Tissue-specific thresholds of mutation burden associated with anti-PD-1/L1 therapy benefit and prognosis in microsatellite-stable cancers.
Journal
Nature cancer
ISSN: 2662-1347
Titre abrégé: Nat Cancer
Pays: England
ID NLM: 101761119
Informations de publication
Date de publication:
25 Mar 2024
25 Mar 2024
Historique:
received:
12
07
2023
accepted:
28
02
2024
medline:
26
3
2024
pubmed:
26
3
2024
entrez:
26
3
2024
Statut:
aheadofprint
Résumé
Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 or its ligand (PD-1/L1) have expanded the treatment landscape against cancers but are effective in only a subset of patients. Tumor mutation burden (TMB) is postulated to be a generic determinant of ICI-dependent tumor rejection. Here we describe the association between TMB and survival outcomes among microsatellite-stable cancers in a real-world clinicogenomic cohort consisting of 70,698 patients distributed across 27 histologies. TMB was associated with survival benefit or detriment depending on tissue and treatment context, with eight cancer types demonstrating a specific association between TMB and improved outcomes upon treatment with anti-PD-1/L1 therapies. Survival benefits were noted over a broad range of TMB cutoffs across cancer types, and a dose-dependent relationship between TMB and outcomes was observed in a subset of cancers. These results have implications for the use of cancer-agnostic and universal TMB cutoffs to guide the use of anti-PD-1/L1 therapies, and they underline the importance of tissue context in the development of ICI biomarkers.
Identifiants
pubmed: 38528112
doi: 10.1038/s43018-024-00752-x
pii: 10.1038/s43018-024-00752-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
Bagchi, S., Yuan, R. & Engleman, E. G. Immune checkpoint inhibitors for the treatment of cancer: clinical impact and mechanisms of response and resistance. Annu. Rev. Pathol. 16, 223–249 (2021).
pubmed: 33197221
doi: 10.1146/annurev-pathol-042020-042741
Robert, C. A decade of immune-checkpoint inhibitors in cancer therapy. Nat. Commun. 11, 3801 (2020).
pubmed: 32732879
pmcid: 7393098
doi: 10.1038/s41467-020-17670-y
Vaddepally, R. K., Kharel, P., Pandey, R., Garje, R. & Chandra, A. B. Review of indications of FDA-approved immune checkpoint inhibitors per NCCN guidelines with the level of evidence. Cancers 12, 738 (2020).
pubmed: 32245016
pmcid: 7140028
doi: 10.3390/cancers12030738
Lei, Y., Li, X., Huang, Q., Zheng, X. & Liu, M. Progress and challenges of predictive biomarkers for immune checkpoint blockade. Front. Oncol. 11, 617335 (2021).
pubmed: 33777757
pmcid: 7992906
doi: 10.3389/fonc.2021.617335
Wang, D. R., Wu, X. L. & Sun, Y. L. Therapeutic targets and biomarkers of tumor immunotherapy: response versus non-response. Signal Transduct. Target. Ther. 7, 331 (2022).
pubmed: 36123348
pmcid: 9485144
doi: 10.1038/s41392-022-01136-2
Gunjur, A. et al. ‘Know thyself’—host factors influencing cancer response to immune checkpoint inhibitors. J. Pathol. 257, 513–525 (2022).
pubmed: 35394069
pmcid: 9320825
doi: 10.1002/path.5907
Lybaert, L. et al. Challenges in neoantigen-directed therapeutics. Cancer Cell 41, 15–40 (2023).
pubmed: 36368320
doi: 10.1016/j.ccell.2022.10.013
Ganesan, S. & Mehnert, J. Biomarkers for response to immune checkpoint blockade. Annu. Rev. Cancer Biol. 4, 331–351 (2020).
doi: 10.1146/annurev-cancerbio-030419-033604
Tran, E. et al. Cancer immunotherapy based on mutation-specific CD4
pubmed: 24812403
pmcid: 6686185
doi: 10.1126/science.1251102
Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014).
pubmed: 25428507
pmcid: 4279952
doi: 10.1038/nature13988
Kreiter, S. et al. Mutant MHC class II epitopes drive therapeutic immune responses to cancer. Nature 520, 692–696 (2015).
pubmed: 25901682
pmcid: 4838069
doi: 10.1038/nature14426
Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).
pubmed: 28596308
pmcid: 5576142
doi: 10.1126/science.aan6733
Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).
pubmed: 26028255
pmcid: 4481136
doi: 10.1056/NEJMoa1500596
Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).
pubmed: 25838375
doi: 10.1126/science.aaa4971
Segal, N. H. et al. Epitope landscape in breast and colorectal cancer. Cancer Res. 68, 889–892 (2008).
pubmed: 18245491
doi: 10.1158/0008-5472.CAN-07-3095
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
pubmed: 25409260
pmcid: 4315319
doi: 10.1056/NEJMoa1406498
Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).
pubmed: 26359337
pmcid: 5054517
doi: 10.1126/science.aad0095
Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).
pubmed: 25765070
pmcid: 4993154
doi: 10.1126/science.aaa1348
Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).
pubmed: 26997480
pmcid: 4808437
doi: 10.1016/j.cell.2016.02.065
Marabelle, A. 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. 21, 1353–1365 (2020).
pubmed: 32919526
doi: 10.1016/S1470-2045(20)30445-9
Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 (2019).
pubmed: 30643254
pmcid: 6365097
doi: 10.1038/s41588-018-0312-8
Valero, C. et al. The association between tumor mutational burden and prognosis is dependent on treatment context. Nat. Genet. 53, 11–15 (2021).
pubmed: 33398197
pmcid: 7796993
doi: 10.1038/s41588-020-00752-4
Valero, C. et al. Response rates to anti-PD-1 immunotherapy in microsatellite-stable solid tumors with 10 or more mutations per megabase. JAMA Oncol. 7, 739–743 (2021).
pubmed: 33599686
doi: 10.1001/jamaoncol.2020.7684
McGrail, D. J. et al. High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types. Ann. Oncol. 32, 661–672 (2021).
pubmed: 33736924
doi: 10.1016/j.annonc.2021.02.006
Merino, D. M. et al. Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project. J. Immunother. Cancer 8, e000147 (2020).
pubmed: 32217756
pmcid: 7174078
doi: 10.1136/jitc-2019-000147
Offin, M. et al. Tumor mutation burden and efficacy of EGFR-tyrosine kinase inhibitors in patients with EGFR-mutant lung cancers. Clin. Cancer Res. 25, 1063–1069 (2019).
pubmed: 30045933
doi: 10.1158/1078-0432.CCR-18-1102
McGranahan, N. & Swanton, C. Neoantigen quality, not quantity. Sci. Transl. Med. 11, eaax7918 (2019).
pubmed: 31434757
doi: 10.1126/scitranslmed.aax7918
Sha, D. et al. Tumor mutational burden as a predictive biomarker in solid tumors. Cancer Discov. 10, 1808–1825 (2020).
pubmed: 33139244
pmcid: 7710563
doi: 10.1158/2159-8290.CD-20-0522
Chowell, D. et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359, 582–587 (2018).
pubmed: 29217585
doi: 10.1126/science.aao4572
Havel, J. J., Chowell, D. & Chan, T. A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19, 133–150 (2019).
pubmed: 30755690
pmcid: 6705396
doi: 10.1038/s41568-019-0116-x
Doroshow, D. B. et al. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat. Rev. Clin. Oncol. 18, 345–362 (2021).
pubmed: 33580222
doi: 10.1038/s41571-021-00473-5
Davis, A. A. & Patel, V. G. The role of PD-L1 expression as a predictive biomarker: an analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors. J. Immunother. Cancer 7, 278 (2019).
pubmed: 31655605
pmcid: 6815032
doi: 10.1186/s40425-019-0768-9
Li, F. et al. The association between CD8
pubmed: 34585125
pmcid: 8452798
doi: 10.1016/j.eclinm.2021.101134
Ott, P. A. et al. T-cell-inflamed gene-expression profile, programmed death ligand 1 expression, and tumor mutational burden predict efficacy in patients treated with pembrolizumab across 20 cancers: KEYNOTE-028. J. Clin. Oncol. 37, 318–327 (2019).
pubmed: 30557521
doi: 10.1200/JCO.2018.78.2276
Finotello, F. et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med. 11, 34 (2019).
pubmed: 31126321
pmcid: 6534875
doi: 10.1186/s13073-019-0638-6
Bao, R., Stapor, D. & Luke, J. J. Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types. Genome Med. 12, 90 (2020).
pubmed: 33106165
pmcid: 7590690
doi: 10.1186/s13073-020-00787-6
Marcus, L. et al. FDA approval summary: pembrolizumab for the treatment of tumor mutational burden-high solid tumors. Clin. Cancer Res. 27, 4685–4689 (2021).
pubmed: 34083238
pmcid: 8416776
doi: 10.1158/1078-0432.CCR-21-0327
Subbiah, V., Solit, D. B., Chan, T. A. & Kurzrock, R. The FDA approval of pembrolizumab for adult and pediatric patients with tumor mutational burden (TMB) ≥10: a decision centered on empowering patients and their physicians. Ann. Oncol. 31, 1115–1118 (2020).
pubmed: 32771306
doi: 10.1016/j.annonc.2020.07.002
Prasad, V. & Addeo, A. The FDA approval of pembrolizumab for patients with TMB > 10 mut/Mb: was it a wise decision? No. Ann. Oncol. 31, 1112–1114 (2020).
pubmed: 32771305
doi: 10.1016/j.annonc.2020.07.001
Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).
pubmed: 28187290
pmcid: 5391692
doi: 10.1016/j.cell.2017.01.017
Jardim, D. L., Goodman, A., de Melo Gagliato, D. & Kurzrock, R. The challenges of tumor mutational burden as an immunotherapy biomarker. Cancer Cell 39, 154–173 (2021).
pubmed: 33125859
doi: 10.1016/j.ccell.2020.10.001
Gupta, R., Mehta, A. & Wajapeyee, N. Transcriptional determinants of cancer immunotherapy response and resistance. Trends Cancer 8, 404–415 (2022).
pubmed: 35125331
pmcid: 9035058
doi: 10.1016/j.trecan.2022.01.008
Nowicki, T. S., Hu-Lieskovan, S. & Ribas, A. Mechanisms of resistance to PD-1 and PD-L1 blockade. Cancer J. 24, 47–53 (2018).
pubmed: 29360728
pmcid: 5785093
doi: 10.1097/PPO.0000000000000303
Richman, L. P., Vonderheide, R. H. & Rech, A. J. Neoantigen dissimilarity to the self-proteome predicts immunogenicity and response to immune checkpoint blockade. Cell Syst. 9, 375–382 (2019).
pubmed: 31606370
pmcid: 6813910
doi: 10.1016/j.cels.2019.08.009
Luksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).
pubmed: 29132144
pmcid: 6137806
doi: 10.1038/nature24473
Wolf, Y. & Sameuls, Y. Neoantigens in cancer immunotherapy: quantity vs. quality. Mol. Oncol. 17, 1457–1459 (2023).
pubmed: 37370255
pmcid: 10399717
doi: 10.1002/1878-0261.13483
Kieffer, Y. et al. Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. Cancer Discov. 10, 1330–1351 (2020).
pubmed: 32434947
doi: 10.1158/2159-8290.CD-19-1384
Niknafs, N. et al. Persistent mutation burden drives sustained anti-tumor immune responses. Nat. Med. 29, 440–449 (2023).
pubmed: 36702947
pmcid: 9941047
doi: 10.1038/s41591-022-02163-w