“Finding cancer genes and pathways is a crucial first step towards therapy…”" />
Browse by Medical Category
“Finding cancer genes and pathways is a crucial first step towards therapy…”
Gad A. Getz, PhD
Associate Professor of Pathology, Harvard Medical SchoolAssistant Pathologist, Massachusetts General HospitalDirector of Bioinformatics, Massachusetts General Hospital Cancer Center and Department of PathologyPaul C. Zamecnik Chair of Oncology, Massachusetts General Hospital Cancer Center Director, Cancer Genome Computational Analysis and Associate Member, Broad InstituteMember, Blue Ribbon Panel, Co-chair of Enhanced Data Sharing working group
Massachusetts General Hospital Cancer Center149 13th Street, 7th FloorCharlestown, MA, 02129Phone: 617-724-7014Email: GGETZ1@mgh.harvard.edu
The Getz Laboratory is focused on cancer genome analysis which includes two major tasks: (i) Characterization – cataloging of all genomic events and the mechanisms that created them during the 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 molecular subtypes of the disease, their markers and relationship to clinical variables.
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. 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, noise 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, CapSeg, dRanger, BreakPointer etc.). We are also developing benchmarking approaches to assess the accuracy of the tools to help guide and interpret the experiments.
Detecting Cancer-Associated Genes
Next, we analyze the detected events across a cohort of samples searching for genes/pathways 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 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). We recently reported the importance of modeling the heterogeneity of these models across patients, sequence contexts and the genome, when searching for cancer genes. We are continuously improving these methods and working towards generating a unified approach that integrates all types of alterations to better detect 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 are developing tools (ABSOLUTE) 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. We are now working to introduce these concepts to clinical trials and eventually clinical care.
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.
Andre Bernards Associate GeneticistAndrew Dunford Associate Computational Biologist IAtanas Kamburov Postdoctoral ScholarAyellet Segre Computational Biologist II, Broad InstituteChet Birger Associate Director, Principal Architect, CGA, Broad InstituteChip Stewart Senior Computational Biologist IIDaniel Rosebrock Associate Computational BiologistDavid Heiman Software EngineerDimitri Livitz Associate Computational BiologistDuyen Nguyen Software EngineerEddie Salinas Senior Software EngineerEila Arich-Landkof Associated ResearcherEsther Rheinbay Postdoctoral AssociateFrancois Aguet Computational Biologist IGordon Saksena Senior Software EngineerGrace Tiao Associate Computational BiologistLidia Graziano Senior Administrative AssistantHailei Zhang Computational Biologist IIgnaty Leshchiner Senior Computational Biologist IJaegil Kim Senior Computational Biologist IJulian Hess Associate Computational Biologist IKane Hadley Software EngineerKeren Yizhak Associated ResearcherKirsten Kubler Associated ScientistManaswi Gupta Associate Computational Biologist IIMaryam Alsalah TechnicianMegan Hanna Manager, Operations, CGAMichael Noble Associate Director, Data Science CGANick Haradhvala Associated ScientistPaz Polak Postdoctoral ScholarPrasanna Parasuraman Postdoctoral ScholarSam Freeman Graduate StudentSam Meier Software EngineerTimothy Defreitas Associate Software EngineerTim Sullivan Associate Computational Biologist IIXiao Li Associate Computational BiologistYosef Maruvka Postdoctoral Scholar
Back to Top