Artificial intelligence–powered spatial analysis of endothelial cells and tumor-infiltrating lymphocytes to predict response to axitinib in adenoid cystic carcinoma.

6122 Background: Despite the limited systemic options for adenoid cystic carcinoma (ACC), VEGFR inhibitors remain a clinical mainstay. Although high stromal tumor-infiltrating lymphocyte (TIL) density has been identified as a predictive biomarker for improved progression-free survival (PFS), its predictive power remains to be fully optimized. We hypothesized that baseline vascular architecture, represented by endothelial cell (EC) density, might modulate the efficacy of axitinib. Methods: We performed a post-hoc exploratory analysis on H 95% CI: 0.11–0.87; P=0.026). Furthermore, this subgroup demonstrated significantly prolonged OS (median NR vs. 24.4 months; HR 0.12; 95% CI: 0.02–0.95; P=0.044). Stratification based on intratumoral densities of EC and TILs showed a similar trend with the High EC/High TIL subgroup (n=10) reporting prolonged PFS (HR 0.32; 95% CI: 0.12-0.87; P=0.025) but not OS (HR 0.46; 95% CI: 0.12-1.74; P=0.251). The individual biomarkers based on median TIL and EC showed a trend towards prolonged survival but were not statistically significant. Conclusions: The co-enrichment of stromal ECs and TIL is associated with significantly prolonged PFS and OS, suggesting that this unique TME architecture may identify R/M ACC patients who derive greater clinical benefit from axitinib. This AI-based spatial analysis using H&E slides offers a practical, scalable biomarker strategy to guide treatment selection in this rare cancer.