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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA176303-02
Application #
8685205
Study Section
Special Emphasis Panel (ZCA1-SRLB-R (J1))
Program Officer
Gerhard, Daniela
Project Start
2013-06-18
Project End
2017-04-30
Budget Start
2014-05-01
Budget End
2015-04-30
Support Year
2
Fiscal Year
2014
Total Cost
$1,013,016
Indirect Cost
$341,321
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109
Nikolova, Olga; Moser, Russell; Kemp, Christopher et al. (2017) Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies. Bioinformatics 33:1362-1369
Gurley, Kay E; Ashley, Amanda K; Moser, Russell D et al. (2017) Synergy between Prkdc and Trp53 regulates stem cell proliferation and GI-ARS after irradiation. Cell Death Differ 24:1853-1860
Pauli, Chantal; Hopkins, Benjamin D; Prandi, Davide et al. (2017) Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine. Cancer Discov 7:462-477
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
Kemp, Christopher J (2015) Animal Models of Chemical Carcinogenesis: Driving Breakthroughs in Cancer Research for 100 Years. Cold Spring Harb Protoc 2015:865-74
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
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
Gönen, 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:

Showing the most recent 10 out of 15 publications