Article Abstract

Tumor mutational and indel burden: a systematic pan-cancer evaluation as prognostic biomarkers

Authors: Hao-Xiang Wu, Zi-Xian Wang, Qi Zhao, Dong-Liang Chen, Ming-Ming He, Lu-Ping Yang, Ying-Nan Wang, Ying Jin, Chao Ren, Hui-Yan Luo, Zhi-Qiang Wang, Feng Wang


Background: Tumor mutational burden (TMB) has been widely studied as a predictive biomarker of response to immune checkpoint inhibitors (ICIs). Besides, evidence suggests frameshift indels are a highly immunogenic mutational class and thus a potentially superior biomarker. However, the general prognostic impact of TMB and indel burden in patients with solid tumors has not been systematically investigated.
Methods: We analyzed 20 primary solid cancer types from The Cancer Genome Atlas (TCGA) database. Clinicopathologic factors, TMB and indel burden were collected or calculated. For each cancer type, the impact of TMB or indel burden on overall survival (OS) was evaluated using the Kaplan-Meier method and Cox regression with the method of inverse probability of treatment weighting.
Results: Twenty cancer types from 6,035 patients were analyzed. Survival analysis showed that TMB had a significant impact on OS in 14 out of these 20 cancer types. According to the general survival impact of TMB, they could be classified into three groups, namely the TMB-Worse (eight cancer types), TMB-Better (six cancer types) and TMB-Similar (six cancer types) group, in which higher TMB was associated with inferior, superior, or similar OS, respectively. The survival impacts of TMB in the TMB-Worse and TMB-Better groups were generally consistent when limited to genes from two FDA-approved panels. Notably, in two out of the six cancer types in the TMB-Similar group, the indel burden significantly affected OS.
Conclusions: TMB, as well as indel burden, has divergent prognostic impact in different cancer types, thus could be incorporated in prognostication and risk stratification. More importantly, the general prognostic impact should be taken into account when establishing the predictive function of TMB to ICI treatment.