Prevalence of High Tumor Mutational Burden and Association With Survival in Patients With Less Common Solid Tumors

Changxia Shao, Gerald Li, Lingkang Huang, Scott Pruitt, Emily Castellanos, Garrett Frampton, Kenneth R Carson, Tamara Snow, Gaurav Singal, David Fabrizio, Brian M Alexander, Fan Jin, Wei Zhou, Changxia Shao, Gerald Li, Lingkang Huang, Scott Pruitt, Emily Castellanos, Garrett Frampton, Kenneth R Carson, Tamara Snow, Gaurav Singal, David Fabrizio, Brian M Alexander, Fan Jin, Wei Zhou

Abstract

Importance: Tumor mutational burden (TMB) is a potential biomarker associated with response to immune checkpoint inhibitor therapies. The prognostic value associated with TMB in the absence of immunotherapy is uncertain.

Objective: To assess the prevalence of high TMB (TMB-H) and its association with overall survival (OS) among patients not treated with immunotherapy with the same 10 tumor types from the KEYNOTE-158 study.

Design, setting, and participants: This retrospective cohort study evaluated the prognostic value of TMB-H, assessed by Foundation Medicine (FMI) and defined as at least 10 mutations/megabase (mut/Mb) in the absence of immunotherapy. Data were sourced from the deidentified Flatiron Health-FMI clinicogenomic database collected up to July 31, 2018. Eligible patients were aged 18 years or older with any of the following solid cancer types: anal, biliary, endometrial, cervical, vulvar, small cell lung, thyroid, salivary gland, mesothelioma, or neuroendocrine tumor. Patients with microsatellite instability-high tumors were excluded from primary analysis. For OS analysis, patients were excluded if immunotherapy started on the FMI report date or earlier or if patients died before January 1, 2012, and patients were censored if immunotherapy was started later than the FMI report date. Data were analyzed from November 2018 to February 2019.

Main outcomes and measures: Overall survival was analyzed using the Kaplan-Meier method and Cox proportional hazards model, adjusting for age, sex, cancer types, practice type, and albumin level.

Results: Of 2589 eligible patients, 1671 (64.5%) were women, and the mean (SD) age was 63.7 (11.7) years. Median (interquartile range) TMB was 2.6 (1.7-6.1) mut/Mb, and 332 patients (12.8%) had TMB-H (≥10 mut/Mb). Prevalence of TMB-H was highest among patients with small cell lung cancer (40.0%; 95% CI, 34.7%-45.6%) and neuroendocrine tumor (29.3%; 95% CI, 22.8%-36.6%) and lowest was among patients with mesothelioma (1.2%; 95% CI, 0.3%-4.4%) and thyroid cancer (2.7%; 95% CI, 1.2%-5.7%). Adjusted hazard ratio for OS of patients not treated with immunotherapy with TMB-H vs those without TMB-H was 0.94 (95% CI, 0.77-1.13). Comparable results were observed when including patients with high microsatellite instability tumors and calculating OS from first observed antineoplastic treatment date.

Conclusions and relevance: These findings suggest that prevalence of TMB-H varies widely depending on tumor type and TMB-H does not appear to be a factor associated with OS among patients across these cancer types treated in the absence of immunotherapy.

Conflict of interest statement

Conflict of Interest Disclosures: Dr Shao reported owning stock in Merck and Co outside the submitted work. Dr Li reported receiving personal fees from Foundation Medicine and having equity ownership in F. Hoffmann-La Roche outside the submitted work. Dr Huang reported owning stock in Merck and Co outside the submitted work. Dr Castellanos reported owning equity in Flatiron Health and stock from Roche outside the submitted work. Dr Carson reported receiving personal fees from Tempus Labs and Flatiron Health outside the submitted work. Dr Snow reported receiving personal fees from and owning stock in Roche and owning equity in Flatiron Health outside the submitted work. Dr Fabrizio reported having a patent for Methods and Systems for Evaluating Tumor Mutational Burden pending. Dr Alexander reported receiving personal fees from Roche during the conduct of the study and grants from Eli Lilly and Co, Puma, and Celgene outside the submitted work. Dr Zhou reported receiving stock and stock options in Merck and Co outside the submitted work. No other disclosures were reported.

Figures

Figure 1.. Distribution of Tumor Types and…
Figure 1.. Distribution of Tumor Types and High Tumor Mutational Burden (TMB-H) Across the Entire Cohort
Analysis included all tumor types. Evaluation of TMB-H was performed using the FoundationOne assay or FoundationOne Companion Diagnostic platforms. TMB-H was defined as 10 or more mutations per megabase. All patients had at least 1 documented clinical visit observed in the clinicogenetic database after January 1, 2011. Meso indicates mesothelioma; NET, neuroendocrine tumor; SCLC, small cell lung cancer.
Figure 2.. Kaplan-Meier Estimate of Overall Survival…
Figure 2.. Kaplan-Meier Estimate of Overall Survival Among Patients With High Tumor Mutational Burden (TMB) and Without High TMB
All patients were included for primary analysis. Survival was defined as the time from Foundation Medicine report date to the date of death due to any cause or censor date. High TMB was defined as 10 or more mutations per megabase.
Figure 3.. Sensitivity Analyses Evaluating Association Between…
Figure 3.. Sensitivity Analyses Evaluating Association Between Tumor Mutational Burden (TMB) and OS Among Patients With All 10 Tumor Types
The study population was derived from patients tested by FoundationOne (F1) assay or F1 Companion Diagnostic (CDx) assay vs any FMI assay. OS was calculated from Foundation Medicine (FMI) report date vs date of first antineoplastic treatment. CGDB indicates clinicogenomic database; HR, hazard ratio; MSI-H, high microsatellite instability; TMB, tumor mutational burden; TMB-H, high TMB (≥10 or ≥13 mutations per megabase).

References

    1. Samstein RM, Lee CH, Shoushtari AN, et al. . Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet. 2019;51(2):202-206. doi:10.1038/s41588-018-0312-8
    1. Singal G, Miller PG, Agarwala V, et al. . Association of patient characteristics and tumor genomics with clinical outcomes among patients with non–small cell lung cancer using a clinicogenomic database. JAMA. 2019;321(14):1391-1399. doi:10.1001/jama.2019.3241
    1. Yarchoan M, Albacker LA, Hopkins AC, et al. . PD-L1 expression and tumor mutational burden are independent biomarkers in most cancers. JCI Insight. 2019;4(6):e126908. doi:10.1172/jci.insight.126908
    1. Alexandrov LB, Nik-Zainal S, Wedge DC, et al. ; Australian Pancreatic Cancer Genome Initiative; ICGC Breast Cancer Consortium; ICGC MMML-Seq Consortium; ICGC PedBrain . Signatures of mutational processes in human cancer. Nature. 2013;500(7463):415-421. doi:10.1038/nature12477
    1. Mariathasan S, Turley SJ, Nickles D, et al. . TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018;554(7693):544-548. doi:10.1038/nature25501
    1. Rizvi NA, Cho BC, Reinmuth N, et al. . LBA6—durvalumab with or without tremelimumab vs platinum-based chemotherapy as first-line treatment for metastatic non-small cell lung cancer: MYSTIC. Ann Oncol. 2018;29(suppl 10):x40-x41. doi:10.1093/annonc/mdy511.005
    1. Rizvi NA, Hellmann MD, Snyder A, et al. . Cancer immunology: mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348(6230):124-128. doi:10.1126/science.aaa1348
    1. Ready N, Hellmann MD, Awad MM, et al. . First-line nivolumab plus ipilimumab in advanced non–small-cell lung cancer (CheckMate 568): outcomes by programmed death ligand 1 and tumor mutational burden as biomarkers. J Clin Oncol. 2019;37(12):992-1000. doi:10.1200/JCO.18.01042
    1. Reck M, Schenker M, Lee KH, et al. . Nivolumab plus ipilimumab versus chemotherapy as first-line treatment in advanced non-small-cell lung cancer with high tumour mutational burden: patient-reported outcomes results from the randomised, open-label, phase III CheckMate 227 trial. Eur J Cancer. 2019;116:137-147. doi:10.1016/j.ejca.2019.05.008
    1. Herbst RS, Lopes G, Kowalski DM, et al. . LBA79—association between tissue TMB (tTMB) and clinical outcomes with pembrolizumab monotherapy (pembro) in PD-L1–positive advanced NSCLC in the KEYNOTE-010 and -042 trials. Ann Oncol. 2019;30(suppl 5):v916-v917. doi:10.1093/annonc/mdz394.077
    1. Hellmann MD, Ciuleanu TE, Pluzanski A, et al. . Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden. N Engl J Med. 2018;378(22):2093-2104. doi:10.1056/NEJMoa1801946
    1. Hellmann MD, Callahan MK, Awad MM, et al. . Tumor mutational burden and efficacy of nivolumab monotherapy and in combination with ipilimumab in small-cell lung cancer. Cancer Cell. 2019;35(2):329. doi:10.1016/j.ccell.2019.01.011
    1. Van Allen EM, Miao D, Schilling B, et al. . Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015;350(6257):207-211. doi:10.1126/science.aad0095
    1. Fabrizio DA, George TJ Jr, Dunne RF, et al. . Beyond microsatellite testing: assessment of tumor mutational burden identifies subsets of colorectal cancer who may respond to immune checkpoint inhibition. J Gastrointest Oncol. 2018;9(4):610-617. doi:10.21037/jgo.2018.05.06
    1. Devarakonda S, Rotolo F, Tsao MS, et al. . Tumor mutation burden as a biomarker in resected non-small-cell lung cancer. J Clin Oncol. 2018;36(30):2995-3006. doi:10.1200/JCO.2018.78.1963
    1. Wang C, Liang H, Lin C, et al. . Molecular subtyping and prognostic assessment based on tumor mutation burden in patients with lung adenocarcinomas. Int J Mol Sci. 2019;20(17):4251. doi:10.3390/ijms20174251
    1. George J, Lim JS, Jang SJ, et al. . Comprehensive genomic profiles of small cell lung cancer. Nature. 2015;524(7563):47-53. doi:10.1038/nature14664
    1. . Study of pembrolizumab (MK-3475) in participants with advanced solid tumors (MK-3475-158/KEYNOTE-158). Accessed October 5, 2020.
    1. Marabelle A, Fakih M, Lopez J, et al. . Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol. 2020;21(10):1353-1365. doi:10.1016/S1470-2045(20)30445-9
    1. Food and Drug Administration . FoundationOne CDx: summary of safety and effectiveness data. Accessed December 19, 2019.
    1. Chalmers ZR, Connelly CF, Fabrizio D, et al. . Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 2017;9(1):34. doi:10.1186/s13073-017-0424-2
    1. Owada-Ozaki Y, Muto S, Takagi H, et al. . Prognostic impact of tumor mutation burden in patients with completely resected non–small cell lung cancer: brief report. J Thorac Oncol. 2018;13(8):1217-1221. doi:10.1016/j.jtho.2018.04.003
    1. Strickland KC, Howitt BE, Shukla SA, et al. . Association and prognostic significance of BRCA1/2-mutation status with neoantigen load, number of tumor-infiltrating lymphocytes and expression of PD-1/PD-L1 in high grade serous ovarian cancer. Oncotarget. 2016;7(12):13587-13598. doi:10.18632/oncotarget.7277
    1. Schwartz S, Tian Y, Cecchi F, et al. . The prognostic role of microsatellite status, tumor mutational burden, and protein expression in CRC. J Clin Oncol. 2018;36(4)(suppl):572. doi:10.1200/JCO.2018.36.4_suppl.572
    1. Simpson D, Ferguson R, Marinez CN, et al. . Mutation burden as a potential prognostic marker of melanoma progression and survival. J Clin Oncol. 2017;35(15)(suppl):9567. doi:10.1200/JCO.2017.35.15_suppl.9567
    1. Birkbak NJ, Kochupurakkal B, Izarzugaza JM, et al. . Tumor mutation burden forecasts outcome in ovarian cancer with BRCA1 or BRCA2 mutations. PLoS One. 2013;8(11):e80023. doi:10.1371/journal.pone.0080023
    1. Park SE, Park K, Lee E, et al. . Clinical implication of tumor mutational burden in patients with HER2-positive refractory metastatic breast cancer. Oncoimmunology. 2018;7(8):e1466768. doi:10.1080/2162402X.2018.1466768
    1. Brown SD, Warren RL, Gibb EA, et al. . Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival. Genome Res. 2014;24(5):743-750. doi:10.1101/gr.165985.113
    1. Zhou KI, Peterson BF, Serritella A, et al. . Spatial and temporal heterogeneity of PD-L1 expression and tumor mutational burden in gastroesophageal adenocarcinoma at baseline diagnosis and after chemotherapy. Clin Cancer Res. 2020;clincanres.2085.2020. doi:10.1158/1078-0432.CCR-20-2085
    1. Hellmann MD, Nathanson T, Rizvi H, et al. . Genomic features of response to combination immunotherapy in patients with advanced non–small-cell lung cancer. Cancer Cell. 2018;33(5):843-852.e4. doi:10.1016/j.ccell.2018.03.018
    1. Zhao P, Li L, Jiang X, Li Q. Mismatch repair deficiency/microsatellite instability-high as a predictor for anti-PD-1/PD-L1 immunotherapy efficacy. J Hematol Oncol. 2019;12(1):54. doi:10.1186/s13045-019-0738-1
    1. FoundationOne CDx . FoundationOne CDx technical information. Accessed December 19, 2019.
    1. Naggara O, Raymond J, Guilbert F, Roy D, Weill A, Altman DG. Analysis by categorizing or dichotomizing continuous variables is inadvisable: an example from the natural history of unruptured aneurysms. AJNR Am J Neuroradiol. 2011;32(3):437-440. doi:10.3174/ajnr.A2425

Source: PubMed

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