Sohrab Shah, PhD

Chief, Member

Contact
Cristina Radu 646.888.8300

About

Sohrab Shah is the Chief of Computational Oncology in the Department of Epidemiology and Biostatistics. Dr. Shah received a PhD in computer science from the University of British Columbia in 2008 and developed his research program in computational biology at BC Cancer Agency and the University of British Columbia starting in 2010. His research focuses on developing and using computational methods to understand cancer evolution and treatment response. This encompasses advanced machine learning and Bayesian statistical methods to analyze and interpret large-scale datasets in cancer research. At MSK, Dr. Shah is building new and innovative capacity in computational methods across the spectrum of data-intensive research activity. This includes multimodal data integration such as genomics and imaging, high-resolution single-cell genomics, and transcriptomics. His translational focus lies in breast cancer and ovarian cancer, in which he has pioneered discovery of prognostic mutational signatures and large-scale studies of mutational landscapes and evolution of these cancers. Dr. Shah is a former Canada Research Chair, is a Komen Scholar, and holds the Nicholls-Biondi Endowed Chair in Computational Oncology at MSK.

Education

PhD

University of British Columbia
Vancouver, British Columbia, Canada

Appointments

Computational Oncology, Department of Epidemiology & Biostatistics

Professor, Weill Cornell Medical College

Gerstner Sloan Kettering Faculty Member

Affiliations

Affiliate Member, New York Genome Center

Affiliate Member, Irving Institute for Clinical and Translational Research, Columbia University

 

Selected Publications

Ceglia N, Sethna Z, Freeman SS, … Shah SP, McPherson A. Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector. Nat Commun. 2023 14(1):4400. PMCID: PMC10359421.

Funnell T*, O’Flanagan C*, Williams M*, … Shah SP, Aparicio S. Single cell genomic variation induced by mutational processes in cancer. Nature 2022; 612(7938):106-115. PMCID: PMC9712114.

Vázquez-García, I., Uhlitz, F., Ceglia, N.  Shah SP. Ovarian cancer mutational processes drive site-specific immune evasion. Nature 2022; 612(7941):778-786. PMCID: PMC9771812.

Boehm KM, Aherne EA, Ellenson L, … Shah SP. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat Cancer. 2022 (6):723-733. PMCID: PMC9239907.

Vanguri RS, Luo J, Aukerman AT, … Shah SP. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat Cancer. 2022 (10):1151-1164. PMCID: PMC9586871.

Salehi S, Kabeer F, Ceglia N… Shah SP. Clonal fitness inferred from timeseries modeling of single cell cancer genomes. Nature 2021;595(7868):585–590. PMCID: PMC8396073.

Laks E, Zahn H, Lai D, McPherson A, Steif A,… Shah SP, Aparicio S. Clonal Decomposition and DNA Replication States Defined by Scaled Single-Cell Genome Sequencing Cell. 2019;179(5):1207-1221.e22. PMCID: PMC6912164.

Funnell T, Zhang AW, Grewal D, … Shah SP. Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models. PLoS Comput Biol. 2019;15(2):e1006799. PMCID: PMC6402697.

Zhang AW… Shah SP. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat Methods. 2019;16(10):1007-15. PMCID: PMC7485597

Campbell KR, Steif A, Laks E… Shah SP. clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers. Genome Biol. 2019;20(1):54. doi: 10.1186/s13059-019-1645-z. PMCID: PMC6417140.

 

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