Pan-cancer subclonal mutation analysis of 7,827 tumors predicts clinical outcome

Abstract

Intra-tumor heterogeneity is an important driver of tumor evolution and therapy response. Advances in precision cancer treatment will require understanding of mutation clonality and subclonal architecture. Currently the slow computational speed of subclonal reconstruction hinders large cohort studies. To overcome this bottleneck, we developed Clonal structure identification through Pairwise Penalization, or CliPP, which clusters subclonal mutations using a regularized likelihood model. CliPP reliably processed whole-genome and whole-exome sequencing data from over 12,000 tumor samples within 24 hours, thus enabling large-scale downstream association analyses between subclonal structures and clinical outcomes. Through a pan-cancer investigation of 7,827 tumors from 32 cancer types, we found that high subclonal mutational load (sML), a measure of latency time in tumor evolution, was significantly associated with better patient outcomes in 16 cancer types with low to moderate tumor mutation burden (TMB). In a cohort of prostate cancer patients participating in an immunotherapy clinical trial, high sML was indicative of favorable response to immune checkpoint blockade. This comprehensive study using CliPP underscores sML as a key feature of cancer. sML may be essential for linking mutation dynamics with immunotherapy response in the large population of non-high TMB cancers.