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Germline variants predictive of tumor mutational burden and immune checkpoint inhibitor efficacy.

ABSTRACT: High tumor mutational burden (TMB) is associated with response to checkpoint blockade in several cancers. We identify pathogenic germline variants associated with increased TMB (GVITMB). GVITMB were found in 7 genes using a pan-cancer approach (APC, FANCL, SLC25A13, ERCC3, MSH6, PMS2, and TP53) and 38 gene sets (e.g., those involved in DNA repair and programmed cell death). GVITMB were also associated with mutational signatures related to the dysfunction of the gene carrying the variant, somatic mutations that further affect the gene or pathway with the variant, or transcriptomic changes concordant with the expected effect of the variant. In a validation cohort of 140 patients with cutaneous melanoma, we found that patients with GVITMB had prolonged progression-free survival (p = 0.0349, hazard ratio = 0.688) and responded favorably (p = 0.0341, odds = 1.842) when treated with immune checkpoint inhibitors. Our results suggest that germline variants can influence the molecular phenotypes of tumors and predict the response to immune checkpoint inhibitors.

SUBMITTER: Chatrath A

PROVIDER: S-EPMC7988326 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Germline variants predictive of tumor mutational burden and immune checkpoint inhibitor efficacy.

Chatrath Ajay A Ratan Aakrosh A Dutta Anindya A

iScience 20210304 3

High tumor mutational burden (TMB) is associated with response to checkpoint blockade in several cancers. We identify pathogenic germline variants associated with increased TMB (GVITMB). GVITMB were found in 7 genes using a pan-cancer approach (APC, FANCL, SLC25A13, ERCC3, MSH6, PMS2, and TP53) and 38 gene sets (e.g., those involved in DNA repair and programmed cell death). GVITMB were also associated with mutational signatures related to the dysf ...[more]

PMID: 33786423

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Project description:Treatment with immune checkpoint inhibitors (ICPIs) extends survival in a proportion of patients across multiple cancers. Tumor mutational burden (TMB)-the number of somatic mutations per DNA megabase (Mb)-has emerged as a proxy for neoantigen burden that is an independent biomarker associated with ICPI outcomes. Based on findings from recent studies, TMB can be reliably estimated using validated algorithms from next-generation sequencing assays that interrogate a sufficiently large subset of the exome as an alternative to whole-exome sequencing. Biological processes contributing to elevated TMB can result from exposure to cigarette smoke and ultraviolet radiation, from deleterious mutations in mismatch repair leading to microsatellite instability, or from mutations in the DNA repair machinery. A variety of clinical studies have shown that patients with higher TMB experience longer survival and greater response rates following treatment with ICPIs compared with those who have lower TMB levels; this includes a prospective randomized clinical trial that found a TMB threshold of ≥10 mutations per Mb to be predictive of longer progression-free survival in patients with non-small cell lung cancer. Multiple trials are underway to validate the predictive values of TMB across cancer types and in patients treated with other immunotherapies. Here we review the rationale, algorithm development methodology, and existing clinical data supporting the use of TMB as a predictive biomarker for treatment with ICPIs. We discuss emerging roles for TMB and its potential future value for stratifying patients according to their likelihood of ICPI treatment response. IMPLICATIONS FOR PRACTICE: Tumor mutational burden (TMB) is a newly established independent predictor of immune checkpoint inhibitor (ICPI) treatment outcome across multiple tumor types. Certain next-generation sequencing-based techniques allow TMB to be reliably estimated from a subset of the exome without the use of whole-exome sequencing, thus facilitating the adoption of TMB assessment in community oncology settings. Analyses of multiple clinical trials across several cancer types have demonstrated that TMB stratifies patients who are receiving ICPIs by response rate and survival. TMB, alongside other genomic biomarkers, may provide complementary information in selecting patients for ICPI-based therapies.

Project description:ObjectiveMolecular profiling is developing to inform treatment in endometrial cancer. Using real world evidence, we sought to evaluate frontline immune checkpoint inhibitor vs chemotherapy effectiveness in advanced endometrial cancer, stratified by Tumor Mutational Burden (TMB) ≥10 mut/MB and microsatellite instability (MSI).MethodsPatients with advanced endometrial cancer in the US-based de-identified Flatiron Health-Foundation Medicine Clinico-Genomic Database were included. Data originated from patients treated between January 2011- March 2022 at 280 US clinics. Next-generation sequencing assays were performed via FoundationOne or FoundationOneCDx. Longitudinal clinical data were derived from electronic health records. Immune checkpoint inhibitor treatment included pembrolizumab, dostarlimab, and nivolumab monotherapies. Time to next treatment, time to treatment discontinuation, and overall survival were assessed with the log-rank test and Cox proportional hazard models with adjusted hazard ratios (aHR) for known prognostic factors. We used the Likelihood ratio test to compare biomarker performance.ResultsA total of 343 patients received chemotherapy and 28 received immune checkpoint inhibitor monotherapy as frontline treatment. Patients who received monotherapy were more likely to be stage III at diagnosis (immune checkpoint inhibitor: 54.6% vs chemotherapy: 15.0%; p<0.001) and more likely to test MSI-high via next-generation sequencing (immune checkpoint inhibitor: 53.6% vs chemotherapy: 19.2%; p<0.001). In MSI-high cancers, single-agent immune checkpoint inhibitor had a more favorable time to next treatment (aHR: 0.18, p=0.001) and overall survival (aHR 0.29, p=0.045). Additional analyses on 70 unique tumor specimens revealed mismatch repair deficiency (dMMR) via immunohistochemistry and MSI-high via next-generation sequencing concordance (91%), with nominal improvement of MSI over dMMR to predict time to treatment discontinuation (p=0.030), time to next treatment (p=0.032), and overall survival (p=0.22). MSI status was concordant with tumor mutational burden ≥10 in 94.3% of cases.ConclusionImmune checkpoint inhibitors may have improved efficacy over chemotherapy in frontline treatment for advanced endometrial cancer defined by MSI-high using next-generation sequencing as a nominally better predictor of outcomes than dMMR with immunohistochemistry. This provides the biologic rationale of active phase III trials.

Project description:Background: Despite an increasing understanding about tumor mutation burden (TMB) in cancer immunity and cancer immunotherapy, the comprehensive cognition between TMB and efficiency of immune checkpoint inhibitors (ICIs) is still lacking. A systematic review and meta-analysis was conducted to evaluate the predictive value of TMB on efficacy of ICIs. Methods: Systematic literature search was conducted on PubMed, EMBASE, Web of Science and Cochrane Library up to June 16, 2019. Pooled odds ratio (OR) of objective response rate (ORR), hazard ratio (HR) of progression-free survival (PFS) and overall survival (OS) were estimated by inverse variance weighted fixed-effects model (I 2 ≤ 50%) or DerSimonian-Laird random-effects model (I 2 > 50%). In addition, heterogeneity analysis, sensitivity analysis, publication bias and subgroup analysis were conducted. Moreover, fractional polynomial regression was conducted to investigate the dose-response relationship between TMB cutoffs and efficacy of ICIs. Furthermore, we assessed ORR by TMB and programmed cell death ligand 1 (PD-L1) expression after layering each other in studies which the two could be both acquired. Results: Three thousand six hundred fifty-seven records were retrieved through database searching, and 29 studies with 4,431 patients were finally included in the meta-analysis. TMB high group had significantly improved ORR (pooled OR 3.31, 95% CI 2.61, 4.19, P < 0.001), PFS (pooled HR 0.59, 95% CI 0.49, 0.71, P < 0.001) and OS (pooled HR 0.68, 95% CI 0.53, 0.89, P = 0.004). Sensitivity analyses illustrated the results were stable, and publication bias was identified in ORR. Subgroup analyses showed the predictive value of TMB was significant in non-small-cell lung cancer (except for the OS) and melanoma. In addition, heterogeneity was substantial in targeted next generation sequencing group but tiny in whole exome sequencing group. Furthermore, TMB and PD-L1 expression were capable to predict improved ORR of ICIs after stratification of each other, with tiny heterogeneity. Conclusions: High tumor mutation burden predicted improved efficacy of immune checkpoint inhibitors in cancers, and targeted next generation sequencing for estimating tumor mutation burden in clinic should be standardized to eliminate heterogeneity in the future. Moreover, tumor mutation burden and programmed cell death ligand 1 expression were independent factors on predicting efficacy of immune checkpoint inhibitors.

Dataset Information

Germline variants predictive of tumor mutational burden and immune checkpoint inhibitor efficacy.

Publications

Germline variants predictive of tumor mutational burden and immune checkpoint inhibitor efficacy.

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