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The Optimal Tumor Mutational Burden Cutoff Value as a Novel Marker for Predicting the Efficacy of Programmed Cell Death-1 Checkpoint Inhibitors in Advanced Gastric Cancer

  • Jae Yeon Jang (Division of Hematology-Oncology, Department of Internal Medicine, Wonju Severance Christian Hospital) ;
  • Youngkyung Jeon (Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Sun Young Jeong (Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Sung Hee Lim (Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Won Ki Kang (Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Jeeyun Lee (Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Seung Tae Kim (Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine)
  • Received : 2023.04.11
  • Accepted : 2023.07.09
  • Published : 2023.07.31

Abstract

Purpose: The optimal tumor mutational burden (TMB) value for predicting treatment response to programmed cell death-1 (PD-1) checkpoint inhibitors in advanced gastric cancer (AGC) remains unclear. We aimed to investigate the optimal TMB cutoff value that could predict the efficacy of PD-1 checkpoint inhibitors in AGC. Materials and Methods: Patients with AGC who received pembrolizumab or nivolumab between October 1, 2020, and July 27, 2021, at Samsung Medical Center in Korea were retrospectively analyzed. The TMB levels were measured using a next-generation sequencing assay. Based on receiver operating characteristic curve analysis, the TMB cutoff value was determined. Results: A total 53 patients were analyzed. The TMB cutoff value for predicting the overall response rate (ORR) to PD-1 checkpoint inhibitors was defined as 13.31 mutations per megabase (mt/Mb) with 56% sensitivity and 95% specificity. Based on this definition, 7 (13.2%) patients were TMB-high (TMB-H). The ORR differed between the TMB-low (TMB-L) and TMB-H (8.7% vs. 71.4%, P=0.001). The progression-free survival and overall survival (OS) for 53 patients were 1.93 (95% confidence interval [CI], 1.600-2.268) and 4.26 months (95% CI, 2.992-5.532). The median OS was longer in the TMB-H (20.8 months; 95% CI, 2.292-39.281) than in the TMB-L (3.31 months; 95% CI, 1.604-5.019; P=0.049). Conclusions: The TMB cutoff value for predicting treatment response in AGC patients who received PD-1 checkpoint inhibitor monotherapy as salvage treatment was 13.31 mt/Mb. When applying the programmed death ligand-1 status to TMB-H, patients who would benefit from PD-1 checkpoint inhibitors can be selected.

Keywords

Acknowledgement

This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number, HR20C0025).

References

  1. Janjigian YY, Shitara K, Moehler M, Garrido M, Salman P, Shen L, et al. First-line nivolumab plus chemotherapy versus chemotherapy alone for advanced gastric, gastro-oesophageal junction, and oesophageal adenocarcinoma (CheckMate 649): a randomised, open-label, phase 3 trial. Lancet 2021;398:27-40. https://doi.org/10.1016/S0140-6736(21)00797-2
  2. Kim TH, Kim IH, Kang SJ, Choi M, Kim BH, Eom BW, et al. Korean practice guidelines for gastric cancer 2022: an evidence-based, multidisciplinary approach. J Gastric Cancer 2023;23:3-106. https://doi.org/10.5230/jgc.2023.23.e11
  3. Maio M, Ascierto PA, Manzyuk L, Motola-Kuba D, Penel N, Cassier PA, et al. Pembrolizumab in microsatellite instability high or mismatch repair deficient cancers: updated analysis from the phase II KEYNOTE-158 study. Ann Oncol 2022;33:929-938. https://doi.org/10.1016/j.annonc.2022.05.519
  4. Marabelle A, Le DT, Ascierto PA, Di Giacomo AM, De Jesus-Acosta A, Delord JP, et al. Efficacy of pembrolizumab in patients with noncolorectal high microsatellite instability/mismatch repair-deficient cancer: results from the phase II KEYNOTE-158 study. J Clin Oncol 2020;38:1-10. https://doi.org/10.1200/JCO.19.02105
  5. Kang YK, Boku N, Satoh T, Ryu MH, Chao Y, Kato K, et al. Nivolumab in patients with advanced gastric or gastro-oesophageal junction cancer refractory to, or intolerant of, at least two previous chemotherapy regimens (ONO-4538-12, ATTRACTION-2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 2017;390:2461-2471. https://doi.org/10.1016/S0140-6736(17)31827-5
  6. Chen L, Kang Y, Tanimoto M, Boku N. ATTRACTION-04 (ONO-4538-37): a randomized, multicenter, phase 2/3 study of nivolumab (Nivo) plus chemotherapy in patients (Pts) with previously untreated advanced or recurrent gastric (G) or gastroesophageal junction (GEJ) cancer. Ann Oncol 2017;28:v266.
  7. Moehler M, Dvorkin M, Boku N, Ozguroglu M, Ryu MH, Muntean AS, et al. Phase III trial of avelumab maintenance after first-line induction chemotherapy versus continuation of chemotherapy in patients with gastric cancers: results from JAVELIN gastric 100. J Clin Oncol 2021;39:966-977. https://doi.org/10.1200/JCO.20.00892
  8. Shitara K, Ozguroglu M, Bang YJ, Di Bartolomeo M, Mandala M, Ryu MH, et al. Pembrolizumab versus paclitaxel for previously treated, advanced gastric or gastro-oesophageal junction cancer (KEYNOTE-061): a randomised, open-label, controlled, phase 3 trial. Lancet 2018;392:123-133. https://doi.org/10.1016/S0140-6736(18)31257-1
  9. Yoon HH, Jin Z, Kour O, Kankeu Fonkoua LA, Shitara K, Gibson MK, et al. Association of PDL1 expression and other variables with benefit from immune checkpoint inhibition in advanced gastroesophageal cancer: systematic review and meta-analysis of 17 phase 3 randomized clinical trials. JAMA Oncol 2022;8:1456-1465. https://doi.org/10.1001/jamaoncol.2022.3707
  10. Janjigian YY, Bendell J, Calvo E, Kim JW, Ascierto PA, Sharma P, et al. CheckMate-032 study: efficacy and safety of nivolumab and nivolumab plus ipilimumab in patients with metastatic esophagogastric cancer. J Clin Oncol 2018;36:2836-2844. https://doi.org/10.1200/JCO.2017.76.6212
  11. Kim JH, Ryu MH, Park YS, Ma J, Lee SY, Kim D, et al. Predictive biomarkers for the efficacy of nivolumab as≥3rd-line therapy in patients with advanced gastric cancer: a subset analysis of ATTRACTION-2 phase III trial. BMC Cancer 2022;22:378.
  12. Chia NY, Tan P. Molecular classification of gastric cancer. Ann Oncol 2016;27:763-769. https://doi.org/10.1093/annonc/mdw040
  13. Wang H, Ding Y, Chen Y, Jiang J, Chen Y, Lu J, et al. A novel genomic classification system of gastric cancer via integrating multidimensional genomic characteristics. Gastric Cancer 2021;24:1227-1241. https://doi.org/10.1007/s10120-021-01201-9
  14. Cai H, Jing C, Chang X, Ding D, Han T, Yang J, et al. Mutational landscape of gastric cancer and clinical application of genomic profiling based on target next-generation sequencing. J Transl Med 2019;17:189.
  15. Kwak Y, Seo AN, Lee HE, Lee HS. Tumor immune response and immunotherapy in gastric cancer. J Pathol Transl Med 2020;54:20-33. https://doi.org/10.4132/jptm.2019.10.08
  16. Joshi SS, Badgwell BD. Current treatment and recent progress in gastric cancer. CA Cancer J Clin 2021;71:264-279. https://doi.org/10.3322/caac.21657
  17. Patel TH, Cecchini M. Targeted therapies in advanced gastric cancer. Curr Treat Options Oncol 2020;21:70.
  18. Marabelle A, Fakih M, Lopez J, Shah M, Shapira-Frommer R, Nakagawa K, 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;21:1353-1365.  https://doi.org/10.1016/S1470-2045(20)30445-9
  19. Jardim DL, Goodman A, de Melo Gagliato D, Kurzrock R. The challenges of tumor mutational burden as an immunotherapy biomarker. Cancer Cell 2021;39:154-173. https://doi.org/10.1016/j.ccell.2020.10.001
  20. Chan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol 2019;30:44-56. https://doi.org/10.1093/annonc/mdy495
  21. Coulie PG, Van den Eynde BJ, van der Bruggen P, Boon T. Tumour antigens recognized by T lymphocytes: at the core of cancer immunotherapy. Nat Rev Cancer 2014;14:135-146. https://doi.org/10.1038/nrc3670
  22. Matsushita H, Vesely MD, Koboldt DC, Rickert CG, Uppaluri R, Magrini VJ, et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 2012;482:400-404. https://doi.org/10.1038/nature10755
  23. Riaz N, Morris L, Havel JJ, Makarov V, Desrichard A, Chan TA. The role of neoantigens in response to immune checkpoint blockade. Int Immunol 2016;28:411-419. https://doi.org/10.1093/intimm/dxw019
  24. Panda A, Betigeri A, Subramanian K, Ross JS, Pavlick DC, Ali S, et al. Identifying a clinically applicable mutational burden threshold as a potential biomarker of response to immune checkpoint therapy in solid tumors. JCO Precis Oncol 2017;2017:PO.17.00146.
  25. Schrock AB, Ouyang C, Sandhu J, Sokol E, Jin D, Ross JS, et al. Tumor mutational burden is predictive of response to immune checkpoint inhibitors in MSI-high metastatic colorectal cancer. Ann Oncol 2019;30:1096-1103. https://doi.org/10.1093/annonc/mdz134
  26. Sha D, Jin Z, Budczies J, Kluck K, Stenzinger A, Sinicrope FA. Tumor mutational burden as a predictive biomarker in solid tumors. Cancer Discov 2020;10:1808-1825. https://doi.org/10.1158/2159-8290.CD-20-0522
  27. Fluss R, Faraggi D, Reiser B. Estimation of the Youden index and its associated cutoff point. Biom J 2005;47:458-472. https://doi.org/10.1002/bimj.200410135
  28. Hajian-Tilaki K. The choice of methods in determining the optimal cut-off value for quantitative diagnostic test evaluation. Stat Methods Med Res 2018;27:2374-2383. https://doi.org/10.1177/0962280216680383
  29. Youden WJ. Index for rating diagnostic tests. Cancer 1950;3:32-35. https://doi.org/10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3
  30. Wu Y, Xu J, Du C, Wu Y, Xia D, Lv W, et al. The predictive value of tumor mutation burden on efficacy of immune checkpoint inhibitors in cancers: a systematic review and meta-analysis. Front Oncol 2019;9:1161.
  31. Wei B, Kang J, Kibukawa M, Arreaza G, Maguire M, Chen L, et al. Evaluation of the TruSight oncology 500 assay for routine clinical testing of tumor mutational burden and clinical utility for predicting response to pembrolizumab. J Mol Diagn 2022;24:600-608. https://doi.org/10.1016/j.jmoldx.2022.01.008
  32. Wang F, Wei XL, Wang FH, Xu N, Shen L, Dai GH, et al. Safety, efficacy and tumor mutational burden as a biomarker of overall survival benefit in chemo-refractory gastric cancer treated with toripalimab, a PD-1 antibody in phase Ib/II clinical trial NCT02915432. Ann Oncol 2019;30:1479-1486. https://doi.org/10.1093/annonc/mdz197
  33. Mishima S, Kawazoe A, Nakamura Y, Sasaki A, Kotani D, Kuboki Y, et al. Clinicopathological and molecular features of responders to nivolumab for patients with advanced gastric cancer. J Immunother Cancer 2019;7:24.
  34. Rizvi H, Sanchez-Vega F, La K, Chatila W, Jonsson P, Halpenny D, et al. Molecular determinants of response to anti-programmed cell death (PD)-1 and anti-programmed death-ligand 1 (PD-L1) blockade in patients with non-small-cell lung cancer profiled with targeted next-generation sequencing. J Clin Oncol 2018;36:633-641. https://doi.org/10.1200/JCO.2017.75.3384
  35. Kang YK, Chen LT, Ryu MH, Oh DY, Oh SC, Chung HC, et al. Nivolumab plus chemotherapy versus placebo plus chemotherapy in patients with HER2-negative, untreated, unresectable advanced or recurrent gastric or gastro-oesophageal junction cancer (ATTRACTION-4): a randomised, multicentre, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol 2022;23:234-247. https://doi.org/10.1016/S1470-2045(21)00692-6
  36. Wu CE, Yeh DW, Pan YR, Huang WK, Chen MH, Chang JW, et al. Chromosomal instability may not be a predictor for immune checkpoint inhibitors from a comprehensive bioinformatics analysis. Life (Basel) 2020;10:276.