Artificial intelligence (AI)–powered spatial analysis of tumor-infiltrating lymphocytes (TIL) for prediction of response to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC).

595 Background: Stromal TIL are a well-recognized prognostic and predictive biomarker in breast cancer. There is a need for tools assisting visual assessment of TIL, to improve reproducibility as well as for convenience. This study aims to assess the clinical significance of AI-powered spatial TIL analysis in the prediction of pathologic complete response (pCR) after NAC in TNBC patients. Methods: H&E stained slides and clinical outcomes data were obtained from stage I – III TNBC patients treated with NAC in two centers in Korea. For spatial TIL analysis, we used Lunit SCOPE IO, an AI-powered H&E Whole-Slide Image (WSI) analyzer, which identifies and quantifies TIL within the cancer or stroma area. Lunit SCOPE IO was developed with a 13.5 x 109 micrometer2 area and 6.2 x 106 TIL from 17,849 H&E WSI of multiple cancer types, annotated by 104 board-certified pathologists. iTIL score and sTIL score were defined as area occupied by TIL in the intratumoral area (%) and the surrounding stroma (%), respectively. Immune phenotype (IP) of each slide was defined from spatial TIL calculation, as inflamed (high TIL density in tumor area), immune-excluded (high TIL density in stroma), or desert (low TIL density overall). Results: A total of 954 TNBC patients treated from 2006 to 2019 were included in this analysis. pCR (ypT0N0) was confirmed in 261 (27.4%) patients. The neoadjuvant regimens used were mostly anthracycline (97.8%) and taxane (75.1%) -based, with 116 (12.1%) patients receiving additional platinum and 41 (4.3%) patients treated as part of immune checkpoint inhibitor or PARP inhibitor clinical trials. The median iTIL score and sTIL score were 4.3% (IQR 3.2 – 5.8) and 8.1% (IQR 6.3 – 13.4), respectively. The mean iTIL score was significantly higher in patients who achieved pCR after NAC (5.8% vs. 4.5%, p < 0.001), and a similar difference was observed with sTIL score (12.1%.1 vs. 9.4%, p < 0.001). iTIL score was found to remain as an independent predictor of pCR along with cT stage and Ki-67 in the multivariable analysis (adjusted odds ratio 1.211 (95% CI 1.125 – 1.304) per 1 point (%) change in the score, p <0.001). By IP groups, 291 (30.5%) patients were classified as inflamed, 502 (52.6%) as excluded, and 161 (16.9%) as desert phenotype. The patients with inflamed phenotype were more likely to achieve pCR (44.7%) than other phenotypes (19.8%, p < 0.001). Conclusions: AI-powered spatial TIL analysis could assess TIL densities in the cancer area and surrounding stroma of TNBC, and TIL density scores and IP classification could predict pCR after NAC.