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An Analysis Of Cellular Networks To Predict Drug Response In Breast Cancer Lines Published In Science Signaling

Stocks in this article: MACK

CAMBRIDGE, Mass., Sept. 20, 2013 (GLOBE NEWSWIRE) -- Merrimack Pharmaceuticals, Inc. (Nasdaq:MACK) today announced that Science Signaling will publish the research article "Profiles of Basal and Stimulated Receptor Signaling Networks Predict Drug Response in Breast Cancer Lines" in its latest issue (Volume 6, ra84). The research article analyzes biochemical characteristics in a collection of breast cell cancer lines and identifies a group of hybrid biomarkers that effectively predicted the response of cells to targeted therapies in vitro. These findings are the result of collaboration between researchers at Merrimack Pharmaceuticals, Inc. and the laboratory of Peter Sorger, Ph.D., in the department of systems biology at Harvard Medical School.

Biomarkers in early breast cancer often determine the treatment course for the disease. A well-known biomarker, HER2 (also known as ErbB2), is traditionally used to pinpoint cases that could benefit from antibody therapies which target ErbB2. However, HER2 and other biomarkers are only predictive in certain subsets of patients. For example, triple negative breast cancers, defined by low levels of traditional biomarkers, show more heterogeneity in both drug response and at the cellular level and carry a poorer prognosis than other breast cancer subtypes.

By combining traditional genetic classifications of breast cancer cells with an innovative proteomics and biochemical analysis, the researchers were able to construct hybrid response biomarkers that effectively predicted the response of cancer cells to targeted therapies in vitro.

"We are delighted that Science Signaling has highlighted our work in the field of network-based biology. This study serves as proof of principle that systematic biochemical analysis of cellular pathways can enable the identification of factors responsible for variation in drug response," said Peter Sorger, Professor of Systems Biology at Harvard Medical School. "In addition, these predictors and biomarkers have the potential to be translated into the clinic to improve therapy selection in cancer patients."

Follow-up studies may include analyzing patient biopsies and associated patient responses to targeted therapies, with the hope of using such data to advance the study of multiparametric response biomarkers and aid in personalizing cancer treatments.

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