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A Cancer Dependency Map to systematically identify genetic and pharmacologic dependencies and the biomarkers that predict them.


Contact depmap@broadinstitute.org for more information

Broad Institute DepMap Funders

Current
IBM
Achilles Consortium
Carlos Slim Foundation
CTD2
HCMI
Calico
Walter and Marina Bornhorst
Former
ICBP
Novartis (CCLE)

Cancer Dependency Map

The mutations that cause cancer cells to grow also confer specific vulnerabilities that normal cells lack. Some of these acquired alterations represent compelling therapeutic targets. The challenge is that, for the overwhelming majority of cancers, we do not fully understand the relationship between the genetic alterations of cancer and the dependencies they cause. To solve this problem, we are creating a “cancer dependency map” by systematically identifying genetic dependencies and small molecule sensitivities and discovering the biomarkers that predict them.

DepMap scientists are profiling hundreds of cancer cell line models for genomic information and sensitivity to genetic and small molecule perturbations. By triangulating information from these and other large-scale datasets, the hope is to define a landscape of genetic targets for therapeutic development, identify patients that who respond to these therapies, and develop a better understanding of the vulnerabilities of cancer.

The DepMap project at the Broad Institute is part of a strategic collaboration with the Wellcome Sanger Institute (Hinxton, UK). By leveraging the expertise and infrastructure available at both organisations, we aim to more rapidly deliver a high-quality DepMap. We anticipate that this foundational dataset will catalyse a new wave of precision cancer medicines.

Dependency Map

Expanding the diversity of cellular models

Precision medicine requires us to understand the genetic diversity across a broad spectrum of human cancers in order to guide treatment based on molecular characteristics. The DepMap will capture this diversity in order to reflect the molecular and epidemiologic diversity of human cancer. In addition to existing cell lines, the Cancer Cell Line Encyclopedia (CCLE) project will greatly expand the collection of characterized cell lines and organoid models through the Cancer Cell Line Factory, initiative.

Creating a genome-scale catalog of genetic vulnerabilities

Developing new cancer therapies is predicated on finding ways to target processes that will selectively kill cancer cells. We believe developing a complete map of the vulnerabilities of cancer cell models will be a key first step towards identifying therapeutics leads. Therefore, we are using genome-wide RNAi and CRISPR loss-of-function screens to systematically identify essential genes across hundreds of human cancers (an earlier version of this initiative has been referred to as Project Achilles).

Establishing a comprehensive resource for drug sensitivity

Targeted molecular therapy is designed to treat cancer by interrupting specific molecular pathways. However, cancers are highly diverse and the same types of cancer may have different molecular targets. Our ability to optimize the clinical application of cancer drugs depends on knowing the effects of those drugs on cells representing the broadest genetic diversity possible. To achieve this goal, we are employing pooled approaches (PRISM) to screen hundreds of cellular models in a high-throughput manner as well as using more traditional screening methods (CTRP) to reveal the effects of single agents in detail.

Enabling cancer dependency research

The utility of data is often limited by the tools we have to access and operate on those data. So in addition to data generation, DepMap is committed to developing new computational models and making those results, along with user-friendly web-based analytical and visualization tools, available through this portal.

Select Publications

Aviad Tsherniak, Francisca Vazquez, Phillip G. Montgomery, Barbara A. Weir, Gregory Kryukov, Glenn S. Cowley, Stanley Gill, William F. Harrington, Sasha Pantel, John M. Krill-Burger, Robin M. Meyers, Levi Ali, Amy Goodale, Yenarae Lee, Guozhi Jiang, Jessica Hsiao, William F. J. Gerath, Sara Howell, Erin Merkel, Mahmoud Ghandi, Levi A. Garraway, David E. Root, Todd R. Golub, Jesse S. Boehm, & William C. Hahn. Defining a Cancer Dependency Map. Cell July 27, 2017. DOI: j.cell.2017.06.010
Robin M. Meyers, Jordan G. Bryan, James M. McFarland, Barbara A. Weir, Ann E. Sizemore, Han Xu, Neekesh V. Dharia, Phillip G. Montgomery, Glenn S. Cowley, Sasha Pantel, Amy Goodale, Yenarae Lee, Levi D. Ali, Guozhi Jiang, Rakela Lubonja, William F. Harrington, Matthew Strickland, Ting Wu, Derek C. Hawes, Victor A. Zhivich, Meghan R. Wyatt, Zohra Kalani, Jaime J. Chang, Michael Okamoto, Kimberly Stegmaier, Todd R. Golub, Jesse S. Boehm, Francisca Vazquez, David E. Root, William C. Hahn, & Aviad Tsherniak. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nature Genetics 2017 October 49:1779–1784. doi:10.1038/ng.3984
Jordi Barretina, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, Adam A. Margoli, Sungjoon Kim,Christopher J. Wilson, Joseph Leha´r, Gregory V. Kryukov, Dmitriy Sonkin, Anupama Reddy, Manway Liu, Lauren Murray, Michael F. Berger, John E. Monahan, Paula Morais, Jodi Meltzer, Adam Korejwa, Judit Jane´-Valbuena, Felipa A. Mapa, Joseph Thibault, Eva Bric-Furlong, Pichai Raman, Aaron Shipway, Ingo H. Engels, Jill Cheng, Guoying K. Yu, Jianjun Yu, Peter Aspesi Jr, Melanie de Silva, Kalpana Jagtap, Michael D. Jones, Li Wang, Charles Hatton, Emanuele Palescandolo, Supriya Gupta, Scott Mahan, Carrie Sougnez, Robert C. Onofrio, Ted Liefeld, Laura MacConaill, Wendy Winckler, Michael Reich, Nanxin Li, Jill P. Mesirov, Stacey B. Gabriel, Gad Getz, Kristin Ardlie, Vivien Chan, Vic E. Myer, Barbara L. Weber, Jeff Porter, Markus Warmuth, Peter Finan, Jennifer L. Harris, Matthew Meyerson, Todd R. Golub, Michael P. Morrissey, William R. Sellers, Robert Schlegel, & Levi A. Garraway. 2012. The Cancer Cell Line Encyclopedia Enables Predictive Modelling of Anticancer Drug Sensitivity. Nature 483 (7391):603–7. https://doi.org/10.1038/nature11003
Channing Yu, Aristotle M. Mannan, Griselda Metta Yvone, Kenneth N. Ross, Yan-Ling Zhang, Melissa A. Marton, Bradley R. Taylor, Andrew Crenshaw, Joshua Z. Gould, Pablo Tamayo, Barbara A. Weir, Aviad Tsherniak, Bang Wong, Levi A. Garraway, Alykhan F. Shamji, Michelle A. Palmer, Michael A. Foley, Wendy Winckler, Stuart L. Schreiber, Andrew L. Kung, & Todd R. Golub. 2016. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines Nature Biotechnology 34 (4): 419–23. https://doi.org/10.1038/nbt.34600
Matthew G. Rees, Brinton Seashore-Ludlow, Jaime H. Cheah, Drew J. Adams, Edmund V. Price, Shubhroz Gill, Sarah Javaid, Matthew E. Coletti, Victor L. Jones, Nicole E. Bodycombe, Christian K. Soule, Benjamin Alexander, Ava Li, Philip Montgomery, Joanne D. Kotz, C. Suk-Yee Hon, Benito Munoz, Ted Liefeld, Vlado Dančík, Daniel A. Haber, Clary B. Clish, Joshua A. Bittker, Michelle Palmer, Bridget K. Wagner, Paul A. Clemons, Alykhan F. Shamji, & Stuart L. Schreiber. 2016. Correlating Chemical Sensitivity and Basal Gene Expression Reveals Mechanism of Action Nature Chemical Biology 12 (2): 109–16. https://doi.org/10.1038/nchembio.1986.
Steven M. Corsello, Joshua A. Bittker, Zihan Liu, Joshua Gould, Patrick McCarren, Jodi E. Hirschman, Stephen E. Johnston, Anita Vrcic, Bang Wong, Mariya Khan, Jacob Asiedu, Rajiv Narayan, Christopher C. Mader, Aravind Subramanian, & Todd R. Golub. 2017. The Drug Repurposing Hub: a next-generation drug library and information resource. Nature Medicine 23, 405–408 (2017).
Cancer Cell Line Encyclopedia Consortium, and Genomics of Drug Sensitivity in Cancer Consortium. 2015. Pharmacogenomic Agreement between Two Cancer Cell Line Data Sets. Nature 528 (7580):84–87. https://doi.org/10.1038/nature15736.

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