DACO Approved Projects

Principal InvestigatorPrimary AffiliationCountryDate Approved for AccessTitle of ProjectLay Summary
1.Stephen FriendSage BionetworksUnited States2013-08-02Network Modeling of CancerCancer 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 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 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.
2.Rune LindingTechnical University of DenmarkDenmark2013-09-20Network Analysis of Cancer KinomesCell signaling networks are the foundation of cell fate and behavior and their aberrant activity is a key mechanism underlying the pathological behavior of cells during tumor development. However, signaling networks are highly complex, involving a large ensemble of dynamic interactions that flux in space and time. Thus, to understand how aberrant cell decisions arise requires a global view of cell signaling networks. My lab have developed powerful computational tools (e.g. NetworKIN and NetPhorest) that can model cellular signaling networks. We have previously deployed these to model DNA damage, cell fate, cell-cell communication, signaling evolution and to compare model organisms. Our next challenge is to model signaling networks during disease progression. To achieve this goal, we are developing new computational tools to predict the impact of cancer mutations on signaling networks and thereby model diseased networks.
3.Mark GersteinYale UniversityUnited States2014-02-26Dysregulation of Regulatory Networks in CancerWe would like to investigate changes to the genome that occur in cancer, and how those changes affect the coordinated activity of genes (gene networks and pathways), as well as the functioning of pathways important to the healthy operation of a human cell. We hope this will reveal insights into how cancer cells function differently from healthy cells, and help us better classify cancer types and subtypes.
4.Richard GibbsBaylor College Of MedicineUnited States2014-03-19ICGC-TCGA DREAM Mutation Calling ChallengeWhole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
5.Dr. Steven J JonesBC Cancer Agency - Michael Smith Genome Sciences CentreCanada2013-11-28Comparison of the somatic mutational spectrum of medulloblastomas with other cancer typesWe are evaluating the relevance of changes found in the genetic make up of pediatric brain cancer patients by comparing these changes to information available from other cancer types. This cross comparison will potentially enable the identification of disease specific, novel or existing approaches to treatment.
6.Ming TsaoUniversity Health NetworkCanada2013-07-08International Cancer Genome Consortium (ICGC): Pancreatic Cancer ProejctCancer can result from changes in a person's genetic material (DNA). By studying the 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 the genetic changes in cancer. As a contributing member of the ICGC, the OICR will generate a comprehensive catalogue of genomic abnormalities found in pancreatic tumours. The target is to collect the requisite 500 independent tumours and their matched controls and fully characterize 350 of these. The main goal of the ICGC collaboration (OICR, University Health Network, University of Toronto) will allow members from sites world wide to advance cancer research by analysis of a large number of genomes through the comparison of genes involved in multiple cancer types.
7.Sachdev SidhuUniversity of TorontoCanada2013-09-13International Cancer Genome Consortium (ICGC): Pancreatic Cancer Project http://www.icgc.org/Cancer can result from changes in a person's genetic material (DNA). By studying the 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 the genetic changes in cancer. As a contributing member of the ICGC, the OICR will generate a comprehensive catalogue of genomic abnormalities found in pancreatic tumours. The target is to collect the requisite 500 independent tumours and their matched controls and fully characterize 350 of these. The main goal of the ICGC collaboration (OICR, University Hospital Network, University of Toronto) will allow members from sites world wide to advance cancer research by analysis of a large number of genomes through the comparison of genes involved in multiple cancer types.
8.Dan RhodesCompendia Biosciences, Inc.United States2013-12-05Integrative analysis of cancer genomesWe aim to study the relationships among different genetic events in cancer. It is our goal to establish an improved molecular sub-classification of cancer that could be useful in cancer diagnosis and treatment.
9.Bodo LangeAlacris TheranosticsGermany2013-07-19Systems Biology of CancerCancer is a complex disease involving many different changes in the genome of the tumor cell. These differences cause tumors with the same pathological classification to respond very differently to the drugs, making therapy decisions difficult. In addition, different patients will show differences in they way they react to a drug, due to differences in their genome. In this project we would like to use the genome/transcriptome information of different tumors/patients to develop individual models, using ModCell, a modelling system available at Alacris Theranostics to predict the effect (and side effects) of individual drugs or drug combinations. This would identify groups of patients which could respond to a particular treatment, and help to improve cancer diagnosis and treatment.
10.Bernard FoxProvidence Cancer Center, Earle A. Chiles Research InstituteUnited States2014-02-25Analysis of Genomic Prostate Cancer Profiles from the International Cancer Genome Consortium for Correlations with Patient’s Phenotypes in a Prostate Cancer Immunotherapy Research StudyComparison of matched normal and prostate cancer genomes has given insights to genomic variations which we propose to examine for correlations with protein, gene expression, and clinical data in our prostate cancer immunotherapy research participants.
11.Albino BacollaThe University of Texas at AustinUnited States2013-07-31NON-B DNA STRUCTURES AND CANCER GENOMESCancer is caused by mutations in the DNA, which may arise as a result of exposure to carcinogens, pollutants, sunlight, smoking habits, inherited variations in the DNA and/or a compromised ability of cells to repair DNA lesions. DNA comprises long sequences of chemical bases. Although it is known that certain combinations of bases are more susceptible to mutation than others, the underlying mechanisms are not fully understood. We think that in addition to the well-known shape of DNA as a double-helix, other shapes also form, such as 4-helix shapes, which render DNA bases more vulnerable to mutation. Similarly, certain bases may be intrinsically more susceptible to chemical attack than others because of their neighboring bases. By collecting large amounts of cancer mutation data we can identify which bases are most easily mutated. By modeling the shapes of DNA, we aim at finding the reasons for mutations that lead to cancer.
12.Mamatha Shekar (Mahadevappa)IlluminaUnited States2014-04-01Integration and analysis of cancer genomic dataWe have developed an online database that integrates a large number of diverse biological datasets to build models of different cancer types. We will use the datasets from ICGC to complement the current models providing researchers with a comprehensive list of genomic factors that may underlie different cancer types and various clinical phases within a given cancer.
13.Dongwan HongNational Cancer CenterSouth Korea2014-04-15An accurate approach to identifying somatic mutations from cancer genome sequencingRecently, next generation sequencing (NGS) has been used to study cancer affecting specific tissues and organs because it has several advantages over previous methods. NGS data analysis methods and software have been used to characterize cancer genomes and identify various types of somatic variants driving cancer progression. Although there are many computational tools for NGS data analysis, most of these methods don't take into account the nature of cancer so that critical steps and considerations for cancer genome analysis are overlooked. Therefore, cancer-specific NGS methods and software are needed to improve early cancer diagnosis, prediction of patient prognosis, patient cancer sub-type classification, and personalized treatments. The goal of our project is to develop an accurate analysis method to detect all types of somatic variants and annotate them in a cancer specific manner.
14.David AdamsWellcome Trust Sanger InstituteUnited Kingdom2013-06-11Integration and Validation of Bioinformatics Software Tools on Cancer GenomesWe are interested in identifying DNA changes in a broad range of cancer tumours that give rise to and drive the development of the cancers themselves. To do this we are using a number of scientific software programs to perform complex computational analyses. We are taking the ‘two-heads-are-better-than-one’ approach, i.e. by overlapping results from many software programs we seek to obtain better and more reliable results in terms of revealing differences in cancer tumour DNA. Our aim in using the ICGC data is to provide gold standard data sets of known DNA changes reported from laboratory experiments, against which we can evaluate how well the scientific software programs are performing and how well the ‘two-heads-are-better-than-one’ approach works.
15.Kelly FrazerUniversity of California, San DiegoUnited States2013-08-16Cancer GeneticsMost cancers have a strong genetic component. Genetic association studies have demonstrated that some patients can inherit a higher genetic risk of specific cancers. The tumor itself is believed to develop from benign to malignant as a consequence of an accumulation of mutation events in genes important for cancer, leading to uncontrollable cell proliferation. By analyzing tumor and matched normal genomic profiles, we seek to identify biomarkers for cancer susceptibility, cancer progression, and genetic changes responsible for tumor growth. By establishing a refined genetic landscape of cancer, we can improve diagnosis and prognosis, classify tumors, open new pathways for drug discovery, and help physicians select the optimal therapeutic option for each individual patient. We would like to use the ICGC as reference data sets to test bioinformatic algorithm development, and to compare expression and mutational profiles to our own tumor collections.
16.Jan KorbelEuropean Molecular Biology LaboratoryGermany2013-08-30Dissecting genetic determinants for complex genomic DNA alterations in cancerUntil recently, researchers have thought that cancer generally occurs in patients as the result of the action of multiple subsequent mutations. We and others, have, however, recently found evidence for an alternative mechanism, which involves massive chromosome rearrangements resulting from a single, one-step catastrophic event, termed 'chromothripsis'. We recently discovered that chromothripsis is particularly abundant in childhood brain tumor patients carrying a hereditary mutation in the p53 gene, a finding that was important for understanding this particular cancer development phenomenon. We will, in addition to p53, try to find whether mutations in other cancer genes are occurring in connection with chromothripsis, to further understand the molecular basis of this phenomenon (and related phenomena) in cancer.
17.Gad GetzBroad InstituteUnited States2013-08-16Genetic characterization of chronic lymphocytic leukemiaChronic lymphocytic leukemia (CLL) is the most common leukemia in the western hemisphere. It remains incurable and its highly variable course is difficult to predict. Through advanced genetic research technologies, we will characterize the genetic landscape in CLL and its implications on the causes of CLL . We performed an analysis of the all genes that encode proteins of 135 samples of CLL, detecting commonly occurring genetic alterations. The increased size of the patient group allows for detection of genetic events that occur in a small proportion of cases as well as the alterations that affect large proportion of cases. We aim to perform an analysis combining these data as well as publicly available data (e.g from the International Cancer Genome Consortium), to better assess the identity and the proportion of cases affected by recurrently mutated genes.
18.Gangqiao ZhouBeijing Proteome Research CenterChina2014-01-23Identification of genome alterations of hepatocellular carcinoma Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Through advanced genetic research technologies, we will characterize somatic landscape in HCC genome and its implications on the causes of HCC. We have sequenced more than 60 HCC-specific sequences and 5 HCC whole genomes, detecting commonly occurring genetic alterations. The increased size of the patient group allows for detection of genetic alterations that occur in a small proportion of cases as well as of those affecting large proportion of cases. Therefore, we want to combine the data from the International Cancer Genome Consortium (ICGC) and our in-house data to identity recurrently mutated genes in HCC.
19.Charles VaskeFive3 GenomicsUnited States2013-08-20Association of pathway signatures with mutational spectrum in cancerEach gene in the genome interacts with other genes in two ways: a gene's activity is regulated by others, and the biological activity of a gene in a cell may require the coordination of other gene's. In tumors, mutations will aberrantly turn genes on or off, resulting in the abnormal behaviors of tumor cells. We will map each tumor's mutations onto the set of genes, and then the corresponding interactions, in an attempt to explain tumor cell's behavior in terms of how these regulatory interactions have been modified. We will provide an overview of what sets of interactions are most often modified together, in order to better understand what biological processes must be co-regulated in cancer.
20.Alex RamosH3 BiomedicineUnited States2013-11-05Mining ICGC data for target identification and patient selection biomarker discovery Cancer is caused by genetic changes in the genome that lead to deregulation of key cancer “driver” genes. We aim to analyze patterns of genetic changes across tumor samples derived from multiple tissue sites to identify new driver genes. Characterization of cancer genomes from such analyses will be a tremendous aid to our understanding of cancer biology and could ultimately lead to novel therapeutic approaches in genetically-defined populations of cancer patients.
21.John CastleTRONGermany2013-11-26Individualized cancer combination therapyImmunotherapies have the potential to improve the outcome of cancer patients. Our institute is running clinical trials testing therapeutic RNA vaccines, in which a patient’s immune system is triggered to attack cancer cells. To trigger an immune response against cancer cells, we seek to identify those genes, including mutated genes, which are found only in the cancer cells. By profiling a collection of cancer samples, we can identify the frequency and co-occurrence of mutations and tumor-specific genes (antigens). We will select the most promising tumor-specific antigens and then proceed to make therapeutic RNA vaccines that encode for these cancer antigens, followed by clinical trials that hopefully demonstrate a benefit for the patient.
22.Manish Gala (Independent Investigator Status)Massachusetts General HospitalUnited States2014-01-17Germline Mutations in Ras/Raf-initiated CarcinogenesisCertain mutations occur with high frequency in cancers at multiple sites in the body. While these mutations can predispose someone to develop cancer from normal tissue, previous studies have demonstrated that additional mutations are required to overcome anti-cancer barriers. We hope to use the ICGC data and other data to help us develop novel computer algorithms to find these helper mutations.
23.Carlos Martinez-A.Centro Nacional BiotecnologiaSpain2013-12-05Study of the implications of the Dido locus in chromosome 20 (death inducer obliterator protein ) in hematological myelodysplasiaThe aim of this research is to study the DNA coding regions of the Dido (death inducer-obliterator protein) gene, to identify its possible implication in the development of a type of cancers that affect the blood. We use the mouse as a model to study MDS/MPD diseases (myelodysplastic/ myeloproliferative diseases, a subgroup of blood cancers). Our recent research showed that alterations in Dido protein expression cause a transplantable disease whose symptoms and signs suggested myelodysplastic diseases. Our findings indicate that Dido might be one of the genes involved in the development of this group of diseases. With access to the International Cancer Genome Consortium (ICGC) datasets, we will study the DNA coding sequences of genes from patients with myelodysplastic diseases and compare them with sequences from healthy donors. With this study, we intend to gather sufficient information about Dido gene function to contribute to develop new treatments and diagnostic tools.
24.Parminder MankooSanofi AventisUnited States2014-02-20Identifying subtype-specific mechanisms and novel drug targets and biomarkers using ICGC genomic and clinical dataThis project aims to further personalized medicine by identifying tumor-driving gene alterations and novel drug targets that are found only in specific cancer subtypes. We will achieve this goal by integrating multi ICGC data types along with clinical data correlation analyses. We will apply novel methods to identify patterns of correlation and mutual exclusivity defining different cancer subtypes. Secondly, we plan to correlate these identified cancer subtypes with clinical outcomes (for example, survival and progress-free survival). This research will improve understanding of the molecular mechanisms of cancers, support target identification, and facilitate patient stratification that will most benefit from subtype-specific therapy. Significant findings will be validated on cell lines and mouse models derived from human tumor, followed by subtype-specific drug development, potentially leading to better personalized patient cancer therapy.
25.Pedro GalanteHospital Sirio-LibanêsBrazil2014-01-24Study of retroposition events in cancerA major set of cancers is caused by alterations in DNA. The identification of these alterations is essential to understand the initiation and progression of this disease. Recently, next generation DNA sequencing (NGS) has enabled us to better characterize various types of somatic variants, such as complex genomic variations involving chromosomal rearrangements and retrotransposable elements, such as LINEs and SINEs (Long/Short Interspersed Nuclear Elements, respectively). In general, the identification of these variations is based on comparisons between sequences from normal and tumor samples. Here, we aim to analyze the amazing set of tumor and normal sequences produced by ICGC to identify complex somatic variations. Our areas of focus are those variations involving retrotransposable elements, such as LINEs, SINEs and other retrocopied elements. We expect to find a small, but interesting set of somatic variations involving these elements in tumors.
26.Jaume PonsPfizer, Inc.United States2013-11-08Discovery and validation of targets in sub types of cancer by mining genetic and expression dataOur goal is to use genetic and gene expression data to develop precision medicine approaches that match targeted therapies to well defined tumor subtypes. The availability of same patient gene expression and genetic data on a large patient base in ICGC makes this a possibility.
27.Nicholas ShackelCentenary InstituteAustralia2014-04-11Discovering Novel Non-Invasive Diagnostic and Prognostic Markers in Hepatocellular CarcinomaThe aim of this research is to utilize available data to identify genome changes occurring in liver cancer and to determine their molecular “signatures”, in order to help determine both the cause of these genome changes together with the outcomes of patients with liver cancer. With access to the International Cancer Genome Consortium (ICGC) dataset, we will be able to screen DNA sequences for changes that occur in liver cancer and to compare them to our own data.
28.Peter CampbellWellcome Trust Sanger InstituteUnited Kingdom2014-03-24Deciphering the mutational landscape and signatures of mutational processes active in the genomes and transcriptomes of human cancersIn this project, we will identify somatic (non-inherited) changes in the DNA sequence of cancer patients. These changes will be separated from germ-line (inherited) changes by comparing the patient's cancer DNA to that of their normal DNA. We will then record the distributions of these DNA rearrangements or changes across different cancer types, and we will study the genetic and genomic context in which they occur. We will further investigate and report the functional consequences of the somatic (non-inherited) genomic changes. We will also explore the relationships between different types of somatic changes such as DNA/gene rearrangements or DNA insertions and/or deletions.
29.Trey IdekerUniversity of California, San DiegoUnited States2014-03-28Network analysis and classification of cancerEmerging evidence shows that cancer is not a disease of single genes but of entire cellular pathways and networks. Analysis of massive, genome-scale datasets across large patient cohorts presents unique challenges from both the perspective of data integration and the development of tools for use in the clinical community. Our project aims to combine multiple types of genome scale data, such as mRNA expression and DNA mutations, in meaningful ways. We will make use of cellular pathway models in order to improve diagnostic and prognostic prediction tools, highlight potential drug targets, and characterize cellular pathways that play active roles in cancer progression.
30.Jonathan DryAstraZenecaUnited States2013-08-01Discovery of novel drug targets and patient opportunities for existing drugs in diverse tumor typesThe goal of our research project with the ICGC controlled data is to develop novel bioinformatics and statistical analysis methods to integrate multiple layers of molecular information across patient tumor samples. The ability to collectively analyze patterns of occurrence between mutations and rearrangements in DNA and RNA, chromosomal amplifications/deletions, and mRNA expression, is of great interest to identify genes and pathways that can be targeted in the treatment of cancer. Access to the ICGC data will allow us to develop methods enhancing the field of next-generation DNA/RNA sequencing data analysis in oncology. We also hope to further understanding of the relationships between different molecular events, helping define new molecular disease contexts and drive identification of novel targets for the treatment of tumor types such as pancreatic, for which novel targeted therapies are severely lacking.
31.David TorrentsBSC-IRB Research Programme in Computational BiologySpain2014-03-28Validation and discovery of new structural rearrangements in human MedulloblastomaCancer is an unfortunately prevalent disease affecting millions of people. Finding adequate therapies is made difficult by the fact that different cancers show different alterations of the information encoded by the genetic material. Moreover these alterations are also quite different between patients affected by the same cancer type. In order to develop successful therapies, researchers need to understand how cancer evolve from normal cells, how it progresses, and what biological features can point to a particular clinical outcome. This is why the main effort in cancer research is now addressed by sequencing genetic material of many affected patients and for many different cancer types. Our project is to apply the computational tools we have developed on these large datasets, in order to find new genetic modifications that can lead to cancer and can be targeted by therapy.
32.George BovaUniversity of Tampere Institute of Biomedical TechnologyFinland2014-03-25Integrated Studies of Cancer GenomicsThe 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.John McPhersonOntario Institute for Cancer ResearchCanada2014-03-07International Cancer Genome Consortium (ICGC): Pancreatic Cancer ProjectCancer 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 tumours. Our target is to collect 500 independent tumours and their matched controls and to fully study and describe 350 of these. The ICGC collaboration will allow members world wide to advance cancer research through analysis of a large number of genomes from multiple cancer types. .
34.Ryan MorinSimon Fraser UniversityCanada2013-07-29Meta-analysis of sequencing data from human tumoursThis 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.
35.Eduardo ReisUniversity of Sao PauloBrazil2014-04-06Establishment of functional genomics platforms for discovery of molecular markers and therapeutical targets in pancreatic cancerDuctal adenocarcinoma (PDAC) is the most prevalent pancreatic tumor, extremely aggressive and whose only curative treatment available is surgical removal in early stages of the disease. Pancreatic cystic tumors may progress to invasive adenocarcinoma and currently there are no markers to safely predict their clinical evolution. In this project we will employ high through put sequencing of RNA/DNA from clinical samples of PDAC and cystic tumors to search for DNA mutations or changes in gene expression with diagnostic or clinical relevance. Data analysis will allow to pinpoint alterations in DNA and/or gene expression that are over-represented in PDAC and cystic tumors that develop into invasive carcinomas, pointing to novel candidate biomarkers for early diagnosis and prognosis, as well as molecular vulnerabilities that may be exploited as therapeutic targets of pancreatic cancer.
36.Joaquin DopazoCentro de Investigacion Principe FelipeSpain2014-04-06Impact of cancer mutations on the interactome structureAlthough genomic projects have discovered many mutations associated to cancer it is often difficult to know whether these are causative mutations or are just a consequence of the evolution of the cancer cells. Since proteins carry out their functions in a complex network of interactions it might occur that the factor that drives a cell from a normal to a cancer state is not the mutated protein itself but the disorder that this malfunctioning protein causes in such network. Our aim is to take advantage of the availability of a large number of cancer samples to look for patterns of changes in the “healthy” network of protein interactions that could be related to cancer.
37.Lan KluweUniversity Medical Center Hamburg-EppendorfGermany2013-06-11Exploring prognostic value of copy number variation in oral carcinomas Oral tumor is the 8th most common cancer. Each oral tumor has its own genetic alterations. Copy number variation (CNV) at various genomic regions is one of those changes and may determine the clinical course. We aim to study the 20 most frequent CNVs based on the ICGC data and to find which ones are associated with preferred clinical course and which ones with less-preferred clinical course. We further aim to find CNVs associated with good therapy responses. These CNVs can be used for determining the likely course of disease of oral tumors and help with the choice of therapies.
38.Sepp HochreiterJohannes Kepler University LinzAustria2013-06-11Detection of copy number variations and segments that are identical by descent in cancer patientsWe are developing new machine learning and bioinformatics methods for the detection of copy number variations and segments that are identical by descent (IBD) on next generation sequencing 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 real genotyping datasets such as those available on the ICGC data portal and hope that they lead to results which verify or complement the outcomes of existing studies.
39.Gloria PetersenMayo ClinicUnited States2013-08-30Applying next generation sequencing studies to identify genetic predisposition to pancreatic cancerPancreatic cancer (PC) has a poor survival rate because it is often found too late. It is vital to improve ways to find persons at high risk, who can have early screening. This project aims to find new genes that increase risk of this cancer. Using the DNA sequences from blood samples of PC patients, we will use a novel approach to screen for changes (gene variants) that are less common, but may confer risk. We will pursue any findings using a second, large sample of Mayo Clinic PC patients’ DNA. We will then look at cancer DNA for changes, and check whether any of the changes in the genes that we find will affect the way that the proteins they make do not work, or work in an altered way.
40.Janet KelsoMax Planck Institute for Evolutionary AnthropologyGermany2014-04-21Mutations and associated changes of gene expression - cancer genes and evolutionary relevant genesFrom the large number of mutations detected in cancers, we want to help identifying mutations that contribute to cancer progression. We want to find cancer mutations that occur more often in specific sets of genes, and clarify if these are associated with changes in gene expression. Our analyses will shed light on the cellular pathway for cell proliferation in cancer. We will also test whether there are genetic variants in cancer genes in current human populations that have been contributed by admixture with archaic humans.
41.Jorge Reis-FilhoMemorial Sloan-Kettering Cancer CenterUnited States2013-06-11Genomic characterisation of rare histological subtypes of breast cancerBreast cancer is not a single disease. Each breast cancer is to some extent unique in terms of its genetic alterations. To understand the genetic changes underlying breast cancer, we will focus on subsets of breast cancer that have distinctive features when viewed under the microscope, as previous experiments suggest that these subsets of breast cancer may share specific genetic changes that give them their distinctive features. By combining sequencing data generated in-house with the ICGC data available, we plan to identify the genetic changes that define these rare subtypes of breast cancer. This information will be employed not only to improve the diagnosis of rare types of breast cancer, but also to identify potential drivers of specific subgroups of breast cancer.
42.Ben RaphaelBrown UniversityUnited States2014-04-04Reconstructing Catastrophic Rearrangements in Cancer caused by ChromothripsisCancer is a disease driven in part by genomic mutations that accumulate during the lifetime of an individual. These range in size from single nucleotide mutations through larger rearrangements that duplicate, delete, or rearrange segments of the genome. The classic model of tumor evolution states that these mutations accumulate incrementally over a long period of time. However, an alternative model has recently been suggested where a single massive genome shattering event (termed Chromothripsis) results in a highly rearranged region on a single chromosome, or across several chromosomes. We are investigating ways to quantify whether or not such an event is supported by DNA sequencing data.
43.Ming TsaoUniversity Health NetworkCanada2013-06-21International Cancer Genome Consortium (ICGC): Pancreatic Cancer ProejctCancer can result from changes in a person's genetic material (DNA). By studying the 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 the genetic changes in cancer. As a contributing member of the ICGC, the OICR will generate a comprehensive catalogue of genomic abnormalities found in pancreatic tumours. The target is to collect the requisite 500 independent tumours and their matched controls and fully characterize 350 of these. The main goal of the ICGC collaboration (OICR, University Health Network, University of Toronto) will allow members from sites world wide to advance cancer research by analysis of a large number of genomes through the comparison of genes involved in multiple cancer types.
44.Xavier EstivillCenter For Genomic Regulation (CRG)Spain2013-07-31Assessing the germline component of genetics susceptibility to common cancerWe propose to evaluate the spectrum of inherited genetic mutations in cancer. We will analyze the whole set of genes in cases of breast cancer, colon cancer, acute lymphocytic leukemia (ALL), and acute myeloid leukemia (AML), to identify genetic variants in the germline (or reproductive cells) of patients that could play a role in cancer predisposition. Our analysis of the germline component of a common cancer of adults, such chronic lymphocytic leukemia (CLL) in the CLL-ICGC project, suggests that a limited number of genes are affected at the germline level in this type of cancer. We will use bioinformatic approaches to assess for recurrent genes and common pathways involved in cancer predisposition. By identifying the genetic component of susceptibility to these four types of common cancers we should be able to contribute to prevention plans for cancer and to define genes and variants that will be selected for functional studies.
45.Xiaoping SuMD Anderson Cancer CenterUnited States2014-03-11Epstein-Barr virus (EBV) analysis of RNA-seq and exome sequencing data in CLLThe SF3B1 mutation is found in 10-15% of chronic lymphocytic leukemia (CLL). Although it is associated with disease progression and resistance to treatment, the effects of this mutation are not fully known. The Epstein-Barr virus (EBV) is also associated with CLL, but very little is known about its role in the disease as well. More could be learned about the significance of the SF3B1 mutation and EBV virus infection by examining genetic data from the ICGC. This research could help doctors develop more accurate ways to predict CLL patients' chances of recovery.
46.Brandon HiggsMedImmuneUnited States2013-09-13Integrative analyses of multi-omics data to identify therapeutic targets in patients with double malignancy syndromeWe will characterize genetic and gene expression abnormalities in patients with both a lung and ovary tumor. DNA sequence will be captured, as will RNA expression to identify mutations (in the DNA) and genes that are abnormally turned-on or -off (in the RNA expression data) that may lead to the onset of cancer as well as the ability of patients to not respond to various cancer treatments. The DNA and RNA expression data from ICGC will allow us to understand how unique or similar these coexisting double malignancies are in our patients when compared to large populations of patients with either primary ovarian or lung tumors. The DNA and RNA expression abnormalities will be compared between our patients and the large cohort of ICGC patients.
47.Rene OpavskyUniversity of Nebraska Medical CenterUnited States2013-09-20Identification of genes hypomethylated in human and mouse lymphoid malignanciesBlood cancers, such as leukemia and lymphoma, are responsible for 10% of all cancer related deaths in the United States. A chemical modification of DNA – termed methylation - is changed in many blood cancers. Our laboratory found one enzyme that causes methylation that has protective functions against cancer. This enzyme controls a group of molecules that may play a role in causing blood cancer. Using available data we will identify genes likely involved in the development of blood cancer. Subsequent experiments will identify genes important in blood cancer and targets for development of drugs that kill cancer cells.
48.Nada JabadoMcGill University health Centre (MUHC)Canada2014-03-21Biomarkers for Pediatric Glioblastoma through Genomics and Epigenomics. The iCHANGE project.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.
49.Zhi LuTsinghua universityChina2014-01-23Characterizaion of functional long non-coding RNAs in different kinds of cancer and detection of new cancer lncRNA biomarkersLong non-coding RNA is a special type of RNA which does not code for a protein. We will integrate different kinds of genomic data in order to identify cancer-related long non-coding RNA genes from throughout the whole genome. We would like to integrate data from ICGC to discover these genes and determine their roles in cancer development and progression. We also hope to detect new markers for the identification of different types of cancer.
50.Keigo MachidaUniversity of Southern CaliforniaUnited States2013-10-24Mutanome of liver cancer using Exome sequencing and RNA-seqCompelling epidemiologic evidence identifies obesity and alcohol as critical co-morbidity factors for the incidence of liver diseases, especially hepatocellular carcinoma (HCC) in hepatitis C virus (HCV), HBV, alcoholic or obese patients. Individual clonal heterogeneity of tumor-initiating cells (TICs) of liver diseases (alcoholics, obesity, virus-associated hepatocellular carcinomas) is characterized and TIC-specific mutations will be targeted by cancer immunotherapy using dendritic cell (DC) vaccination. To find out cancer-specific mutant epitopes (cancer-specific mutant proteins) for cancer immunotherapy, next-generation DNA sequence data from ICGC will be processed to find out cancer-specific mutations. We will identify mutations associated with liver diseases and compare these findings against sequencing data collected on liver diseases. To identify specific genes and pathways implicated in the pathogenesis of liver diseases, candidate genes and pathways may represent current or future therapeutic pathways.
51.Li DingWashington University in St. LouisUnited States2013-11-05Cancer Susceptibility Variant Discovery in High Throughput Sequencing DataCancer 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.
52.Joshua StuartUniversity of California Santa CruzUnited States2013-11-08ICGC-TCGA DREAM ChallengeMutations, or changes to the genome sequence of a cell, can lead to cancer. It is therefore of extreme importance to catalog all of the mutations observed in tumors to gain information about the disease. Dozens of methods exist to identify mutations. However, there is currently very little agreement between any two methods. The discrepancies make it difficult to compare and combine results from different studies. The ICGC-TCGA DREAM organization is launching the Somatic Mutation Calling Challenge to identify the best method(s). The most accurate techniques that emerge from this activity will be made available to the research community and adopted as the standards for large scale analysis of CGHub datasets and for a whole genome pan-cancer analysis conducted by ICGC in the coming years.
53.Yen-Yi HoUniversity of MinnesotaUnited States2014-02-21Pathway Analysis Using Cancer Mutation DataIn this project, we will study the genes found to be altered in cancer tissues among patients with pancreatic cancer. We would like to use quantitative approaches to examine whether genes in certain chemical reactions in a cell are more frequently altered than expected by chance alone. We will compare the genetic alterations between in human pancreatic patients and in mice with pancreatic cancer. Our goal is to identify common altered chemical reactions in a cell that might contribute to the development of pancreatic cancer.
54.Lars KaderaliInstitute for Medical Informatics and Biometry, TU DresdenGermany2013-12-19The Role of Viruses in Tumor FormationCancers arise from normal cells that have undergone certain change or damage in their genetic makeup (their genome). In order to improve cancer treatment it is important to better understand what genetic changes in cells are responsible for the development of a specific tumor. In recent years it has become clear that, at least for some tumor types, one of those genetic changes can be a virus infection. A well-known example is human papillomavirus that can cause cervical cancer. The aim of our research is to build up sensitive methods to detect viral infection in a tissue sample, given DNA next generation sequencing data. The ICGC provides sequencing data from tumor and normal samples that we want to use to build and validate our methods.
55.David TorrentsBarcelona Supercomputing CenterSpain2013-12-19ICGC Somatic Variant Calling Pipeline Benchmarking AnalysisThe somatic variant calling pipeline benchmarking exercise’s goal is to find somatic mutations and to compare programs across tumors and across sequencing technologies. ICGC data is used to compare the genome reads of a tumor and normal tissue. This exercise will help assess various technologies used in whole genome analysis, the ways in which structural variations can be identified, and the resources and types of analysis that are required to provide high confidence results.
56.Daoud MeerzamanNational Cancer InstituteUnited States2013-12-19ICGC-TCGA Dream ChallengeWhole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
57.Gad GetzBroad InstituteUnited States2013-12-11 Pan-cancer analysis to detect genomic alterations in tumors.While enormous strides have been made in cancer research in the last decade, cancer is still a leading cause of death worldwide. The onset of new sequencing technologies, along with the directed assembly of patient samples by groups worldwide, is allowing researchers to identify the common genomic alterations in numerous tumor types. To date however, these data sets have been too small, and hence underpowered, to detect the many infrequent or rare events that drive cancer. Our goal in this project is to assemble and analyze a data set of adequate size to be powered to discover the complete array of genomic alterations, both common and rare, found in cancer .
58.Tak-Wah LamHKU-BGI Bioinformatics Algorithms and Core Technology Research LaboratoryHong Kong2014-02-17ICGC-TCGA DREAM Genomic Mutation Calling ChallengeWhole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer- induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standard- ized, carefully-evaluated approaches for both research and clinical practice.
59.Arash AlizadehStanford UniversityUnited States2014-02-06Integrative Clinical Cancer Genomics 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.
60.Xueda HuCancer Institute, Chinese Academy of Medical ScienceChina2014-02-24The landscape of RNA-editing in human cancersAs we know, cancer results from nucleotide alterations in human genome. RNA editing is defined as the nucleotide sequence change of RNA transcripts relative to that of the encoding DNA. Similar as function in DNA sequence mutations, RNA editing has been connected to cancer development and progression. In this study, we will use the exome and RNA-seq data from the same individuals deposited in ICGC database, compare their sequence differences and make a comprehensive profile of RNA editing in a variety of human cancers. This research is expected to provide new diagnostic and prognostic markers and might contribute to early detection of cancer and monitoring of therapy response.
61.Ken ChenThe University of Texas MD Anderson Cancer CenterUnited States2014-04-14Identification and characterization of genomic structural aberrationsOne 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.
62.Ira HallUniversity of VirginiaUnited States2014-01-24An ultra-fast open source variant calling pipeline for clinical genome interpretationCancer is a genomic disease caused in part by the accumulation of mutations. Genome sequencing offers an opportunity to profile the unique set of mutations that are present in each individual tumor, and therefore to direct personalized cancer treatment. However, detecting and interpreting somatic mutations from raw genome sequencing data in a timely manner remains a major challenge in the field. Current computational methods suffer from limited sensitivity and accuracy, and can take weeks to run on a standard dataset, with involvement from multiple skilled researchers. The goal of this project is to develop ultra-fast computational methods to map and interpret cancer genome mutations with minimal human involvement. Specifically, we will use ICGC data to test and refine our computational pipeline for mapping and interpreting cancer genome mutations, and we will participate in the ICGC DREAM Mutation Calling challenge to compare our methods to others in the field.
63.Ruibin XiPeking UniversityChina2014-01-22Mutation detection with high-throughput sequencing dataAll 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.
64.David TorrentsBSC-IRB Research Programme in Computational BiologySpain2014-01-15ICGC-TCGA-DREAM Somatic Mutation Calling ChallengeWhole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
65.Chandrasekhar KanduriUniversity of GothenburgSweden2014-01-24Functional role of long noncoding RNAs in Medulloblastoma Medulloblastoma are heterogeneous tumors and one of the most malignant brain tumors in children. Previous investigations have mostly relied on protein coding genes in classifying medulloblastomas as well as understanding the molecular basis of medulloblastoma's path to disease (pathogenesis). Though there is a significant progress in our understanding of reasons underlying the medulloblastoma pathogenesis, still a significant portion of medulloblastoma tumors are untreatable. Hence we are interested in understanding the functional role of noncoding RNAs in medulloblastoma pathogenesis to check whether they provide answers to hitherto poorly understood questions. We have already initiated our investigations using the available cell lines representing different medulloblastoma subgroups. In order to obtain clinical significance to our preliminary observations, we would like to extend our investigations to data generated by International cancer genome consortium. This will immensely help us our efforts to understand the functional role of noncoding RNAs in medulloblastoma pathogenesis.
66.Rebecca FitzgeraldUniversity Of CambridgeUnited Kingdom2014-04-15ICGC Oesophageal Adenocarcinoma http://www.compbio.group.cam.ac.uk/research/icgcOesophageal and junctional adenocarcinoma (OAC) are types of cancer which affect the esophagus and the area where it connects to the stomach. The groups of Rebecca Fitzgerald and Simon Tavare, in collaboration with the OCCAMS network of health centres, are collecting and performing whole genome sequencing on up to 500 selected cases of OAC. We wish to access ICGC data on related types of cancer in order to compare them with this dataset. It is hoped that our comprehensive analysis of large numbers of patient samples will contribute to the understanding of the causes of OAC.
67.Tae Hyun HwangUniversity of Texas Medical Center at DallasUnited States2014-02-06Finding somatic mutations and structural variations using next-generation sequencing dataWe recently developed new computational approaches to identify mutations and structural variations in DNA which are linked with cancer progression and development. We would like to apply these approaches to ICGC data, and are also participating in the ICGC-TCGA DREAM Mutation Calling challenge.
68.David TorrentsBSC-IRB Research Programme in Computational BiologySpain2014-03-10ICGC Somatic Variant Calling Benchmarking 1.2 AnalysisThis project’s goal is to use computer programs to find cancer-related mutations and to compare those programs using ICGC data gathered from different types of tumors using different technologies. We will use a program we have developed called SMUFIN, which will compare paired data from tumors and normal tissue to directly identify all types of variants. We will then analyze data first from a type of blood cancer called CLL (chronic lymphocytic leukemia) and then from a type of brain tumor called a medulloblastoma, which should be more challenging.
69.Tatsuhiko TsunodaRIKENJapan2014-04-04RIKEN ICGC Pan-cancer analysis 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.
70.Gilles THOMASSynergie Lyon CancerFrance2014-01-24ICGC-TCGA DREAM Mutation Calling challenge https://www.synapse.org/#!Synapse:syn312572/wiki/Whole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
71.Steven SalzbergJohns Hopkins UniversityUnited States2014-02-17ICGC-TCGA DREAM Mutation Calling challengeWhole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
72.Henry YangCancer Science Institute of SingaporeSingapore2014-02-21ICGC-TCGA DREAM Mutation Calling challengeWhole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
73.Nancy ZhangUniversity of PennsylvaniaUnited States2014-03-18Detection of Somatic Structural Variation in TumorsWhole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
74.David MittelmanGene by GeneUnited States2014-02-17ICGC-TCGA DREAM Mutation Calling challenge https://www.synapse.org/#!Synapse:syn312572/wiki/Whole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
75.Paul BoutrosOntario Institute for Cancer ResearchCanada2014-02-17ICGC-TCGA DREAM Mutation Calling ChallengeThe ICGC-TCGA DREAM Genomic Mutation Calling Challenge (herein, The Challenge) is an international effort to improve standard methods for identifying cancer-associated mutations and rearrangements in whole-genome sequencing (WGS) data. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open collaborative Challenge. The goal of this somatic mutation calling (SMC) Challenge is to identify the most accurate mutation detection tools, and establish the state-of-the-art. The tools in this Challenge must use as input WGS data from tumour and normal samples and output mutation calls associated with cancer.
76.Niranjan NagarajanGenome Institute of Singapore, A*STARSingapore2014-02-17ICGC-TCGA DREAM Mutation Calling challenge (https://www.synapse.org/#!Synapse:syn312572) Whole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
77.Yudi PawitanKarolinska InstitutetSweden2014-03-10ICGC-TCGA DREAM Mutation Calling challengeWhole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
78.Benjamin LehnerCenter For Genomic Regulation (CRG)Spain2014-03-19Regional Variation in Somatic Mutation Rates in Human Cancer GenomesGenetic mutations are the basis of many diseases including cancer. These mutations occur at different rates depending on their location in relation to other genes. Our project aims to integrate ICGC data with other data from around the world in order to help understand how the organization of genetic material in the cell affects how often it mutates. We will also investigate how the mutation rates seen in cancer cells are different from those of healthy cells
79.Stephen TsuiThe Chinese University of Hong KongHong Kong2014-03-24Discovery of Hepatocellular carcinoma driver mutationsHepatocellular carcinoma (HCC) is the second leading cause of death in Hong Kong. Finding the genetic cause of this liver cancer type is of great interest because it will aid in clinical diagnosis and customization of healthcare plans. It is established that specific mutations in the patients' genome, accumulated over time, cause HCC and possibly other cancers as well. Our study aims to find out which specific mutations cause HCC by inventing an algorithm that accounts for observations in high-throughput genome sequencing data. By comparing cancer tissue and normal tissues adjacent (control) to the tumor from the same patient, patterns of mutations in the tumor cells' genome stand out behind the control's background. The algorithm shall account for the impact of various such patterns by adjusting a score added to each observed pattern. We hope this algorithm will contribute as a novel approach in diagnosis and treatment for HCC subtypes.
80.Keith CallenbergRedPath Integrated Pathology, Inc.United States2014-03-24ICGC-TCGA DREAM Mutation Calling ChallengeWhole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
81.Stephanie KreisUniversity of LuxembourgLuxembourg2014-04-11Melanomics: Advancing our understanding of of melanoma biology by integrating data from melanoma genomes, transcriptomes and miRNomesThe global incidence of melanoma continues to rise faster than any other malignancy. Melanoma is a disease showing a wide range of clinical behavior from relatively harmless to aggressive metastatic disease. Primary melanoma is most often a result of UV light through sun exposure, which can cause accumulation of various types of mutations. We have characterized tissue and blood samples from patients with primary melanoma (as well as matching healthy skin) by generating miRNome, whole genome, and transcriptome data sets. The goal of this project is to identify (i) disease-stage-specific DNA, mRNA, and miRNA profiles in healthy versus primary and metastatic tumour lesions and (ii) to integrate and compare our data sets with each other as well as with publically available data sets from melanoma samples. With this, we hope to find new players and pathways that could be therapeutically targeted to better treat advanced melanoma.
82.Becky DreesSpiral Genetics, Inc.United States2014-04-08ICGC-TCGA DREAM Genomic Mutation Calling ChallengeWhole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
83.Toby BloomNew York Genome CenterUnited States2014-04-04DREAM challenge on somatic variant callingWhole-genome sequencing is a method for reading the complete DNA sequence of a cell sample. The ICGC-TCGA DREAM Genomic Mutation Calling Challenge is an international effort to create standard methods for identifying cancer-induced mutations in whole-genome sequencing data. A global competition is being launched to find the most accurate techniques for using computers to identify these mutations. This will allow groups around the world to adopt standardized, carefully-evaluated approaches for both research and clinical practice.
84.Jan KorbelEuropean Molecular Biology LaboratoryGermany2014-04-04Pan-Cancer Analysis of Whole Genomes: PAWGThis 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.
85.Jeffrey BaileyUniversity of Massachusetts Medical SchoolUnited States2014-04-08Molecular and genetic pathogenesis of hematologic malignanciesWe are investigating the molecular and genetic causes of cancer that affect blood, bone marrow and lymph nodes. Our primary focus is B cell cancers including understanding the causes of Burkitt lymphoma, particularly the endemic form which is the most prevalent pediatric cancer in sub-Saharan Africa. Combining our data with ICGC data, we will compare and contrast different cancer and cell types to improve diagnosis and treatment.
86.Jason WongUniversity of New South WalesAustralia2014-04-14Cis-regulatory mutations in cancerOnly about 1% of the human genome directly codes for proteins. The remaining “non-coding” portion of genome (that don't make proteins) is poorly understood and it is only recently that scientist have begun exploring what these other parts of the genome do and why they are important in dictating how cells in our bodies work. Equipped with this emerging knowledge, this project aims to use the ICGC genome sequencing datasets to determine whether there are mutations outside of protein coding regions that can potentially cause cancer. The discovery of new cancer causing mutations will have the potential to provide new ways to determine patient treatment regimes and offer new targets for therapy.
87.Christos ChinopoulosSemmelweis UniversityHungary2014-04-11Bioenergetic Bail-in Mechanisms of Mitochondria in CancerMitochondria are the powerhouses of the cell. In cancer cells mitochondrial functions are altered, in order to adapt to the environment and also to resist cell death. Under adverse conditions, the risk of mitochondria switching from energy producers to energy consumers is credible. Yet, cancer mitochondria manage to evade those risks, and even play a major role in tumor progression. An extensive body of work in our laboratory has identified potential mechanisms that afford cancer mitochondria with the ability to adapt to such adverse conditions. These mechanisms appear to be non-operational in healthy human adult brain, but preliminary experiments showed that they are switched on in aggressive brain tumors. The purpose of requesting access to ICGC Controlled Data is to capitalize on this line of research and expand our searches on cancer repositories to the maximum extent possible.
88.James CostelloUniversity of Colorado BoulderUnited States2014-04-17The mutational landscape of bladder cancerIt is expected that genes frequently mutated in a cancer reflect some functional association to the disease. We must first identify what these genes are. This project will focus on identifying the mutations present in a group of over 300 bladder cancer patients. We then intend to study the highly mutated genes to determine what functional role these genes have in bladder cancer.