Professor of Pathology, Harvard Medical School Investigator, Massachusetts General Hospital Director of Bioinformatics, Massachusetts General Hospital Cancer Center and Department of Pathology Paul C. Zamecnik Chair of Oncology, Massachusetts General Hospital Cancer Center Director, Cancer Genome Computational Analysis and Institute Member, Broad Institute Contact email: firstname.lastname@example.org
Cancer is a disease of the genome that is driven by a combination of possible germline risk-alleles together with a set of “driver” somatic mutations that are acquired during the
clonal expansion of increasingly fitter clones. In order to generate a comprehensive list of all germline and somatic events that occurred during life and the development of the cancer, we are developing and applying highly sensitive and specific tools for detecting different types of mutations in massively-parallel sequencing data. The volume and complexity of these data require developing computational tools using state-of-the-art statistical and machine learning approaches to extract the signal from the noise (e.g., MuTect, dRanger, MSMuTect, etc.).
Detecting cancer-associated genes
Next, we analyze the detected events across a cohort of samples searching for genes/ pathways, as well as non-coding variants, that show significant signals of positive selection. To that end, we construct a statistical model of the background mutational processes and then detect genes that deviate from it. As part of constructing the models, we study and infer the mutational processes (using SignatureAnalyzer) that affected the samples (carcinogens, defects in repair mechanisms, etc.) and their timing.
We have developed tools for detecting significantly gained or lost genes in cancer (GISTIC) and genes with increased density or irregular patterns of mutations (MutSig suite, CLUMPS/ EMPRINT, MSMutSig, NetSig). Our work demonstrated the need to accurately model the heterogeneity of mutability across patients, sequence contexts and genome, when searching for cancer genes.
Heterogeneity and clonal evolution of cancer
Cancer samples are heterogeneous, containing a mixture of normal cells and cancer cells that often represents multiple subclones. We developed and continue to develop tools (ABSOLUTE, Phylogic, PhylogicNDT) for characterizing the heterogeneity of cancer samples using copy-number and mutation data measured on bulk samples and now also using single cells. Using these tools, we can infer which mutations are clonal or sub-clonal, as well as estimate the number of subclones and their distribution over space and time. This enables us to study resistance to therapy and introduce these concepts into clinical trials.
Pre-cancer to cancer transition and cancer cell-of-origin
By studying mutations in normal cells and pre-cancer lesions, we can detect clonal expansions in pre-malignant tissues. We have observed clonal expansions across essentially all normal tissue types. Leveraging the association between mutation density and the epigenetic state of a cell, we study the tissue- and cell-of-origin of all major cancer types enabling better understanding the transition from pre-malignant clones to cancer.
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