Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden

Abstract

Background: High tumor mutational burden (TMB) is an emerging biomarker of sensitivity to immune checkpoint inhibitors and has been shown to be more significantly associated with response to PD-1 and PD-L1 blockade immunotherapy than PD-1 or PD-L1 expression, as measured by immunohistochemistry (IHC). The distribution of TMB and the subset of patients with high TMB has not been well characterized in the majority of cancer types.

Methods: In this study, we compare TMB measured by a targeted comprehensive genomic profiling (CGP) assay to TMB measured by exome sequencing and simulate the expected variance in TMB when sequencing less than the whole exome. We then describe the distribution of TMB across a diverse cohort of 100,000 cancer cases and test for association between somatic alterations and TMB in over 100 tumor types.

Results: We demonstrate that measurements of TMB from comprehensive genomic profiling are strongly reflective of measurements from whole exome sequencing and model that below 0.5 Mb the variance in measurement increases significantly. We find that a subset of patients exhibits high TMB across almost all types of cancer, including many rare tumor types, and characterize the relationship between high TMB and microsatellite instability status. We find that TMB increases significantly with age, showing a 2.4-fold difference between age 10 and age 90 years. Finally, we investigate the molecular basis of TMB and identify genes and mutations associated with TMB level. We identify a cluster of somatic mutations in the promoter of the gene PMS2, which occur in 10% of skin cancers and are highly associated with increased TMB.

Conclusions: These results show that a CGP assay targeting ~1.1 Mb of coding genome can accurately assess TMB compared with sequencing the whole exome. Using this method, we find that many disease types have a substantial portion of patients with high TMB who might benefit from immunotherapy. Finally, we identify novel, recurrent promoter mutations in PMS2, which may be another example of regulatory mutations contributing to tumorigenesis.

Keywords: Cancer genomics; Mismatch repair; PMS2; Tumor mutational burden.

Figures

Fig. 1

Accuracy and precision of comprehensive genomic profiling for assessing tumor mutation burden. a Comparison of tumor mutation burden measured by whole exome sequencing versus comprehensive genomic profiling. Tumor mutation burden (mutations/Mb) was measured in 29 samples by whole exome sequencing of matched tumor and normal samples and by comprehensive genomic profiling (see “Methods” for more details). The line y = x is plotted in red. b Tumor mutation burden measured by comprehensive genomic profiling in 60 pairs of replicates. The line y = x is plotted in red. c Results of simulations of percentage deviation from actual TMB when sampling different numbers of megabases sequenced. Median observed deviation is shown in black and 10% and 90% confidence interval are shown in grey. Lines are smoothed using a cubic smoothing spline with smoothing parameter = 0.6. Left: results of simulations with TMB equal to 100 mutations/Mb. Center: results of simulations with TMB equal to 20 mutations/Mb. The median line was smoothed with smoothing parameter = 0.8. Right: results of simulations with TMB equal to 10 mutations/Mb. The median line was smoothed with smoothing parameter = 0.8

Fig. 2

The landscape of tumor mutation burden. For all disease types with greater than 100 samples, the median mutation burden is plotted for each disease type. The left and right edges of the boxes correspond to the 25th and 75th percentiles. Whiskers extend to the highest value that is within 1.5 × IQR of the hinge, where IQR is the inter-quartile range, or distance between the first and third quartiles. Points beyond this are plotted individually. Tissue types of interest are shown in color, as follows: skin, green; lung, orange; bladder, purple; kidney, pink; other, white. The area above 20 mutations/Mb, which we have designated as high TMB, is colored in grey

Fig. 3

The relationship between tumor mutation burden and microsatellite instability. a Specimens for which we measured both TMB and microsatellite instability. MSI calls were only available for 62,150 samples from the most recent versions of the assay. Specimens with TMB low and called as MSI-Stable are shown in light grey, specimens with high TMB (mutations/Mb >20) are shown in blue, and specimens called as MSI-High are shown in dark grey. b The proportion of samples called as MSI and TMB high (dark blue), TMB high and MSI-Stable (light blue), and TMB low and MSI-High (grey) for each of the disease types with greater than 0.3% of samples called as either TMB or MSI-High

Fig. 4

Associating mutations in cancer genes with tumor mutational burden. a Coefficient from linear model. Genes are sorted by this ratio. Genes involved in mismatch repair (MSH2, MSH6, MLH1, PMS2) are highlighted in blue. DNA polymerase ε (POLE) is highlighted in orange. b Plot of mutation burden in specimens with known or likely driver mutations in any of the mismatch repair genes listed above (MMR+), n = 859, and of specimens without such a mutation (MMR−), n = 91,579. c Plot of mutation burden in specimens with known or likely driver mutations in POLE (n = 102) and specimens without such mutations (n = 92,336)

Fig. 5

Recurrent PMS2 mutations are associated with increased mutation burden and are stratified by disease type. a Location of recurrent PMS2 promoter mutations upstream of the transcription start site. Locations showing multiple dinucleotide events are marked with a blue box. b Mutation burden in PMS2 mutant versus wild-type specimens. For the indicated disease and selected mutation or collection of mutations, tumors were classified as Mut + or Mut−. Mutation burden for these two sample populations is plotted. Whiskers extend to the highest value that is within 1.5 × IQR of the hinge, where IQR is the inter-quartile range, or distance between the first and third quartiles. Points beyond this are not shown. c Percentage of specimens with PMS2 promoter mutations in select disease types. The percentage of specimens with any of the PMS2 promoter mutations is plotted