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UPCI Contributes to National ‘Checklist’ for Personalized Testing in Cancer Clinical Trials

PITTSBURGH, Oct. 16, 2013 – A University of Pittsburgh Cancer Institute (UPCI) professor contributed to guidelines that will be used by the National Cancer Institute (NCI) to evaluate proposals for clinical trials that include biological assessments of individual cancer risk and treatment recommendations.

William L. Bigbee, Ph.D., previous leader of the Cancer Biomarkers Facility at UPCI and outgoing chair of the National Institutes of Health Cancer Biomarkers Study Section, is co-author of an article in the Oct. 1, 2013 issue of the journal Nature that includes a proposed, 30-point NCI “checklist” of criteria for the use of “omics-based” predictors in clinical trials.

Tests based on “omics” are those that use computational modeling to interpret molecular measurements of blood, tissue or other bodily samples in order to recommend a clinical course of action, such as cancer therapy or preventative surgery. Genomics, transcriptomics and proteomics are among the fields of study that are the basis of these tests.

“Omics-based tests are very powerful, emerging tools that are revolutionizing medicine, particularly in predicting and treating cancer,” said Dr. Bigbee, also professor of pathology in Pitt’s School of Medicine. “However, there are many variables and opportunities for error, including study design, patient selection, biological sample integrity, and data analysis and management. The NCI checklist is intended to provide clear expectations and guidelines for the development and implementation of omics-based tests and will hopefully eliminate unintentional errors.”

The Nature paper, written by lead author Lisa M. McShane, Ph.D., and senior author Barbara A. Conley, M.D., both of NCI, was published in conjunction with an “explanation and elaboration” paper in the journal BMC Medicine.

The checklist is the result of a 2011 Institute of Medicine review of the omics field and the recommendations of a 2011 NCI workshop bringing together scientists and stakeholders in this research. The review and workshop arose following widely publicized cases of premature advancement of omics-based tests used to guide treatment decisions.

The checklist is divided into these five sections:

  • specimen issues
  • assay issues
  • model development, specification and preliminary performance evaluation
  • clinical trial design
  • ethical, legal and regulatory issues

Based on his research expertise in cancer biomarkers, Dr. Bigbee’s primary contributions to the checklist involved the specimen and assay issues sections.

“There’s a whole series of issues involving the biological specimens selected for omics-based testing on which research conclusions and clinical recommendations will be made,” said Dr. Bigbee. “From which patients will biological specimens be obtained? Are the selected patients representative of all relevant patients with a given malignancy? How are these specimens collected, processed and stored to ensure reproducible results?”

For example, if a research project seeks to use omics-based testing to determine the best course of treatment for an individual cancer patient, it is important that the predictor be developed and tested in specimens from patients with similar clinical characteristics, including stage of disease.

“The field is rife with promising results in patients with late-stage cancer that may or may not be relevant to patients with early-stage cancer,” said Dr. Bigbee. “We don’t want to give the wrong toxic drugs to patients or give them too much or too little. The goal of personalized medicine is to give the right therapy to the right patient at the right time.”

Additional co-authors include Margaret M. Cavenagh, M.S., Tracy G. Lively, Ph.D., P. Mickey Williams, Ph.D., Mei-Yin C. Polley, Ph.D., Kelly Y. Kim, Ph.D., James V. Tricoli, Ph.D., Deborah J. Shuman, Richard M. Simon, D.Sc., and James H. Doroshow, M.D., all of NCI; David A. Eberhard, M.D., Ph.D., of the University of North Carolina; Jill P. Mesirov, Ph.D., of the Massachusetts Institute of Technology and Harvard University; and Jeremy M.G. Taylor, Ph.D., of the University of Michigan.

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