| Dr. Stephen Friend | Sage Bionetworks | United States | 2010-12-20 | 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. |
| Dr. Daniel D Von Hoff, MD. | Translational Genomics Research Institute (TGen) | United States | 2011-02-07 | Pancreatic Cancer Genomics | With a 5-year survival rate of less than 4%, pancreatic cancer is the fourth leading cause of cancer death in the United States. Current chemotherapy and radiation therapy are largely ineffective in the treatment of this disease. Instead of generic cytotoxins (traditional arsenal of the oncologist), the field is seeking to develop “smart drugs” that target critical aberrations that are unique to cancer cells. This approach is meeting with some success, however, patients’ tumors still eventually progress on these therapies. Most investigators envision using combination of agents to hit multiple targets present in a patient’s tumor. By identifying additional tumor targets we hope to provide patients with new therapeutic alternatives. We plan to evaluate next-generation technologies such as tumor and non-tumor sequencing to precisely define the molecular markers that are responsible for growth and differentiation in each patient. |
| Dr. David Jackson | LIFE Biosystems | Germany | 2011-05-03 | 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. |
| Dr. Michael Heuser | Hannover Medical School | Germany | 2011-05-18 | Characterization of novel mutations in AML for their effects on myeloid differentiation. | Acute myeloid leukemia is a very aggressive disease of the white blood cells, and novel treatments are needed. All-trans retinoic acid (ATRA) is an agent that has already been shown to be effective in one special form of leukaemia (acute promyeloic leukaemia). Whether ATRA is effective in other types of AML is a matter of current investigations. We are interested in identifying novel mutations that may be responsible for the fact that many leukemia patients don’t benefit from ATRA treatment. Genes of primary interest will be members of the retinoic acid receptor pathway. If mutations in these genes can be identified they will be functionally evaluated for their effect on ATRA sensitivity and leukemia development in vivo. Targeting these mutated proteins with new drugs may improve treatment of AML patients.
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| Mr. Christopher H Moy | GlaxoSmithKline | United States | 2011-08-08 | Translational Studies for Clinical and Preclinical Data Exploration in Cancer Genetics | GlaxoSmithKline Oncology R&D has numerous new cancer treatment currently in development. One of the critical challenges to improved treatment outcomes is to match the appropriate treatments to the patient. The primary objective of our translation projects is to define the nature of tumors in a detailed scientific manner such that the best option can be made available to patients receiving experimental treatments in the clinic. These detailed studies of tumors will help direct our scientific strategies for patient selection and provide insight to designing new classes of therapeutics for cancer patients worldwide. |
| Prof. Gary Bader | University of Toronto | Canada | 2011-10-07 | Impact of cancer-specific DNA mutations on protein-protein interaction networks | The cell is a complex machine made of many interacting parts. Parts include proteins, encoded by genes in the genome, and other molecules, like fats and sugars. The goal of our research is to develop a map of how parts of the cell work together. This map can then be used to learn how genes function in cellular processes and what happens when specific genes fail in disease, such as cancer. We are using ICGC data to understand how cancer mutations affect proteins and their interactions to change the map and influence disease. With this information, researchers can better understand the underlying causes of cancer in their quest to develop more timely diagnosis and better medical treatments. |
| Prof. Rune Linding | Technical University of Denmark | Denmark | 2011-10-20 | 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. |
| Prof. Peter Park | Harvard Medical School | United States | 2011-11-22 | Analysis of structural variations using whole-genome sequencing data | A large number of diseases, most notably cancer, involve various structural alterations in the genome. Thus, precise characterization of these structural variations in both normal and diseased individuals is important for understanding their impact on human health and disease. In this project, computational methods will be developed to accurately detect these variations from a large amount of data obtained using the latest DNA sequencing technologies. This would help us understand genome variations that might be related to initiation or progression of various cancers. |
| Dr. Steven J Jones | BC Cancer Agency - Michael Smith Genome Sciences Centre | Canada | 2011-12-01 | Comparison of the somatic mutational spectrum of medulloblastomas with other cancer types | To evaluate the relevance of changes found in the genetic make up of pediatric brain cancer patients, these changes have will be compared to information available from other cancer types. This cross comparison will potentially enable the identification of disease specific, novel or existing approaches to treatment. |
| Prof. Mark Gerstein | Yale University | United States | 2011-12-13 | 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. |
| Dan Rhodes | Compendia Biosciences, Inc. | United States | 2012-01-10 | 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. |
