학술논문
Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies
Document Type
article
Author
Wolf, Denise M; Yau, Christina; Wulfkuhle, Julia; Brown-Swigart, Lamorna; Gallagher, Rosa I; Lee, Pei Rong Evelyn; Zhu, Zelos; Magbanua, Mark J; Sayaman, Rosalyn; O’Grady, Nicholas; Basu, Amrita; Delson, Amy; Coppé, Jean Philippe; Lu, Ruixiao; Braun, Jerome; Investigators, I-SPY2; Asare, Smita M; Sit, Laura; Matthews, Jeffrey B; Perlmutter, Jane; Hylton, Nola; Liu, Minetta C; Pohlmann, Paula; Symmans, W Fraser; Rugo, Hope S; Isaacs, Claudine; DeMichele, Angela M; Yee, Douglas; Berry, Donald A; Pusztai, Lajos; Petricoin, Emanuel F; Hirst, Gillian L; Esserman, Laura J; van 't Veer, Laura J
Source
Cancer Cell. 40(6)
Subject
Language
Abstract
Using pre-treatment gene expression, protein/phosphoprotein, and clinical data from the I-SPY2 neoadjuvant platform trial (NCT01042379), we create alternative breast cancer subtypes incorporating tumor biology beyond clinical hormone receptor (HR) and human epidermal growth factor receptor-2 (HER2) status to better predict drug responses. We assess the predictive performance of mechanism-of-action biomarkers from ∼990 patients treated with 10 regimens targeting diverse biology. We explore >11 subtyping schemas and identify treatment-subtype pairs maximizing the pathologic complete response (pCR) rate over the population. The best performing schemas incorporate Immune, DNA repair, and HER2/Luminal phenotypes. Subsequent treatment allocation increases the overall pCR rate to 63% from 51% using HR/HER2-based treatment selection. pCR gains from reclassification and improved patient selection are highest in HR+ subsets (>15%). As new treatments are introduced, the subtyping schema determines the minimum response needed to show efficacy. This data platform provides an unprecedented resource and supports the usage of response-based subtypes to guide future treatment prioritization.