Optimizing cancer immunotherapy response prediction by tumor aneuploidy score and fraction of copy number alterations

Abstract Identifying patients with low tumor mutation burden (TMB) that are likely to respond to cancer immunotherapy is an important, yet highly challenging clinical need. Using 3,139 patients across 17 different cancer types, we comprehensively studied the ability of two common copy number alteration (CNA) scores – the tumor aneuploidy score (AS) and the fraction of genome encompassed by copy number alterations (FGA) – to predict survival following immunotherapy in both pan-cancer and individual cancer types. We propose an elbow-point based method to optimize the cutoff used for calling CNAs. The optimized AS and FGA scores show significantly improved predictive performance compared to the arbitrary cutoffs reported in the literature. However, our data suggests that the use of AS and FGA for predicting immunotherapy response is currently limited to only a few cancer types. Therefore, larger sample sizes are needed to evaluate the clinical utility of these measures for patient stratification in other cancer types.