An enhanced prognostic score for overall survival of patients with cancer derived from a large real-world cohort.

Abstract

By understanding prognostic biomarkers, we gain insights into disease biology and may improve design, conduct, and data analysis of clinical trials and real-world data. In this context, we used the Flatiron Health Electronic Health Record-derived deidentified database that provides treatment outcome and biomarker data from >280 oncology centers in the USA, organized into 17 cohorts defined by cancer type. In 122694 patients, we analyzed demographic, clinical, routine hematology, and blood chemistry parameters within a Cox proportional hazard framework to derive a multivariable prognostic risk model for overall survival (OS), the 'Real wOrld PROgnostic score (ROPRO)'. We validated ROPRO in two independent phase I and III clinical studies. A total of 27 variables contributed independently and homogeneously across cancer indications to OS. In the largest cohort (advanced non-small-cell lung cancer), for example, patients with elevated ROPRO scores (upper 10%) had a 7.91-fold (95% confidence interval 7.45-8.39) increased death hazard compared with patients with low scores (lower 10%). Median survival was 23.9 months (23.3-24.5) in the lowest ROPRO quartile Q1, 14.8 months (14.4-15.2) in Q2, 9.4 months (9.1-9.7) in Q3, and 4.7 months (4.6-4.8) in Q4. The ROPRO model performance indicators [C-index= 0.747 (standard error 0.001), 3-month area under the curve (AUC)= 0.822 (0.819-0.825)] strongly outperformed those of the Royal Marsden Hospital Score [C-index= 0.54 (standard error 0.0005), 3-month AUC= 0.579 (0.577-0.581)]. We confirmed the high prognostic relevance of ROPRO in clinical Phase 1 and III trials. The ROPRO provides improved prognostic power for OS. In oncology clinical development, it has great potential for applications in patient stratification, patient enrichment strategies, data interpretation, and early decision-making in clinical studies.