Computationally Derived Spatial Immune Signature Identifies Trastuzumab Responders in HER2+ Breast Cancer: NSABP B41 Clinical Trial Validation.

Abstract

Trastuzumab-based chemotherapy has improved outcomes in human epidermal growth factor receptor 2-positive (HER2+) breast cancer, but treatment benefit varies among patients. Predictive signatures are needed to identify patients most likely to respond to these therapies. We developed DeSTIL (Density and Spatial architecture of Tumor-Infiltrating Lymphocytes), a computational signature derived from hematoxylin and eosin slides. The signature captures spatial organization of immune cells and interactions with non-immune cells. DeSTIL was trained on HER2+ breast cancer slides from The Cancer Genome Atlas (n = 250) and validated in phase III 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. In NSABP B41, DeSTIL-positive patients (n=61) showed significantly improved EFS with trastuzumab compared to the combination arm (HR = 0.09, 95% 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 pCR analysis, a classifier trained on University Hospitals Cleveland slides achieved an AUC of 0.70 in the training cohort and 0.63 in the trastuzumab arm of the NSABP B-41 validation cohort. 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.