Data from Computationally Derived Spatial Immune Signature Identifies Trastuzumab Responders in HER2+ Breast Cancer: NSABP B-41 Clinical Trial Validation

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AbstractPurpose: Trastuzumab-based chemotherapy has improved outcomes in human epidermal growth factor receptor 2 (HER2)–positive breast cancer, but treatment benefit varies among patients. Predictive signatures are needed to identify patients most likely to respond to these therapies. Experimental Design: We developed Density and Spatial architecture of Tumor-Infiltrating Lymphocytes (DeSTIL), a computational signature derived from hematoxylin and eosin slides. The signature captures spatial organization of immune cells and interactions with nonimmune cells. DeSTIL was trained on HER2+ breast cancer slides from The Cancer Genome Atlas (n = 250) and validated in a phase III National Surgical Adjuvant Breast and Bowel Project (NSABP) B-41 randomized clinical trial (n = 221), which compared chemotherapy plus trastuzumab, lapatinib, or combination. The DeSTIL scores were dichotomized into positive and negative groups, and event-free survival (EFS) was assessed using Cox proportional hazards with interaction terms. Results: In NSABP B-41, DeSTIL-positive patients (n = 61) showed significantly improved event-free survival (EFS) with trastuzumab compared with the combination arm [hazard ratio (HR) = 0.09; 95% confidence interval (CI) = 0.01–0.77; P = 0.006] and a significant signature–treatment interaction (P = 0.024). No EFS difference was observed in DeSTIL-negative patients (n = 160). Gene expression analysis supported the image-derived signature stratifying DeSTIL-positive and DeSTIL-negative tumors. In an exploratory pathologic complete response analysis, a classifier trained on University Hospitals Cleveland slides achieved AUCs of 0.70 in the training cohort and 0.63 in the trastuzumab arm of the NSABP B-41 validation cohort. Conclusions: DeSTIL identifies a subset of HER2+ patients who derive greater benefit from trastuzumab. These findings support the potential of computationally derived immune architecture to inform selection of standard HER2-targeted therapies.

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