TORONTO, Dec. 18, 2012 /CNW/ - Dr. Tom Hudson, President and Scientific Director of the Ontario Institute for Cancer Research (OICR), today announced that scientists at the Institute have developed a new computer-based method to identify gene signatures that can serve as prognostic biomarkers for cancer. The researchers, Drs. Guanming Wu, Research Associate, and Lincoln Stein, Platform Leader and Principal Investigator, Informatics and Bio-computing Platform, used this technique to discover gene expression signatures related to survival in breast and ovarian cancers. Their study was published in the journal Genome Biology.

To find the gene signatures, Wu and Stein used a collection of algorithms to interrogate gene expression datasets from both breast and ovarian cancer patients. This involves two steps. First, one algorithm mines the data for modules of interacting genes that play a role in the disease process, independent of clinical status. Then a second algorithm scans the genomic dataset for modules that have clinical significance.

Wu and Stein's application of this method to breast cancer found a 31-gene signature that is strongly correlated with patient survival in estrogen receptor positive breast cancer. Application of the method to high-grade serous ovarian cancer discovered a 75-gene signature that is correlated with patient survival. Discovery of the gene signature for ovarian cancer fills an unmet clinical need as few prognostic signatures have been found for this disease.

"A key aspect of OICR's mission is to translate our growing understanding of the genomic alterations underlying cancers into clinically useful tools. By creating a new method able to find reliable prognostic biomarkers, we hope to enable a new paradigm of precision medicine in which treatment decisions are based on the exact nature of the patient's disease," says Stein.

Biomarkers are biological entities such as genes and proteins that indicate the presence and state of a disease. Testing for biomarkers gives clinicians information that can be used to diagnose disease and choose therapies that will be of the greatest benefit to patients while sparing them the negative side effects of those that would be of no help. Treating patients in this way is known as personalized medicine and reliable biomarkers are central to its application.

The gene signatures found using Wu and Stein's approach is thought to be more reliable than those found by traditional methods, as they rely upon ensembles of interacting genes rather than a small number of unrelated genes. Another advantage to Wu and Stein's method is that it can probably by applied to a variety of different cancer types.

"We have released the signature-finding algorithm as an open source package to allow the research community to experiment and extend the method," says Stein. His group is already working on an adaptation of the method to allow biomarkers to be identified using genome sequencing data. This has the potential to reduce the cost and complexity of diagnostic tests based on biomarker signatures.

Ontario Institute for Cancer Research

OICR is an innovative cancer research and development institute dedicated to prevention, early detection, diagnosis and treatment of cancer. The Institute is an independent, not-for-profit corporation, supported by the Government of Ontario. The annual budget for OICR, its research partners and collaborators exceeds $150 million. This supports more than 1,600 investigators, clinician scientists, research staff and trainees located at its headquarters and in research institutes and academia across the Province of Ontario. It has research hubs in Hamilton, Kingston, London, Ottawa, Thunder Bay and Toronto. OICR has key research efforts underway in small molecules, biologics, stem cells, imaging, genomics, informatics and bio-computing, from early stage research to Phase III clinical trials. For more information, please visit the website at http://www.oicr.on.ca.

SOURCE: Ontario Institute for Cancer Research

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Researchers use novel method to identify prognostic gene signatures for survival in breast and ovarian cancers

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