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
Distinct cellular dynamics associated with response to CAR-T therapy for refractory B-cell lymphoma
Haradhvala NJ, Leick MB, Maurer K, Gohil SH et al. Nature Medicine 2022
Published 12 Sep 2022
Tangent normalization for somatic copy-number inference in cancer genome analysis
Gao GF, Oh C, Saksena G et al. Bioinformatics 2022
Published 30 Aug 2022
High-resolution profiling of lung adenocarcinoma identifies expression subtypes with specific biomarkers and clinically relevant vulnerabilities
Roh W, Geffen Y, Cha H et al. Cancer Research 2022
Published 30 Aug 2022
Molecular map of chronic lymphocytic leukemia and its impact on outcome
Knisbacher BA, Lin Z, Hahn CK, Nadeu F, Duran-Ferrer M et al. Nature Genetics 2022
Published 04 Aug 2022
Genetic subtypes of smoldering multiple myeloma are associated with distinct pathogenic phenotypes and clinical outcomes
Bustoros M, Anand S et al. Nature Communications 2022
Published 15 Jun 2022
Prevalence of monoclonal gammopathies and clinical outcomes in a high-risk US population screened by mass spectrometry: a multicentre cohort study
El-Khoury H, Lee DJ, Alberge JB et al. Lancet Haematology 2022
Published 25 Mar 2022