Abstract 6270: Al-derived tumor-infiltrating lymphocytes predicts risk of invasive breast cancer recurrence in ductal carcinoma in situ (DCIS)

Abstract Background: The abundance of tumor-infiltrating lymphocytes (TILs) has demonstrated prognostic value in breast cancer recurrence. However, manual TIL assessment is both time-consuming and prone to inter-observer variability. This study aimed to evaluate the performance of two AI-based TIL scoring pipelines, emphasizing the added value of incorporating tissue-based context insights for automated TILs scoring. Methods: We compared two in-house AI-based TILs scoring pipelines using the Translational Breast Cancer Research Consortium DCIS cohort. The first pipeline employs single-cell classification to identify epithelial, lymphocyte, stromal, and other cell types, followed by spatial analysis of immune populations relative to epithelial cells through clustering (AI-TILs-cluster). The second pipeline builds on this foundation by integrating a tumor microenvironment segmentation model as context guidance to compute the proportion of lymphoid cells within the epithelium and stromal compartments (AI-TILs-seg). In a cohort of 45 retrospectively collected hematoxylin and eosin (HE)-stained whole-slide tissue sections from 38 patients with available pathologists manual TILs, we evaluated the correlation of both scores and the median manual TIL score across the pathologists. Additionally, we assessed the prognostic value of the AI-derived TILs in 232 patients (530 HE slides) with up to 228 follow-up months (median: 78 months) by examining the association of per-patient AI-TILs with time to invasive breast cancer recurrence. This analysis was conducted using a multivariate Cox Proportional Hazards model that included patient age, tumor grade, DCIS lesion size, and estrogen receptors (ER) and progesterone receptors (PR) status. Results: AI-TILs-cluster was not significantly correlated with median pathologist TILs, AI-TILs-seg, however, demonstrated a moderate correlation (Spearman’s rho = 0.43, p = 0.003). This finding was expected as the automatic scores were not designed to replicate the manual TILs. AI-TILs-seg was associated with an increased risk of invasive breast cancer (Hazard Ratio = 1.067, 95% Confidence Interval: 1.017-1.119, p= 0.008) independent of other variables. In comparison, the multivariate analysis including AI-TILs-cluster did not show significance in predicting the risk of invasive breast cancer. Conclusion: These findings suggest that incorporating contextual tumor microenvironment segmentation can enhance AI-derived TIL scoring, leading to better alignment with pathologist assessments and improved clinical relevance. Future work will focus on validating this scoring approach in larger cohorts and improving correlation with pathologist TILs. Citation Format: Sara Ranjbar, Xiaoxi Pan, Karina Pinao, Caner Ercan, Roberto Salgado, Hugo M. Horlings, Allison Hall, Lorraine M. King, Carlo Maley, E Shelly Hwang, Yinyin Yuan, Simon Castillo, Pingjun Chen. Al-derived tumor-infiltrating lymphocytes predicts risk of invasive breast cancer recurrence in ductal carcinoma in situ (DCIS) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6270.