Large-scale efforts to characterize tumor genomes have uncovered a highly heterogeneous landscape of molecular alterations that distinguish tumor cells from normal cells. A small number of these alterations are causal 'driver' events that confer neoplastic properties to tumors, such as inappropriate growth and proliferation; however, the majority of these alterations are thought to be 'passenger' events that accumulate in tumor cells by chance over the course of tumor progression. Discriminating drivers from passengers is a pressing need in cancer research and will be critical for understanding the molecular origins of tumors, identifying novel targets for drug development, uncovering mechanisms of resistance to therapeutics, and ultimately selecting the most effective therapies for patients. Current efforts t discriminate drivers from passengers rely on statistical over-representation of events in a population of tumors or their predicted effects on protein activity. However, it is now well appreciated that cancer is not a disease of single mutations, nor of genes, but of groups of genes working together in molecular networks and pathways. Cellular behaviors result from complex networks of interactions among biological molecules within the cell, such that driver mutations confer neoplastic behaviors to tumor cells by altering network structure and function. In this grant I propose to model molecular alterations detected in tumors as network perturbations and use these models to discriminate drivers from passengers. These network models will allow us to study cancer in new ways: they will be used to 1) study the biological network effects of known driver mutations versus other human genetic variation, 2) develop hypotheses about the mechanisms by which driver mutations confer neoplastic behaviors to tumor cells, 3) compare patterns of altered network structure across tumor populations, 4) evaluate the combined effect of mutations collocated within a biological network, and 5) predict the set of driver mutations and perturbed pathways in selected individual tumor genomes. I will extend the models to include molecular events overlapping functional non-protein coding elements now known to cover 80% of the human genome, and quantify how acquired alterations observed in a tumor interact with inherited variants in the patient's genome. Finally, will work with established collaborators to experimentally validate novel computational findings uncovered by network perturbation modeling. This project will provide a more global view of the driver landscape in tumors and supply the cancer research community with a suite of computational tools for modeling the consequences of molecular aberrations and targeted interventions in cancer.
Over the last decade, incredible effort has gone into the characterization of tumor genomes in order to determine the causes of cancer inform the development of effective cancer drugs and enable personalized cancer medicine. However, tumor cells also harbor a large number of spurious differences from normal cells that mask the true cancer causing 'driver' events. This project proposes a new strategy to identify drivers by modeling how molecular changes detected in tumors rewire biological networks and change the behavior of tumor cells.
| Marty Pyke, Rachel; Thompson, Wesley Kurt; Salem, Rany M et al. (2018) Evolutionary Pressure against MHC Class II Binding Cancer Mutations. Cell 175:416-428.e13 |
| Zhang, Wei; Bojorquez-Gomez, Ana; Velez, Daniel Ortiz et al. (2018) A global transcriptional network connecting noncoding mutations to changes in tumor gene expression. Nat Genet 50:613-620 |
| Capriotti, Emidio; Ozturk, Kivilcim; Carter, Hannah (2018) Integrating molecular networks with genetic variant interpretation for precision medicine. Wiley Interdiscip Rev Syst Biol Med :e1443 |
| Dow, Michelle; Pyke, Rachel M; Tsui, Brian Y et al. (2018) Integrative genomic analysis of mouse and human hepatocellular carcinoma. Proc Natl Acad Sci U S A 115:E9879-E9888 |
| Ozturk, Kivilcim; Dow, Michelle; Carlin, Daniel E et al. (2018) The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine. J Mol Biol 430:2875-2899 |
| Carter, Hannah; Ideker, Trey (2017) Common genetic variation in the germline influences where and how tumors develop. Mol Cell Oncol 4:e1302905 |
| Engin, H Billur; Carlin, Daniel; Pratt, Dexter et al. (2017) Modeling of RAS complexes supports roles in cancer for less studied partners. BMC Biophys 10:5 |
| Marty, Rachel; Kaabinejadian, Saghar; Rossell, David et al. (2017) MHC-I Genotype Restricts the Oncogenic Mutational Landscape. Cell 171:1272-1283.e15 |
| Buckley, Alexandra R; Standish, Kristopher A; Bhutani, Kunal et al. (2017) Pan-cancer analysis reveals technical artifacts in TCGA germline variant calls. BMC Genomics 18:458 |
| Wang, Tina; Tsui, Brian; Kreisberg, Jason F et al. (2017) Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction and rapamycin treatment. Genome Biol 18:57 |
Showing the most recent 10 out of 17 publications
