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Last Updated: 09/23/14

Evaluation of Predictive Signatures
of Prostate Cancer

Dan Mercola, M.D., Ph.D.
University of California, Irvine

Dr. Mercola’s program will refine and validate profiles that predict relapse in prostate cancer patients. These profiles will help distinguish indolent disease from disease that will progress. Dr. Mercola has developed anovel algorithm, during work carried out during the Director’s Challenge program, that enables the assignment of molecular signatures to different cell types present in the prostate tumor. This algorithm captures important information about tumor-stromal interactions taking place in the diseased gland. Based on this algorithm, ~1,100 genes have been associated with relapse. The profile has been refined to 200 high priority relapse-associated genes. The profile will be refined further, confirmed using independent analytical strategies and validated in an observational clinical validation trial.


  • The project includes investigators form UC Irvine, Sidney Kimmel Cancer Center (SKCC), UC San Diego, the Burnham Institute, the Scripps Research Institute, Northwestern University, the Translational Genomics Institute (TGen), the Sun Health Research Institute and the SKCC/Sharp HealthCare Urology Research Group.
  • Individual institutions and a community based specimen procurement network will provide tissue specimens.


  • Refine and confirm the prognostic signature for prostate cancer.
  • Develop a high throughput PCR assay for 200 prioritized genes from the prognostic signature for evaluating the signature in fresh frozen and paraffin embedded specimens.
  • Generate tissue micro-arrays (TMAs) containing 1000 prostate specimens for signature validation by immunohistochemistry.
  • Carry out an observational clinical validation trial.

Featured Publications:

Koziol JA et al (2009) The wisdom of the commons: ensemble tree classifiers for prostate cancer prognosis. Bioinformatics 25: 54-60. PMID: 18628288

Development by the Mercola SPECS project of a classifier to distinguish indolent from aggressive prostate cancer.

Jia Z et al (2011) Diagnosis of Prostate Cancer Using Differentially Expressed Genes in Stroma. Cancer Research 71(7): 2476-2487. PMID: 21459804

Describes the development of a stroma-specific classifier for nearby tumor