Fighting Cancer with Machine learning
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Research Focus

We are building the Cancer Dependency Map: an effort to systematically identify the chemical and genetic vulnerabilities of different cancer cells, as well as their predictive biomarkers.

Precision cancer medicine

We build predictive models to predict cancer vulnerabilities from genomic profiles of tumors and cancer cell lines.

Cancer Targets Identification

We integrate functional screening and 'omics data to identify novel cancer targets as well as drugs for repurposing.

CRISPR/Cas9
screens

We develop computational methods and tools to facilitate the analysis of CRISPR screening in cancer models.

Small-molecule screens

We analyze highly-multiplexed small-molecule screening data from the PRISM platform to discover novel cancer therapeutic leads.

Our Team

Aviad Tsherniak

Founder and Scientific Advisor

Philip Montgomery

Sr Principal Software Engineer

Mike Burger

Senior Computational Associate

Neekesh Dharia

Postdoctoral Scholar

James McFarland

Group Leader

Josephine Lee

Senior Software Engineer

Josh Dempster

Data Scientist II

Allie Warren

Associate Computational Biologist II

Jérémie Kalfon

Associate Computational Biologist II

Andrew Tang

Sr. Visual Designer

Mustafa Kocak

Computational Scientist II

Josh Pan

Postdoctoral fellow

Yejia Chen

Software Engineer

Ashir Borah

Associate Computational Biologist I

Gwen Miller

Broad Cancer Scholar - Associate Computational Biologist

William Colgan

Associate Computational Biologist I

Andrew Boghossian

Associate Computational Biologist I

Nishant Jha

Software Engineer

Javad Noorbakhsh

Computational Scientist II

Lena Joesch-Cohen

Associate Computational Biologist I

Isabella Boyle

Associate Computational Biologist I

Kwabena Ofori-Atta

Associate Computational Biologist I

David Wu

Associate Computational Biologist I

Jessica Cheng

Associate Software Engineer

Alumni

Han Xu

Associate Professor, MD Anderson

Robin Meyers

Graduate Student, Genetics, Stanford University

Jared Jacobsen

Studying for AI Research

Li Wang

Computational Biologist, 10X Genomics

Jordan Bryan

Graduate Student, Statistics, Duke University

Kailash Nakagawa

Undergraduate Student, Stanford University

Quinton Wessells

Graduate Student, Biomedical Informatics, Stanford University

Remi Marenco

Bioinformation Lead, Cancer Cell Line Factory

Guillaume Kugener

Medical Student, USC

Zandra Ho

Medical Student, Brown

Andy Jones

Graduate Student, Computer Science, Princeton University

Jordan Rossen

Graduate Student, Epidemiology, Harvard University

Vickie Wang

MD-PhD student, Neuroscience, University of Pennsylvania

Mariya Kazachkova

Graduate Student, Biomedical Sciences, UCSD

Phoebe Moh

Graduate Student, Computer Science, University of Maryland

CDS Outings

Join Us

Associate Software Engineer

Apply here

We are seeking an exceptional software developer to join the Cancer Data Science group to build tools and portals. In the Cancer Data Science group, we leverage data science approaches to understand cancer biology and support translational efforts.

Publications

Selected publications

Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

James McFarland et al.
Nature Communications
August 2020
Check out the accompanying CDS Blog post!

Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

Andrew Jones et al.
Preprint
March 2020

Global computational alignment of tumor and cell line transcriptional profiles

Allison Warren et al.
Preprint
March 2020

Gene expression has more power for predicting in vitro cancer cell vulnerabilities than genomics

Joshua M. Dempster et al.
Preprint
February 2020

Discovering the anticancer potential of non-oncology drugs by systematic viability profiling

Steven M. Corsello et al.
Nature Cancer
January 2020

Agreement between two large pan-cancer CRISPR-Cas9 gene dependency datasets

Joshua M. Dempster et al.
Nature Communications
December 2019
16th most read Nature Communications paper in life and biological sciences in 2019!

Extracting Biological Insights from the Project Achilles Genome-Scale CRISPR Screens in Cancer Cell Lines

Joshua M. Dempster et al.
Preprint
July 2019

WRN helicase is a synthetic lethal target in microsatellite unstable cancers

Edmond M. Chan et al.
Nature
April 2019

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

James M. McFarland et al.
Nature Communications
November 2018

Accompanying website: https://depmap.org/R2-D2/

Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells

Robin M. Meyers et al.
Nature Genetics
October 2017

Accompanying website: https://depmap.org/ceres/

Defining a Cancer Dependency Map

Aviad Tsherniak et al.
Cell
July 2017

Accompanying website: https://depmap.org/rnai

ATARiS: Computational quantification of gene suppression phenotypes from multisample RNAi screens

Diane D. Shao et al.
Genome Research
December 2012

Other publications

Genomic copy number dictates a gene-independent cell response to CRISPR-Cas9 targeting

Andrew J. Aguirre et al.
Cancer Discovery
June 2016

High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines

Channing Yu et al.
Nature biotechnology
February 2016

Genome-wide interrogation of human cancers identifies EGLN1 dependency in clear cell ovarian cancers

Colles Price et al.
Cancer research
March 2019

MDM2 and MDM4 are Therapeutic Vulnerabilities in Malignant Rhabdoid Tumors

Howard TP et al.
Cancer Resarch
February 2019

Genetic and transcriptional evolution alters cancer cell line drug response

Uri Ben-David et al.
Nature
August 2018

Targetable vulnerabilities in T-and NK-cell lymphomas identified through preclinical models

Samuel Y. Ng et al.
Nature communications
May 2018

CRISPR-Cas9 screen reveals a MYCN-amplified neuroblastoma dependency on EZH2

Liying Chen et al.
The Journal of clinical investigation
December 2017

MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells

Gregory V. Kryukov et al.
Science
March 2016

Contact Us

James McFarland

Cancer Data Science
Broad Institute of MIT and Harvard
415 Main Street
Cambridge, MA 02142

Email: jmmcfarl at broadinstitute.org


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