.Elia BiganzoliUniversity of MilanItaly2019-06-222019-08-05
Increased body mass index (BMI) has been recognized as a risk factor for developing breast cancer and has also been associated with adverse survival. Here we aim to use the ICGC Controlled Data to investigate the associations between the patient’s BMI at diagnosis and the biological characteristics of the tumor using the “560 breast cancer genomes cohort” (Nik-Zainal et al. Nature 2016). Given the importance of the immune tumor microenvironment in the context of increased BMI, we will also evaluate the association and relationship between several immune variables and the available biological, clinical and pathological characteristics of these breast cancers. The project will be conducted in collaboration with the J.C. Heuson Breast Cancer Translational Research Laboratory, Université Libre de Bruxelles, Institut Jules Bordet, Brussels.
28.Mascha BinderUniversity Hospital Halle (Saale)Germany2019-02-222020-02-22
Chronic Lymphocytic Leukemia (CLL) represents the most common leukemia in western countries and is characterized by a very variable disease course. While some patients only require very litttle or no treatment at all, others quickly develop resistances to commonly used drugs. Understanding the differences between this two subgroups and how to individually tailor the therapy to the patients is a largely unmet clinical need. In our research project we are concentrating significance on a certain family of surface molecules (SLAMF receptors) that are involved in immune control and their the prognostic and pathobiological significance in CLL. We are therefor working with a cellular model system where we try to reproduce the data from a clinically well characterized cohort of CLL patients. With the ICGC data we are hoping to validate our findings and further strengthening the translational applicabilty of our research.
29.Christoph BockCeMM Research Center for Molecular Medicine of the Austrian Academy of SciencesAustria2019-01-102020-01-09
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.
30.Jonathan BondUniversity College DublinIreland2018-11-232019-11-23
The best way to improve treatments is to try to understand the reasons why they sometimes don’t work. This is not straightforward, as the ‘internal wiring’ of a leukemia cell is very complicated, and the leukemia cell is very good at ‘rewiring’ itself to escape being killed by the therapies we currently give. We use a scientific approach called ‘Systems Biology’ to try and better understand how this happens. This approach involves making computer models of the gene and protein networks that keep a leukemia cell alive. We wish to use ICGC controlled data to see whether some of the mutations found in leukemia cells might interact with each other. We think that this analysis will help to identify hidden ‘Achilles’ heels’ in the leukemia cells, which might help us find more precise and effective cures for children with blood cancers.
31.Guillaume BourqueMcGill UniversityCanada2019-01-292020-01-29
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
32.George BovaUniversity of Tampere Institute of Biomedical TechnologyFinland2018-10-222019-10-21
The University of Tampere, Finland Institute of Biomedical Technology (IBT) is a collaborator in the ongoing ICGC Prostate Cancer-UK project, whose principal aim is to define the genomic basis of prostate cancer, and to use this information to improve prevention, diagnosis, and therapy of this common disease. Prof. Bova is a PI in the prostate cancer-UK project group, and he is mainly focused on analysis of metastatic prostate cancer samples which are a critical component of the ICGC Prostate Cancer-UK project. At IBT, working together with Prof. Visakorpi and Nykter and team members, we will use a combination of the ICGC data and data generated locally to build models to support the development of a system to enable effective prevention, diagnosis, and treatment of cancer tailored to each patient and their unique characteristics.
33.Adrian BrackenTrinity College DublinIreland2019-06-172020-06-17
This research project is focused on the genetics of breast cancer. We know that breast cancer often runs in families, and some women inherit a risk of developing breast cancer. Landmark studies on these families led to the discovery of cancer-causing mutations in the BRCA1 and BRCA2 genes. However, mutations in these two genes are only detected in one in five women with familial breast cancer. Therefore to identify the mutations responsible in the majority of women, we sequenced the DNA of Irish patients from families with strong patterns of breast cancer, but with normal BRCA1/2 genes. We discovered several additional mutations we believe are responsible for causing cancer in these women. We now wish to search for these inherited mutations in the ICGC data, to determine their frequency in a large international sample of patients.
34.Joshua Breunigcedars-sinai medical centerUnited States2019-01-312020-01-31
Pediatric high-grade glioma (a malignant tumor that occurs in the brain or spinal cord) has a very low survival rate. This has remained mostly unchanged over many decades. We have created a mouse model that allows us to rapidly recreate the tumor types found in pediatric patient population, reflecting the individual nature of human cancer. We aim to use ICGC controlled data to compare with our models to check if they accurately reflect human tumors, and if the commonalities can be used to discover new therapeutics. Also, if validated, the models can be used as drug discovery and testing platforms that would be predictive of the response to treatments in patients.
35.Robert BristowCancer Research UK, Manchester InstituteUnited Kingdom2018-12-192019-12-19
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.
36.Angela BrooksUniversity of California Santa CruzUnited States2019-06-202020-06-19
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.
37.Benedikt BrorsGerman Cancer Research CenterGermany2019-01-152020-01-14
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
38.Benedikt BrorsGerman Cancer Research CenterGermany2018-11-262019-11-25
Ninety percent of pancreatic cancers are driven by a mutation in a single gene called KRAS, which is difficult to address therapeutically. The remaining 10% of tumors are not characterized very well. We discovered a previously unknown driving mechanism in a subset of tumors lacking a KRAS mutation. We would like to corroborate the significance of our finding by screening the ICGC pancreatic cancer cohort for similar events to explore their prevalence.
39.Jehoshua BruckCalifornia Institute of Technology (Caltech)United States2018-08-312019-08-30
Instabilities in repeat regions in DNA have been identified in cancer patients. Given a large amount of repeat regions in human DNA, our study aims at finding specific repeat regions which can be attributed to different kinds of cancer based on variations in the number of repeats and mutations. In this regard, we will use DNA data for cancer patients provided by ICGC by extracting tandem repeat (TR) regions from their DNA and estimating the mutation rate there. TR regions are periodic sequences in DNA. For example, ACACACAC is a TR region with “AC” being repeated. These regions constitute 3% of the human genome. The mutation rates in these regions are particularly high and their estimates can be used to differentiate between healthy and cancerous people and also to study the variation among different cancers. This study can be useful in early cancer detection at a minimal computation cost.
40.Kenneth BuetowArizona State UniversityUnited States2019-01-232020-01-22
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.
41.Mario CaceresUniversitat Autònoma de BarcelonaSpain2018-11-062019-11-05
Inversions are one type of genetic variants that affect a large fraction of the human genome and that have been implicated in functional differences between individuals. Nevertheless, they have been poorly studied due to technical challenges in their detection, which has precluded determining their role in disease susceptibility. In addition, it has been recently shown that many inversions have appeared independently multiple times in different individuals and their effects have been largely missed by current studies. Therefore, this project aims to carry out a complete analysis of the functional effects of human inversions, including their association with different common complex diseases and other health related traits. In particular, by using the available ICGC Controlled Data corresponding to sequence information from different types of cancer, we will be able to check the role of inversions on the genetic predisposition to the disease, which can result in potential significant social benefits.
42.Peter CampbellWellcome Trust Sanger InstituteUnited Kingdom2019-04-262020-04-25
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
43.Scott CarterDana-Farber Cancer InstituteUnited States2018-11-192019-11-18
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
44.Maria CastroUniversity of MichiganUnited States2019-03-252020-03-25
Our research group studies different types of brain tumors. Currently we are focused in one type of a highly aggressive brain tumor, called high grade glioma (HGG), which occurs in the pediatric-adolescent population. This HGG type is incurable, with patients exhibiting a median survival of 18.0 months. It has been observed that pediatric HGGs are different in many aspects from the adult counterparts, explaining why the extrapolation of chemotherapeutics from the adult HGG failed to improve the clinical outcome in the pediatric population. We have developed a pediatric HGG mouse model that mirrors the human disease and is a valuable tool to study the tumor’s biology and possible therapeutic approaches. We are interested in comparing the data obtained from the mouse model with the human data. The ICGC Controlled Data, in combination with other databases, will be used for this purpose.
45.Julide CelebiIcahn School of Medicine at Mount SinaiUnited States2019-03-222020-03-20
A widely accepted genetic classification scheme of melanomas (a type of skin cancer) as aggressive and high-risk has not translated into the clinical setting. We propose to utilize a unique gene signature that we recently identified to classify melanomas as high- or low-risk and to identify the predictive value of our gene signature on patient outcomes. We will utilize ICGC controlled data to identify our gene signature and to identify critically mutated genes associated with primary cutaneous melanomas (the most common subtype of malignant melanoma).
46.Frederic CharronUniversity of MontrealCanada2019-05-152020-05-03
Medulloblastoma is one of the most common pediatric brain tumors. Although many mutations have been characterized as drivers of medulloblastoma formation, little is known about the mechanisms that control the growth and progression from precancerous lesions to malignant tumors. We recently found that one of the mechanisms controlling medulloblastoma progression is cell senescence (a phenomenon by which normal cells can no longer divide). This research project aims to find new mutated genes and signaling mechanisms that lead to inactivation of cell senescence in medulloblastoma. We will use the ICGC controlled datasets to identify medulloblastoma mutations in genes that have been linked to cell senescence in studies of other cancers. Sequenced tumors curated and maintained by ICGC will allow us to uncover novel regulators of medulloblastoma progression that could be used in the development of pre-clinical medulloblastoma models to explore novel targeted therapies.
47.Claude ChelalaBarts Cancer InstituteUnited Kingdom2018-12-202019-12-19
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.
48.Ken ChenThe University of Texas MD Anderson Cancer CenterUnited States2019-03-152020-03-13
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.
49.Yiwen ChenThe University of Texas MD Anderson Cancer CenterUnited States2018-10-102019-10-09
The goal of this project is to develop principle and computationally efficient algorithms that enable integrative analysis of ICGC data to elucidate the involvement of particular genes and pathways in carcinogenesis and therapeutic response. We will develop and test our algorithms using the ICGC data from all cancer types. This methodological work will advance the understanding of the genetic basis of each of the primary conditions included in the requested datasets.
50.Yiwen ChenThe University of Texas MD Anderson Cancer CenterUnited States2018-10-192019-10-18
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
51.Xi ChenUniversity of MiamiUnited States2019-05-012020-04-29
Triple negative breast cancer (TNBC) is a molecularly diverse disease. We have previously showed that TNBC can be separated into four distinct subtypes. We propose to use ICGC dataset to perform genomic data analysis to understand the biological differences between TNBC subtypes.
52.Arul ChinnaiyanUniversity of MichiganUnited States2019-03-252020-03-25
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
53.Jung Kyoon ChoiKAISTSouth Korea2019-05-082020-05-03
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
54.Olivier CinquinUniversity of California, IrvineUnited States2019-05-152020-05-08
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.
55.Giovanni CirielloUniversity of LausanneSwitzerland2019-05-242020-05-22
Cancer is characterized by multiple molecular alterations. By examining thousands of patients, it becomes apparent that certain mutations often appear together while others are never or rarely seen in the same tumor. These co-dependent alterations reflect functional interactions; hence their identification provide insight on cooperation and function of altered genes in cancer. We have developed statistical approaches to systematically identify co-dependent alterations across many patients and tumor types and discovered co-dependent alterations leading to the development of more aggressive tumors and/or altered response to therapy. So far, we focused on alterations targeting genes (the “coding” part of the genome), we aim now at studying alterations in the "non-coding" part of the genome (including ICGC controlled data), whose function and interactions remain largely unknown. Our goal is to understand how response to therapy depends not on single altered genes, but on combinations of alterations in the coding and non-coding genome.
56.Harry CliffordCambridge Cancer GenomicsUnited Kingdom2019-03-182020-03-18
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.
57.Jon CokerOmniTier Inc.United States2019-01-292020-01-29
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.
58.Sara CooperHudsonAlpha Institute for BiotechnologyUnited States2019-02-282020-02-25
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.
59.Joakim CronaUppsala UniversitySweden2019-01-172020-01-16
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.
60.Istvan CsabaiEotvos Lorand University BudapestHungary2019-02-142020-02-13
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.
61.Edwin CuppenUniversity Medical Center, Utrecht, The NetherlandsNetherlands2018-10-192019-10-18
Tumor metastasis involves the dissemination of cancer cells of primary tumors to distant organ sites. Within this research project, we will systematically compare the genetic characteristics of primary and metastatic cancer in ICGC data and search for shared and distinct mutation mechanisms that are active in individual patients. The results of this study will provide a better understanding of the factors that drive cancer formation and metastatic processes.
62.James DaiFred Hutchinson Cancer Research CenterUnited States2019-05-252019-07-09
The incidence of esophageal adenocarcinoma (EAC) has risen drastically in Western countries. Genomic features of western EAC have been studied in US and UK, featuring a high mutation rate and particular mutation signatures that may be related to gastroesophageal acid reflux diseases. It was reported that most cases of EAC in Western countries arose from Barrett’s esophagus (BE), a serious complication of gastroesophageal acid reflux disease. We have studied EAC in China and found that BE is rarely associated with EAC in China. We have genomic data from 10 Chinese EAC cases and their matched controls. The ICGC ESAD-UK data will allow us compare genomic profiles and signatures between UK EAC and Chinese EAC. We hypothesize that Chinese EAC may arise via a different disease process and have distinct genomic features from UK EAC. Through the comparison study, the findings will be helpful to understand the mechanisms of developing EAC.
63.Charles DavidTsinghua universityChina2019-05-282019-07-12
The past decade has seen a surge in studies designed to look for genetic variations that affect an individual's susceptibility to cancer. These studies have enabled researchers to zero in on small regions of the human genome where specific sequence variations predispose individuals to the development of cancer. Many such "hotspots" have been identified in a variety of cancers, but the exact function of only a tiny fraction of these is understood. In pancreatic cancer, a susceptibility hotspot resides next to a gene, KLF5, which is critical for the emergence of pancreatic cancer. We previously showed that switching off KLF5 appropriately is essential to block incipient tumors, and our data suggest that this genetic hotspot is may control the cell's ability to do this. To examine this, we will use the ICGC's human pancreatic cancer data to examine genetic variation around KLF5, and its impact on KLF5 regulation.
64.Marcela Davila LopezUniversity of GothenburgSweden2018-11-262019-11-25
We are conducting a research project towards personalized prevention and treatment of gallbladder cancer in Chile, where we plan to generate genetic information from tumor tissue and blood. In order to compare the molecular profiles of gallbladder cancer diagnosed in Chile and Japan, we apply here for access to deposited ICGC controlled data on Japanese population described in the article by Nakamura et al. (2015) “Genomic spectra of biliary tract cancer” (doi:10.1038/ng.3375).
65.Subhajyoti DeRutgers UniversityUnited States2018-12-132019-12-13
Tumor samples acquire a large number of changes in their DNA. Whole genome sequencing is a method for reading the complete DNA sequence of normal and cancer cells. We will use the cancer genomic data from the ICGC to evaluate the accuracy of our software, which in turn can help adopt standardized, carefully evaluated approaches for both research and clinical practice.
66.Francisco De La VegaFabric Genomics, Inc.United States2019-03-082020-03-06
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
67.Michael DeanNational Cancer InstituteUnited States2019-05-012020-04-24
Genetic studies have demonstrated that some individuals have a higher underlying genetic risk of developing cancer. The tumors themselves arise from alterations in genes that encode crucial regulators of cell growth and genome stability. In this project, we plan to identify genetic alterations (germline and somatic) by using ICGC controlled data that influence cancerous growth and investigate how these alterations influence key cellular processes.
68.Francesca DemichelisCentre for Integrative Biology (CIBIO) - Universita degli studi di Trento - ItalyItaly2019-06-202020-06-12
Human cancer is a widely spread disease often characterized by the disruption of multiple genes that often become the driving force of tumor cells. The aim of this research project is to identify new potential drug targets based on disrupted genes. Specifically, the search for drug targets is based on a cell's property, named synthetic lethality, where the concomitant disruption of two genes is fatal for the cell. In practice, when such targets are identified, the corresponding treatment is selectively effective only on tumor cells. In order to enhance the chances of nominating these drug targets, we implement a search using mathematical methods to learn from the genomes of thousands of patients’ cancer cells and experimentally validate the most promising findings. The data generated within the ICGC project will be used to select pairs of likely synthetic lethal genes. Funded by the European Research Council (ERC-CoG-2014).
69.Emmanouil DermitzakisUniversity of GenevaSwitzerland2019-02-082020-02-08
Somatic mutations (non-heritable changes) found in regions of the genome that do not encode for proteins have not been as extensively investigated for their role in tumour formation as the ones in the protein-coding genome. These somatic mutations affect the expression of genes by inducing changes in gene regulatory regions and have attracted a lot of interest due to their potential importance in cancer development. Two complementary methods have been developed in our laboratory that investigate those changes in regulatory regions between normal and tumour samples and identify potential somatic mutations that drive cancer. The goal of this project is to compare and benchmark the two methods, check the overlap between the two and investigate which method provides more biologically plausible results. Then, using ICGC Controlled Data we will extend the analysis to discover regulatory regions potentially driving tumour formation in other cancer types.
70.Li DingWashington University in St. LouisUnited States2018-11-092019-11-08
Cancer results in each individual from a combination of inherited genetic susceptibility and environmental exposures. Two important goals of personalized medicine for cancer are to identify individuals at high risk for cancer due to their genetic make-up and to identify the best treatment plan based on specific mutations that are present in patients' tumors. These goals will only be realized when each individual’s inherited and tumor genetic code can be read and analyzed in the clinical setting. We will use ICGC data to assist with the development of computer tools and to conduct analyses that aim to discover genes with rare genetic variants that increase cancer risk and to understand how this variation affects genetic mutations in the tumor. This project will accelerate the overall understanding of cancer genetics and its application to human health.
71.Thomas DrakeUniversity of GlasgowUnited Kingdom2019-03-012020-03-01
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.
72.Yotam DrierFaculty of Medicine, The Hebrew University of JerusalemIsrael2019-04-042020-04-04
Only 2% of our DNA codes for genes. The function of the other 98% "Non-coding DNA" is often still a mystery, but we do know now that it contains many elements, known as "DNA regulatory elements", that regulate basic DNA related processes. Such processes include determining the way our long DNA is compacted and folded, and the rate in which each gene is produced. Cancer is typically caused by DNA alterations causing cells to multiply out of control. While a lot is known on the how alteration in genes cause cancer, the roles of alterations in regulatory DNA elements across cancer remains an open question. We will utilize controlled ICGC data to detect and model the outcome of alterations of regulatory DNA elements, and study how they drive cancer and impact response to therapy.
73.Anindya DuttaUniversity of VirginiaUnited States2019-02-202020-02-19
Differences in our DNA that we inherit from our parents may be useful for predicting what diseases a patient may acquire and selecting drugs that are best for treating a particular patient. Doctors often use several different sources of clinical information to predict how aggressive a cancer will be following a cancer diagnosis in order to decide how aggressive the treatment for the cancer should be. In our previous paper, we found that differences in our DNA that we inherit from our parents can be used by doctors to improve their ability to predict how aggressive a cancer in the brain might be. We now hope to expand our study to cancers outside of the brain.
74.John EdwardsWashington University in St. LouisUnited States2019-05-152020-05-01
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.
75.Rosalind EelesInstitute of Cancer Research, UKUnited Kingdom2019-05-242019-07-08
This is an international research project involving prostate cancer research groups from seven countries, which form the Pan Prostate Cancer Group: PPCG (http://panprostate.org/). Our primary objective is to integrate different data types based on measurements in cancerous and normal cells from over 2000 prostate cancer patients generated by ICGC, TCGA, and other groups. Our aim is to translate our findings to provide clinical benefit, such as accurately predicting aggressive disease that will need radical treatment, gaining new insights into tumorigenesis for better diagnosis; and determining differences in prostate cancer across different ethnic populations. We will apply a uniform set of algorithms to process and analyse the data, developed by the PPCG members and overseen by the Sanger Institute, DKFZ, and OICR. This will eliminate differences that are due to different ways of analyzing the data. This project is modelling itself on the successful ICGC PanCancer Analysis of Whole Genomes (http://docs.icgc.org/pcawg/).
76.Ran ElkonTel Aviv UniversityIsrael2019-05-202019-07-04
Somatic mutations are spontaneous genetic alternations accumulating throughout one’s life leading to different types of cancer. Until recently, research was focused on identification of somatic mutations that enhance cancer development by modifying genes that encode for proteins. However, these DNA regions cover only less than 3% of our genome. The maturation of novel technologies that allow rapid determination of the DNA sequence of whole genomes now enables for the first time the discovery of somatic mutations that occur in regions in the genome that do not encode for proteins. Somatic mutations that occur in these genomic regions are called "noncoding mutations". The ICGC Controlled Data already include thousands of tumor-normal paired whole genome sequences from numerous cancer types. We will use this rich data resource aiming at the identification of additional noncoding genomic regions that are frequently mutated in cancer, elucidating novel aspects in cancer biology.
77.Benjamin EvansUniversity of East AngliaUnited Kingdom2019-01-242020-01-24
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.
78.Eduardo EyrasPompeu Fabra UniversitySpain2018-11-192019-11-19
The analysis of cancer genomes has allowed the identification of targeted treatments. Still, a considerable number of cancer patients do not contain any of the known alterations described so far. These tumors are named pan-negative and these patients cannot benefit from the available therapies. It is therefore of utmost relevance to expand the catalogue of actionable alterations. Recent results have shown that alterations in pre-mRNA splicing bear major importance in the understanding of cancer. In this project we propose to investigate the role of splicing alterations in pan-negative tumors. Using ICGC controlled data, we plan to use information about genome-wide somatic mutations to identify those that fall in splicing regulatory sequences and will measure their impact in splicing using RNA sequencing data from the same samples. We hope this work will provide new insights into the mechanisms of pan-negative tumors and will help identifying novel targets of therapy.
79.Kyle FarhIllumina, IncUnited States2019-04-012020-04-01
Each person's genome contains dozens of rare mutations. The effects of most of these variants are unknown. In this project, we plan to analyze these rare variants using novel statistical methods and artificial intelligence tools and characterize their impact on the risk of developing various types of cancer. Controlled access data will be needed for this project in order to understand which variants are present in patients with which subtypes of cancer.
80.Pedro Favoretto GalanteHospital Sirio-LibanêsBrazil2019-02-202020-02-19
The emergence of cancer accounts for a large number of changes in DNA. This project aims to identify and validate specific elements from DNA, also known as retrocopies, that influence the progression of cancer in humans. For that, we will use whole genome sequencing data of normal and cancer cells. We will use the cancer genomic data from the ICGC to search for those elements and evaluate the accuracy of our software developed for this purpose.
81.Pedro Favoretto GalanteHospital Sirio-LibanêsBrazil2019-02-202020-02-19
The emergence of cancer accounts for a large number of changes in DNA. This project aims to identify and validate specific elements from DNA, also known as retrocopies, that influence the progression of cancer in humans. For that, we will use whole genome sequencing data of normal and cancer cells. We will use the cancer genomic data from the ICGC to search for those elements and evaluate the accuracy of our software developed for this purpose.
82.Christopher Flowers, MD, FASCOEmory UniversityUnited States2019-01-102020-01-09
It has been recently reported that patients with Chronic Lymphocytic Leukemia (CLL) do not respond to novel immunotherapy approaches aimed at using the immune system to fight the disease. However, patients whose CLL transforms to an aggressive lymphoma respond better. This project aims to use ICGC-controlled data to find differences in gene expression profile between CLL cells that have transformed from ones that have not transformed in order to understand why non-transformed CLL do not respond to these novel immunotherapies.
83.Bernard FoxProvidence Cancer Center, Earle A. Chiles Research InstituteUnited States2018-11-122019-11-12
Comparison of matched normal and cancer genomes has given insights to genomic variations which we propose to examine for correlations with protein, gene expression, and clinical data in our cancer immunotherapy research participants using both genomes for the cancers they are being treated for, and across tumor tissue type cancer genomes (using ICGC controlled data).
84.Guido FranzosoImperial College LondonUnited Kingdom2019-06-242019-06-27
Professor Franzoso and colleagues have developed a new cancer drug, known as DTP3, which they are conducting to trial in multiple myeloma patients. DTP3 kills myeloma cells in laboratory tests and mice, without causing any toxic side effects, the main problem with other cancer drugs. The team has discovered how DTP3 also kills other types of blood cancer, including acute myeloid leukaemia (AML). Over 20,000 people develop AML in the UK and US each year. Unfortunately, current therapies are toxic and ineffective for many patients, resulting in a high relapse rate and poor prognosis. The researchers have tested DTP3 in cellular models of aggressive AML subtypes, where other drugs do not work, and found it is highly effective. They now seek support from the ICGC to comprehensively study their drug in AML with the aim of starting a new trial to develop an effective treatment for currently untreatable AML patients.
85.Juliet FrenchQIMR Berghofer Medical Research InstituteAustralia2018-12-212019-12-21
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.
86.Peter FrommoltIndivumed GroupGermany2019-06-032020-06-02
Human cancer represents a complex disease that can be characterized by changes in the DNA sequence of the patient genome. As part of our effort to understand how molecular processes change in cancer, we will identify these modifications of the DNA using technologies which can decipher the entire DNA sequence of tissue samples from cancerous and normal patient tissues. In order to validate our own analysis workflow used to identify DNA changes that occur in the cancer tissue, we will analyse a small subset of ICGC data that has been previously characterized extensively. This validation process will allow us to deploy our analytical processes on patient clinical sample data with the highest confidence. This in turn will help us push forward our understanding of the changes of DNA that underlay complex cancerous disease processes.
87.Audrey FuUniversity of IdahoUnited States2018-12-192019-12-18
It is a challenge to learn which gene regulates which other gene directly from genomic data. Correlation is often used as a proxy of such a causal relationship, but similar levels of correlation can arise from different underlying processes. Therefore we are developing statistical models and machine learning algorithms that infer gene regulatory networks by combining genetic data and molecular measurements, such as gene expression. Combining ICGC controlled data with data from the GTEx (health individuals) and TCGA (cancer patients) will be helpful for us to examine the effectiveness of the methods we are developing and comparing. We will apply our methods primarily to breast and ovarian cancers.
88.Georg FuellenRostock University Medical CenterGermany2018-12-142019-12-13
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.
89.Melissa FullwoodCancer Science Institute of SingaporeSingapore2019-06-182019-08-02
Chromatin interactions are when two remote regions of the genome come into close spatial proximity and form a physical loop. Chromatin interactions have been found to play important roles in regulating the expression of genes. We have developed a computational method to predict chromatin interactions. By applying the method to datasets from ICGC controlled data and other sources, we wish to understand the different chromatin interaction landscapes in different subtypes of diseases. By associating the predicted chromatin interaction changes with differentially expressed genes that can be identified from ICGC datasets, we wish to understand how chromatin interaction changes could lead to gene regulation failures.
90.Phillip FutrealMD Anderson Cancer CenterUnited States2018-11-022019-11-01
We use genetic profiling to determine whether mutations in cancer can predict response to various therapies and explain why some families have a lot of cancer. For those patients who are resistant to therapies, we would like to use their genetic profiles to find new alternative therapies that could work for them. We are studying multiple types of cancer and will use the ICGC datasets to confirm any observations that we see.
91.Juan Fuxman BassBoston UniversityUnited States2019-04-042020-04-02
Cancer is caused by changes in the DNA sequence of our genome. Most changes have been identified in regions that produce proteins; however, less is known about the consequences of changes in regions that control when and where proteins are being produced (regulatory regions). In this project, we aim to identify DNA changes in regulatory regions that have implications in cancer, either as a general cancer mechanism or as differences between multiple cancer types. To achieve this main goal, we will use ICGC collected DNA sequence data and measurements of the extent at which genes are turned on, and integrate this data with computational analyses and experimental studies. Altogether, this project will identify novel cancer mechanisms which will pave the way for the development of new therapeutics.
92.Manish GalaMassachusetts General HospitalUnited States2019-04-082020-04-08
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.
93.Steven GallingerUniversity Health NetworkCanada2019-05-082020-05-06
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.
94.Ying GaoShanghai Institutes of Biological SciencesChina2019-04-232020-04-21
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.
95.Giuseppe GasparreUniversità di Bologna, Dipartimento di Scienze Mediche e ChirurgicheItaly2019-01-312020-01-30
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.
96.Roland GeisbergerSCRI-LIMCR, Salzburg, AustriaAustria2019-01-242020-01-24
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.
97.Mark GersteinYale UniversityUnited States2019-01-312020-02-07
Despite the discovery of tens of thousands of mutations in cancer patients, few are readily interpretable in terms of their effects on known cancer genes. It is unclear how these newly discovered variants relate to cancer. Are they simply neutral passengers created by error-prone DNA replication in cancer genomes? Alternatively, are key cancer-driving variants lurking among this pool of mutations? Or perhaps, do some variants regulate the expression of known cancer genes? Using data from the ENCODE Consortium, which has catalogued connections between hundreds of regulatory proteins and their targets throughout the genome, we are searching for mutations that might disrupt the regulation of key genes and thus help cause cancer. As part of the Pan-Cancer Analysis Working Group, we are also examining whether, in addition to key driver mutations, the thousands of other mutations in each tumor might have subtle effects that together impact cancer progression and patient survival.
98.Jonathan GoekeGenome Institute of SingaporeSingapore2019-03-042020-03-02
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
99.Juan GonzalezBarcelona Institute for Global HealthSpain2019-04-292020-04-29
As men age or become ill with cancer, their blood cells seem to lose the chromosome Y. The loss of chromosome Y (LOY) is also a common feature of cancerous tissues themselves. Therefore, we need to understand whether the loss of the chromosome is a cause or consequence of the disease to determine its usefulness as a potential biomarker. We propose to quantify the dramatic reduction on chromosome Y function produced by LOY. The measurement of this quantity will allow us to answer the fundamental question of whether LOY predisposes to disease and to investigate the extent to which it is a common feature of different types of cancer that can predict disease risk. The ICGC data will be used to validate the results we obtain with data from other studies, helping to increase the reliability of our findings.
100.Robert GoodingQueen's UniversityCanada2019-04-242020-04-24
We are studying the manner in which some cancers may be able to be treated by identifying genetic structures that are called fusion genes - where 2 genes which originally were well separated are now combined, or fused together. Such structures are important because some cancers can develop or progress due to fusion genes, an occurrence that produces new genes that can accelerate cancer (oncogenes), or suppress genes which when present can limit the proliferation of cancer (tumour suppressor genes). Fortunately, for an array of cancers these fusion genes are therapeutic targets. This research project will use the ICGC Controlled Data for cancers associated with the urinary and reproductive systems to identify fusion genes that appear with sufficiently high frequency that they could be targeted by such therapies.
101.Chris GoodnowGarvan Institute of Medical ResearchAustralia2019-03-222020-03-20
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.
102.John GordanUCSFUnited States2018-12-072019-12-07
Many human cancers arise in the context of viral infection, but the extent to which these infections persist in tumors, and/or whether ongoing viral infection within the tumor predicts tumor behavior or treatment response, is not well understood. We will apply new methods to characterize viral behavior in tumors, then use the results of this analysis to study cancer-associated mutations using ICGC controlled data, starting first in hepatocellular carcinoma, the primary tumor of the liver. This analysis is intended to help us discover new treatment targets in this challenging cancer and could later be applied to other virus-associated cancers.
103.Dmitry GordeninNational Institute of Environmental Health Sciences, NIHUnited States2019-03-132020-03-11
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.
104.Malachi GriffithWashington University in St. LouisUnited States2019-02-282020-02-27
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.
105.Anita GrigoriadisKing's College LondonUnited Kingdom2019-06-172019-08-01
The cancer genome is characterised by a variety of alterations that accumulated over time. We and others have shown that specific changes in the cancer genome can provide read-outs of defects in the DNA repair mechanisms. Moreover, these alterations can also be informative for the prediction of treatment response. We would like to use the ICGC data to perform a comprehensive analysis on cancer genomes of their alterations. This will further refine our methods and provide a deeper knowledge of their prevalence and potentially their formation. Ultimately we will then test these alterations in cancer genomes of clinical trial data available to us.
106.Sean GrimmondThe University of MelbourneAustralia2019-04-182020-04-16
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.
107.Thomas GruenewaldLudwig Maximilian University of MunichGermany2019-02-132020-02-12
Newly occurring mutations in the genome (somatic mutations) can alter genes that protect from cancer (tumor suppressors), or that are strictly regulated as they drive cancer (oncogenes). Whether such mutations lead to cancer or not (tumor initiation), and the aggressiveness and growth rates of the cancer cells (tumor progression) appear to be dependent on preexisting heritable genetic variations (germline variants). We aim to decipher the interaction of somatic mutations and germline variants on tumor initiation and progression with data from the International Cancer Genome Consortium (ICGC) on prostate carcinoma. As ICGC provides data from prostate carcinoma patients on somatic mutations, germline variants and clinics, genetic interactions and their impact on clinical course can be well investigated. Therefore, we expect to get further insights into the cooperation of somatic mutations and germline variants and its impact on tumor initiation and progression, and, moreover, detect new prognostic biological indicators for prostate carcinoma.
108.Zeynep GumusIcahn School of Medicine at Mount SinaiUnited States2019-06-032019-07-18
Inherited genetic variation contributes to the risk of developing different cancer types. Here, I propose to use data from ICGC to identify and characterize genetic markers that predispose individuals to the development of cancer. Results of these studies will be widely disseminated to the scientific community through publication in peer-reviewed journals.
109.Xingyi GuoVanderbilt University Medical CenterUnited States2018-11-162019-11-15
Both genetic variants and non-inherited genetic changes play important roles in the etiology of various cancer types. A large number of somatic changes have been identified in cancer genomes by the International Cancer Genome Consortium (ICGC). In addition to non-inherited genetic changes, previous large genetic studies have identified numerous common genetic locations associated with cancer risk. In this application, we propose to analyze the ICGC whole genome sequencing data to identify genetic changes and evaluate the association between inherited genetic variants and non-inherited genetic changes for all available cancer types from the ICGC.
110.Alexander GusevDana-Farber Cancer InstituteUnited States2019-02-062020-02-05
The mechanisms of cancer are driven by inherited variants as well as non-inherited variants that occur in the cell. Though specific examples exist for both types of variation acting as a cancer driver, the genome-wide interplay between these two features remains an important area of research. The goal of this project is to investigate the impact of inherited genetic variation on patterns of non-inherited variation and cellular activity. The project is divided into three main aims. First, we will use the ICGC controlled data to identify specific inherited variants that are associated (and likely driving) cellular activity. Second, we will assess whether these variants can explain the associations identified in large-scale studies of cancer risk. Third, we will use the ICGC controlled data to assess whether cancer risk variants have a significant effect on patterns of non-inherited changes.
111.Ivo GutCentro Nacional de Analisis Genomico (CNAG)Spain2019-06-102019-07-25
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
112.Ivo GutCentro Nacional de Analisis Genomico (CNAG)Spain2018-09-172019-09-16
The ICGC data sets with the tumor-normal sample pairs is, to our knowledge, one of the most comprehensive datasets to assess the performance of the programs used to call non-inherited mutations (somatic mutations) to date. We would like to access the ICGC Controlled Data to download the sequenced reads and predicted variants of the chronic lymphocytic leukaemia (a type of blood cancer) and medulloblastoma (a cancerous brain tumor) control-tumor samples. These data sets are going to be used as a gold standard to benchmark all the steps of our variant calling workflows. We are interested in finding possible caveats and also ways to improve the quality of our pipelines, specially by identifying common technical patterns in false positive and false negative variants.
113.David GutteryUniversity of LeicesterUnited Kingdom2019-01-302020-01-28
Numerous studies have highlighted many genetic changes resulting in human cancers. Analysis of these changes and alterations gives the possibility of personalising treatment based on the changes found in each patient's cancer. Recent studies have focused on how cancers can evade the immune system and how scientist can find ways of combating this. The aim of this study is to determine how specific changes in a cancer patient's DNA can affect how the tumour is able to evade the immune system in breast, endometrial and colorectal cancers. Our analysis of the ICGC controlled data will generate a large bank of pilot data that will be used towards larger studies investigating how we can combat this and develop various options for treatment. Eventually, we will use this information to develop a blood test used to detect and monitor how cancer tries to evade the immune system.
114.Leng HanUniversity of Texas Health Science Center at HoustonUnited States2018-12-102019-12-09
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
115.Mira HanUniversity of Nevada, Las VegasUnited States2019-02-262020-02-25
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.
116.Douglas HanahanEcole Polytechnique Federale de LausanneSwitzerland2018-09-212019-09-20
Pancreatic Neuroendocrine tumor (PanNET) is a rare type of Pancreatic cancer. In this project, we try to find out why some patients develop metastasis while some others have relatively benign tumors. For this sake, we study a mouse model of PanNET which represents the human disease. Our study shows that many genes are important for the tumor to be aggressive and metastatic. By inhibiting these genes, we could demonstrate that the mouse tumor will start getting smaller and eventually disappear. As a next step, we need to validate our findings with humans. For this reason, we need to have good quality human clinical data from patients. ICGC Controlled Data acquired very rich clinical data from 89 patients with PanNET. This dataset will be critically important for our research, especially in showing the applicability of our finding (mouse model) to human disease.
117.David HausslerUniversity of California Santa CruzUnited States2018-09-182019-09-17
The focal point of this project is comparing gene-level expression estimates of an individual pediatric patient's tumor to the gene-level expression estimates of thousands of pediatric and adult tumors, called the research compendium. The gene-level expression estimates for some of the ICGC controlled access datasets will be calculated and included in the research compendium. This comparison is called a pan-cancer analysis, because the individual's data is being compared to data from many different cancer types. Although pediatric cancer is rare, by comparing each case to the research compendium, similarities can be spotted. This approach could be an efficient method of determining which cancer fighting treatments developed for adult tumors might be good candidates in specific pediatric cancers.
118.Sampsa HautaniemiUniversity of HelsinkiFinland2019-06-122020-06-03
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.
119.Xin HeUniversity of ChicagoUnited States2018-12-202019-12-20
Cancer cells have mutations in important genes that cause undesired cell growth and proliferation. Finding these genes is an important research problem as it can lead to potential drug targets to treat cancer. DNA from thousands of cancer patients have been sequenced. However, to translate this large dataset to knowledge of cancer genes is difficult and require powerful computational methods. This project aims to develop such a method. Using ICGC controlled data, its general idea is to incorporate external biological information of DNA mutations to better interpret DNA sequences from patients. The project will lead to an expanded list of cancer genes, which will help researchers develop better drugs.
120.Chunjiang HeSchool of Basic Medical Sciences, Wuhan UniversityChina2019-05-072020-05-07
This project aims to explore gene function in cancer development. Using the ICGC controlled data, we will collect approximately 500 tumor samples from more than 20 types of cancer to discover which genes are vital in the development of cancer. This research will contribute to the drug design and immunotherapy for cancer treatment.
121.Benoit HedanCNRSFrance2018-12-072019-12-06
The "Canine genetics" team led by Catherine ANDRE has been working during the last 20 years on human/dog homologous genetic diseases and especially cancers. Canine cancers with similar to human subtypes are frequent in dogs but rare and not well known in human. The final objectives are to identify the genetic bases of these cancers and to propose therapeutic targets, clinical trials "first in dogs" for veterinary and human medicine benefits. We identified specific alterations in canine melanomas and sarcomas. Our specific aims are to test if these alterations are also involved in some human cancers, even in rare cases. In this context, the ICGC controlled data will be used to screen for these potential rare alterations in human cancers, but known to be important in cancer developpement based on canine cancer data.
122.Sepp HochreiterJohannes Kepler University LinzAustria2019-03-262020-03-24
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.
123.Sheri HolmenHuntsman Cancer Institute, University of UtahUnited States2018-11-262019-11-26
Recently approved therapies have shown strong promise in treating advanced stages of melanoma, a form of skin cancer, but treating patients whose cancer has spread to the brain (brain metastasis) remains challenging and the prognosis for these patients is grim. The goal of our research is to use the ICGC-controlled data, in addition to other datasets, to identify prognostic genetic signatures for those patients at highest risk for the development of brain metastases. Additionally, analysis of these data will provide important insights in to the potential effectiveness of combining treatments that target common alterations in melanoma. Finally, our work using these ICGC-controlled data will enable initiation of future interventional studies that will use specific compounds that target melanoma directly, or immune system-modulating therapies, to determine whether these treatments are effective to prevent brain metastasis in those patients at highest risk.
124.Richard HoulstonInstitute of Cancer Research, UKUnited Kingdom2019-02-082020-02-07
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.
125.Christopher HovensThe University of MelbourneAustralia2018-11-222019-11-22
Prostate cancer is now the most commonly diagnosed cancer in the Western world but only 10% of men with it, will die from it. Our current ability to discriminate between cancers which are harmless and those that are life threatening is poor. This project will examine the genetic make up of cancer clones that are present in high risk prostate cancer and define and trace the spread of those cancers that break away from the prostate and lodge in distant sites, causing death. We utilize ICGC genomic datasets that have detailed genetic information on common cancer types that have spread around the body away from the primary organ where they first arose, this includes, prostate, breast and colorectal cancers.
126.Kuei-Yang HsiaoNational Chung Hsing University, TaiwanTaiwan2018-08-302019-08-29
The development of colorectal cancer involves complexed molecular events. In the past decades, the scientists have focused on those genes producing proteins. However, the growing body of evidence indicates that ‘junk genes’ which don’t produce proteins may also play critical roles during the development of cancer. In this proposed study, we will use computational tools to analyze the ICGC Controlled Data to investigate the expression of those ‘junk genes’. Through completing this project, the potential novel therapeutic targets may be revealed and facilitate the development of new therapeutic strategy.
127.Andrew HsiehFred Hutchinson Cancer Research CenterUnited States2018-12-192019-12-18
Genetic material in the form of DNA serves as a template at the start of a series of discrete steps that ultimately lead to the generation of proteins, the building blocks of cells, tissues, and whole organisms. Proteins perform critical cellular functions such as growth and division, and are required for the proper functioning of the cell. Although the role of mutations within DNA in prostate cancer has been well described, very little is known about how changes in protein synthesis promote cancer, despite the functional significance of this process. My work focuses on elucidating the functional role of critical regulatory regions upstream of the start site of protein synthesis in advanced prostate cancer. Currently, it is unknown how these regulatory regions contribute to cancer. The ICGC controlled dataset will be essential in helping us determine clinical relevance of these genomic regions in a larger cohort of patient samples.
128.Zhibin HuNanjing Medical UniversityChina2019-01-212020-01-20
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.
129.Xihao HuGV20 TherapeuticsUnited States2019-06-142020-05-15
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.
130.Xihao HuGV20 TherapeuticsUnited States2019-06-112020-05-21
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.
131.Yu HuangShanghai Institute of Materia Medica, Chinese Academy of SciencesChina2019-02-272020-02-26
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.
132.Jiandong Huang1. Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesChina2019-05-102020-05-10
The Hepatitis B virus sequence can integrate into the human liver cell genome and give rise to the occurrence of liver cancer. The peptides (organic chemical molecules) translated from these integration sequences can be used as toxins to stimulate human immune responses, thus killing this kind of tumor cell. Methods have been developed to predict these peptides and ICGC controlled data will be used for method validation and accuracy improvement.
133.Sarah HuetCentre de Recherche en Cancérologie de LyonFrance2019-03-182020-03-18
Follicular lymphoma (FL) is the most frequent indolent lymphoma. Patient outcomes are, however, highly heterogeneous, and some patients undergo rapid progression of the disease. The aim of this project is analyze the molecular profile of FL tumors to identify alterations that might predict the risk of progression. Using the massive amounts of ICGC data on FL, we will look for a specific type of alteration called "aberrant RNA splicing" in order to distinguish groups of tumors with distinct biology and/or clinical features. This could allow to better identify, at the time of diagnosis, the patients who need specific therapies because they are at high risk of disease progression.
134.Thomas IlligHannover Medical SchoolGermany2019-05-082020-05-06
Liver cancer is the fifth most frequent cancer type and, with more than 750,000 reported deaths per year, the second leading cause of cancer-related death worldwide. The ICGC liver cancer datasets provide detailed insight into the genetic landscape of liver tumors. Based on this knowledge, we want to analyze tumor-promoting mechanisms, investigate the tumor cell response to its environment and develop novel therapies for liver cancer. As liver cancer represents an ideal model system of solid cancer, our goals are of relevance beyond liver cancer for other tumor diseases.
135.Mohammad IlyasUniversity of NottinghamUnited Kingdom2019-03-282020-03-25
All cells (including cancer cells) are able to acquire energy through a process known as oxidative metabolism. A side effect of this process, known as “oxidation-induced mutation,” is the production of chemicals which can cause damage to DNA, potentially leading to gene mutation and the development of cancer. In this project we will use data from the ICGC to investigate the role (if any) of oxidation-induced mutation in the development of colorectal cancer. We will first check the ICGC colorectal cancer Whole Genome Sequencing data to look for specific patterns of oxidation-induced mutations. We will then see whether the oxidation-induced mutations are associated with clinical or pathological features. Our study will tell us whether oxidation-induced mutations are important in the development of colorectal cancer. If so, it may be possible to prevent or treat colorectal cancer in the future by reversing or inhibiting the oxidation-induced mutations.
136.Marcin ImielinskiNew York Genome CenterUnited States2019-05-242020-05-24
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
137.Takashi ItoKyushu UniversityJapan2018-09-202019-09-19
All the cells in our body have a mechanism called DNA methylation, a chemical modification of DNA that controls gene expression and shuts off the activity of genes. Each cell uses DNA methylation to properly select a unique set of genes to establish its identity. However, DNA methylation improperly functions in cancer cells. For instance, cancer cells use DNA methylation to silence a group of genes that serve as a brake against cell division, thereby enabling their uncontrolled growth. In this project, we will use the ICGC data on DNA methylation to construct a database for the DNA methylation patten of various cancers and their corresponding normal tissues. The database will help researchers to understand the roles for DNA methylation in cancers. It will be also useful for the search of novel types of disease markers and drug targets.
138.Pierre-Etienne JacquesUniversité de SherbrookeCanada2018-08-152019-08-14
Cancer cells rapidly accumulate mutations in their genome, which facilitates their growth and allows them to invade healthy tissues and frequently resist therapy. One of the ways in which tumors accumulate these mutations is by decreasing their ability to accurately repair DNA damage. Indeed, cancer cells frequently have inactivating mutations in proteins that help repair their DNA. These defects may be leveraged by the tumors to inhibit the still intact DNA repair pathways. We will use cancer genomic data from ICGC to identify molecular signatures that are associated with specific defects in DNA repair proteins. These signatures will allow us to determine whether a cancer has a defect in specific repair pathways and will also inform us on the potential compensatory DNA repair strategies that allow tumors to sustain their uncontrolled growth. This knowledge may guide the development of new treatment methods based on the specific genetic alterations of cancers.
139.Roman JaksikSilesian University of TechnologyPoland2019-06-042019-07-19
Leukemia is a cancer that leads to abnormalities of white blood cells, caused by two types of mutations, which increase the proliferation rate of the cells and prevent them from fully maturating. Despite a similar background leukemia is a very diverse disease showing four distinct types and an even larger number of subtypes, with various symptoms and treatment responses, a background of which is still poorly understood. The main goal of this project is to identify abnormalities in acute myeloid leukemia (AML) cells, which is the most common cause of leukemia-related mortality among adults, and acute lymphoblastic leukemia (ALL), the most common cancer among children, responsible for over 30% of cancer-related premature deaths in Poland. For this purpose ICGC data will be used. This study may contribute to a much more effective leukemia treatment by allowing to develop new ways of classifying cancer and predicting treatment outcomes based on information contained in the DNA.
140.Zhaoshi JiangGilead SciencesUnited States2018-10-192019-10-18
Hepatocellular carcinoma (HCC) is a complex cancer without effective treatment. A big reason for this is that many distinct factors, including virus infection and life style, can cause HCC. Clinically, HCC patients can be classified as subgroups based on these factors. Therefore, understanding HCC biology in subgroups may reveal novel disease mechanisms specific to certain population. This ICGC controlled data set (EGAD00001001880) contains genome-wide profiling for gene activity and detailed clinical information for 300 HCC patients. We will look into this invaluable large data set and identify genes that expressed specifically in one subgroup. Such research may help us understand disease mechanisms in HCC subgroups, and provide novel drug targets for HCC.
141.Zhaoshi JiangGilead SciencesUnited States2019-05-082020-05-08
The human digestive track is colonized by large variety of bacteria which interact with human immune system in complex and to this day not well understood ways. So far, only very few studies have shown that certain bacterial species can influence patient’s response to cancer treatment. This effect of bacterial composition in the human digestive tract is, however, difficult to identify because the bacterial composition often changes due to unrelated factors. Data from ICGC controlled data studies provide unique opportunity to study bacterial effect on cancer treatment because similar interactions are not frequently documented. Our efforts will focus on understanding bacterial differences between patients who experienced positive or negative bacterial effect on their cancer treatment and the variation introduced by life-style differences.
142.Peng JinEmory University School of MedicineUnited States2019-02-122020-02-12
Medulloblastoma, a tumor of the cerebellum, is the most common malignant brain tumor in children, but only few genetic alterations involved in tumor progression have been identified. During the postnatal stage, DNA modifications without change in DNA sequence lead to brain development by regulating gene expression related to neural functions. Aberrant DNA modifications in cancer, however, can contribute to abnormal gene expression favorable to tumor growth and its invasive phenotype. In this project, we aim to understand how DNA modifications affect gene expression related to Medulloblastoma progression. Therefore, we would like to conduct a meta-analysis of ICGC datasets and our own experimental data.
143.Sylvie JOBFrench National Cancer LeagueFrance2019-04-232020-04-23
Bile duct cancer (ICC) is a heterogeneous severe malignant disease in which the standard anticancer therapies are mostly ineffective. Immunotherapies are drugs that target the microenvironment of tumors rather than the tumors themselves. Immunotherapies, showed promising results in some patients with advanced liver cancer, but their possible effects against ICC have not yet been studied. The goal of our research is to use mathematical models based on genomic and clinical data (including ICGC controlled data) to study the microenvironment of a large number of ICC and to determine which patient could respond to immunotherapies.
144.Steven JonesBC Cancer, Part Of The Provincial Health Services AuthorityCanada2019-04-232020-04-21
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.
145.Steven JonesBC Cancer, Part Of The Provincial Health Services AuthorityCanada2018-10-192019-10-18
We have developed a classification model for predicting the correct cancer type of a patient solely using the genomic profiles of their tumour. We will be using the ICGC datasets to evaluate this model and to expand the knowledge-base of the classifiers. The classifiers can then be used to characterize rare/unknown/metastatic cancers.
146.Young Seok JuKAISTSouth Korea2019-05-152020-05-15
The research project is about learning "mutational signatures" from samples of mutations, especially from samples of cancer genome. Mutational signature can be thought of as a trail of mutational footprints left by mutational processes. Through careful analysis of mutational signatures, we can study and learn more about the nature of cancer and mutation in general. Our research project is to build a more robust framework to learn mutational signatures. For that, we need a sufficient amount of high quality samples. In this respect, more than 560 breast cancer samples of the ICGC controlled data will be invaluable resources. Lastly, we want to make sure that we do not intend to make use of any information possibly related to personal identification such as heritable information.
147.Kenji KabashimaKyoto UniversityJapan2019-06-182019-08-02
This study aims to investigate why angiosarcoma (AS) develops. AS is a type of soft tissue sarcoma. In cancers and sarcomas, genetic information ("genes") is damaged. These damages are called mutations. Mutations cause normal cells to become malignant. In common cancers such as lung cancers and melanomas, researchers have found specific damaged genes. Such information has been used to develop new drugs that target damaged genes. However, mutations that cause AS are not studied well because AS is a rare tumor. We are using International Cancer Genome Consortium (ICGC) data to look for new mutations of AS. We hope that our study will help better understand biology of AS and eventually lead to development of new drugs for AS.
148.Cigall KadochDana-Farber Cancer InstituteUnited States2018-11-162019-11-15
We study molecular machines, called chromatin remodelers, which bind to DNA and help regulate which genes are expressed in a cell. In particular, we study the mammalian BAF (SWI/SNF) complex, a large molecular machine that is made of numerous proteins. It has become apparent that many of the genes encoding proteins that make up the BAF complex are mutated in certain cancers. These mutations cause BAF complexes in cells to malfunction, leading to changes in the expression of various genes, contributing to the development of cancer. We aim to use the ICGC Controlled Data to ask if the sequences of DNA these machines bind to are also often mutated in cancer.
149.Rachel KarchinJohns Hopkins UniversityUnited States2018-10-312019-10-30
Many DNA mutations implicated in cancer causation are found in a substantial fraction of patients. However there exist a very large number of mutations that are only seen in a few patients. We are developing a computational method to predict which of these rare mutations are involved in cancer origination and progression. ICGC Controlled Data will be used as a source of mutations in cancers of multiple types.
150.George KassiotisThe Francis Crick InstituteUnited Kingdom2019-05-272020-05-27
We have been investigating a group of ancient viruses that have been hiding in our DNA for many thousands of years. They originated from infection of our ancestors with now extinct retroviruses and are known as endogenous retroviruses (ERVs). ERVs do not normally cause harm because our cells have found ways to switch them off. However, ERVs can sometimes reactivate and, although no longer infectious, they can nevertheless affect the function of our DNA and of our immune system. Indeed, ERVs are implicated in the initiation, progression and immune surveillance of human cancer. We now plan to investigate their utility or harmfulness in Esophageal adenocarcinoma (EAC). We will analyse the activity of ERVs in a unique ICGC Controlled Data set from over 100 EAC patients. Upon completion of this project, we should have a clearer idea of reasons for and the impact of ERV reactivation in EAC.
151.Keisuke KataokaNational Cancer CenterJapan2018-09-122019-09-11
Recent scientific advances have enabled us to systematically identify tumor genomic abnormalities. However, detection of genomic abnormalities still remains challenging. Therefore, this study aims to comprehensively characterize the entire landscape of genomic abnormalities in a variety of cancers using our newly created pipelines. We would compare the characteristics of these genomic abnormalities both within and across cancer types, and we attempt to identify the critical abnormalities in tumor genomes. Clearly describing the genomic abnormalities and identifying the novel abnormalities would be of great significance for achieving Genomics-Driven Precision Medicine. To perform this research, we are planning to run our newly developed pipelines on the data deposited by ICGC.
152.Mamoru KatoNational Cancer CenterJapan2018-10-162019-10-15
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
153.Bahram KermaniCrystal Genetics, Inc.United States2019-02-282020-02-27
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.
154.Paul KhavariStanford UniversityUnited States2019-02-062020-02-06
Cancer arises from normal cells which have accumulated DNA mutations causing them to proliferate out from under the control of normal mechanisms. Large scale efforts to sequence tumor DNA have revealed mutations within genes which drive cancer initiation and progression. However, genes make up only 1-2% of the genome and therefore the vast majority of cancer mutations occur in DNA outside of genes, known as non-coding regions. While these mutations are not in genes themselves, several examples of these mutations have been found to affect the levels of these genes in a manner that can drive cancer. We will use ICGC data to identify non-coding regions of the genome that are recurrently mutated in cancer, and prioritize these DNA sequences for follow-up studies to understand mechanisms of how they promote tumor growth. Understanding the function of mutations to non-coding regions involved in cancer will help identify new therapeutic opportunities.
155.EKTA KHURANAJoan & Sanford I. Weill Medical College of Cornell UniversityUnited States2019-04-092020-04-03
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.
156.Youngwook KimSUNGKYUNKWAN UNIVERSITYSouth Korea2019-05-142019-06-27
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
157.Kyeong-Kyu KimSUNGKYUNKWAN UNIVERSITYSouth Korea2019-04-032020-04-01
The goal of the current study lies in the detailed understanding of particular cancer-causing mutations and their potential in the genomic context. Additionally, method-development for predicting cancer-causing mutations in the regulatory regions (regions of the genome that control gene expression) followed by experimental validation is the long-term goal of the project. Hence, information on the diseased genome’s mutations with sampling details is mandatory for such research and analysis. Initially, we will consider all genomic regions and later more detailed analysis will be carried out. The significance of those mutations will be judged in contrast to the important factors like their frequency, position, and relation to the regulatory region of the genome. The ICGC controlled data would be helpful for our project to locate recurrent cancer mutations in genomic regions.
158.Jong-Seo KimSeoul National UniversitySouth Korea2019-04-032020-04-01
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.
159.JUNGSUN KIMOregon Health and Science UniversityUnited States2019-05-102020-05-08
Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis, mainly because the tumors are diagnosed too late for effective treatment or for developing suitable therapeutics. Studying PDAC precursors will, therefore, provide the best opportunity for discovering early diagnostics. However, current human cancer models give a static end-point state of PDAC and do not recapitulate the early PDAC precursors. This lack of a suitable early model is faltering our efforts to discover early-detection-biomarkers for the disease. We previously demonstrated as a proof-of-concept that the reprogramming based PDAC cell system provides unprecedented experimental access to the pancreatic cancer precursor. We aim to streamline the generation of this reprogramming based PDAC cells from more PDAC patients. Herein, we apply for access to the ICGC PDAC RNA-Seq dataset to evaluate how authentic our model is.
160.Oliver KohlbacherEberhard-Karls-University TübingenGermany2019-06-112019-07-19
In recent years, immune therapies have become important and especially immune checkpoint blockade (such as blocking membrane receptors of T-Lymphocytes to suppress immune response) has shown therapeutic potential leading to their introduction into standard care for cancer patients. The proposed study will provide new knowledge of the immune biology of cancers and which can be used to find new therapeutic targets for personalized medicine. Another goal is the development and improvement of methods for immune therapies, especially personalized cancer treatments. These methods include bioinformatics approaches (computer-assisted analyses performed by trained researchers) on next-generation-sequencing (technology to get DNA/RNA sequence information) and mutation data (as from ICGC controlled data). Furthermore, statistical analyses of mutations in different cancer types will be performed, including analyses that will be used to study the interactions of tumours and the immune system.
161.Jan KorbelEuropean Molecular Biology LaboratoryGermany2019-06-092019-07-24
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
162.Amnon KorenCornell UniversityUnited States2019-06-142019-07-25
The DNA sequences of tumors hold information with regards to specific genetic alteration that are present in any given tumor. In addition, tumor DNA sequences contain information with regards to their functional state. For instance, processes that affect the stability of DNA can be observed in tumor DNA sequences because they influence the relative abundance of sequences along the tumor's chromosomes. These processes represent underlying tumor biology; however, they have not been systematically studied and are not well understood. In this project we will study DNA sequence abundance along tumor genome sequences from ICGC and link them to various underlying biological processes in different cancer types.
163.Carl KoschmannUniversity of MichiganUnited States2019-01-252020-01-25
Diffuse intrinsic pontine glioma (DIPG) is a pediatric brainstem tumor with poor prognosis. This study will allow us to explore whether a gene that is upregulated in DIPG called ID1 can be targeted. The reason ID1 is upregulated will be in part elucidated by analysis of ICGC controlled data
164.Lutz KrauseThe University of Queensland Diamantina InstituteAustralia2019-06-152019-07-30
Our aim is to investigate genetic variants that predispose to cancer as well as variants that are associated with cancer progression and patient outcome. Therefore, we will use the ICGA data to search for genetic variants that are enriched in cancer cohorts compared to control cohorts at selected locations in the genome. Our second aim will integrate ICGC data from cancer cohorts to identify changes on DNA and gene expression level that are associated with patient survival.
165.Atsushi KumanogohOsaka universityJapan2019-06-052020-06-05
Our research focuses on a kind of genetic molecule called RNA that is produced from DNA in the cell. Previous research using ICGC Controlled Data has shown that frequently mutated "hot spots" in the human genome are often located within genes that guide the cell's metabolism of RNA molecules. Although these mutations may affect the development of cancer, no researchers have used the data set to investigate this possibility. Therefore, we intend to intensively analyze the ICGC data, including RNA sequence data, using a large-scale computer cluster in Japan.
166.Nathan LackKoc UniversityTurkey2018-11-022019-11-01
Prostate cancer is an extremely common disease that affects an estimated one out of every seven North American men in their lifetime. It has been demonstrated in simple experimental models that the growth of prostate cancer can cause DNA damage at specific genetic locations. To test if these same mutations arise in patient tumours, this project will use ICGC controlled data to characterize the type and frequency of mutations that happen near certain genetic elements from clinical data.
167.Thomas LaFramboiseCase Western Reserve UniversityUnited States2019-01-032020-01-02
Cancer is largely driven by mutations in DNA. However, the vast majority of mutations are "bystanders", and do not drive the disease. It is challenging to determine which mutations are important. In our project, we hope to let the tumor tell us which mutations are important by statistically assessing which mutations are repeatedly duplicated across many patient cancers. To achieve this, we require the large patient data sets that the ICGC curates.
168.Hugo LamRoche Sequencing SolutionsUnited States2018-12-212019-12-20
Somatic mutations are the changes in DNA which cells in an individual accumulate over its lifetime. They are key to understanding the cancer forms, progresses and identifying potential treatments. Detecting them is challenging due to multiple reasons. A tumor generally includes multiple subpopulations and is generally contaminated with normal cells. Furthermore, DNA sequencing errors and biases can complicate detection, especially when the proportion of cells harboring somatic mutations is low. In addition, these low prevalence mutations can be more costly to detect. We plan to use synthetic and real ICGC data (DREAM Challenge datasets and others) to assess the accuracy of somatic mutation calling algorithms and develop better methods to address the limitations of existing techniques.
169.Charles LangleyUC DavisUnited States2019-06-022019-07-13
The centromere is the locus on the chromosome that directs replicated sisters chromosomes to daughter cells. Thus plays a central role in inheritance. Centromeric regions of the human genome are highly repetitive and still not fully assembled. They remain enigmatic, difficult regions in which to analyze variation.  This is despite the obvious critical roles that they play in genome stability and the regulation of gene expression. Recently we have made an advance in the interpretation of population genomic data from these regions that now opens them up to much more powerful research.  Taking a fresh look at the potential associations of clearly classified centromeric region genotypes with appearance and development of cancers is our top priority and access to the data in the ICGC is opportune.
170.Eunjung Alice LeeChildren's Hospital BostonUnited States2019-06-072019-07-22
More than half of the human genome originated from transposable elements (TEs), also called ‘jumping genes,’ that copy their sequence into other chromosomal locations. Most TEs lost their jumping ability, but some retain this ability and generate heterogeneous genomic configurations in humans. Abnormal TE insertions are known to cause diseases such as hemophilia. Several studies including our own studies have also shown that TEs are desuppresed and generate extensive new insertions in some human cancers. However, whether they play an active role in tumor development remains unknown. We will address this question by analyzing a large set of cancer genome data from the ICGC using Tea (transposable element analyzer), a tool we have developed.
171.Sanghyuk LeeEwha Womans UnivSouth Korea2019-04-052020-04-03
Breast cancer is the second leading cause of cancer death among women. Small molecules such as hormone or growth factors promote cell growth and proliferation by binding to their matching receptors on the cell surface. Breast cancer can be divided into multiple types according to these receptors’ abundance, and treatment strategy critically depends on these classifications. For example, HER2-positive breast cancer tests positive for a protein called human epidermal growth factor receptor 2 (HER2), which promotes the growth of cancer cells. In about 25% of breast cancers, the HER2 gene makes excess copies of itself although the origin of this copy number amplifications (CNAs) has not been resolved yet. Recently, several studies reported that cells have many "extra" chromosomes that exist in a small circular form. Using ICGC’s whole genome sequencing data, we want to explore the extra-chromosomal contribution of CNAs that could be related to clinical outcomes.
172.Norman LeeGeorge Washington UniversityUnited States2019-05-092020-05-09
Prostate cancer is the development of cancer in the prostate. We will study the different prostate tumor genomic variations in the Moroccan population in order to identify factors that predict recurrence following surgery, radiotherapy, and hormone therapy. Our research project seeks to identify different pathways of tumor progression, revealing the necessary genomic information to use in stratified treatment strategies and personalized follow-up. Such a study will enable the selection of the best possible treatment for each patient and avoid unnecessary interventions, pain and cost for the patients. We are developing an analysis computer tool and we will use the ICGC Controlled Data to create and validate it.
173.Benjamin LehnerCenter For Genomic Regulation (CRG)Spain2019-06-202020-06-18
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.
174.Christophe LemetreQuentis TherapeuticsUnited States2019-03-062020-03-06
Some genomic alterations in cancer cells affect biological pathways that are activated by exposure to adverse conditions (in a “stress response”). Using ICGC Controlled Data, we aim to explore and quantify these alterations across different types of cancer and correlate our results with the available physical and clinical data. This would help us understand the make up of pan-cancer sub-populations according to that very specific alteration and determine the likelihood of the population to respond to a molecule targeting the pathway in which the event in involved.
175.Eric LetouzeINSERM - National Institute of Health and Medical ResearchFrance2018-12-142019-12-13
The genome of cancer cells is modified by different mechanisms, including modifications of single bases in the DNA sequence (mutations) or chromosomal rearrangements. Whole genome sequencing of large tumor series, as performed by the ICGC project, allows us to analyze these changes with unprecedented precision, and to better understand the molecular mechanisms at the origin of these DNA modifications. In the past years, large-scale analyses of mutations have revealed signatures of known (tobacco, UV light) and new mutational processes operative in different cancers. Our team is specialized in liver cancers. We have identified different processes generating mutations and chromosomal rearrangements in liver cancers. The current project aims at verifying if these mechanisms are restricted to the liver or also operative in other cancer types.
176.Yilong LiTotientUnited States2019-02-212020-02-21
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.
177.Mulin Jun LiTianjin medical universityChina2018-09-072019-09-06
Evidences indicate that structure variants (SVs) can affect gene expression (gene products) by disrupting Topological Associated Domains (TADs) of nuclear chromatin. To inspect whether cancer recurrent SVs could reorganize the gene expression pattern through affecting TAD configuration, we are going to develop a computational framework based on pan-cancer SV events. Using the ICGC controlled dataset, we will detect hotspot of recurrent SVs and then associate them with gene expression changes within same TAD. We believe that this methodology will facilitate the interpretation of biological function of SVs in the development of human cancers.
178.Han LiangThe University of Texas MD Anderson Cancer CenterUnited States2018-10-172019-10-16
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
179.James LillardMorehouse School of MedicineUnited States2018-10-092019-10-08
The great success of cancer genomics studies is partly due to the development of rigorous data analysis pipelines created by highly qualified statistical geneticists. However, it is possible that more information can be extracted from these datasets using alternative approaches. We plan to explore the use of the ICGC controlled datasets to elucidate the importance of cell signaling molecules in cancer progression. In particular, we will apply validated statistical methodologies to gain new insights into the genetics and the molecular signaling networks of cancer. A major goal of our study is to use these findings as a way to better: 1) predict clinical outcomes of cancer patients and 2) identify patients that would benefit from more selective therapies.
180.Zhen LinTulane UniversityUnited States2019-01-112020-01-10
Infectious micro-organism such as viruses (Epstein-Barr virus (EBV)) and bacteria (helicobacter pylori) can cause cancers. To further explore the infectious pathogens associated with lymphomas, we propose to analyze the requested data sets and explore associations between infectious micro-organisms and the development of cancers. Using ICGC controlled data, we plan to further elucidate the role of infectious pathogens in the development of lymphomas by investigating both viral and cellular genetic information in primary lymphomas.
181.Shaoping LingGenome WisdomChina2019-05-262019-07-09
Mutations, or changes to the genome sequence of a cell, can lead to cancer. It is therefore of extreme importance to catalog all of the mutations observed in tumors to gain information about the disease. Dozens of methods exist to identify mutations. However, there is currently very little agreement between any two methods. The discrepancies make it difficult to compare and combine results from different studies. We will use controlled access ICGC data to develop a series of tools that can efficiently and accurately detect mutations in major types of cancer , and apply these tools to the ICGC data and look for new mutations that are important for tumor development.
182.Yu LiuPediatric Translational Medicine Institute, Shanghai Children's Medical Center, Shanghai Jiaotong University School of MedicineChina2018-11-022019-11-02
Some regions of the human genome contain code that the cell uses to produce proteins and other regions do not. Genetic variations in these "noncoding regions" could cause tumors by activating genes that are involved in cancer. We developed a computational pipeline to discover new variations of this type. We plan to use ICGC data to improve our pipeline, which should help to expand our insights into the human genome and the molecular mechanisms underlying cancer.
183.Xue-Song LiuShanghaiTech UniversityChina2018-11-162019-11-15
Cancer therapies that enhance human body`s immune response, so called immunotherapy, are transforming the treatment of cancer. However, only a fraction of patients show response to immunotherapy, and there is an unmet need for markers that will identify patients more likely to respond to immunotherapy. Our research goals are to identify novel features and patterns of DNA alteration in cancer, and use these features as marker for cancer immunotherapy response prediction. We will develop novel analysis methods to analyze ICGC controlled data, to identify novel signatures and patterns of DNA alteration. This study will help to determine which cancer patients will respond best to different types of therapy
184.Josep M LlovetInstitut D'Investigacions Biomediques August Pi i Sunyer (IDIBAPS)Spain2019-06-112019-07-25
Hepatocellular carcinoma (HCC) accounts for the majority of primary liver cancers (90%) and is characterized by a rising incidence and mortality –as opposed to other cancer types- as well as limited therapeutic options, thus representing a major public health issue worldwide. Several liver diseases such as hepatitis or metabolic syndrome contribute to the establishment of HCC, which in the majority of cases develops under a cirrhotic context. Our aim is to identify biomarkers to detect patients at risk and the aberrant cellular mechanisms among these individuals, to ultimately contribute to the development of better prevention strategies and precision medicine therapeutic approaches for patients. To achieve these aims, we plan to conduct genomic analyses incorporating the ICGC data.
185.Martin LoewerTRONGermany2019-04-172020-04-09
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.
186.Sherene LoiPeter MacCallum Cancer CentreAustralia2019-05-152020-05-07
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.
187.Nuria Lopez-BigasInstitute for Research in Biomedicine (IRB Barcelona)Spain2019-01-172020-01-16
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
188.Justo Lorenzo BermejoUniversity Hospital HeidelbergGermany2019-06-032019-07-18
We are conducting a research project towards personalized prevention and treatment of gallbladder cancer in Chile, where we plan to generate genetic information from tumor tissue and blood. In order to compare the molecular profiles of gallbladder cancer diagnosed in Chile and Japan, we apply here for access to deposited ICGC controlled data on Japanese population described in the article by Nakamura et al. (2015) “Genomic spectra of biliary tract cancer” (doi:10.1038/ng.3375).
189.Ana LosadaSpanish National Cancer Research Centre (CNIO)Spain2019-05-282020-05-22
Ewing sarcoma is the second most common bone cancer in children and young adults. In most patients, the fusion of two genes generates a novel protein, EWS-FLI1, which does not exist in normal cells. In addition, around 20% of the tumours have also a mutation in a protein called SA2, and the presence of this mutation correlates with poor outcome. Basic research in our group aims to understand the function of SA2, which is part of a complex named cohesin. Now we want to apply our knowledge of cohesin-SA2 functions to explore how the absence of this protein complex may cooperate with EWS-FLI1 to promote more aggresive tumours. For that, we will analyze ICGC controlled data from Ewing sarcoma tumour samples carrying or not mutations in SA2. We hope that our results may help find better ways to treat Ewing sarcoma patients.
190.Jason LuQIAGEN, REDWOOD CITYUnited States2018-11-092019-11-08
Cancer cells have changes in DNA sequences which play a fundamental role in maintaining cell growth and other normal functions. Identifying these genome alterations is essential for understanding the initiation of cancer, how a tumor progresses, and why a treatment is effective for some patients but not for others. Using ICGC data, we aim to develop computational methods and tools to identify genome changes specifically occurring in each patient, providing the basis for developing targeted therapy for cancer.
191.Eamonn MaherUniversity Of CambridgeUnited Kingdom2018-11-052019-11-04
Renal cell carcinoma (RCC) is the most prevalent kidney cancer, responsible for >100K deaths/year and with a rising incidence in Western countries. If detected early, surgical removal of RCC can be curative but the prognosis for metastatic disease is extremely poor. Despite recent progresses in cancer genomics, RCC is one of the very few tumour types for which the new acquired knowledge has not been translated into new medicines. Whilst cancer has been mostly studied from the tumour perspective, scientific evidence is showing that inherited alterations may be crucial for the development of the disease and response to therapy. Thus, our project tackles a major challenge in renal cancer: to identify critical cancer players and to establish the basis for new therapies by exploring the inherited genome of RCC patients. ICGC Data will be a valuable source to complete our dataset and to get deeper knowledge of genome alterations.
192.Vsevolod MakeevVavilov Institute of General Genetics, Russian Academy of SciencesRussian Federation2019-04-172020-04-10
Cancer transformation of a cell is believed to be caused by so-called 'driver mutations', changes in the cell's genome that disrupt mechanisms controlling the cell's identity. Recent findings demonstrate that most of these mutations are found in DNA segments that do not encode proteins by themselves but control what tissue and what time the protein is synthesized. The mechanisms of this control are facilitated by special regulatory proteins, interacting with particular DNA sites. The objective of our research is to understand how occurrences of different types of mutations of chromosomes in cancer depend on neighboring genome segments performing particular functions: 1) how mutations are correlated with each other; 2) how the mutation process depends on local genome activity and other processes; and 3) what factors determine the subsequent fate of the mutated cells and their progeny.
193.Lara MangraviteSage BionetworksUnited States2019-03-222020-03-20
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.
194.Hiroyuki ManoNational Cancer CenterJapan2018-11-012019-11-01
Cancer can result from genetic alterations (changes in a person's genetic material). Some of the genetic alterations can be good targets of drugs (therapeutic targets). Triple negative breast cancer (TNBC) is a type of breast cancer, and more likely to recur than other types of breast cancer. In this study, genetic alterations of sixteen cases of Japanese TNBC were analyzed in order to discover therapeutic targets of TNBC. We wish to access ICGC data on related types of cancer in order to compare them with this dataset. It is hoped that the comprehensive analysis of large dataset will contribute to the identification of new therapeutic targets.
195.Kathleen MarchalGhent UniversityBelgium2019-02-182020-02-17
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.
196.Yuki MatsumotoAnicom Specialty Medical Institute Inc.Japan2019-01-212020-01-20
Canine cancer is a good model for spontaneously occurring cancer in humans. A canine oral cancer called malignant melanoma, which frequently occurs in Miniature Dachshunds in Japan, is considered similar to a human oral cancer called mucous melanoma. To prove the molecular similarity of these diseases, DNA sequences from canine oral cancer will be compared with those from human oral cancer (registered in “ICGC Controlled Data”). The resulting information will be very useful for understanding the similarity of human and canine cancers.
197.Ilya MazoArgentys Informatics LLCUnited States2019-02-142020-02-14
We are interested in studying the 'dark matter' of the human genome, i.e the parts of genome other than genes. More than 40% of the human genome is represented by leftovers from ancient viral-like genetic sequences (called retroelements). Most have been rendered inactive in the course of evolution, every individual still has copies of some potentially active retroelements that can reinsert or cause other genes to insert into other places, causing mutations of sorts. In several types of cancers such events have been shown to take place at much higher rates, the fact we are trying to exploit to develop novel cancer diagnostics approaches. This became possible with the advent of next generation sequencing technologies that can produce the whole human genome data, specifically from cancer patient samples. Our approach is based on novel algorithms that analyse the cancer genome data to identify and measure the active retroelements.
198.Sam MbulaiteyeNational Institutes of HealthUnited States2018-08-172019-08-16
Burkitt lymphoma (BL) is a cancer of immune cells that occurs as three forms: sporadic (rare), endemic (common), and immunodeficiency-associated (epidemic) BL. Discovered 50 years ago, BL became a good example to learn how changes in DNA and some infections may cause cancer. Recent discoveries of DNA changes in BL tumors have rekindled interest in heritable DNA changes that affect the chance of getting BL. We are requesting access to sequence information obtained from active DNA of normal samples from cases in Germany (n=33) studied under the ICGC protocol. We will add this information to that from 124 single or familial BL cases studied at NCI (65 from US and 22 from Guatemala; 28 single endemic cases from Tanzania 6 familial endemic cases from Uganda and Tanzania). Mathematical analysis of DNA sequences will be performed to identify changes that may explain why some may get BL and others not.  
199.Frank McKeonUniversity of HoustonUnited States2019-01-112020-01-10
It is now clear that cancers are the end-stage of a multi-year and even multi-decade evolution from non-cancerous and pre-cancerous lesions. We know very little about why some precursor lesions simply stop progression and others go on to develop malignant cancer. Towards this end, we have been analyzing these lesions at the level of single cell that can be expanded for DNA sequencing to assess all mutations at each stage. This analysis is providing data at higher resolution than possible by analysis of whole biopsies of these different lesions, but suffers from expense that prohibits an analysis of large numbers of patients. Therefore leveraging these studies by combination with those of the ICGC will strengthen the conclusions that can be drawn from the clonal analysis we have performed to date.
200.Jörg MencheCeMM Research Center for Molecular Medicine of the Austrian Academy of SciencesAustria2019-03-062020-03-06
Mutational processes in cancer genomes leave certain footprints, or signatures, specific to the processes that are causing them. Analysis of these mutational signatures is a potent method to reveal the processes which create the plethora of mutations in various cancer types. These mutations enable cancers find new avenues to evade the immune system, develop resistance to treatment, metastasize, etc. Thus, unraveling these defective processes holds the potential to predict disease progression, treatment response, or develop novel treatment strategies. In this project, we will investigate the mutational signatures present in liver cancers associated with viruses, specifically HBV (hepatitis B). We will aim at illuminating the mechanistic associations between the cellular defence systems against viruses and pathways exploited by liver cancers to enrich their genomic diversity, through an analysis of ICGC mutational data of hepatocellular carcinoma (liver cancer) patients.
201.Tong MengTongji UniversityChina2018-12-212019-12-20
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.
202.Eilish MiddlehurstConcR LTD.United Kingdom2018-10-042019-10-04
Currently 50% of diagnosed of cancer patients will die of cancer, of these 50%, 90% of the deaths will be caused by treatment resistance. ConcR is developing computational methods of predicting which genetic mutations will arise in a specific patients cancer, and which therapeutics the patient will develop resistance to over the course of their disease. ConcR is developing a software based tool to enable clinicians to adapt treatments proactively, reducing the occurrence of treatment resistance and therefore mortality associated with cancer. ConcR software will allow for effective interpretation of this data and be an enabling technology for precision oncology. ConcR will be utilising the data sets on the International Genome Consortium (ICGC) platform to further develop and test our predictive capabilities. All results of the predictions made from the analysis of these data sets will be published to the ICGC.
203.Markku MiettinenNCIUnited States2018-09-202019-09-19
Angiosarcomas are very aggressive cancers originating from blood and lymphatic vessels either spontaneously or after radiation for treatment of other cancers. Since it is rare disease, we know little about its biology. Recently, we understood that many cancers happen because of alterations in genes that control different functions in the human cells, especially proliferation. Recent evidence suggests that angiosarcomas may arise from mutations that increase the vascular proliferation in a small number of patients. We want to perform a more comprehensive study on a larger scale using ICGC and other data sources to discover the underlying mechanism of angiosarcomas and identify specific targets for treatment
204.Kaja MilanowskaArdigenPoland2018-09-182019-09-18
Next-generation sequencing (analyzing DNA sequences) has become the preferred method for interrogating different types of sequence variation amongst collections of samples. Recently it has become a cost-effectiveness method to support treatment selection for patients, including patients suffering from different cancer types. Still it is a great challenge to find a precise and standardized method to select and direct patients to the treatment the most beneficial for them. We would like to retrospectively test our classification method on ICGC controlled data. The data will allow us to assess the accuracy of our method in the context of cancer. Our intention is use the data to test our method and potentially make some biological discoveries through our analysis.
205.Seema MitalThe Hospital for Sick ChildrenCanada2019-04-042020-04-04
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.
206.Satoru MiyanoThe Institute of Medical Science, The University of TokyoJapan2018-08-172019-08-16
Detection of DNA mutations in cancer cells from DNA sequencing data of each patient is one of the most important steps in cancer research and precision medicine. We are developing a mathematical method that is able to use multiple information sources helpful for finding out such mutations accurately by combining different types of statistical models. Using the ICGC Controlled Data, we will test and improve the ability of the method.
207.Takuya MizunoYamaguchi UniversityJapan2018-12-172019-12-17
Canine cancer is a good model for spontaneously occurring cancer in humans. A canine oral cancer called malignant melanoma, which frequently occurs in Miniature Dachshunds in Japan, is considered similar to a human oral cancer called mucous melanoma. To prove the molecular similarity of these diseases, DNA sequences from canine oral cancer will be compared with those from human oral cancer (registered in “ICGC Controlled Data”). The resulting information will be very useful for understanding the similarity of human and canine cancers.
208.Ryan MorinBC Cancer, Part Of The Provincial Health Services AuthorityCanada2019-06-202019-07-26
This study aims to search through the massive amounts of ICGC data to find the mutations that are most important to cancer cells. These mutations can activate cancer-promoting genes known as oncogenes or inactivate other important genes known as tumour suppressor genes. Such genetic changes are what allow cancer cells to survive. Discovering the important changes of individual cancer types may allow us to develop new drugs that can effectively eliminate cancer cells with less toxicity than classic cancer treatments. These changes can also provide the basis of sophisticated clinical tests that can help us better personalize/individualize cancer therapy thus ultimately improving patient outcomes.
209.Kazem MousavizadehIran university of medical sciencesIran2018-11-232019-11-22
Cancers are divided into two types: solid tumors and hematologic malignancies.Hematologic malignancies affect lymph and blood systems and are divided into types based on whether they are fast or slow-growing. Chronic myeloid leukemia (CML) is a kind of slow-growing hematologic malignancy that is based on blood forming cells in the bone marrow. However, it can convert to a kind of fast-growing malignancy called acute myeloid leukemia (AML) which is hard to treat. In this project we aim to find new targets for AML treatment which may help patients to survive longer and improve their quality of life. We would like to access the ICGC's recent data on CML in order to find these genetic targets more accurately.
210.Ola MyklebostUniversity of Bergen, NorwayNorway2019-05-312020-05-29
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.
211.Sven NahnsenEberhard-Karls-University TübingenGermany2019-01-072020-01-07
We are working on establishing a hybrid infrastructure to enable the analysis of protected ICGC data in the cloud and integrate the results with our locally computed private data. The routine research of cancer data requires large investments in infrastructure for analysis. Despite the benefits of cloud-providers (e.g. highspeed computing), there are still technical difficulties in using these infrastructures to analyze medical data for privacy reasons. Cloud, in this context, refers here to ICGC repositories that are available through Amazon-AWS. One big issue is to ensure that individual patient data is kept private at all times. Our approach will be able to fulfill these requirements, as we can calculate important statistics on ICGC in the cloud and subsequently download and integrate the results with our locally computed data. This will have the benefit of not conflicting with regulations in a medical context while preserving the ability to access ICGC data.
212.So NakagawaTokai University School of MedicineJapan2018-12-122019-12-12
About half the human genome corresponds to repetitive sequences including transposable elements (TEs), which are generally thought to be “junk” DNA that do not fulfill any function. Recently, several studies have revealed that some TEs could be involved in various biological processes and in many diseases such as cancers. However, molecular function of many TEs are yet unclear. In particular, some TEs contains open-reading frames (ORFs) (readable part of the DNA) that potentially express various peptides (short chains of amino acids) and/or proteins. These TE-derived peptides/proteins could be harmful and may be involved in progression of cancers. We have been developing a database for TE-derived ORFs named gEVE. In this study, using the gEVE database with the ICGC database, we are going to obtain TE-derived ORFs that are expressed in various cancer cells/tissues. Those elements could be candidates for cancer markers and therapeutic targets for various cancer patients.
213.Jin-Wu NamHanyang UniversitySouth Korea2019-02-202020-02-19
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.
214.Arcadi NavarroPompeu Fabra UniversitySpain2018-09-272019-09-26
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
215.Paweł NiewiadomskiUniversity of WarsawPoland2018-09-102019-09-09
Medulloblastoma is the most common brain tumor in children. Genetically modified laboratory mice are commonly used to model human medulloblastoma and devise new therapies. We discovered that there are significant differences between mouse and human medulloblastoma. The goal of this project is to understand why the tumors in the two species are so different, and what implications this might have for the use of mouse models in preclinical studies of this disease. ICGC data will allow us to determine how gene expression (gene products) in medulloblastoma tumors changes as a function of mutations present in the cancer cells.
216.Sergey NikolaevGustave RoussyFrance2018-11-202019-11-20
Basal Cell Carcinoma and melanoma are skin cancers which accumulate mutations in their genomes induced by ultraviolet light. We plan to perform genomic profiling of 100 genomes of Basal Cell Carcinoma and to use genomic profiles of ICGC as a reference data set in order to study the effect of ultraviolet light in skin cancers. Specifically would like to investigate the mechanisms of the reparation of the DNA damages induced by ultraviolet light, and to compare them between melanoma and Basal Cell Carcinoma.
217.Hieu NimAustralian Regenerative Medicine InstituteAustralia2019-06-252019-07-25
This project seeks to employ data-driven analysis techniques to reliably stratify between low-risk and aggressive tumours. Utilising our own laboratory data and next-generation sequencing data from ICGC, the project will apply bioinformatics techniques to search for the missing genetic links between a breast cancer mutation and poor prognosis in prostate cancer.
218.Beifang NiuComputer Network Information CenterChina2018-11-222019-11-22
Microsatellites are repetitive DNA sequences. Microsatellite instability (MSI) is a form of mutation that occurs in some tumors due to defects in the cell's ability to repair DNA. The mutation of microsatellites will transform normal cells into malignant tumor cells and eventually lead to malignant tumors. MSI detection is significant for tumor early diagnosis and prognosis. In this project, we will use the data from the International Cancer Genome Consortium (ICGC) to develop a convenient and accurate tool to detect MSI status to assist cancer diagnosis and prognosis. Firstly, we will analyze the data from ICGC to find microsatellite sites. Then we will judge whether the sample is stable or not, basing on the stability of the satellite site we found.
219.Rienk OffringaGerman Cancer Research CenterGermany2019-03-152020-03-14
Immunotherapeutic strategies have recently shown great promise for the treatment of several cancer types, including melanoma and lung cancer, but have so far failed to show clear clinical benefit in patients with pancreatic cancer. Nevertheless, curative treatments for pancreatic cancer, in particular pancreatic ductal adenocarcinoma (the most common type of pancreatic cancer), are still lacking, so it is important to learn how immunotherapy could be successfully applied to this cancer type. Our in house analyses with limited data sets have revealed immunological differences between pancreatic tumors that could help to identify patients who may benefit from immunotherapy. The use of the more extensive the ICGC data sets will enable us to look into these differences in greater detail.
220.Seishi OgawaKyoto UniversityJapan2018-10-312019-10-30
The dynamics of clonal evolution from non-malignant mammary epithelial cells to invasive breast cancers are poorly understood. In this study, we will analyze the genetic alterations of non-invasive breast cancers and precancerous lesions by analyzing DNA sequences. To investigate the alterations correlated with invasiveness, we would like to analyze the ICGC sequencing data of invasive breast cancer cohort and compare them to our non-invasive cohort data.
221.Alberto OrfaoCentro de Investigacion del CancerSpain2019-03-182020-03-18
One type of human immune cells called B cells have a group of molecules on their surface (called B-cell receptors, or BCRs) that are used to recognize the presence of other molecules called antigens. In patients with a type of blood cancer called Chronic Lymphocytic Leukemia (CLL), analyzing these BCRs can help to distinguish patients with different prognosis. As the primary BCR analyses at the molecular level are not enough to clarify the clinical destiny of some patients, in this project we seek to add the variable of biochemical composition. The Cancer Research Center of University of Salamanca (USAL) and the Bioinformatic Service of USAL will use the ICGC CLL data to develop new bioinformatics protocols to identify genomic alterations of CLL patients using both the widely known molecular BCR features as well as the biochemical composition of BCR.
222.Zbyszek OtwinowskiThe University of Texas Southwestern Medical CenterUnited States2018-12-212019-12-20
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.
223.Laxmi ParidaIBM ResearchUnited States2019-01-172020-01-16
A core challenge in understanding and treating cancer is the molecular complexity and heterogeneity observed in and across patient diseases. To date, a great deal of research has been focused on regions of the genome that encode genes, leaving the vast majority of the genome under-studied with respect to its prognostic or diagnostic power. Our goal is to apply computational methods and artificial intelligence algorithms on ICGC genomic and clinical data in the hopes of discovering patterns among all the different parts of genome that can distinguish between different major and minor types of cancer. Such insights could lead to improved diagnosis and care of cancer patients.
224.Ji Wan ParkHallym UniversitySouth Korea2018-11-192019-11-18
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
225.Peter ParkHarvard Medical SchoolUnited States2019-03-132020-03-11
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.
226.Peter ParkHarvard Medical SchoolUnited States2019-04-032020-04-01
Genomic DNA is tightly packaged inside human cells. The special protein molecules and other factors facilitate this packaging. Specifics of the packaging determine how easy it is to read out the information from each gene in the genome. It was discovered that the DNA-packaging proteins are mutated and malfunction in a number of cancers, including pediatric brain tumors. Using the ICGC controlled data we will investigate in which ways the production of these proteins is disrupted in cancer cells. Our findings will help to shed new light into how genomic DNA is ‘handled’ in cell during normal development and into how its ‘mishandling’ can lead to cancer.
227.Ramon ParsonsIcahn School of Medicine at Mount SinaiUnited States2018-08-302019-08-29
The development, progression of cancer and response to drugs are known to be driven by a combination of genetic and non-genetic modifications in cancer cell chromosomes and the evolution of cells within the tumor. Using molecular and clinical data from ICGC, we are interested to investigate specific molecular patterns as well as micro-organisms composition (presence of microorganisms genomes in tumor samples) in several types of cancer; including to study the genomic landscape of triple negative breast cancer (the subtype of breast cancer less understood and druggable so far) and to decipher the p53 network and how it maintains expression of other tumor suppressor genes in colon cancers. The lab will concentrate mainly these studies on seven cancers but we will extend our studies to other cancers in specific projects to investigate if we find the same patterns.
228.Lorenzo PasqualiInstitut de Recerca Germans Trias i PujolSpain2019-06-252019-07-23
Pancreatic neuroendocrine tumors (PNETs) are endocrine tumors arising in the pancreas. The major molecular mechanisms underlying PNETs have not been yet elucidated and research evidence points to the involvement of both genetic and epigenetic mechanisms. We now aim to integrate epigenetic markers and gene expression with PNETs genetics. Access to ICGC data will allow us to relate epigenetic changes to DNA modifications. Such analyses will help us to unmask molecular pathways that drive the formation of PNETs. The acquisition of deeper knowledge of genetic and epigenetic mechanisms underlying cancer development and progression can set the base for a new diagnostic classification and medical therapy for pancreatic neuroendocrine tumors.
229.Jakob PedersenAarhus UniversityDenmark2019-04-292020-04-23
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.
230.Martin PeiferUniversity of CologneGermany2019-05-072020-05-01
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
231.Paivi PeltomakiUniversity of HelsinkiFinland2019-01-112020-01-10
Lynch syndrome is the most prevalent human cancer syndrome. Individuals with Lynch syndrome are predisposed to cancers of multiple organs, but the reasons why certain organs are particularly prone to cancer development are unknown. The tendency to acquire molecular changes may differ between different organs and is also likely to be different in individuals with inherited predisposition (Lynch syndrome) compared to individuals without such predisposition (sporadic cases). To test these hypotheses, we are generating comprehensive mutational profiles for established (colorectal & ovarian) and putative Lynch syndrome carcinomas (breast) in our laboratory and will compare the results with mutation data available for corresponding sporadic carcinomas to be retrieved from ICGC. This research is expected to shed light on the mechanisms of organ-specific cancer susceptibility and identify molecular targets for tailored treatment and cancer prevention.
232.Dmitri PervouchineSkolkovo Institute of Science and TechnologyRussian Federation2018-09-102019-09-09
We believe that genome’s “dark matter” -- so called long non-coding RNAs (functional molecules transcribed from DNA that do not translate into proteins) -- can sometime encode short proteins. These proteins were not found before because they are too short (thus referred to as micropeptides). Using bioinformatics, we predicted a number of novel micropeptides and now would like to test their association with cancer followed by experimental validation. This project proposes a previously unexplored paradigm for cancer mechanisms, and will deliver new targets for cancer therapy.
233.Paul PharoahUniversity Of CambridgeUnited Kingdom2019-05-162019-06-29
Inherited differences in our genetic makeup - our genome - underlie the tendency for cancer to run in families. The aim of this study is to look for differences in the regions of the genome that we think are biologically (or functionally) important in order to identify variants that might be associated with risk of ovarian cancer. We will therefore analyse the genomes of women with ovarian cancer that have been generated as part of the ovarian cancer component of the ICGC and compare the frequency of variants in functionally interesting regions of the genome with less active regions of the geome to identify risk associated regions. These will then be validated by targeted sequencing in large-scale ovarian cancer studies that are not part of the ICGC.
234.Salvatore PiscuoglioUniversity of BaselSwitzerland2018-08-172019-08-16
Long non-coding RNA (lncRNA) is a type of RNA that has not be subjected to the same extent of attention and research compared to RNA from protein-coding genes. While protein-coding genes account for <2% of the human genome, a substantial proportion of the remaining genome leads to the production of lncRNAs, which are involved in many important cellular mechanisms that might lead to cancer. Compared to protein-coding genes, research into their role in cancer development is still at its infancy. Here we propose to leverage the extensive data generated by the LIRI-JP project, accessed through ICGC Controlled Data, to improve our understand of the role lncRNAs play in cancer development.
235.Sarka PospisilovaCEITEC Masaryk UniversityCzech Republic2018-10-042019-10-03
The aim of the study is to use the requested dataset to optimize our pipeline for investigating mutation patterns in chronic lymphocytic leukemia (CLL). We will apply a broad range of methods in an attempt to identify distinct CLL subgroups. We will leverage observations obtained in the requested ICGC dataset to comprehensively explore a dataset that has been generated at our institute. We plan to develop an algorithm for better classification of leukemia patients with a goal of effective personalized care.
236.shobha potluriLyell Immunopharma Inc.United States2019-05-022020-05-02
The datasets from the ICGC samples will be used for validation of targets pursued at Lyell. The ICGC data will be combined with datasets from other datasets to compare and contrast the presence of the target across multiple tumor types. Access to raw data is critical to ensure that the diversely generated datasets are run through the same pipelines to allow for valid comparisons across datasets. In our proposed work, no attempt will be made to re-identify the Research Participants. The raw data will be processed with same pipelines that data from other datasets will be run through and the processed data will be combined
237.Xose PuenteUniversity of OviedoSpain2018-10-092019-10-08
Recent advances in cancer genomics have improved our ability to identify the genomic mutations present in tumor cells. The presence of a specific mutation in a tumor can be used for diagnosis and to decide the best treatment for a particular patient, sometimes with drugs aimed at counteracting the effect that that particular mutation exerts on the cell. However, our understanding of the human genome is still limited, and it is difficult to predict what effect will have a particular mutation. In some cases, current algorithms fail to correctly predict the effect of a mutation, leading scientists to miss some important mutations. Our study will use ICGC Controlled data to integrate genomic data (genomic mutations) with functional data generated by ICGC to identify these mutations that are frequently missed by current approaches.
238.Trevor PughUniversity Health NetworkCanada2018-11-232019-11-22
Recent advancements in genomic approaches have given rise to a number of tumour sequencing and biobank initiatives such as the International Cancer Genome Consortium (ICGC). While gathering such data are valuable alone, there is great understanding to be gained through integration and comparison across different models and data types. Specifically, we will use ICGC data to understand the type of immune cells that recognize tumours in children. Our goal is to incorporate the data from ICGC and other international initiatives and comprehensively characterize immune features of childhood cancers that may inform outcome of treatment in children. Findings from our proposed project will lead to more effective research, treatments, and diagnostic tests for childhood cancer.
239.roberto puzoneIRCCS Ospedale Policlinico San MartinoItaly2018-11-272019-11-27
Most cancer are heterogeneous genetic disease in which many genes are involved, and some inherited gene mutations are already known to be associated with different therapies, cancer progression and prognosis. Our study investigates into a potential increase of the known specific risk associated with some inherited point mutations (SNV), in cancers which have acquired mutations in genes which are known to strongly promote specific cancer development (driver genes). Because these SNV are in totally different DNA positions than the driver genes no direct influence can be thought, thus the effect should involve the genes as a network (pathways). Using ICGC controlled data, we will focus on high incidence cancer such as lung, breast, ovarian, and colon cancer, to perform an investigation that will include comparison among the different cancers.
240.Jun QinShanghai Institutes for Biological Sciences, Chinese Academy of SciencesChina2018-11-092019-11-09
Prostate cancer (PCa) is the second most common cancer diagnosed in men. While the indolent prostate tumors may be treated easily, the patients with aggressive prostate cancer often have the worse clinical outcome with characteristics such as the rapid relapse. Our long-term objective is to elucidate the underlying mechanism driving the indolent tumors progressing into the aggressive PCa. Recent studies have found that a variation in a single nucleotide (single nucleotide polymorphisms, SNPs) that occurs at a specific position in the genome could be associated with PCa risk. In this project, using the ICGC data and patients’ clinical information, we aim to identify the PCa risk-associated SNPs which are also correlated with disease relapse. Subsequently, we are designed to determine how these SNPs affect disease progression by experimental approaches. We believe the knowledge acquired in our studies will potentially lead to the development of therapeutic approaches.
241.Hong QuPeking UniversityChina2018-11-122019-11-11
We will use ICGC data to help identify genetic sequences involved in cancer metastasis which may help doctors to diagnose different types of cancer or predict patient outcomes. We will also integrate our genetic findings with ICGC's clinical information, such as patient survival periods, in order to develop methods of diagnosing different types of colorectal cancer.
242.Kun QuUniversity of Science and Technology of ChinaChina2019-06-032020-06-03
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.
243.Sabarinathan RadhakrishnanNational Centre for Biological SciencesIndia2019-06-112020-06-11
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.
244.Milan RadovichIndiana University School of MedicineUnited States2019-02-132020-02-13
Cancer is a disease of DNA. When changes (or typos) in the genetic code occur in the DNA of a normal cell, this leads to transformation of that normal cell to malignancy. Cancers have a variety of ways in which to mutate DNA. One of these methods is to take stretches of DNA sequence, and literally copy-and-paste these sequences to disrupt other areas of the genome that are designed to prevent cancer. These DNA sequences that copy-and-paste are known as transposable elements, or more commonly known as "jumping genes". In this application, we would like to use ICGC genomic data to study these transposable elements to provide additional clues on the causes of cancer and potentially shed light on new therapeutic interventions.
245.Emad RakhaUniversity of NottinghamUnited Kingdom2019-04-182020-04-18
It is now known that breast cancer exhibits a number of distinct types, which have different impact on patient quality of life following diagnosis as well as the response to different treatment types. Using the ICGC Controlled data will pave the way to the discovery for novel key proteins involved in the development and progression of breast cancer. This data will be explored to identify key factors, which make some breast cancers behave aggressively and therefore increase the patient’s risk of fatality as compared to other cancers that are quite indolent. Identifying these factors would better cancer management as they could be also used as targets for treating patients.
246.Karthik RamanIndian Institute of Technology, MadrasIndia2018-09-102019-09-09
Although vast amounts of genomic data have been generated worldwide from human cells in diseases like cancer, analytic approaches used for extracting information from raw data remains in their infancy. This project aims at possible identification of previously unknown mutations and other changes that occur in the cell that drive tumour formation, to understand the progression of tumour in specific cancer subtypes and across all cancer types. To do this we require ICGC data at different levels(genomic, gene expression) and across cancer types. We aim to build an easy-to-use open source end-to-end tool for the analysis of cancer datasets.
247.Gunnar RatschEidgenoessische Technische Hochschule ZuerichSwitzerland2019-02-142020-02-13
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.
248.Kristin ReicheFraunhofer Institute for Cell Therapy and ImmunologyGermany2019-01-142020-01-13
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.
249.Eduardo ReisUniversity of Sao PauloBrazil2019-05-222020-05-20
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.
250.Eduardo ReisUniversity of Sao PauloBrazil2019-05-222020-05-20
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.
251.Marc RemkeGerman Consortium for Translational Cancer Research (DKTK)Germany2019-01-212020-01-20
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.
252.Daniel RenoufBC Cancer, Part Of The Provincial Health Services AuthorityCanada2018-11-152019-11-15
Pancreatic cancer is a devastating disease with very poor survival rates. Recent studies have identified a subset of pancreatic cancer patients whose tumors are more detectable by the immune system, and therefore benefit from greater survival rates. However, it remains unclear why this occurs for only a subset of pancreas tumors. Our group aims to determine why some pancreas tumors are more detectable by the immune system than others, by leveraging data from both primary and metastatic pancreas cancer patients made available by the ICGC consortium and the BC Cancer Agency, respectively. By utilizing state-of-the-art computational techniques, we hope to shed light on mechanisms accounting for the different immunological subtypes of pancreas cancer and generate an impetus for more effective treatment decisions for this debilitating disease.
253.William RitchieCNRSFrance2019-05-302020-05-28
Current computational approaches for analyzing the human genome in disease rely on our current knowledge of biology. We are creating an artificial intelligence capable of directing scientists to disease-related regions of the genome without the requirement of any prior clinical or biological knowledge. This will enable us to discover novel genomic regions that are worthy of further exploration and fully utilize the wealth of information contained in this research. The ICGC controlled data enables us to test that our algorithm will be of clinical relevance and allow us to adapt the artificial intelligence component to ensure that the computational resources it uses are not too high for use on hospital and research computers.
254.Davide RobbianiRockefeller UniversityUnited States2019-02-132020-02-12
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.
255.Davide RobbianiRockefeller UniversityUnited States2018-12-142019-12-13
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).
256.Steven RobertsWashington State UniversityUnited States2019-04-302020-04-23
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.
257.Brian RoodChildren's National Medical CenterUnited States2019-05-152020-05-15
We have identified small runs of repeated DNA code that seem to be able to distinguish individuals with the brain tumor medulloblastoma from healthy people. These elements are called microsatellites. Most interestingly, the DNA we used to find these elements is from the normal cells in the body, not the tumor DNA. Therefore, these cancer associated microsatellites exist before the tumor is formed and thus could mark a predisposition to cancer. The objective of this proposal is to attempt to determine the effect that these microsatellites may be having on the way that DNA instructions are interpreted by the cell to create proteins. Using ICGC controlled data, we will first analyze DNA sequences from each subject to count the number of repeats in each medulloblastoma associated location and then look at the RNA sequences from the nearby genes to determine if there are changes in the way it is processed.
258.Frank RosenbauerUniversity of MuensterGermany2018-10-292019-10-28
The proper function of a cell depends on the precise control of gene activation and inactivation. This process is coordinated by a large set of regulatory elements. Mutations in these regulatory elements have been implicated in the development and progression of various tumors. The ICGC datasets will be used to precisely identify these mutations across the genomes of a cohort of lymphoma patients. We aim to identify regulatory mutations that contribute to tumor development in order to find new approaches for cancer diagnosis and treatment.
259.Jeffrey RosenfeldRutgers UniversityUnited States2019-05-282019-07-12
Normal cancer sequencing for personalized medicine and the determination of eligibility for immunotherapy is based upon the sequencing of a few hundred genes in a patient's tumor. The assumption is that this data is sufficient to guide treatment decisions without looking at the full 20,000 genes in the tumor, the non-coding parts of the genome (that don't make proteins) and the genome of the patients' healthy cells. We will use the ICGC data to determine whether this assumption is correct or whether the expanded data in ICGC which includes the whole genome of both a patients' normal DNA and the tumor will improve our results.
260.Kai RothkammUniversity Medical Center Hamburg-EppendorfGermany2018-09-052019-09-04
Human cancer frequently harbours changes in the DNA. Recent studies have demonstrated that such “mutational signatures” reflect previous genotoxic insults (e.g. smoking, sun exposure) and DNA repair deficiencies, i.e. an inability of cells to fully restore damaged DNA. When cancer patients receive chemotherapy or radiotherapy, the outcome is influenced by the capacity of tumour cells to repair DNA damage. The development of reliable methods for the detection of mutational signatures representing DNA repair deficiencies is the aim of the proposed study. These methods would aid the prediction of treatment outcome in cancer patients receiving radio- and chemotherapy. They would also enable each individual patient to receive the best possible treatment, based on the vulnerabilities of their tumour. The ICGC Controlled Data will be used to establish detection methods for different DNA repair deficiencies and determine how often such vulnerabilities affect different cancer types.
261.Robert RussellHeidelberg University, BioquantGermany2018-10-172019-09-27
In this study, we plan to characterize genetic variants from ICGC patients through Mechismo (mechismo.russelllab.org/) an approach that integrates various types of biological information into computational models that predict the functional consequences of mutations. We will initially focus our analysis on known cancer genes from the Cancer Gene Census (https://cancer.sanger.ac.uk/census), and then extend it to related genes within the same biological pathway (https://reactome.org/; in collaboration with Guanming Wu, OHSU, and Licoln Stein, OICR, groups). The ultimate goal of the analysis is to identify variants, genes and/or whole biological processes that might represent critical susceptibility factors either predisposing or driving cancer.
262.Martin SchaeferEuropean Institute of OncologyItaly2019-01-102020-01-09
Tumors are composed of a multitude of different types of malignant cells, exist in a diverse microenvironment of healthy cells such as fibroblasts or immune cells and are sometimes populated by bacteria. Sequencing data contains not just genetic information of the tumor itself but also from all these different cell types. The aim of this project is to develop and apply methods to extract the information of the cellular complexity of a tumor and its environment. The raw sequencing data from ICGC will help us to better understand the cellular composition of the complex system cancer in different tissues.
263.Paul ScheetMD Anderson Cancer CenterUnited States2019-03-182020-03-18
One type of alteration in the human genome is called allelic imbalance (AI). These include duplication and deletion of different segments of the genome. Many of these AI events have been found in tumors and have been implicated in cancer development. Our lab has developed software that can detect these events in samples a with very small percentage of tumor cells - in samples with as low as 1% tumor cells for example. Given this ability, we will use ICGC data to build on the current knowledge of AI's role in cancer initiation and progression by studying the genomes of adjacent normal samples (relative to the tumor locations) of ICGC subjects. It has been shown that these adjacent normal samples are often not truly normal, but exhibit a small percentage of tumor cells. We will sensitively profile AI in these samples and attempt to determine their biological and clinical implications.
264.Joshua SchiffmanHuntsman Cancer Institute, University of UtahUnited States2019-06-142019-07-29
Ewing sarcoma is a tumor that has relatively few mutations. It is likely, then, that the development of Ewing sarcoma is driven by other mechanisms. One such mechanism is through duplications or deletions of segments of chromosomes in cancer cells. We will use the ICGC Controlled Data from Ewing sarcoma tumors to determine which segments of chromosomes in Ewing sarcoma have been duplicated or deleted. We will combine that data with clinical outcome data, to determine which of these duplications or deletions are the greatest impact on patient survival. We hope to understand these mechanisms so that better treatments can be developed.
265.Ulrich SchuellerResearch Institute Children's Cancer Center HamburgGermany2019-02-052020-02-05
Pediatric brain tumors mainly result from pathological alterations during brain development. While some of these alterations seem to be unique, others are frequently found in different types of brain tumors. Some of these recurrent mutations affect genes which not only seem to play important parts in the context of cancer development, but are also found to be mutated in different severe neurodevelopmental disorders. With the help of the data from the International Cancer Genome Consortium (ICGC) we can further track down and elucidate the connection between mutations in these genes, their resulting expression profiles and tumor development, in order to identify potential therapeutic strategies and risk factors for pediatric brain tumors.
266.Nikolaus SchultzSloan Kettering Institute for Cancer ResearchUnited States2018-08-312019-08-30
Tumor samples usually contain many gene mutations. However, typically only a small fraction of these promote tumor growth (they are often referred to as "drivers"), while the majority are neutral ("passengers"). Identifying the functional mutations (drivers) in a tumor sample remains a challenge. We have recently developed two novel methods for the identification of functional mutations, based on recurrence in large sets of cancer samples in specific individual amino acids (linear hotspots) or in amino acids with close proximity in a protein structure (3-D hotspots). We plan to apply these methods to data from the ICGC.
267.Benjamin Schuster-BoecklerUniversity of OxfordUnited Kingdom2018-11-292019-11-28
Changes to the genetic material that happen during our lifetime can lead to cancer. These so-called "mutations" occur spontaneously, but many different environmental influences and life-style choices can make mutations more likely. By using the ICGC controlled data, the goal of our research is to find out more about the mechanisms by which lifestyle and the environment influence the appearance of mutations, and thereby the likelihood of developing cancer.
268.Russell SchwartzCarnegie Mellon UniversityUnited States2018-12-192019-12-17
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.
269.Roland SchwarzMax Delbrück Center for Molecular MedicineGermany2018-10-292019-10-28
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
270.Colin SempleInstitute of Genetics and Molecular Medicine, University of EdinburghUnited Kingdom2019-03-142020-03-12
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.
271.Cathal SeoigheNational University of Ireland, GalwayIreland2019-02-272020-02-26
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.
272.Ruty ShaiSheba Medical CenterIsrael2018-11-162019-11-15
The central question addressed by the present study is the role of certain genetic regulatory factors in the prognosis of a type of cancer called medulloblastoma, which is the most common malignant brain tumor in children. Using data from the ICGC, we hope to highlight factors that can be used to predict tumor aggressiveness and that can be targeted in the treatment of medulloblastoma.
273.Tatsuhiro ShibataNational Cancer CenterJapan2018-10-012019-09-30
Recent studies reported that the total number of mutations was associated with clinical response to immune checkpoint inhibitors in melanoma (skin care), lung cancer and others. The non-self antigens ("from the external environment") produced by somatic mutations (acquired genetic alterations) are called neo-antigen. Therefore, the landscape of neo-antigen in individual patient is expected to contribute to the personalized immunotherapies. However, several previous studies reported more complex association between neo-antigen and anti-tumor immune responses. To better understand the biological significance of neo-antigens in immunological features of tumor, we will perform comprehensive analyses using various sequencing techniques and ICGC data. We will identify neo-antigen, and then investigate the critical factors that contribute to differences in immune system within each tumor type. Through this study, we attempt to uncover clinically useful biomarkers (indicators we use to examine biological processes) for cancer immunotherapy.
274.Tatsuhiro ShibataNational Cancer Center JapanJapan2019-06-172020-06-17
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.
275.Hyun-Tae ShinVeterans Medical Research InstituteSouth Korea2019-04-232020-04-19
Structure variations (SVs) are large-scale genomic changes and cause breaking and linking of genes and regulatory regions. This has been proven to play a significant role in cancer development and detection of SVs is important in clinical decision making for cancer therapy. The object of our research is to develop software that is accurate enough to be used in clinical practice. We will use the ICGC data to verify the performance of the software.
276.Adam ShlienThe Hospital for Sick ChildrenCanada2019-02-112020-02-10
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.
277.Adam ShlienThe Hospital for Sick ChildrenCanada2019-01-252020-01-24
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.
278.Israel SilvaA. C. Camargo Cancer CenterBrazil2019-06-022019-07-17
Certain viruses are detected in cancer. One of these viruses, named Epstein-Barr virus (EBV), is present in a small set of gastric adenocarcinomas (~10%). Whether EBV infection leads to DNA damage in gastric carcinogenesis, and if so by which mechanism, is a subject of ongoing investigation. Using ICGC controlled data, aim to develop an integrative analysis to identify DNA changes specifically related to DNA-modifying enzymes. Therefore, understanding the role of these enzymes on the immune response against EBV infection may provide us with an understanding of how EBV, and other virus infections, lead to overall mutations that lead to gastric cancer.
279.Israel SilvaA. C. Camargo Cancer CenterBrazil2019-06-022019-07-17
Certain viruses are detected in cancer. One of these viruses, named Epstein-Barr virus (EBV), is present in a small set of gastric adenocarcinomas (~10%). Whether EBV infection leads to DNA damage in gastric carcinogenesis, and if so by which mechanism, is a subject of ongoing investigation. Using ICGC controlled data, aim to develop an integrative analysis to identify DNA changes specifically related to DNA-modifying enzymes. Therefore, understanding the role of these enzymes on the immune response against EBV infection may provide us with an understanding of how EBV, and other virus infections, lead to overall mutations that lead to gastric cancer.
280.Sigrid SkanlandUniversity of OsloNorway2019-05-272019-07-11
Chronic lymphocytic leukemia (CLL) is a common malignancy of immune cells which covers 40% of all leukemia cases in the Western world. The disease is initially slow-growing and a “watch and wait” approach is recommended for patients without symptomatic disease. Traditionally, frontline chemoimmune therapy has been the conventional choice. However, over the past three years, novel therapeutic possibilities have revolutionized CLL management and continuously demand a better understanding of the highly heterogeneous features of the disease so that maximum patient benefit can be obtained. We wish to analyze ICGC controlled transcriptional data from CLL patients in order to identify alterations which can indicate drug or drug combinations best suitable for the individual patient. The ultimate goal of the project is to assist clinical decisions in individualized cancer therapy.
281.Alona SosinskyGenomics EnglandUnited Kingdom2018-10-092019-10-08
The 100 000 Genomes Project aims to improve clinical care for cancer patients through personalised medicine. To date, thousands of patients have received reports based on analyses of their tumour genetics that could help doctors make diagnoses, pick therapies and enrol them in relevant clinical trials. Tumour genomes contain "germline" variants that are inherited from parents as well as "somatic" mutations that arise during the patient's own life. In order to uncover these variants, we are routinely collecting tumour and blood samples for each patient. We then apply computational algorithms that analyse this data and return a small subset of variants that may be driving tumour development. Blood cancers can be more challenging to analyse because they often contaminate the tissues we use as reference for the patient's normal genome. We are developing a new approach to overcome this obstacle and will test it on well-studied genomes from ICGC projects.
282.Adam SowalskyNational Cancer InstituteUnited States2019-04-152020-04-15
Our goal is to use the genomic data deposited in these controlled-access databases for learning why some cancers are more aggressive than others. When we examine mutations to DNA and why certain genes turn up or down, our goal is also to understand why some prostate cancers become drug resistant. Overall, our goal is to use this data for research aimed ultimately at reducing the pain and suffering associated with diagnosis and death from prostate cancer.
283.Paul SpellmanOregon Health and Science UniversityUnited States2018-11-272019-11-26
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
284.Lincoln Stein Ontario Institute for Cancer ResearchCanada2018-10-262019-10-25
Cancer can result from changes in a person's genetic material (DNA). By studying genetic changes, researchers can learn what causes cancer. This will lead to new ways to prevent, detect and treat cancer. The International Cancer Genome Consortium (ICGC) was created to coordinate a large number of research projects. The ICGC will develop a comprehensive catalogue of genetic changes that occur in cancer. These will be benchmarked against other cancer types to ensure data is of the highest quality. As a contributing member of the ICGC, the Ontario Institute for Cancer Research will generate a comprehensive catalogue of genomic abnormalities found in pancreatic and prostate tumours. Our target is to collect and study 500 independent tumours of each type and their matched controls. The ICGC collaboration will allow members worldwide to advance cancer research through analysis of a large number of genomes from multiple cancer types.
285.Lincoln SteinOntario Institute for Cancer ResearchCanada2019-01-212019-12-17
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.
286.Marc-Henri SternInstitut CurieFrance2019-05-282019-07-12
Cancer is often linked with acquired abnormalities of the tumor genome, such as mutations, gains and losses of parts, and other aberrant structures. Some tumors are characterized by an increased rate for such abnormalities, a process named genomic instability. Our research project is devoted to unravelling the origins of genomic instabilities in cancers. Our approach consists in the systematic analysis of cancer genome architecture with relation to the genes altered in various types of cancer. By analyzing ICGC controlled data, we aim at deciphering associations and functional links between gene alterations and the genomic instability patterns. Taking into consideration genomic instability could improve tumor molecular classifications, prognosis and prediction of response to treatment.
287.David SturgillNational Institute of Health (NIH), Bethesda, Maryland, USAUnited States2019-01-182020-01-18
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.
288.Mikita SuyamaKyushu UniversityJapan2019-06-062020-06-02
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.
289.YUTAKA SUZUKIUniversity of TokyoJapan2019-01-092020-01-09
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 previous sequencers, in order to validate the accuracy and the coverage of the new approach. 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.
290.Fredrik SwartlingUppsala UniversitySweden2018-10-162019-10-15
We are generating various models of childhood brain tumors in mice and have performed global analysis of the activity levels (expression) of genes in tumors from these models. We focus on cancer pathways that are often upregulated in the most aggressive subtypes of the most common malignant childhood brain tumor, medulloblastoma. Now we would like to compare our data with similar data obtained from human samples in ICGC Controlled Data. Comparing our samples with newly generated ICGC data of medulloblastoma would give us much better accuracy as compared to analysis performed using older published data of medulloblastoma publically available elsewhere. The goal of our studies is to generate clinically relevant models that closely resemble the tumors found in childhood brain tumor patients. The models can then be used to test new promising therapies for patients affected with these devastating diseases.
291.Gergely SzollosiEotvos Lorand University BudapestHungary2019-04-032020-04-01
Cancer is a genetic disease fueled by evolution within subsequent generations of cells. Despite advances in the molecular biology of cancer-associated genes, our understanding of the mechanisms that lead to cancer is limited. Cancer death rates have changed little in the last few decades. To address these problems, a new field called "physics of cancer" has emerged. Most tissues have a hierarchical structure, with cell types ranging from stem cells, through more specialized cells, to fully specialized cells. Our aim is to understand the breakdown of the hierarchical organization of healthy tissues and the emergence of tumors, using evolutionary models, computer simulations, and sequence analysis. Using the data provided by the ICGC, we will evaluate how individual tumors evolve and branch into distinct lineages.
292.Atsushi TakaiKyoto UniversityJapan2019-02-222020-02-19
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.
293.Keiichi TamaiMiyagi Cancer Center Research InstituteJapan2019-03-122020-03-12
Our goal in this project is to elucidate the therapeutic targets of cancer, especially head and neck cancer as well as lung cancer. These cancers are relatively common in world wide, but little is known about the critical target for effective therapy. Using ICGC data, we plan to use information about genome-wide somatic mutations and RNA expression to identify the targets.
294.Mohamed TawhidThompson Rivers UniversityCanada2018-09-212019-09-20
One of the main reasons of death is cancer. Unfortunately, most of the cases diagnoses of cancer happen at the late stage, it appears late to heal which increases the number of people who die because of cancer. If the diagnose happens in its early stage, then there is a possibility to increase the curing of the suffered peoples. There is a desire and need to deal with cancer in its early stage. Our research aims to apply image segmentation and data mining approaches to predict and detect cancer in its early stage when the input data is in the form of images. Using ICGC datasets, we analyze various existing image processing techniques used for early detection and prediction of cancer. Also, we develop computational models via Predictive modeling in which we can analyze massive amounts of data in order to predict healthcare outcomes for individual patients.
295.Martin TaylorThe University of EdinburghUnited Kingdom2019-01-242020-01-23
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.
296.Michael TaylorThe Hospital for Sick ChildrenCanada2019-02-202020-02-19
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.
297.Soo-Hwang TeoCancer Research MalaysiaMalaysia2018-09-282019-09-27
Asian women have previously had a lower risk of breast cancer, but this is rapidly changing because of changing lifestyle choices. To date, although Asians have a different genetic makeup from Caucasians, this information is not used in how we screen for and treat breast cancer in Asia. We found that a genetic variant in the gene-editing protein APOBEC3B is present in 50% of Asians and 10% of Caucasians. Interestingly, this variant is associated with an increased risk to cancer, and the cancers which develop have a different immune profile. We are currently conducting a detailed analysis of Asian breast cancers. We plan to use data generated by ICGC to validate our methodology and as an external Caucasian dataset against which we can compare our results, in the hopes of providing definitive evidence on whether breast cancers in Asian women are similar to breast cancers in Caucasian women.
298.Amanda TolandThe Ohio State UniversityUnited States2018-08-172019-08-16
The frequency of mutations in genes driving tumor growth for cancers such as breast and colon differs between ethnic and racial groups and is known to affect tumor aggressiveness and prognosis. It is well established that ethnicity and race are factors that impact cancer rates and outcomes. Some of the cancer disparities between ethnic and racial groups are due to socioeconomic and environmental risk exposures (i.e. smoking, poor diet, high stress living conditions, place of residence, access to health care), but studies also suggest that even when these are adjusted for differences remain that are likely to reflect biological diversity. This study will test whether inherited variations, that differ in frequency between racial groups, influence the rate of TP53 and PIK3CA mutations in breast cancer. We will use ICGC breast cancer datasets that have both mutation data and genetic variation data.
299.David TorrentsBarcelona Supercomputing CenterSpain2019-06-252019-07-04
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
300.David TorrentsBarcelona Supercomputing CenterSpain2018-11-132019-11-13
This is a research project to determine the impact of how genomic analysis can be used for making oncology decisions and personalize treatments. The project aims to identify the specific genomic variants in a known location on a chromosome that can provide an increased knowledge of the genes that could be involved in the development and progression of the disease. By using dataset of tumour-normal sample pairs from chronic lymphocytic leukaemia (type of blood and bone marrow cancer) and medulloblastoma (cancerous brain tumor) (Alioto et al 2015), we will be able to calibrate the different programs by means of the comparison between their results, and the results verified in the ICGC dataset. That way will be possible to achieve the highest specificity and sensitivity for detecting the alterations that occur along the life in an individual. All this work will allow us to further our knowledge on the variants effects.
301.David TorrentsBSC-IRB Research Programme in Computational BiologySpain2019-06-072020-06-07
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.
302.Olga TroyanskayaPrinceton UniversityUnited States2019-06-122020-05-07
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.
303.Olga TroyanskayaSimons FoundationUnited States2019-06-212020-06-21
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.
304.Cord UphoffLeibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbHGermany2019-05-202019-07-04
Growth and survival of tumor cells rely on continuous activation of cell signaling pathways. Mutations of signal transduction genes have been described in different forms of Non-Hodgkin lymphomas. Novel drugs for individualized therapies are being developed targeting mutant proteins and leading the malignant cells to apoptosis, i.e. inducing cell death. Involving a process called “alternative splicing” genes can be translated into proteins of different sizes. Novel studies have shown that signaling genes/proteins (e.g. STAT3) can be affected by alternative splicing and that the proteins generated can have different cellular functions. Our preliminary experiments show that genes in signaling pathways other than the JAK/STAT pathway can be targets of alternative splicing. We are applying to get access to ICGC data to find out whether these results can be confirmed for patients. Inhibition of the affected pathway may then turn out to provide novel treatment options for a subgroup of lymphoma patients.
305.Eliezer Van AllenDana-Farber Cancer InstituteUnited States2019-06-212020-06-20
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.
306.Marc van de VijverAMCNetherlands2019-06-192019-07-19
Breast tumors consist of a mixture of cancer cells and non-cancer cells. For example, cells belonging to the immune system can infiltrate the tumor and communicate with the cancer cells. This may lead to an alteration in the tumor and potentially have an effect on clinical outcome in the patient. The project aims to understand patterns of interaction between the cancer cells and immune cells and look for associations in the genome of the tumor. Genomic data of ICGC of invasive breast cancer will be used alongside new information of the immune infiltrate in these tumors. We will evaluate the association and relationship between several immune variables and the available biological and clinical characteristics of these breast cancers.
307.Ryan Van LaarGeneseq BiosciencesAustralia2018-08-282019-08-28
Despite public health efforts, the mortality rate of melanoma has steadily increased over the past 50 years. Geneseq Biosciences has developed and validated a genetic signature of melanoma that can be measured in skin tissue and/or blood. ICGC data will be used to further explore the clinical utility of our genomic signature for melanoma to increase or understanding of how the tumor changes from early to late stage disease. Our research may lead to new tools to reduce the over and under-diagnosis of melanoma, as well as providing doctors a new tool to personalise treatment options.
308.Peter Van LooFrancis Crick InstituteUnited Kingdom2019-05-022020-04-26
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
309.Ashok VenkitaramanUniversity Of CambridgeUnited Kingdom2019-06-182019-08-02
Cancer cells differ from normal cells because they exhibit abnormal behaviours like uncontrolled growth. Cancer cells evolve these abnormal behaviours by rapidly changing the information encoded in their genomes. The propensity for such rapid change is known as genomic instability, but how it occurs is not known. In this study, we wish to identify factors that are associated with genome instability in different types of cancer. We will study DNA and RNA sequencing information from multiple cancers, including ICGC controlled data, to attempt to identify recurrent characteristics that are associated with genomic instability. We hope through this work to better understand the development of cancer, in order to find new approaches to detect and treat the disease.
310.Roel VerhaakJackson Laboratory for Genomic MedicineUnited States2019-01-042020-01-03
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
311.Mauno VihinenLund UniversitySweden2018-10-102019-10-09
The goal of all medicine is to treat individually each patient. This goal is difficult to achieve. But the facility to incorporate a patient’s genetic information into the picture provided by traditional signs, symptoms, personal and family history, imaging and other laboratory studies will for the first time allow medicine to become truly personalized. Based on the information provided by ICGC controlled data, this project aims at contributing to personalized medicine in cancers by developing novel accurate methods for stratification of cancer cases for clinical and research applications. Since cancer is unique for each patient, it is important to find subgroups of patients who can benefit of different therapeutic options available.
312.Robert VonderheideUniversity of PennsylvaniaUnited States2019-04-232020-04-21
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.
313.Claes WadeliusUppsala UniversitySweden2019-01-292020-01-28
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.
314.Junbai WangOslo University HospitalNorway2019-05-012020-04-29
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.
315.Jinhua WangUniversity of MinnesotaUnited States2019-06-112019-07-26
Understanding the functional impact of DNA mutations will help the research community to find better treatment for cancer. Mutations deposited in the ICGC controlled dataset will be used to help us build better computational predictive models for functional annotation of DNA changes in cancer patients.
316.Wyeth WassermanThe University of British ColumbiaCanada2018-09-132019-09-12
Rapidly-developed technologies are making it possible to routinely measure the entire DNA sequence, the genome, of cancer patients. By developing new analysis methods, we now have an opportunity to investigate beyond the 2% of the DNA that contains genes, and begin closing the discovery gap in the remaining 98% of the genome. We aim to deliver bioinformatics tools for analyzing changes in the DNA of cancer patients. Using the new tools, we will investigate how on/off switches in the DNA control the activity of genes in individual cancer patients and across different cancers, allowing us to form a picture about which changes cause illness. The tools will be developed, tested and benchmarked on different datasets of whole genomes, including cancer genomes that are part of the ICGC Controlled Data.
317.Ian WatsonMcGill UniversityCanada2019-03-222020-03-20
Our research project aims to understand how mutated genes identified in melanoma sequencing data promote disease progression in order to develop novel therapeutic strategies to treat metastatic disease. Our primary objective is to identify significantly mutated genes that promote cancer progression. This task is challenging due to the fact that, in comparison with other cancers, melanoma has a high number of mutations caused by UV exposure. We will use ICGC data to better characterize this mutational process and develop algorithms that discriminate between driver mutations (those that contribute to disease progression) and passenger mutations (those that do not affect disease progression). Our second objective is to determine the mechanisms of action of these mutated genes by performing integrative analyses with clinical data.
318.Rebecca WattersUniversity of PittsburghUnited States2019-01-242020-01-23
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.
319.David WedgeUniversity of OxfordUnited Kingdom2018-10-312019-10-30
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
320.David WedgeUniversity of OxfordUnited Kingdom2019-01-242020-01-24
This project is about providing breakthrough advances through analysis of a very large series of Whole Genome DNA data from prostate cancer contributed by many of the leading scientists and clinicians working in prostate cancer genomics. The project will be able to address a number of scientific themes using ICGC Controlled Data. These include the analysis of ethnic differences in genetic alterations, and the interaction between inherited alterations and those that appear in cancer. We will also analyse the patterns and types of mutation that occur in prostate cancer. We aim to identify mutations in non-coding DNA associated with prostate cancer. Finally, we aim to find clinically relevant subgroups of prostate cancer.
321.Joachim WeischenfeldtRigshospitaletDenmark2019-04-152020-04-13
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.
322.Bart WestermanVU university medical centerNetherlands2018-12-192019-12-18
Differences between tumor cells on the DNA level is seen as a major cause of resistance to therapy. This resistance can be caused by the outgrowth of cells with DNA mutations that were already present in the tumor at the time of diagnosis or by new DNA mutations. We have generated a prediction model to identify heterogeneous mutations and want to investigate whether we can confirm these predictions independently based on data in the ICGC database.
323.Nicola WhiffinImperial College LondonUnited Kingdom2018-12-102019-12-10
Identifying the genetic cause of a disease can be used for diagnosis, to identify at-risk family members, for pre-natal screening and to dictate clinical treatments. For many diseases, clinical genetic testing has therefore become widespread. However, for most diseases, we only find a suspected disease-causing variant in ~30% of suspected genetic cases. Most research has focused almost exclusively on regions of the genome that are known to code directly for protein, where predicting the effect of variants is relatively straightforward. This leaves about 98% of the genome un-studied. We know that much of this remaining sequence is involved in regulating the levels of proteins and that variants that perturb this regulation could be involved in disease. Here, we will identify small sub-classes of these un-studied variants, with predicted regulatory effects and will use the ICGC Controlled Data to test for their involvement in cancer.
324.Daniel WilliamsonNewcastle UniversityUnited Kingdom2018-09-102019-09-09
Medulloblastoma is an aggressive brain tumor, it’s the most common brain tumor in children. We want to investigate the molecular “story” behind Medulloblastomas, the genetic code of a cell gives us clues as to how the disease works and how severe it might be. In various studies different groups of samples have been investigated using differing methods. We aim to consistently analyze many Medulloblastoma samples including data from the ICGC and elsewhere using up to date variant calling and structural variation software. We plan to use these observations to better understand which patients have more aggressive forms of the disease, different subgroups have different clinical outcomes, and patterns of mutations can indicate which subgroup a patient has. In addition to knowing the subgroup, individual mutations may also indicate disease progression. We will use our findings to develop more accurate biomarker based test to better inform treatment based on patient prognosis.
325.Gane WongUniversity of AlbertaCanada2019-06-102019-07-25
We are interested in the molecular mechanisms by which cancer cells evade chemotherapy and eventually become resistant to treatment. Our work on breast cancer patients has identified a novel mechanism whereby tumours exposed to chemotherapy acquire resistance by mutating the genes targeted by chemotherapy (i.e. the tubulin gene family) in many different ways that all produce much the same effect on the 3-dimensional shape of the proteins encoded by these genes. We want to confirm this result in other cancers, e.g. ovarian, through access to the tutor sequences available from the ICGC website.
326.Swee Seong WongLifeomicUnited States2019-03-222020-03-22
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.
327.Kui WuBeijing Genomics Institute-ShenZhenChina2018-08-202019-08-19
Endocrine tumours are relatively rare malignancies that are heterogeneous in terms of location, symptoms and response to treatment. This project aims to compare endocrine tumor subtypes systematically to investigate their tumor initiation mechanisms and identify genetic features that can be used in future clinical molecular diagnosis. We will combine controlled ICGC data with other data that we have collated for the analysis of pattern of mutations, structural variations and molecular subtype classification.
328.Ruibin XiPeking UniversityChina2019-03-142020-02-13
All cancers are results of DNA mutations and hence the study of mutations in tumor genomes can provide tremendous help in finding treatments of cancer. The ICGC data profiled DNA information of thousands of tumor genomes. Comprehensive analysis of these data can help us to identity critical mutations in tumor genomes. In this project, we will develop a series of tools that can efficiently and accurately analyze the DNA data. We will apply these tools to the ICGC data and look for new mutations that are important for tumor development.
329.Zhenyu XuSophia GeneticsSwitzerland2019-02-202020-02-18
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.
330.Ying XuUniversity of GeorgiaUnited States2018-08-222019-08-21
One of the complexities of studying cancer is its large variability across individuals. However, a hallmark of cancer tissue cells is that they all have lower intracellular pH and higher extracellular pH compared to normal human cells. Our research aims to systemically explore multi-level changes during cancer cell division, and gain insight into how this reversed pH may be at the root of cancer development. ICGC controlled data, a collection that provides cancer-related data from different countries, will be used to improve our current model and potentially explain cancer at a more fundamental level.
331.Lixing YangUniversity of ChicagoUnited States2019-05-152020-05-03
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
332.Patricio YankilevichIBioBAArgentina2019-06-062020-06-04
The Genetic Risk of an individual, which is the probability of carrying a specific disease associated to genetic mutations, can be assessed in different ways. The current development consider a Genetic Risk Assessment system based on machine learning models built from genetic datasets of the ICGC Data Portal. The objective is to develop classifiers for genetic profiles associated with cancer patients in order to help and support local medical doctors. The developed model will be able to assess risk of individuals depending on the classification between positive cancer diagnosis and healthy controls. The same approach will be used to discriminate tumor subtypes within a primary site.
333.Kai YeSchool of the Electronic and Information Engineering, Xi’an Jiaotong UniversityChina2019-06-182019-07-30
The identification of DNA mutations causing various forms of cancer would shed light on disease progression and provide tremendous help in identifying personalized treatment strategies. In this study, we will develop a new methodology to discover various mutations in highly repetitive regions of the genome. This, along with other available tools on the market would provide us with a comprehensive catalog of DNA mutations. The ICGC controlled data includes numerous tumor DNA sequences in both protein coding and non-coding regions. We will apply our new methodology to all ICGC data and search for novel DNA mutations that are potentially vital for disease diagnose or treatment selection.
334.Iwei YehUniversity of California, San FranciscoUnited States2018-09-102019-09-09
Our project seeks to better characterize melanomas that arise at sun-protected sites. These melanomas have distinct features as compared to melanomas from sun-exposed skin. Melanoma occurs when a normal cell sustains damage to its DNA or genetic material which leads to abnormal function. Some of the DNA changes can be opposed by specific therapies. For this reason, analyzing the DNA of sun-protected melanomas may aid in treating patients. However, completely analyzing the DNA of a cancer is very expensive. The changes that arise in sun-protected melanomas are different that those in sun-exposed melanomas. We will use the ICGC data to determine which regions of DNA are most high yield to test in sun-protected melanomas. We will also attempt to identify additional treatable changes in these deadly tumors.
335.Bauke YlstraVU university medical centerNetherlands2018-10-092019-10-08
Recent research has revealed massive differences in the number and organization of chromosomes between healthy tissue and cancer cells. This has led to novel insights into how these changes occur, like the identification of chromothripsis, a process where chromosomes are shattered and stitched back together in a different order. We identified a new phenomenon where certain stretches of DNA are present in extraordinarily large numbers and called it chromorexis. This phenomenon was associated with poor prognosis, suggesting a possible role in cancer formation and progression. We will use the ICGC controlled data to identify how frequently this phenomenon occurs, what the underlying mechanism is and whether there is indeed a relationship with prognosis.
336.Sung-Soo YoonSeoul National UniversitySouth Korea2019-01-042020-01-03
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.
337.Sung-Soo YoonSeoul National University HospitalSouth Korea2019-06-242019-08-08
Mutations in the FLT3 gene drive the development of cancer. Variations in single base pairs in the DNA sequence of this gene are not clearly understood. This study aims to investigate the influence of these variations using the data deposited in the ICGC and the Korean acute myeloid leukemia cohorts.
338.Jiri ZavadilInternational Agency for Research on Cancer (IARC)France2019-03-072020-03-05
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.
339.Qimin ZhanPeking UniversityChina2019-03-292020-03-29
Gastroesophageal Junction (GEJ) Carcinoma, a rare type of esophagus, has various molecular characterizations in diverse populations. To elucidate the underlying genetic changes, we will perform a comprehensive and comparative analysis between Asian and Western populations based on genomic and clinical data (including ICGC controlled data) donated from ESAD-UK project and Chinese ethnics which potentially leads to better diagnosis and treatment of GEJ Carcinoma in Clinical Practice.
340.Yan ZhangThe Ohio State UniversityUnited States2019-02-282020-02-27
Structural variations (SVs) are genetic mutations that affect large regions of DNA in the genome (at least 50bp). Such changes on the genome have been known to cause numerous diseases, and also contribute the formation and invasion of cancer tissues. The Pancancer Analysis of Whole Genomes (PCAWG) study is generating a comprehensive dataset of various SV types in cancer samples. However, there is still a gap between the genetic mutations and their functional impact. Thus we propose to develop computational and statistical tools to make the linkage, and apply our tools to the SV set and sequencing data generated by PCAWG and ICGC. From our study, we expect to reveal the distribution of these genetic mutations on the genome, and infer their potentially functional impact. These computational predictions will be further studied experimentally.
341.Zemin ZhangPeking UniversityChina2018-10-292019-10-28
Cancer generation is an evolution process during which the genome of cancer cells accumulate large number of alteration. Some key functional alterations drive this process. We will analyze the ICGC data, which contains thousands of cancer samples across multiple cancer types, identify and characterize key functional alterations. These functional alterations' characterization will improve our understanding of cancer and also provide candidate drug targets for therapy.
342.Jinghui ZhangSt. Jude Children's Research HospitalUnited States2019-06-042019-07-19
Cancer chemotherapy treatments can cause changes (mutations) in DNA, which may lead cancer to become resistant to treatment. We recently found that chemotherapies used to treat childhood leukemia may cause these types of mutations, as determined by analyzing the "fingerprint" (signatures) of mutations in treated leukemia samples. However, leukemia patients receive many types of treatment, and it's unclear which chemotherapies are the culprit. Using ICGC controlled data, we will test whether the mutation fingerprints observed in childhood leukemia are also found in adult cancers, which may help narrow down which chemotherapies are causing these mutations in leukemia, and potentially determine whether the same thing also happens in adult cancers. Additionally, we identified deletions in DNA sequences upstream of adult breast cancer related genes in pediatric cancer patients. We wish to explore whether ICGC controlled data also contains such deletions. This may help us improve detection of breast cancer susceptible individuals.
343.Bin ZhuNational Cancer InstituteUnited States2018-09-142019-09-13
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.
344.Aviad ZickHebrew University-Hadassah Medical CenterIsrael2018-10-012019-09-30
Cancer treatment can be guided by understanding which genetic mutations are present in the tumor. One type of mutation associated with cancer involves the presence of amplified numbers of certain genes. For instance, extra copies of the ERBB2 gene is a sign indicating aggressive breast tumors that can also predict which drugs are likely to produce a benefit. These mutations can be detected using standard tests, which allow us to classify different types of cancer based on their genetic characteristics. Using ICGC data from tumors with different kinds of amplifications, our goal is to create a sub-grouping of tumors based ERBB2 gene characteristics such as type, size and number. If this project identifies new correlations between genetic characteristics and clinical outcomes, it may contribute to health care by indicating the basis for new types of cancer treatment.
345.Oliver ZillGenentechUnited States2019-03-142020-03-12
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.
346.Lihua Zounorthwestern universityUnited States2018-12-072019-12-06
This is an international research project among the researchers of the International Cancer Genome Consortium (ICGC) and its regional components, including the The Cancer Genome Atlas (TCGA). The project aims to understand common and distinguishing patterns of variation among a diverse set of cancer types. We will first perform uniform computational processing of the whole genome sequencing data from the tumors and normal control DNA of more than 2000 donors. This will eliminate differences that are due to different ways of analyzing the data. In collaboration with other researchers in the Pan-Cancer Analysis of Whole Genomes: PAWG project we will then address research questions relating to the types and subtypes of cancer, patterns of mutation in genes and their regulatory regions, large-scale structural changes in the genome, and the evolution of cancer cells, among others. These questions will be related to clinical outcomes among the tissue donors to this project.