Prospective-retrospective HeCOG phase III trial validation of Polaris TME for the prediction of TILs from whole-slide images in high-risk early breast cancer.

557 Background: Tumor-infiltrating lymphocytes (TILs) are an established prognostic and predictive biomarker in early breast cancer. However, manual histopathologic scoring can introduce interobserver variability and limit throughput in analyzing at scale in routine practice. Recent advances in artificial intelligence (AI) offer the potential to standardize TILs quantification and enhance reproducibility. Our aim is to evaluate whether deep learning-based tumor microenvironment (TME) quantification from digitized hematoxylin- and eosin (H median age 53 22-79; 638 (63%) were hormone receptor-positive/HER2-negative, 394 (32%) HER2-positive, 176 (14%) triple-negative breast cancer (TNBC), and 6 patients of unknown subtype. Median follow-up was 116.59 (53.45 –157.06) months. When blindly deployed on the external cohorts, the model showed a significant correlation with the manual TILs scoring (ρ=0.60, p5% TILs and AUC of 0.92 (0.88–0.95) for the >50% TILs. Predicted low-TILs tumors were associated with significantly lower 10-year iDFS in TNBC (55.1% versus 70.9%, p=0.034), with a similar pattern observed in HER2-positive (55.3% versus 64.8%, p=0.096), whereas there was no difference in 10-year iDFS between the predicted TILs groups for luminal cancers. Conclusions: These results indicate that Polaris TME accurately quantifies TILs derived from digitized H&E slides and provides prognostic measure for long-term iDFS in high-risk early breast cancer. Prospective validation and harmonization efforts to facilitate clinical implementation are warranted.