Background In clinical trials that assess novel therapeutic agents in patients with non-small-cell lung cancer (NSCLC), early endpoints (e.g. progression-free survival [PFS] and objective response rate) are often evaluated as indicators of biological drug activity, and are used as surrogate endpoints for overall survival (OS). Compiling trial-level data could help to develop a predictive framework to ascertain correlation trends between treatment effects for early (e.g. odds ratio [OR] for PFS at 6 months) and late endpoints (e.g. hazard ratio [HR] OS).
Methods A dataset was compiled, which included 81 randomized, controlled trials (RCTs; Phase II–IV) of NSCLC (Stages I–IV), with 35 drugs and 156 observations. The dataset was collected from multiple source databases, including Citeline, TrialTrove, clinicaltrials.gov, and PubMed. We applied random-effects meta-analysis to correlate a variety of treatment effects for early endpoints with HR OS. We performed meta-regression analyses across different data-strata, stratified by the mechanism of action (MoA) of the investigational product (programmed death protein-1/programmed death-ligand 1 [PD-1/PD-L1], epidermal growth factor receptor [EGFR], vascular endothelial growth factor receptor, and DNA damage response).
Results Low (Spearman’s rho 0.3–<0.5) to moderate (rho 0.5–<0.7) correlations were observed between HR OS and (1) HR PFS, (2) OR PFS 4 months, and (3) OR PFS 6 months for PD-1/PD-L1 trials, EGFR trials, and all trials combined (Random-effects meta-regression; P<0.05). Similar correlations were observed between each of the early endpoint treatment effects and HR OS. For example, the moderate correlation observed between OR PFS 4 months and HR OS (rho-0.579; 95% confidence interval [CI]-0.800,-0.274; meta-regression R2= 72.5%) was similar to that between OR PFS 6 months and HR OS (rho-0.633; 95% CI-0.802, -0.383; R2=86.1%) for PD-1/PD-L1 trials. Note, the reported rho values are negative as a HR<1, and an OR>1, indicate benefit with the investigational product.
Conclusions Using a comprehensive summary data set in the NSCLC space, we observed low-to-moderate correlations between treatment effects for early endpoints and HR OS across RCTs of agents with different MoAs, including trials of PD-1/PD-L1 checkpoint inhibitors. Exploration of additional endpoints, beyond RECIST, is required to identify other early indicators of efficacy that might predict HR OS. By incorporating additional trial-level parameters and building composite biomarkers using machine intelligence methods, in collaboration with innovative trial design efforts, we envisage to improve the prediction of HR OS from early endpoints.
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