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Development and Validation of a Novel Platform-Independent Metastasis Signature in Human Breast Cancer
Authors:Shuang G. Zhao  Mark Shilkrut  Corey Speers  Meilan Liu  Kari Wilder-Romans  Theodore S. Lawrence  Lori J. Pierce  Felix Y. Feng
Affiliation:1Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America;2Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, Michigan, United States of America;3Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America;Cedars Sinai Medical Center, UNITED STATES
Abstract:PurposeThe molecular drivers of metastasis in breast cancer are not well understood. Therefore, we sought to identify the biological processes underlying distant progression and define a prognostic signature for metastatic potential in breast cancer.ResultsWe identified a broad range of metastatic potential that was independent of intrinsic breast cancer subtypes. 146 genes were significantly associated with metastasis progression and were linked to cancer-related biological functions, including cell migration/adhesion, Jak-STAT, TGF-beta, and Wnt signaling. These genes were used to develop a platform-independent gene expression signature (M-Sig), which was trained and subsequently validated on 5 independent cohorts totaling nearly 1800 breast cancer patients with all p-values < 0.005 and hazard ratios ranging from approximately 2.5 to 3. On multivariate analysis accounting for standard clinicopathologic prognostic variables, M-Sig remained the strongest prognostic factor for metastatic progression, with p-values < 0.001 and hazard ratios > 2 in three different cohorts.ConclusionM-Sig is strongly prognostic for metastatic progression, and may provide clinical utility in combination with treatment prediction tools to better guide patient care. In addition, the platform-independent nature of the signature makes it an excellent research tool as it can be directly applied onto existing, and future, datasets.
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