Cancer Biology is In the midst of major transition - from a primary focus on isolated components (e.g., oncogenes) to an integrated focus on systems (e.g., pathways). This transition has become feasible only recently, following two decades of intensive work on the molecular biology of cancer (leading to the identification of many oncogenes) and on the human genome (providing the ability to take comprehensive global views of biological systems). What is needed is the ability to recognize the 'modules' of cellular activity that are critical to the cancer cell, thereby defining molecular signatures of such activity that could be used to build predictive models of cancer cell behavior. Accomplishing these goals will require a multidisciplinary effort that integrates cancer biology with high throughput data generating capabilities and computational biology expertise. We therefore propose a Center which will specialize in such integrative cancer biology, and we will focus initially on defining signatures of protein kinases, now recognized to be critical to the pathogenesis of most if not all human cancers.
In Aim 1, we will use systematic gain- and loss- of function experiments to develop signatures of activation' with which we will build and test computational models to predict kinase activity in tumors.
In Aim 2, we will study the dependence of cancer cells on all protein kinases, thereby developing signatures of essentiality' that will be used to predict in a new sample which kinases are required for tumor survival.
In Aim 3, we will develop 'signatures of modulation, in which we will identify small organic molecules capable of perturbing kinase signaling networks using novel computational and high throughput screening methods based on the detection of gene expression signatures.
In Aim 4, we will establish community outreach and training through the development and deployment of computational biology tools aimed at modeling problems in cancer biology, and we will establish an educational program through which the next generation of scientists working at the interface of computational science and cancer biology will be trained. These ambitious goals will be realized by bringing together the cancer biology expertise of the Dana-Farber Cancer Institute and the genomic and computational expertise of the Broad Institute, a Harvard-MIT research collaboration focused on fulfilling the promise of genomic medicine.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Specialized Center--Cooperative Agreements (U54)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Gallahan, Daniel L
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Dana-Farber Cancer Institute
United States
Zip Code
Gönen, Mehmet; Weir, Barbara A; Cowley, Glenn S et al. (2017) A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines. Cell Syst 5:485-497.e3
Geller, Leore T; Barzily-Rokni, Michal; Danino, Tal et al. (2017) Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. Science 357:1156-1160
Okondo, Marian C; Johnson, Darren C; Sridharan, Ramya et al. (2017) DPP8 and DPP9 inhibition induces pro-caspase-1-dependent monocyte and macrophage pyroptosis. Nat Chem Biol 13:46-53
Tsherniak, Aviad; Vazquez, Francisca; Montgomery, Phil G et al. (2017) Defining a Cancer Dependency Map. Cell 170:564-576.e16
Ilic, Nina; Birsoy, K?vanç; Aguirre, Andrew J et al. (2017) PIK3CA mutant tumors depend on oxoglutarate dehydrogenase. Proc Natl Acad Sci U S A 114:E3434-E3443
Zhu, Xiaodong; Girardo, David; Govek, Eve-Ellen et al. (2016) Role of Tet1/3 Genes and Chromatin Remodeling Genes in Cerebellar Circuit Formation. Neuron 89:100-12
Yu, Channing; Mannan, Aristotle M; Yvone, Griselda Metta et al. (2016) High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat Biotechnol 34:419-23
Tirosh, Itay; Izar, Benjamin; Prakadan, Sanjay M et al. (2016) Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352:189-96
Kryukov, Gregory V; Wilson, Frederick H; Ruth, Jason R et al. (2016) MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells. Science 351:1214-8
Godec, Jernej; Tan, Yan; Liberzon, Arthur et al. (2016) Compendium of Immune Signatures Identifies Conserved and Species-Specific Biology in Response to Inflammation. Immunity 44:194-206

Showing the most recent 10 out of 42 publications