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Rameen Beroukhim, MD, PhD

Assistant Professor, Department of Medicine, Harvard Medical School

Attending Physician, Medicine, Brigham And Women's Hospital

Associate Member, Cancer Program, Broad Institute of MIT and Harvard

Assistant Professor, Medical Oncology and Cancer Biology, Dana-Farber Cancer Institute

Contact Info

Rameen Beroukhim
Dana-Farber Cancer Institute
450 Brookline Avenue

Boston, MA, 02215
Phone not available.


Michael Donohoe
Dana-Farber Cancer Institute
Phone: 617-632-6488

DF/HCC Program Affiliation

Cancer Genetics

Lab Website

Beroukhim Laboratory

Research Abstract

The Beroukhim laboratory focuses on understanding the somatic genetics of cancer, primarily in identifying alterations in chromosomal structure (including copy-number gains and losses and loss of heterozygosity) that contribute to tumor growth, and characterizing the biological effects of these alterations. The aims of these experiments are to understand the biological basis of the various cancer subtypes to guide development of therapeutics, and to develop prognostic and predictive markers to guide the application of those therapeutics.

Much of our effort focuses on developing computational methods with general applicability to the study of the somatic genetics of cancer. An example is the Genomic Identification of Significant Targets In Cancer (GISTIC) algorithm, a statistical technique that distinguishes chromosomal alterations that are likely to drive tumorigenesis from alterations that may result from random chance alone. We and our collaborators have used this approach to identify novel oncogenes in lung adenocarcinoma (NKX2-1), lung and esophageal squamous cell cancers (SOX2), and colon cancer (CDK8). We have also used the technique to identify prognostic and predictive indicators in a variety of cancers. Recently, we have developed these methods to perform unified analyses across over 3000 cancers of several dozen types, in which we identified two new amplified oncogenes (MCL1 and BCL2L1) and an overarching unity to cancer genomes. We are currently extending these approaches to additional cancer types and higher quality, integrated datasets.


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