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

Abstract

BackgroundHigh 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.MethodsIn 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.ResultsWe 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.ConclusionsThese 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-017-0424-2) contains supplementary material, which is available to authorized users.

Emerging therapeutic targets in myeloproliferative neoplasms and peripheral T-cell leukemia and lymphomas

“…The last decade of cancer research has been significantly shaped by major advances in next-generation sequencing technologies that have led to the analysis of more than 100,000 whole cancer genomes [4]. Approximately 4 million coding mutations have been identified, defining 500 genes that act as functional cancer drivers [5,6], many of which participate in core cancer pathways [7].…”

Mutation load and an effector T-cell gene signature may distinguish immunologically distinct and clinically relevant lymphoma subsets

“…Synonymous mutations were counted after filtering to reduce sampling noise and as an attempt to capture mutational processes contributing to neoantigen generation. 20 Noncoding alterations, alterations listed as "known somatic alterations" in the Catalogue of Somatic Mutations in Cancer database, alterations predicted to be germ line by the somatic-germ line-zygosity algorithm, known germ line alterations in the Single Nucleotide Polymorphism database, and germ line alterations occurring with $2 counts in the Exome Aggregation Consortium database were filtered and not counted. A breakdown of the total mutation count in each sample and the mutation load per Mb can be found in supplemental Table 1.…”

“…In addition to identifying a number of potentially deleterious mutations, the FoundationOne Heme platform was also used to estimate mutation load (per megabase) using Foundation Medicine's mutation load algorithm. 6,20 To estimate mutation load, base substitutions and indels within a 1.2-Mb region were identified by DNA sequencing; any alteration known or likely to be associated with tumorigenesis was removed, because the F1H assay is biased toward functional mutations in cancer. Synonymous mutations were counted after filtering to reduce sampling noise and as an attempt to capture mutational processes contributing to neoantigen generation.…”

Precision Medicine in Oncology and Immuno-Oncology: Where We Stand and Where We're Headed

“…In this context, a correlation between mutational load - a surrogate marker for tumor neoantigen load - and outcome upon the blockage of T-cell checkpoint inhibitors has been observed in NSCLC [6] and other indications. In the meantime, an extensive investigation described the distribution of tumor mutational burden across a diverse cohort of 100,000 cancer cases and tested for association between somatic alterations and tumor mutational burden in over 100 tumor types [7]. …”