Predicting which cancer patients will best respond to therapy is an enormous health care issue. It has been recently suggested, based on early reads of whole exome sequencing data, that there may only be ~12 molecular pathways that drive the development and progression of cancer. Many experimental therapies are now targeting these pathways. We believe that gene expression signatures may be one of the best ways to judge the activation of a particular molecular pathway, by providing a """"""""molecular summary"""""""" of the activity of many genes. Moreover, we believe that the selective addition of mutational assessment may improve the resolving power of a hybrid, multi-analyte (DNA + RNA) test that may be used to guide patients to the most effective drugs. We have recently developed gene expression signatures to measure the activation of two of the most important pathways in colon cancer, RAS and PI3K, for which there is an increasing availability of pathway targeted therapeutics. Due to the complex nature of these pathways, simple analysis of canonical single gene mutations only identifies the response characteristics of a proportion (<30%) of the population. Here, we propose to technically validate the existing RAS and PI3K signatures and to refine their activity through novel mutational assessment. Multi-analyte signatures/ algorithms will be clinically validated in a CLIA environment with a cohort of colorectal cancer patients treated with cetuximab therapy. This approach will prepare signatures for clinical application in the near future.

Public Health Relevance

This proposal seeks to identify a reliable means to identify the right drugs for the right cancer patients. Current approaches over treat many patients to help an unknown few, with substantial costs and toxicities. The application of molecular signatures to individualize therapy holds significant promise to personalize cancer care, improve response rates, reduces toxicity and associated costs. Here we will combine RNA gene expression signatures with gene mutation assessments to identify responders and non-responders to cetuximab therapy.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
3U01CA157960-03S1
Application #
8894152
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Ogunbiyi, Peter
Project Start
2012-09-19
Project End
2017-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Spartanburg Regional Medical Center
Department
Type
DUNS #
City
Spartanburg
State
SC
Country
United States
Zip Code
29303
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Omolo, Bernard; Yang, Mingli; Lo, Fang Yin et al. (2016) Adaptation of a RAS pathway activation signature from FF to FFPE tissues in colorectal cancer. BMC Med Genomics 9:65
Schell, Michael J; Yang, Mingli; Missiaglia, Edoardo et al. (2016) A Composite Gene Expression Signature Optimizes Prediction of Colorectal Cancer Metastasis and Outcome. Clin Cancer Res 22:734-45
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