Abstract
Tumor mutation burden (TMB) is a well-known efficacy predictor for checkpoint inhibitor immunotherapies. Currently, TMB assessment relies on DNA sequencing data. Gene expression profiling by RNA sequencing (RNAseq) is another type of analysis that can inform clinical decision-making and including TMB estimation may strongly benefit this approach, especially for the formalin-fixed, paraffin-embedded (FFPE) tissue samples. Here, we for the first time compared TMB levels deduced from whole exome sequencing (WES) and RNAseq profiles of the same FFPE biosamples in single-sample mode. We took TCGA project data with mean sequencing depth 23 million gene-mapped reads (MGMRs) and found 0.46 (Pearson)–0.59 (Spearman) correlation with standard mutation calling pipelines. This was converted into low (<10) and high (>10) TMB per megabase classifier with area under the curve (AUC) 0.757, and application of machine learning increased AUC till 0.854. We then compared 73 experimental pairs of WES and RNAseq profiles with lower (mean 11 MGMRs) and higher (mean 68 MGMRs) RNA sequencing depths. For higher depth, we observed ~1 AUC for the high/low TMB classifier and 0.85 (Pearson)–0.95 (Spearman) correlation with standard mutation calling pipelines. For the lower depth, the AUC was below the high-quality threshold of 0.7. Thus, we conclude that using RNA sequencing of tumor materials from FFPE blocks with enough coverage can afford for high-quality discrimination of tumors with high and low TMB levels in a single-sample mode.
Highlights
Tumor mutation burden (TMB) per million base pairs is a wellknown efficacy predictor for checkpoint inhibitor immunotherapy [1]
Data could be obtained for only 53 tumor cases because the absolute majority of the paired The Cancer Genome Atlas (TCGA) data were generated for fresh-frozen tissue samples
No discernable sequencing batch bias was detected during both correlation analysis and primary component analysis: samples from different TCGA sequencing centers were randomly clustered on the PCA (Figure S1)
Summary
Tumor mutation burden (TMB) per million base pairs is a wellknown efficacy predictor for checkpoint inhibitor immunotherapy [1]. In commercial FDA-approved FoundationOne CDx test for unpaired single tumor samples, TMB is defined as the number of somatic mutations per million base pairs (megabase) of the protein-coding sequence analyzed— including both substitutions and indels, but irrespective of the functional consequences of the variants [3]. TMB assessment is commercially available in the form of clinical and research use-only diagnostic tests [5]. In June 2020, TMB was approved for the use of immune checkpoint inhibitor pembrolizumab in the treatment of patients with advanced or metastatic solid cancers, and FoundationOne CDx assay was approved as a companion diagnostic test
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