Machine learning risk stratification in a US-based database to identify subgroups of patients with head and neck cancer who benefit from adding chemotherapy to pembrolizumab.

Abstract

e18007 Background: First-line treatment for recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC) includes pembrolizumab or pembrolizumab plus chemotherapy, guided by PD-L1 when available. Although both were established as standards in KEYNOTE-048, they were not directly compared, leaving optimal patient selection unclear. We hypothesized that machine learning (ML)–predicted baseline prognosis modifies chemotherapy benefit, with higher-risk patients more likely to benefit from chemotherapy’s rapid clinical effects. Methods: Using the Flatiron Health Research Database, we identified patients with R/M HNSCC treated with first-line pembrolizumab or pembrolizumab plus chemotherapy and positive or unknown PD-L1. A gradient-boosted survival model predicted 6-month survival from baseline clinical variables using cross-validation, then calibrated via isotonic regression. Heterogeneity of absolute treatment benefit was evaluated using overlap-weighted regression with 2-year restricted mean survival time (RMST) pseudo-observations. We summarized the continuous treatment-effect function using a crossover point, defined as the baseline 6-month survival probability at which the estimated RMST benefit of adding chemotherapy reached a clinically meaningful magnitude (≥30 days). Patients were stratified by this survival probability, and survival was compared between treatments using inverse probability treatment weighting (IPTW). Results: Among 1,736 patients, 1,095 received pembrolizumab and 641 received pembrolizumab plus chemotherapy. Median age was 68 years, 78% were male, median follow-up was 24 months, and PD-L1 was positive in 17.9%, with 82.1% unknown. The model achieved 6-month AUC 0.75 with good calibration (Brier 0.17). Chemotherapy benefit increased as predicted survival worsened: for every 10 percentage-point decrease in predicted 6-month survival, patients gained 24 days in 2-year RMST with combination therapy (p<0.001). The crossover point corresponded to a baseline 6-month survival probability of 64%. Patients below the crossover survival probability (31.2%)—characterized by worse ECOG, weight loss, lower HPV positivity, bone metastases, and hypoalbuminemia—derived significant benefit from adding chemotherapy in the IPTW-adjusted survival analysis, while those above (68.8%) showed no benefit (Table 1). Conclusions: An ML model trained on nationally-representative data identified subgroups of patients with R/M HNSCC who benefit from adding chemotherapy to pembrolizumab. RMST differences stratified by crossover survival probability. 6-month Survival <64% 6-month Survival ≥64% 1-year RMST Δ 52.5 (22.2-80.1) 1.0 (-13.1-15.3) 2-year RMST Δ 84.4 (27.0-142.3) -17.3 (-51.6-18.2) RMST differences (Pembro+Chemo - Pembro) in days; 95% CI in parentheses.