e18006 Background: Immunotherapy (IO) has emerged as the standard of care for recurrent and metastatic HNSCC. However, conventional PD-L1 expression levels offer limited utility for predicting therapeutic response. This study evaluates the application of a deep-learning framework, ENLIGHT-DP, to predict response to IO in HNSCC directly from common H HR: 0.343, p=0.0003 for EMS-H vs. EMS-L). This is superior to the PFS separation using PD-L1 (HR: 0.69, p=0.14 for PDL1-H vs. rest; HR: 0.523, p=0.0055 for PDL1-H vs. PDL1-L). ENLIGHT-DP was also borderline significant in stratifying with respect to OS (HR:0.55 , p=0.07 for EMS-H vs rest; HR: 0.577, p=0.16 for EMS-H vs. EMS-L), while PD-L1 was not predictive of OS (HR: 1.12, p=0.71 for PDL1-H vs. rest; HR: 1.1, p = 0.8 for PDL1-H vs PDL1-L). ENLIGHT-DP generalizes to the external cohorts with a ROC AUC of 0.70 in the HMC cohort and 0.67 in the BIO2 cohort, vs. 0.68 in the VGHTPE cohort. Conclusions: ENLIGHT-DP demonstrated a superior prediction performance to IO ORR, PFS, and OS in HNSCC, suggesting a potential clinical utility for treatment guidance.
Chen et al. (Thu,) studied this question.