Authors: Xiaoxi Pan | Caner Ercan | Zhongya Wang | Roland Bassett | K. Gonzales | Clinton Yam | Lei Huo | Yinyin Yuan
Source: Clinical Cancer Research (article, 2025-06-13)
Field: Materials Science — Biomaterials

Abstract PS1-07: Al-Derived Tumor-Infiltrating Lymphocytes Enhance the Model with Baseline Stromal Tumor-Infiltrating Lymphocytes and Ki-67 in Predicting Pathologic Complete Response in an Early-Stage Triple-Negative Breast Cancer Prospective Trial Abstract Background: The tumor immune microenvironment is crucial in shaping the response to neoadjuvant chemotherapy in triple-negative breast cancer (TNBC). Stromal tumor-infiltrating lymphocytes (sTILs) and Ki-67 are important biomarkers to predict treatment response in TNBC (Abuhadra et al., 2023). However, manual assessment of sTILs faces challenges due to variability in interpretation across different observers (Van Bockstal et al. 2021). To obtain a consistent predictive model for treatment response with chemotherapy in TNBC, we utilized artificial intelligence (AI) methods for enhancement. Material &amp; methods: We collected 408 hematoxylin and eosin (H&amp;E)-stained images from ARTEMIS (NCT02276443) arm 1 pretreatment core biopsies, divided into discovery and validation cohorts as per a previous work (Abuhadra et al., 2023). We also used the same criteria to evaluate pathologic complete response (pCR), Ki-67, and sTILs. H&amp;E images exhibiting metaplasia and giant cells were excluded, resulting in 201 slides for the discovery cohort and 193 slides for the validation cohort, with one slide per case. To calculate the TILs score, we applied AI pipelines on H&amp;E images to identify epithelial, lymphoid, stromal, and other cells (AbdulJabbar et al., 2020), and to segment tissue into tumor, stroma, parenchyma, necrosis/hemorrhage, and adipose tissue (unpublished). To ensure the accuracy of cell classification, we combined the two pipelines to refine cell recognition. Specifically, cell types identified outside the tumor area but misclassified as epithelial cells underwent a secondary prediction. AI-derived TILs (AI-TILs) was calculated as the proportion of identified lymphoid cells within the tumor and stromal tissues, excluding other cell types. Results: To evaluate the AI pipelines, 20 images from discovery cohort were manually annotated by three pathologists, resulting in 8010 cells. We achieved an average balanced accuracy of 91.2% across the identified cell types. In the discovery cohort (n=201; pCR rate 42%, 85/201), AI-TILs was notably associated with manual sTILs (Spearman’s rho=0.49, P&amp;lt;0.001). In a multivariable model consisting of AI-TILs and Ki-67 to predict pCR, with AI-TILs cutoff (0.173, range: 0.03-0.73) established through recursive partitioning analysis, we found that higher AI-TILs levels were significantly associated with pCR (Odds ratio, 4.57, 95% CI=1.63-12.80, P=0.004). Manual sTILs showed a similar pattern (Odds ratio, 3.71, 95% CI=1.97-6.99, P&amp;lt;0.001) in a multivariable model with Ki-67. When combining Ki-67, sTILs and AI-TILs, the significant association between AI-TILs and pCR was retained (Odds ratio: 3.01, 95% CI=1.04-8.72, P=0.042). Using the same model, we achieved an AUC of 0.74 and a precision of 0.68, marginally improving the model consisting of Ki-67 and sTILs (AUC 0.71, precision 0.68). In the validation cohort (n=193; pCR rate 42%, 82/193), the AI-TILs score performed consistently, maintaining correlation with sTILs (rho=0.39, P&amp;lt;0.001). By employing the model constructed in the discovery dataset with Ki-67, sTILs, and AI-TILs to predict pCR, we achieved an AUC of 0.69 and a precision of 0.67, comparable to the model with Ki-67 and sTILs alone (AUC 0.68, precision 0.66). Calibration plots and Hosmer-Lemeshow test indicated that the model including AI-TILs aligned better with the actual data compared to the model without AI-TILs. Conclusion: This study demonstrates that AI-TILs from baseline biopsies can be a promising biomarker to enhance the prediction for neoadjuvant chemotherapy response in TNBC, alongside existing predictors such as manual sTILs, underscoring the potential clinical value of AI-TILs due to its reproducibility and objectivity. Citation Format: Xiaoxi Pan, Caner Ercan, Zhongya Wang, Roland Bassett Jr., Karina B Pinao Gonzales, Clinton Yam, Lei Huo, Yinyin Yuan. Al-Derived Tumor-Infiltrating Lymphocytes Enhance the Model with Baseline Stromal Tumor-Infiltrating Lymphocytes and Ki-67 in Predicting Pathologic Complete Response in an Early-Stage Triple-Negative Breast Cancer Prospective Trial [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr PS1-07. Stromal cell ; Medicine ; Tumor-infiltrating lymphocytes ; Stage (stratigraphy) ; Pathology ; Oncology ; Internal medicine ; Cancer ; Immunotherapy ; Biology Nanoparticle-Based Drug Delivery ; Cancer Cells and Metastasis