The emergence of cancer genomics, combined with increased understanding of the molecular basis of oncogenesis, has stimulated hope that treatment will improve by becoming more targeted and individualized in nature. Cancer genomics studies established a number of critical cancer genes, leading to a number of successful targeted therapies (e.g. Gleevec, Herceptin and Plexxikon). Despite these successes, most cancers do not have a targeted therapy and when one exists, response is highly variable, even among patients that share the targeted mutation and tumor type. To move cancer into the era of personalized therapies, it becomes important to identify the alterations driving tumor progression in each tumor, determine the network that links these aberrations, and identify factors that predict sensitivity to targeted therapies. As projects such as The Cancer Genome Atlas (TCGA) amass cancer cell genomes at a breathtaking pace, a staggering genetic complexity is revealed. To interpret cancer genomes, a key computational challenge is to separate the wheat from the chaff and define both what are the key alterations likely to be functionally driving cancer and then, after defining such genes, begin to identify mechanisms of action and therapeutic implications. Leveraging components from our published methods, CONEXIC (Akavia et.al Cell 2010) and LirNet (Lee et.al, PLOS Gen 2009), we will develop machine-learning algorithms that integrate cancer genomic data to do just that. We will apply the methods we develop to melanoma, glioblastoma, ovarian, breast and colon cancer and experimentally follow up on our computational findings, towards a better understanding of each of these deadly cancers. The approaches developed in this grant will accelerate discovery to rapidly extract the maximal value from modern genomic studies and help carry cancer genomics from the diagnostic to the therapeutic realm.
This work aims to develop methods that help dissect the genetic complexity of individual cancers. For each tumor we aim to identify which mutations arm a cell with the abilities to abnormally grow or evade drug treatment, providing a foundation of tools towards personalized cancer treatment. We will apply these methods for discovery in some of the most aggressive cancers that currently lack good therapeutic solutions including glioblastoma, ovarian cancer and melanoma.
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| DiGiuseppe, Joseph A; Cardinali, Jolene L; Rezuke, William N et al. (2018) PhenoGraph and viSNE facilitate the identification of abnormal T-cell populations in routine clinical flow cytometric data. Cytometry B Clin Cytom 94:588-601 |
| Wei, Spencer C; Levine, Jacob H; Cogdill, Alexandria P et al. (2017) Distinct Cellular Mechanisms Underlie Anti-CTLA-4 and Anti-PD-1 Checkpoint Blockade. Cell 170:1120-1133.e17 |
| Lavin, Yonit; Kobayashi, Soma; Leader, Andrew et al. (2017) Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses. Cell 169:750-765.e17 |
| Chevrier, Stéphane; Levine, Jacob Harrison; Zanotelli, Vito Riccardo Tomaso et al. (2017) An Immune Atlas of Clear Cell Renal Cell Carcinoma. Cell 169:736-749.e18 |
| Setty, Manu; Tadmor, Michelle D; Reich-Zeliger, Shlomit et al. (2016) Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat Biotechnol 34:637-45 |
| Marcotte, Richard; Sayad, Azin; Brown, Kevin R et al. (2016) Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance. Cell 164:293-309 |
| Prabhakaran, Sandhya; Azizi, Elham; Carr, Ambrose et al. (2016) Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data. JMLR Workshop Conf Proc 48:1070-1079 |
| Chen, Bo-Juen; Litvin, Oren; Ungar, Lyle et al. (2015) Context Sensitive Modeling of Cancer Drug Sensitivity. PLoS One 10:e0133850 |
| DiGiuseppe, Joseph A; Tadmor, Michelle D; Pe'er, Dana (2015) Detection of minimal residual disease in B lymphoblastic leukemia using viSNE. Cytometry B Clin Cytom 88:294-304 |
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