|1.||Dr. David Jackson||LIFE Biosystems||Germany||2013-04-26||Variant Characterization for Prediction of Drug Responsiveness||A crucial step towards the personalization of cancer medicine involves the marriage of molecular information and biomedical knowledge with patient specific clinico-molecular data. Effective cancer treatment is dependent on such an approach, since often patients respond very differently to treatment despite sharing the same cancer type. The key to understanding treatment response or resistance lies in deciphering the complex interplay of individual molecular variability in the tumor and the rest of a patient’s body. The aim of our project is to use the ICGC data as a reference to compare to patient-specific information with molecular information about drug mode of action and the functional implications of gene variants. This will make personalized cancer treatment decision support feasible and will help to bring the benefit of molecular disease data to the clinic and treating physician. |
|2.||Stephen Friend||Sage Bionetworks||United States||2013-08-02||Network Modeling of Cancer||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 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. |
|3.||Rune Linding||Technical University of Denmark||Denmark||2013-09-20||Network Analysis of Cancer Kinomes||Cell 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. |
|4.||Mark Gerstein||Yale University||United States||2013-12-12||Dysregulation of Regulatory Networks in Cancer||We 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. |
|5.||Robert Bradley||Fred Hutchinson Cancer Research Center||United States||2013-03-19||Dysregulation of gene expression in tumors||The improper regulation or expression of particular genes is known to play important roles in cancer development. Recent technological developments have enabled individual laboratories, as well as large collaborations, to study the expression levels of hundreds or thousands of genes simultaneously in samples from cancer patients as well as healthy individuals. These studies will allow us to greatly increase our understanding of how cancers form and grow. In order to identify genes that are important for cancer development, we create computational methods to analyze these "gene expression datasets," thereby identifying potential future therapeutic targets. |
|6.||Jason Lu||OmicSoft Corporation||United States||2013-02-18||Systematic identification of genome alterations in cancer||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. 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. |
|7.||Dr. Steven J Jones||BC Cancer Agency - Michael Smith Genome Sciences Centre||Canada||2013-11-28||Comparison of the somatic mutational spectrum of medulloblastomas with other cancer types||We 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. |
|8.||Ming Tsao||University Health Network||Canada||2013-07-08||International Cancer Genome Consortium (ICGC): Pancreatic Cancer Proejct||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 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. |
|9.||Sachdev Sidhu||University of Toronto||Canada||2013-09-13||International 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. |
|10.||Dan Rhodes||Compendia Biosciences, Inc.||United States||2013-12-05||Integrative analysis of cancer genomes||We 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. |
|11.||Bodo Lange||Alacris Theranostics||Germany||2013-07-19||Systems Biology of Cancer||Cancer is a complex disease involving many different changes in the genome of the tumor cell. These differences cause tumors with the same pathological classification to respond very differently to the drugs, making therapy decisions difficult. In addition, different patients will show differences in they way they react to a drug, due to differences in their genome. In this project we would like to use the genome/transcriptome information of different tumors/patients to develop individual models, using ModCell, a modelling system available at Alacris Theranostics to predict the effect (and side effects) of individual drugs or drug combinations. This would identify groups of patients which could respond to a particular treatment, and help to improve cancer diagnosis and treatment. |
|12.||Bernard Fox||Providence Cancer Center, Earle A. Chiles Research Institute||United States||2013-03-26||Analysis of Genomic Prostate Cancer Profiles from the International Cancer Genome Consortium for Correlations with Patient’s Phenotypes in a Prostate Cancer Immunotherapy Research Study||Comparison 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. |
|13.||Albino Bacolla||The University of Texas at Austin||United States||2013-07-31||NON-B DNA STRUCTURES AND CANCER GENOMES||Cancer 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. |
|14.||Mamatha Shekar (Mahadevappa)||NextBio||United States||2013-11-27||Integration and analysis of cancer genomic data||We 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 genes and mutations that may underlie different cancer types and various clinical phases within a given cancer. |
|15.||Dongwan Hong||National Cancer Center||South Korea||2013-06-21||An accurate approach to identifying somatic mutations from cancer genome sequencing||Recently, 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. |
|16.||David Adams||Wellcome Trust Sanger Institute||United Kingdom||2013-06-11||Integration and Validation of Bioinformatics Software Tools on Cancer Genomes||We 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. |
|17.||Kelly Frazer||University of California, San Diego||United States||2013-08-16||Cancer Genetics||Most 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. |
|18.||Jan Korbel||European Molecular Biology Laboratory||Germany||2013-08-30||Dissecting genetic determinants for complex genomic DNA alterations in cancer||Until 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. |
|19.||Gad Getz||Broad Institute||United States||2013-08-16||Genetic characterization of chronic lymphocytic leukemia||Chronic 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. |
|20.||Charles Vaske||Five3 Genomics||United States||2013-08-20||Association of pathway signatures with mutational spectrum in cancer||Each gene in the genome interacts with other genes in two ways: a gene's activity is regulated by others, and the biological activity of a gene in a cell may require the coordination of other gene's. In tumors, mutations will aberrantly turn genes on or off, resulting in the abnormal behaviors of tumor cells. We will map each tumor's mutations onto the set of genes, and then the corresponding interactions, in an attempt to explain tumor cell's behavior in terms of how these regulatory interactions have been modified. We will provide an overview of what sets of interactions are most often modified together, in order to better understand what biological processes must be co-regulated in cancer. |
|21.||Alex Ramos||H3 Biomedicine||United States||2013-11-05||Mining 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. |
|22.||John Castle||TRON||Germany||2013-11-26||Individualized cancer combination therapy||Immunotherapies 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. |
|23.||Benedikt Brors||German Cancer Research Center||Germany||2013-03-06||Correlation between DNA methylation and other epigenetic changes in CLL||Cancer is a disease caused by acquired changes in the genome during lifetime. There is accumulating evidence that this is not entirely due to direct changes in the DNA (the 'genome material'), but also by changes in associated proteins, or by chemical modification of DNA called methylation. We are investigating such changes, which are called epigenetic since they are not directly inherited by copying of the DNA, in a disease called chronic lymphocytic leukemia (CLL). One of the ICGC networks has already investigated such changes in CLL and published the results in a scientific journal. We want to use these data to construct a map of typical patterns of DNA methylation in CLL. We will then use our own data on additional epigenetic changes to relate these to the findings that have been already published. |
|24.||Manish Gala (Independent Investigator Status)||Massachusetts General Hospital||United States||2013-02-18||Germline Mutations in Ras/Raf-initiated Carcinogenesis||Certain 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. |
|25.||Tatsuaki Morokata||Astellas Pharma Inc.||Japan||2013-02-08||Investigation of innovative drug targets and biomarkers for cancer therapeutics.||Astellas Pharma Inc. is committed to providing highly effective drugs to oncology patients. We believe that understanding the complexity of cancer is absolutely necessary to the development of drugs with higher efficacy and fewer side effects, and that the data of the International Cancer Genome Consortium (ICGC) will provide us with a deep understanding of the genetic context of cancer cells and tissues. By applying computer analyses to the ICGC data, we will explore some genomic alterations specifically observed in some types of cancer. We will then combine the results with clinical information to identify mutated proteins which may cause cancer, and experimentally validate the ability to induce tumor formation. We believe that analyses such as these using the ICGC data will provide us the opportunity to find novel drug targets or biomarkers and make it possible to develop cancer medicines for defined patient populations with precise diagnoses. |
|26.||Carlos Martinez-A.||Centro Nacional Biotecnologia||Spain||2013-12-05||Study of the implications of the Dido locus in chromosome 20 (death inducer obliterator protein ) in hematological myelodysplasia||The 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. |
|27.||Claude Chelala||Barts Cancer Institute, Queen Mary University of London||United Kingdom||2013-12-04||Integrated meta analysis of pancreatic ductal adenocarcinoma studies towards prognostic biomarker discovery||Pancreatic cancer is amongst the leading cause of cancer deaths in the world with an overall 5 year survival of less than 5%. Over the past decade, a number of research efforts aimed at studying gene expression profiles have identified patient groups (disease subtypes) that respond differently to the same treatment. We are compiling these studies for meta analysis in order to validate the reproducibility of disease subtypes across multiple studies. Moreover, the project aims to identify potentially new subtypes that are correlated with patient's clinical outcome, and subsequently test their robustness and reproducibility on independent patient cohorts. |
|28.||Sami Kilpinen||MediSapiens Ltd||Finland||2013-12-10||Next-generation of bioinformatic tools for personalized medicine||MediSapiens Ltd is developing innovative software tools for both drug development and personalized medicine. While modern technology allows rather efficient genetic analysis of individuals, the interpretation of the resulting information remains a considerable challenge. With proper tools one could use these existing large scale datasets to interpret data from individual patients. This would enable e.g. tailoring medical treatment to suit the patient and illness better. In drug development, the ability to visually explore and analyze complex and large datasets is essential for the development of effective new therapies for cancer. |
|29.||Parminder Mankoo||Sanofi Aventis||United States||2013-02-07||Identifying subtype-specific mechanisms and novel drug targets and biomarkers using ICGC genomic and clinical data||This 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. |
|30.||Kamalakar Gulukota||NorthShore University HealthSystem||United States||2013-11-25||Genomics of Pancreatic Cancer||Pancreatic cancer is a very aggressive tumor resulting in death in about a year in most cases. However, there are some patients who survive 5 years and more after diagnosis. Our project attempts to look at these patients in an attempt to determine the characteristics of these tumors. The goal is to provide a more reliable prognosis to patients' data on the basis of the molecular characterization of their tumor. |
|31.||Pedro Galante||Hospital Sirio-Libanês||Brazil||2013-02-07||Study of retroposition events in cancer||A major set of cancers is caused by alterations in DNA. The identification of these alterations is essential for 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 focuses are those variations involving retrotransposable elements, such as LINEs, SINEs and other retrocopied elements. We expected to find a small, but interesting set of somatic variations involving these elements in tumors. |
|32.||Jaume Pons||Pfizer, Inc.||United States||2013-11-08||Discovery and validation of targets in sub types of cancer by mining genetic and expression data||Our 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. |
|33.||Lars Kaderali||University of Technology Dresden||Germany||2013-04-04||Non-coding RNAs in cancer development||While it is well known that mutations in our genomic information are associated with cancer, much less is known about how such mutations affect regulatory mechanisms in the cell. In this project, we focus on the role of alterations in regulatory regions of our genome (so-called noncoding RNA), and their role in cancer development. For this purpose, we will develop and apply novel bioinformatics tools to model cellular regulatory mechanisms, map noncoding RNA data to these networks, and analyze the role these networks play in tumor progression based on the sequencing data from the ICGC consortium. We hope that through this procedure we will not only get a clearer idea of the role of noncoding RNAs in cancer, but possibly also identify new points of attack to impede cancer progression. |
|34.||Keyue Ding||Chongqing Medical University||China||2013-04-19||Genetic mechanisms of virus-related hepatocellular carcinoma||Hepatocellular carcinoma (HCC) is the fifth most common cancer in men and the seventh in women. Major risk factors for HCC include infection with hepatitis B virus (HBV) or hepatitis C virus (HCV), alcoholic liver disease, and most probably nonalcoholic fatty liver disease. In the proposed study, we aim to use data from the ICGC data portal to systematically characterize mutation patterns in HCC with different causes. We will also sequence DNA from cancer tissues, adjacent normal tissues and immune system cells in ten patients with HBV-associated HCC. Using this data, we aim to identify "driver genes" involved in HCC development. |
|35.||Nicholas Shackel||Centenary Institute||Australia||2013-05-13||Discovering Novel Non-Invasive Diagnostic and Prognostic Markers in Hepatocellular Carcinoma||The 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. |
|36.||Peter Campbell||Wellcome Trust Sanger Institute||United Kingdom||2013-03-11||Deciphering the mutational landscape and signatures of mutational processes active in the genomes and transcriptomes of human cancers||In 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. |
|37.||Sergi Castellvi-Bel||Institut D'Investigacions Biomediques August Pi i Sunyer (IDIBAPS)||Spain||2013-04-03||Identification of new genes for predisposition to colorectal cancer by exome sequencing||The main objective of this research project is the identification of new genes responsible for familial colorectal cancer (CRC) predisposition by sequencing the part of the human genome that codifies for proteins (whole-exome sequencing) in selected cases with familial aggregation but no alteration of genes involved in the known CRC hereditary forms (polyposis forms and Lynch syndrome).
Access to sequencing data files corresponding to the germline coding genome of 105 cases from the Chronic Lymphocytic Leukemia (CLL) ICGC project will permit to perform a control of the filtering and prioritization strategy used in our dataset in an independent dataset. Those variants shared in the CRC and CLL datasets will be most probably false positives findings, not likely to be involved in CRC genetic predisposition. |
|38.||Trey Ideker||University of California, San Diego||United States||2013-04-30||Network analysis and classification of cancer||Emerging 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. |
|39.||Jonathan Dry||AstraZeneca||United States||2013-08-01||Discovery of novel drug targets and patient opportunities for existing drugs in diverse tumor types||The 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. |
|40.||David Torrents||BSC-IRB Research Programme in Computational Biology||Spain||2013-05-03||Validation and discovery of new structural rearrangements in human Medulloblastoma||Cancer 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. |
|41.||George Bova||University of Tampere Institute of Biomedical Technology||Finland||2013-04-26||Integrated Studies of Cancer Genomics||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. |
|42.||John McPherson||Ontario Institute for Cancer Research||Canada||2013-11-22||International Cancer Genome Consortium (ICGC): Pancreatic Cancer Project||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 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.
|43.||Ryan Morin||Simon Fraser University||Canada||2013-07-29||Meta-analysis of sequencing data from human tumours||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. |
|44.||Eduardo Reis||University of Sao Paulo||Brazil||2013-05-21||Establishment of functional genomics platforms for discovery of molecular markers and therapeutical
targets in pancreatic cancer||Ductal 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. |
|45.||Joaquin Dopazo||Centro de Investigacion Principe Felipe||Spain||2013-05-21||Impact of cancer mutations on the interactome structure||Although 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. |
|46.||Lan Kluwe||University Medical Center Hamburg-Eppendorf||Germany||2013-06-11||Exploring 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. |
|47.||Sepp Hochreiter||Johannes Kepler University Linz||Austria||2013-06-11||Detection of copy number variations and segments that are identical by descent in cancer patients||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 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. |
|48.||Gloria Petersen||Mayo Clinic||United States||2013-08-30||Applying next generation sequencing studies to identify genetic predisposition to pancreatic cancer||Pancreatic 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. |
|49.||Janet Kelso||Max Planck Institute for Evolutionary Anthropology||Germany||2013-06-21||Mutations and associated changes of gene expression - cancer genes and evolutionary relevant genes||From 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. |
|50.||Jorge Reis-Filho||Memorial Sloan-Kettering Cancer Center||United States||2013-06-11||Genomic characterisation of rare histological subtypes of breast cancer||Breast 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. |
|51.||Ming Tsao||University Health Network||Canada||2013-06-21||International Cancer Genome Consortium (ICGC): Pancreatic Cancer Proejct||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 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. |
|52.||Xavier Estivill||Center For Genomic Regulation (CRG)||Spain||2013-07-31||Assessing the germline component of genetics susceptibility to common cancer||We 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. |
|53.||Brandon Higgs||MedImmune||United States||2013-09-13||Integrative analyses of multi-omics data to identify therapeutic targets in patients with double malignancy syndrome||We 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. |
|54.||Rene Opavsky||University of Nebraska Medical Center||United States||2013-09-20||Identification of genes hypomethylated in human and mouse lymphoid malignancies||Blood 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. |
|55.||Nada Jabado||McGill University health Centre (MUHC)||Canada||2013-12-04||Biomarkers 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. |
|56.||Keigo Machida||University of Southern California||United States||2013-10-24||Mutanome of liver cancer using Exome sequencing and RNA-seq||Compelling 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. |
|57.||Li Ding||Washington University in St. Louis||United States||2013-11-05||Cancer Susceptibility Variant Discovery in High Throughput Sequencing Data||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. |
|58.||Joshua Stuart||University of California Santa Cruz||United States||2013-11-08||ICGC-TCGA DREAM Challenge||Mutations, or changes to the genome sequence of a cell, can lead to cancer. It is therefore of extreme importance to catalog all of the mutations observed in tumors to gain information about the disease. Dozens of methods exist to identify mutations. However, there is currently very little agreement between any two methods. The discrepancies make it difficult to compare and combine results from different studies. The ICGC-TCGA DREAM organization is launching the Somatic Mutation Calling Challenge to identify the best method(s). The most accurate techniques that emerge from this activity will be made available to the research community and adopted as the standards for large scale analysis of CGHub datasets and for a whole genome pan-cancer analysis conducted by ICGC in the coming years. |
|59.||Gad Getz||Broad Institute||United States||2013-12-11|| 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 . |