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

Denise M Wolf; Christina Yau; Julia Wulfkuhle; Lamorna Brown-Swigart; Rosa I Gallagher; Pei Rong Evelyn Lee; Zelos Zhu; Mark J Magbanua; Rosalyn Sayaman; Nicholas O'Grady; Amrita Basu; Amy Delson; Jean Philippe Coppé; Ruixiao Lu; Jerome Braun; I-SPY2 Investigators; Smita M Asare; Laura Sit; Jeffrey B Matthews; Jane Perlmutter; Nola Hylton; Minetta C Liu; Paula Pohlmann; W Fraser Symmans; Hope S Rugo; Claudine Isaacs; Angela M DeMichele; Douglas Yee; Donald A Berry; Lajos Pusztai; Emanuel F Petricoin; Gillian L Hirst; Laura J Esserman; Laura J van 't Veer

doi:10.1016/j.ccell.2022.05.005

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

Cancer Cell. 2022 Jun 13;40(6):609-623.e6. doi: 10.1016/j.ccell.2022.05.005. Epub 2022 May 26.

Authors

Denise M Wolf¹, Christina Yau², Julia Wulfkuhle³, Lamorna Brown-Swigart⁴, Rosa I Gallagher³, Pei Rong Evelyn Lee⁴, Zelos Zhu⁵, Mark J Magbanua⁴, Rosalyn Sayaman⁴, Nicholas O'Grady⁵, Amrita Basu⁵, Amy Delson⁶, Jean Philippe Coppé⁴, Ruixiao Lu⁷, Jerome Braun⁷; I-SPY2 Investigators; Smita M Asare⁷, Laura Sit⁵, Jeffrey B Matthews⁵, Jane Perlmutter⁸, Nola Hylton⁹, Minetta C Liu¹⁰, Paula Pohlmann¹¹, W Fraser Symmans¹², Hope S Rugo¹³, Claudine Isaacs¹⁴, Angela M DeMichele¹⁵, Douglas Yee¹⁶, Donald A Berry¹⁷, Lajos Pusztai¹⁸, Emanuel F Petricoin³, Gillian L Hirst⁵, Laura J Esserman⁵, Laura J van 't Veer¹⁹

Affiliations

¹ Department of Laboratory Medicine, University of California, San Francisco, 2340 Sutter Street, San Francisco, CA 94143, USA. Electronic address: denise.wolf@ucsf.edu.
² Department of Surgery, University of California, San Francisco, San Francisco, CA 94143, USA. Electronic address: cyau@buckinstitute.org.
³ Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA 20110, USA.
⁴ Department of Laboratory Medicine, University of California, San Francisco, 2340 Sutter Street, San Francisco, CA 94143, USA.
⁵ Department of Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.
⁶ Breast Science Advocacy Core, University of California, San Francisco, San Francisco, CA 94143, USA.
⁷ Quantum Leap Healthcare Collaborative, San Francisco, CA 94118, USA.
⁸ Gemini Group, Ann Arbor, MI 48107, USA.
⁹ Department of Radiology, University of California, San Francisco, San Francisco, CA 94143, USA.
¹⁰ Department of Surgery, Mayo Clinic, Rochester, MN 55905, USA.
¹¹ MedStar Georgetown University Hospital, Georgetown University, Washington, DC 20057, USA.
¹² Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹³ Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA 94158, USA.
¹⁴ Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA.
¹⁵ Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
¹⁶ Department of Medicine, University of Minnesota, Minneapolis, MN 55455, USA.
¹⁷ Berry Consultants, LLC, Austin, TX 78746, USA.
¹⁸ Yale School of Medicine, Yale University, New Haven, CT 06510, USA.
¹⁹ Department of Laboratory Medicine, University of California, San Francisco, 2340 Sutter Street, San Francisco, CA 94143, USA. Electronic address: laura.vantveer@ucsf.edu.

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.

Keywords: DNA repair; Immune; Luminal; breast cancer; clinical trial; immunotherapy; multiple arms; platinum; response prediction; subtyping.

Publication types

Clinical Trial
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Antineoplastic Combined Chemotherapy Protocols / therapeutic use
Biomarkers, Tumor / metabolism
Breast Neoplasms* / drug therapy
Breast Neoplasms* / genetics
Female
Humans
Neoadjuvant Therapy
Receptor, ErbB-2 / genetics
Receptors, Estrogen / metabolism
Receptors, Progesterone / metabolism

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

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