Gad A. Getz, PhD

Associate Professor of Pathology, Harvard Medical School
Assistant Pathologist, 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 Associate Member, Broad Institute
Member, Blue Ribbon Panel, Co-chair of Enhanced Data Sharing working group


Massachusetts General Hospital Cancer Center
149 13th Street, 7th Floor
Charlestown, MA, 02129
Phone: 617-724-7014
Email: GGETZ1@mgh.harvard.edu

Overview

The Getz Laboratory is focused on cancer genome analysis which includes two major steps: (i) Characterization – cataloging of all genomic events and the mechanisms that created them during the clonal evolution of the cancer, including events at the DNA, RNA and protein levels in normal and tumor samples from an individual patient; and (ii) Interpretation – analysis of the characterization data across a cohort of patients with the aim of identifying the alterations in genes and pathways that cause cancer or increase its risk as well as identifying mutational processes, tumor evolution and heterogeneity, molecular subtypes of the disease, their markers and relationship to clinical variables. In addition to developing tools for high throughput analysis of cancer data and experimentally testing the findings, the Getz lab develops platforms that enable to manage and execute these large-scale analytical pipelines, including Firehose and a cloud-based version FireCloud, and builds portals that enable the research community to interact with the data and results of the analyses.

Characterizing the Cancer Genome

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. Mutations occur at all levels and scales, including DNA point mutations, small insertions and deletions, larger genomic rearrangements and copy-number alterations, as well as epigenetic, transcriptional and proteomic changes. To generate a comprehensive list of all germline and somatic events that occurred during and prior to development of the cancer, we are developing and applying highly sensitive and specific tools for detecting these events in sequencing data. The complexity of the underlying cancer genomes requires the use of state-of-the-art statistical and machine learning approaches to most efficiently extract the signal from the noise (tools we developed include MuTect, Indelocator, SegSeq, CapSeg, dRanger, BreakPointer and others).

Detecting Cancer-Associated Genes

Once we detect the events in the cancer genomes, we analyze them across a cohort of samples searching for genes (and pathways) that show significant signals of positive selection, e.g. the number of mutations exceeds what is expected by chance. To do so, we construct a detailed statistical model of the background mutational processes and detect genes that deviate from this model. We have developed tools for detecting genes which are significantly gained or lost in cancer (GISTIC) and genes with increased density or irregular patterns of mutations (MutSig, CLUMPS). We recently reported the importance of modeling the heterogeneity of these mutational processes across patients, sequence contexts and along the genome, when searching for cancer-associated genes. We are improving these methods and working towards a unified method that takes into account all types of alterations and incorporates prior knowledge to better detect cancer genes and driver alterations.

 

Heterogeneity and clonal evolution of cancer

Cancer samples are heterogeneous, containing a mixture of normal (i.e. non-cancer) cells and a population of cancer cells that often represents multiple subclones. Keeping in mind that cancer is a dynamic system, these subclones may represent the remaining cells of less-fit clones which have not yet been overtaken by the expanding most-fit clone or they may represent interacting sub-clones that co-evolved to support each other and reached an equilibrium or a combination of these scenarios. Our lab has been developing tools (ABSOLUTE) for characterizing the heterogeneity of cancer samples using copy-number, mutational and other data measured on bulk samples and now also getting into the analysis of single cells. Using these tools, we can infer which mutations are clonal (i.e. exist in all cancer cells) or sub-clonal (i.e. exist in subclones), as well as estimate the number of subclones and monitor their evolution over time or space by studying multiple samples from the same patient. Recently, we demonstrated that sub-clonal driver mutations are associated with outcome, emphasizing the importance of including clonal information in clinical trials.

Mutational Process

Mutations are the product of multiple processes that damage, repair, replicate and deliberately alter DNA. We use mutation data to study these processes, understand their mutational signatures, infer their molecular mechanisms and identify alterations that are associated with their activity. We recently found an association between mutations in ERCC2 and a specific mutational signature. By studying asymmetries in mutational processes we were able to detect that a mechanism that works on the lagging strand of DNA while it is replicated and a new mutational process that generates mutations on the non-transcribed strand.

Read more about the Getz Lab from the Center for Cancer Research Annual Report and the Pathology Basic Science Research Brochure.

Selected Publications

Bibliography of Gad Getz via PubMed

Kim J, Mouw KW, Polak P, Braunstein LZ, Kamburov A, Tiao G, Kwiatkowski DJ, Rosenberg JE, Van Allen EM, D'Andrea AD, Getz G. Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat Genet. 2016; 48(6):600-6.

Haradhvala NJ, Polak P, Stojanov P, Covington KR, Shinbrot E, Hess JM, Rheinbay E, Kim J, Maruvka YE, Braunstein LZ, Kamburov A, Hanawalt PC, Wheeler DA, Koren A, Lawrence MS, Getz G. Mutational strand asymmetries in cancer genomes reveal mechanisms of DNA damage and repair. Cell. 2016; 164(3):538-49.

Lawrence MS, Stojanov P, Mermel CH, Robinson JT, Garraway LA, Golub TR, Meyerson M, Gabriel SB, Lander ES*, Getz G*. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature. 2014; 505:495–501.( *Co-corresponding authors)

Lawrence MS, etal, Lander ES*, Getz G*. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013; 499(7457):214-8. .( *Co-corresponding authors)

Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013; 31(3):213-9.

Carter SL, Cibulskis K, Helman E, McKenna A, Shen H, Zack T, Laird PW, Onofrio RC, Winckler W, Weir BA, Beroukhim R, Pellman D, Levine DA, Lander ES, Meyerson M, Getz G. Absolute quantification of somatic DNA alterations in human cancer. Nat Biotechnol. 2012; 30(5):413-21.

Group Members

Francois Aguet, PhD
Maryam Alsalah
Eila Arich-Landkof
Rotem Ben-Hamo Deutsh, PhD
Chet Birger, PhD
Timothy Defreitas
Andrew Dunford
Samuel Freeman
Gad Getz, PhD
Manaswi Gupta
Kane Hadley
Megan Hanna
Nicholas Haradhvala
David Heiman
Julian Hess
Atanas Kamburov, PhD
Jaegil Kim, PhD
Kirsten Kubler, MD, PhD
Ignat Leshchiner, PhD
Dimitri Livitz
Sam Meier
Yosef Maruvka, PhD
Michael Noble
Prasanna Parasuraman, PhD
Paz Polak, PhD
Esther Rheinbay, PhD
Daniel Rosebrock
Gordon Saksena
Eddie Salinas
Ayellet Segre, PhD
Chip Stewart, PhD
Timothy Sullivan
Grace Tiao
Keren Yizhak, PhD
Hailei Zhang, PhD

 

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