Targeted therapies represent the future of oncology treatment. Toward this end, we have recently identified and subsequently validated novel drug targets in four independent settings that are effective and specific to tumors bearing mutations in common oncogenes and tumor suppressor genes. Our functional genetic approach, based on the genetic principle of synthetic lethality, utilizes a state-of-the-art high-throughput RNA interference and small molecule screening platform, genetically defined cell systems as well as patient-derived cultures for target discovery, advanced computational biology methods using publically available datasets to prioritize targets, and patient-derived xenografts to validate thee novel targets. Through the development and expansion of this integrative discovery engine, we will generate a gold standard synthetic lethal database for several major oncogenes/tumor suppressor genes and identify novel drug targets for three major cancer types. In the very near future this pipeline could be utilized for iterative clinical trial design and personalized cancer treatment.
This application focuses on three cancers in urgent need of better therapies: head and neck squamous cell carcinoma, triple negative breast cancer, and pancreatic ductal adenocarcinoma. There are currently no effective targeted therapies for these tumors and patients with these cancers exhibit poor outcome. Mutation of the tumor suppressor TP53 plays a major role each of these cancers and is associated with more aggressive treatment resistant disease. We have developed an efficient and accurate method to identify the weakness of cancer cells, including those carrying mutations in TP53. We show that targeting these weaknesses with drugs is effective in preclinical models of human cancer. Here we propose to expand this approach to identify and validate novel drug targets for these highly aggressive, treatment-resistant tumors.
|Cancer Target Discovery and Development Network (2016) Transforming Big Data into Cancer-Relevant Insight: An Initial, Multi-Tier Approach to Assess Reproducibility and Relevance. Mol Cancer Res 14:675-82|
|Jang, In Sock; Dienstmann, Rodrigo; Margolin, Adam A et al. (2015) Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors. Pac Symp Biocomput :32-43|
|Ewing, Adam D; Houlahan, Kathleen E; Hu, Yin et al. (2015) Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nat Methods 12:623-30|
|Kemp, Christopher J (2015) Animal Models of Chemical Carcinogenesis: Driving Breakthroughs in Cancer Research for 100 Years. Cold Spring Harb Protoc 2015:865-74|
|Kemp, Christopher J; Moore, James M; Moser, Russell et al. (2014) CTCF haploinsufficiency destabilizes DNA methylation and predisposes to cancer. Cell Rep 7:1020-9|
|GÃ¶nen, Mehmet; Margolin, Adam A (2014) Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning. Bioinformatics 30:i556-63|
|Moser, Russell; Xu, Chang; Kao, Michael et al. (2014) Functional kinomics identifies candidate therapeutic targets in head and neck cancer. Clin Cancer Res 20:4274-88|
|Cermelli, Silvia; Jang, In Sock; Bernard, Brady et al. (2014) Synthetic lethal screens as a means to understand and treat MYC-driven cancers. Cold Spring Harb Perspect Med 4:|
|Jang, In Sock; Neto, Elias Chaibub; Guinney, Juistin et al. (2014) Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data. Pac Symp Biocomput :63-74|
|Neto, Elias Chaibub; Jang, In Sock; Friend, Stephen H et al. (2014) The Stream algorithm: computationally efficient ridge-regression via Bayesian model averaging, and applications to pharmacogenomic prediction of cancer cell line sensitivity. Pac Symp Biocomput :27-38|
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