Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies

Wolf, Denise M.; Yau, Christina; Wulfkuhle, Julia; Swigart, Lamorna Brown; Gallagher, Rosa I.; Lee, Pei Rong Evelyn; Zhu, Zelos; Magbanua, Mark Jesus M.; Sayaman, Rosalyn W.; O’Grady, Nicholas; Basu, Amrita; Delson, Amy L.; Coppé, Jean Philippe; Lu, Ruixiao; Braun, Jerome J.; Asare, Smita; Sit, Laura; Matthews, Jeffrey B.; Perlmutter, Jane; Hylton, Nola M.; Liu, Minetta C.; Pohlmann, Paula R.; Symmans, W. Fraser; Rugo, Hope S.; Isaacs, Claudine; DeMichele, Angela; Yee, Douglas; Berry, Donald A.; Pusztai, Lajos; Petricoin, Emanuel F.; Hirst, Gillian L.; Esserman, Laura J.; Veer, Laura van ‘t

doi:10.1016/j.ccell.2022.05.005

articleCancer CellMay 26, 2022HYBRID OA

Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies

DMDenise M. Wolf CYChristina Yau JWJulia Wulfkuhle LBLamorna Brown Swigart RIRosa I. Gallagher

University of California, San Francisco · George Mason University · +11 more institutions

PubMed

Indexed incrossrefpubmed

Abstract

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.

Citation impact

319

total citations

FWCI: 27.16
Percentile: 100%
References: 54

Citations per year

Authors

33

Topics & keywords

Topics

Keywords

Oncology
Breast cancer
Subtyping
Biology
Bioinformatics
Medicine
Clinical trial
Disease

UN Sustainable Development Goals

Good health and well-being

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