Artificial intelligence‐powered spatial analysis of tumor‐infiltrating lymphocytes as a predictive biomarker for axitinib in adenoid cystic carcinoma

Dong-Hyun Kim, Yoojoo Lim, Chan-Young Ock, Gahee Park, Seonwook Park, Heon Song, Minuk Ma, Mohammad Mostafavi, Eun Joo Kang, Myung-Ju An, Keun-Wook Lee, Jung Hye Kwon, Yeawon Yang, Yoon Hee Choi, Min Kyoung Kim, Jun Ho Ji, Tak Yun, sung-bae Kim, Bhumsuk Keam

Abstract:

Background

This study analyzed the predictive value of artificial intelligence (AI)-powered tumor-infiltrating lymphocyte (TIL) analysis in recurrent or metastatic (R/M) adenoid cystic carcinoma (ACC) treated with axitinib. Methods

Patients from a multicenter, prospective phase II trial evaluating axitinib efficacy in R/M ACC were included in this study. H&E whole-side images of archival tumor tissues were analyzed by Lunit SCOPE IO, an AI-powered spatial TIL analyzer. Results

Twenty-seven patients were included in the analysis. The best response was stable disease, and the median progression-free survival (PFS) was 11.1 months (95% CI, 9.2–13.7 months). Median TIL densities in the cancer and surrounding stroma were 25.8/mm2 (IQR, 8.3–73.0) and 180.4/mm2 (IQR, 69.6–342.8), respectively. Patients with stromal TIL density >342.5/mm2 exhibited longer PFS (p = 0.012). Conclusions

Cancer and stromal area TIL infiltration were generally low in R/M ACC. Higher stromal TIL infiltration was associated with a longer PFS with axitinib treatment.