Machine learning risk stratification in a US-based database to identify subgroups of patients with PD-L1-high NSCLC who benefit from adding chemotherapy to pembrolizumab.

Abstract

e20523 Background: First-line treatment options for advanced non-small cell lung cancer (aNSCLC) with PD-L1 TPS ≥50% include pembrolizumab or pembrolizumab plus platinum-doublet chemotherapy. Without head-to-head randomized data, optimal patient selection remains unclear. We hypothesized that machine learning–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 aNSCLC and PD-L1 CPS TPS ≥50% treated with first-line pembrolizumab or pembrolizumab plus chemotherapy. A gradient-boosted survival model predicted 6-month survival from baseline clinical variables (demographics, cancer features, ECOG, laboratory values, and comorbidities) 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 of treatment weighting (IPTW). Results: Among 1,434 eligible patients, 930 received pembrolizumab and 504 received pembrolizumab plus chemotherapy. Median age was 71 years, 53% were male, and median follow-up was 38 months. The model achieved 6-month AUC 0.76 with good calibration (Brier score 0.17). Chemotherapy benefit increased as baseline predicted survival worsened: for every 10 percentage-point decrease in predicted 6-month survival, patients gained 17 days in 2-year RMST with combination therapy (p = 0.051). The crossover point corresponded to a baseline 6-month survival probability of 70%. Patients below the crossover survival probability (30.8%)—characterized by worse ECOG, weight loss, bone and liver metastases, anemia and hypoalbuminemia—derived significant benefit from adding chemotherapy in the IPTW-adjusted survival analysis, while those above (69.2%) showed no benefit (Table 1). Conclusions: A machine learning model trained on nationally-representative data identified subgroups of patients with PD-L1-high aNSCLC who benefit from adding chemotherapy to pembrolizumab. RMST differences stratified by crossover survival probability. 6-month Survival <70% 6-month Survival ≥70% 1-year RMST Δ 57.9 (22.7-88.7) 2.0 (-15.7-20.4) 2-year RMST Δ 93.7 (22.9-155.1) 3.6 (-36.2-44.3) RMST differences (Pembro+Chemo - Pembro) in days; 95% CI in parentheses.