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.

利用治疗前基因表达、蛋白质/磷蛋白以及来自 I-SPY2 新辅助平台试验 (NCT01042379) 的临床数据，我们创建了替代乳腺癌亚型，将肿瘤生物学纳入临床激素受体 (HR) 和人表皮生长因子受体 2 之外(HER2) 状态以更好地预测药物反应。我们评估了约 990 名接受 10 种针对不同生物学治疗方案治疗的患者的作用机制生物标志物的预测性能。我们探索超过 11 种亚型模式，并确定治疗亚型对，以最大限度地提高人群的病理完全缓解 (pCR) 率。表现最佳的模式包括免疫、DNA 修复和 HER2/Luminal 表型。使用基于 HR/HER2 的治疗选择，后续治疗分配将总体 pCR 率从 51% 提高到 63%。重新分类和改进患者选择带来的 pCR 收益在 HR ⁺子集中最高 (>15%)。随着新疗法的推出，子分型模式决定了显示疗效所需的最低反应。该数据平台提供了前所未有的资源，并支持使用基于响应的子类型来指导未来的治疗优先顺序。