Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors

Abstract

Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, varies across malignancies. Panel sequencing-based estimates of TMB have largely replaced whole-exome sequencing-derived TMB in the clinic. Retrospective evidence suggests that TMB can predict the efficacy of immune checkpoint inhibitors, and data from KEYNOTE-158 led to the recent FDA approval of pembrolizumab for the TMB-high tumor subgroup. Unmet needs include prospective validation of TMB cutoffs in relationship to tumor type and patient outcomes. Furthermore, standardization and harmonization of TMB measurement across test platforms are important to the successful implementation of TMB in clinical practice. SIGNIFICANCE: Evaluation of TMB as a predictive biomarker creates the need to harmonize panel-based TMB estimation and standardize its reporting. TMB can improve the predictive accuracy for immunotherapy outcomes, and has the potential to expand the candidate pool of patients for treatment with immune checkpoint inhibitors.

Figure 1.

Panel-sequencing derived TMB. Confidence intervals (CIs) of panel sequencing-derived measurement of TMB showing the stochastic variability due to limited panel size. CIs can be reconstructed (TMB - lower limit in % × TMB, TMB + upper limit in % × TMB) from the upper and lower limits that are presented as percentages relative to the TMB. CIs were calculated using the Clopper-Pearson method (134).

Figure 2.

Analysis of TMB and mutational signatures in 24 cancer types (8273 tumors, TCGA pan-cancer atlas). Numbers of missense mutations detected by WES were converted to TMB per Mb using the correspondence of 199 mutations to 10 mut/Mb (50). A, Violin plots show markedly different median TMB levels and TMB variability in different cancer types. For most cancer types, unimodal distributions of TMB were observed, but bimodal distributions were observed in adenocarcinomas of the colorectum and stomach, and a multimodal distribution was observed in uterine adenocarcinoma. Upper panel: Percentage of tumors with TMB above 10 mut/Mb including MSI-H adenocarcinoma of uterus, colorectum and stomach (dark bars). B, Heatmap and hierarchical clustering of 26 single base substitution (SBS) signatures using the Manhattan distance and the average linkage method (135). SBS signatures are annotated by the known or putative underlying mutational processes. SBS signatures that are not linked to an underlying mutational process (other SBS) were pooled. Mutational processes that can cause a very high TMB and hypermutation include POLE/POLD1 mutations (SBS10a, 10b, 14 and 20), DNA mismatch repair deficiency (SBS6, SBS15, SBS21, SBS15 and SBS44), UV light (SBS7a and 7b), tobacco smoking (SBS4), AID/APOBEC activation (SBS2 and SBS13) and the three clock-like processes (SBS1 and SBS5). MSI = microsatellite instability, MMRD = mismatch repair deficiency, HRD = homologous recombination deficiency, BERD = base excision repair deficiency. A detailed description of the methods for analysis of mutational signatures can be found at (https://www.nature.com/articles/nature12477). C, Correlation analysis of PD-L1 mRNA expression and TMB level. Significant positive correlations were observed in 5 cancer types, while correlations were not significant in the remaining 19 cancer types. R = Spearman correlation, * = significant after Bonferroni correction (p < 0.05/24). Somatic mutation and mRNA expression data were obtained from [https://gdc.cancer.gov/about-data/publications/pancanatlas], MSI data from [https://gdac.broadinstitute.org] and the levels of the SBS mutational signatures from [https://www.synapse.org/#!Synapse:syn11804040].

Figure 3.

Impact of TMB cutoff choice on sensitivity and specificity of response prediction. Analysis of the Miao et al. cohort (66) of ICB-treated patients with microsatellite-stable solid tumors and clinical annotation of complete response, partial response (CR/PR) or progressive disease (PD). Numbers of missense mutations detected by WES were converted to TMB per Mb using the correspondence of 199 mutations and 10 mut/Mb (50). A, melanoma subcohort (n=125). B, lung cancer subcohort (n=36). C, bladder cancer subcohort (n=23). D, entire cohort of mixed cancer types (n=193).