The Getz Lab
Lab Overview

The Getz lab is focused on cancer genome analysis, which includes (i) somatic events that cause cancer or lead to development of resistance, (ii) germline events that increase the risk for getting cancer, and (iii) using these events to identify subtypes of the disease and their relationship to clinical parameters and/or treatment outcomes. The team is building tools that are part of a robust analytical pipeline to analyze data coming from various national/international collaborative cancer genome projects, and these tools are revolutionizing how we analyze cancer genomes and use them in clinical settings.

Cancer genome analysis in the Getz lab includes two major steps: (i) Characterization – cataloging of all genomic events and the mechanisms that created them during the clonal evolution of the cancer, comparing events at the DNA, RNA and protein levels between tumor and normal 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.

Recent Papers
Clinical Outcomes and Evolution of Clonal Hematopoiesis in Patients with Newly Diagnosed Multiple Myeloma
Mouhieddine TH, Nzerem C et al. Cancer Res Commun 2023
Published 18 Dec 2023
Pan-cancer proteogenomocs connects oncogenic drivers to functional states
Li Y, Porta-Pardo E, Tokheim C, Bailey MH, Yaron TM et al. Cell 2023
Published 31 Aug 2023
Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation
Geffen Y, Anand S, Akiyama Y, Yaron TM, Song Y et al. Cell 2023
Published 14 Aug 2023
Protogenomic data and resources for pan-cancer analysis
Li Y, Dou Y, Da Veiga Leprevost F, Geffen Y, Calinawan AP et al. Cancer Cell 2023
Published 14 Aug 2023
Genetic events associated with venetoclax resistance in CLL identified by whole-exome sequencing of patient samples
Khalsa JK, Cha J, Utro F, Naeem A, Murali I et al. Blood 2023
Published 03 Aug 2023
Inferring early genetic progression in cancers with unobtainable premalignant disease
Leshchiner I, Mroz EA, Cha J et al. Nature Cancer 2023
Published 24 Apr 2023
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