MHC class II genotypes are independent predictors of anti-PD1 immunotherapy response in melanoma.


Journal

Communications medicine
ISSN: 2730-664X
Titre abrégé: Commun Med (Lond)
Pays: England
ID NLM: 9918250414506676

Informations de publication

Date de publication:
30 Sep 2024
Historique:
received: 03 08 2023
accepted: 17 09 2024
medline: 1 10 2024
pubmed: 1 10 2024
entrez: 30 9 2024
Statut: epublish

Résumé

Immune checkpoint blockade is a highly successful anti-cancer immunotherapy. Both CTLA4 and PD1 checkpoint blockers are clinically available for melanoma treatment, with anti-PD1 therapy reaching response rates of 35-40%. These responses, which are mediated via neoantigen presentation by the polymorphic MHC complex, are hard to predict and the tumor mutation burden is currently one of the few available biomarkers. While MHC genotypes are expected to determine therapy responses, association studies have remained largely elusive. We developed an overall MHC genotype binding score (MGBS), indicative of a patient's MHC class I (MHC-I) and class II (MHC-II) neoantigen binding capacity and solely based on the germline MHC-I (MGBS-I) and MHC-II (MGBS-II) genotypes. These scores were then correlated to survival and clinical responses following anti-PD1 immunotherapy in a previously published dataset of 144 melanoma patients. We demonstrate that MGBS scores are TMB-independent predictors of anti-PD1 immunotherapy responses in melanoma. Opposite outcomes were found for both MHC classes, with high MGBS-I and MGBS-II predicting good and bad outcomes, respectively. Interestingly, high MGBS-II is mainly associated with treatment response failure in a subgroup of anti-CTLA4 pretreated patients. Our results suggest that MGBS, calculated solely from the MHC genotype, has clinical potential as a non-invasive and tumor-independent biomarker to guide anti-cancer immunotherapy in melanoma. Many cancer patients are successfully treated with immunotherapy, which boosts the immune system to eliminate cancer cells. While this therapy is successful in around half of skin cancer melanoma patients, it is currently hard to determine in advance which patients respond well. Immune cells react to tumor proteins that are presented at the cancer cell surface by molecules called MHC. These are unique for every patient. We aimed to determine whether the ability of MHC to bind to tumor proteins determines how well therapy works and developed a new way to quantify this interaction. Surprisingly, less ability for tumor proteins to bind to the unconventional class II MHC resulted in better clinical outcome in patients with melanoma. Our results provide new understanding of tumor-immune interaction and the new method may help determine which patients with melanoma will respond to therapy.

Sections du résumé

BACKGROUND BACKGROUND
Immune checkpoint blockade is a highly successful anti-cancer immunotherapy. Both CTLA4 and PD1 checkpoint blockers are clinically available for melanoma treatment, with anti-PD1 therapy reaching response rates of 35-40%. These responses, which are mediated via neoantigen presentation by the polymorphic MHC complex, are hard to predict and the tumor mutation burden is currently one of the few available biomarkers. While MHC genotypes are expected to determine therapy responses, association studies have remained largely elusive.
METHODS METHODS
We developed an overall MHC genotype binding score (MGBS), indicative of a patient's MHC class I (MHC-I) and class II (MHC-II) neoantigen binding capacity and solely based on the germline MHC-I (MGBS-I) and MHC-II (MGBS-II) genotypes. These scores were then correlated to survival and clinical responses following anti-PD1 immunotherapy in a previously published dataset of 144 melanoma patients.
RESULTS RESULTS
We demonstrate that MGBS scores are TMB-independent predictors of anti-PD1 immunotherapy responses in melanoma. Opposite outcomes were found for both MHC classes, with high MGBS-I and MGBS-II predicting good and bad outcomes, respectively. Interestingly, high MGBS-II is mainly associated with treatment response failure in a subgroup of anti-CTLA4 pretreated patients.
CONCLUSIONS CONCLUSIONS
Our results suggest that MGBS, calculated solely from the MHC genotype, has clinical potential as a non-invasive and tumor-independent biomarker to guide anti-cancer immunotherapy in melanoma.
Many cancer patients are successfully treated with immunotherapy, which boosts the immune system to eliminate cancer cells. While this therapy is successful in around half of skin cancer melanoma patients, it is currently hard to determine in advance which patients respond well. Immune cells react to tumor proteins that are presented at the cancer cell surface by molecules called MHC. These are unique for every patient. We aimed to determine whether the ability of MHC to bind to tumor proteins determines how well therapy works and developed a new way to quantify this interaction. Surprisingly, less ability for tumor proteins to bind to the unconventional class II MHC resulted in better clinical outcome in patients with melanoma. Our results provide new understanding of tumor-immune interaction and the new method may help determine which patients with melanoma will respond to therapy.

Autres résumés

Type: plain-language-summary (eng)
Many cancer patients are successfully treated with immunotherapy, which boosts the immune system to eliminate cancer cells. While this therapy is successful in around half of skin cancer melanoma patients, it is currently hard to determine in advance which patients respond well. Immune cells react to tumor proteins that are presented at the cancer cell surface by molecules called MHC. These are unique for every patient. We aimed to determine whether the ability of MHC to bind to tumor proteins determines how well therapy works and developed a new way to quantify this interaction. Surprisingly, less ability for tumor proteins to bind to the unconventional class II MHC resulted in better clinical outcome in patients with melanoma. Our results provide new understanding of tumor-immune interaction and the new method may help determine which patients with melanoma will respond to therapy.

Identifiants

pubmed: 39349759
doi: 10.1038/s43856-024-00612-w
pii: 10.1038/s43856-024-00612-w
doi:

Types de publication

Journal Article

Langues

eng

Pagination

184

Subventions

Organisme : Bijzonder Onderzoeksfonds (Special Research Fund)
ID : BOF.STG.2019.0073.01

Informations de copyright

© 2024. The Author(s).

Références

Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).
doi: 10.1126/science.aar4060 pubmed: 29567705 pmcid: 7391259
Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012).
doi: 10.1038/nrc3239 pubmed: 22437870 pmcid: 4856023
Topalian, S. L. et al. Survival, durable tumor remission, and long-term safety in patients with advanced melanoma receiving nivolumab. J. Clin. Oncol. 32, 1020–1030 (2014).
doi: 10.1200/JCO.2013.53.0105 pubmed: 24590637 pmcid: 4811023
Hodi, F. S. et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363, 711–723 (2010).
doi: 10.1056/NEJMoa1003466 pubmed: 20525992 pmcid: 3549297
Weber, J. S. et al. Sequential administration of nivolumab and ipilimumab with a planned switch in patients with advanced melanoma (CheckMate 064): an open-label, randomised, phase 2 trial. Lancet Oncol. 17, 943–955 (2016).
doi: 10.1016/S1470-2045(16)30126-7 pubmed: 27269740 pmcid: 5474305
Hodi, F. S. et al. TMB and inflammatory gene expression associated with clinical outcomes following immunotherapy in advanced melanoma. https://doi.org/10.1158/2326-6066.CIR-20-0983 (2021).
Larkin, J. et al. Five-year survival with combined nivolumab and ipilimumab in advanced melanoma. N. Engl. J. Med. 381, 1535–1546 (2019).
doi: 10.1056/NEJMoa1910836 pubmed: 31562797
Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).
doi: 10.1126/science.aaa1348 pubmed: 25765070 pmcid: 4993154
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
doi: 10.1056/NEJMoa1406498 pubmed: 25409260 pmcid: 4315319
Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).
doi: 10.1126/science.aad0095 pubmed: 26359337 pmcid: 5054517
Cristescu, R. et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 362, eaar3593 (2018).
doi: 10.1126/science.aar3593 pubmed: 30309915 pmcid: 6718162
Havel, J. J., Chowell, D. & Chan, T. A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19, 133–150 (2019).
doi: 10.1038/s41568-019-0116-x pubmed: 30755690 pmcid: 6705396
Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet 51, 202–206 (2019).
doi: 10.1038/s41588-018-0312-8 pubmed: 30643254 pmcid: 6365097
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).
doi: 10.1158/1078-0432.CCR-21-0327 pubmed: 34083238 pmcid: 8416776
Wang, Y. et al. FDA-approved and emerging next generation predictive biomarkers for immune checkpoint inhibitors in cancer patients. Front. Oncol. 11, 683419 (2021).
doi: 10.3389/fonc.2021.683419 pubmed: 34164344 pmcid: 8216110
Chowell, D. et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359, 582–587 (2018).
doi: 10.1126/science.aao4572 pubmed: 29217585
Alspach, E. et al. MHC-II neoantigens shape tumour immunity and response to immunotherapy. Nature 1–6. https://doi.org/10.1038/s41586-019-1671-8 (2019).
Axelrod, M. L., Cook, R. S., Johnson, D. B. & Balko, J. M. Biological consequences of MHC-II expression by tumor cells in cancer. Clin. Cancer Res. 25, 2392–2402 (2019).
doi: 10.1158/1078-0432.CCR-18-3200 pubmed: 30463850
Liu, D. et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat. Med. 25, 1916–1927 (2019).
doi: 10.1038/s41591-019-0654-5 pubmed: 31792460 pmcid: 6898788
Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).
doi: 10.1016/j.cell.2016.02.065 pubmed: 26997480 pmcid: 4808437
Gide, T. N. et al. Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1/Anti-CTLA-4 combined therapy. Cancer Cell 35, 238–255.e6 (2019).
doi: 10.1016/j.ccell.2019.01.003 pubmed: 30753825
Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with Nivolumab. Cell 171, 934–949.e15 (2017).
doi: 10.1016/j.cell.2017.09.028 pubmed: 29033130 pmcid: 5685550
Gonzalez-Galarza, F. F. et al. Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools. Nucleic Acids Res. 48, D783–D788 (2020).
pubmed: 31722398
Claeys, A., Merseburger, P., Staut, J., Marchal, K. & Van den Eynden, J. Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data. BMC Genom. 24, 1–14 (2023).
doi: 10.1186/s12864-023-09351-z
Jurtz, V. et al. NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 (2017).
doi: 10.4049/jimmunol.1700893 pubmed: 28978689
Nielsen, M. & Andreatta, M. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med. 8, 33 (2016).
doi: 10.1186/s13073-016-0288-x pubmed: 27029192 pmcid: 4812631
Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).
doi: 10.1016/j.cell.2014.12.033 pubmed: 25594174 pmcid: 4856474
Goodman, A. M. et al. MHC-I genotype and tumor mutational burden predict response to immunotherapy. Genome Med. 12, 45. https://doi.org/10.1186/s13073-020-00743-4 .
Anagnostou, V. et al. Integrative tumor and immune cell multi-omic analyses predict response to immune checkpoint blockade in melanoma. Cell Rep. Med. 1, 100139 (2020).
doi: 10.1016/j.xcrm.2020.100139 pubmed: 33294860 pmcid: 7691441
Campbell, K. M. et al. Prior anti-CTLA-4 therapy impacts molecular characteristics associated with anti-PD-1 response in advanced melanoma. Cancer Cell 41, 791–806.e4 (2023).
doi: 10.1016/j.ccell.2023.03.010 pubmed: 37037616 pmcid: 10187051
Rodig, S. J. et al. MHC proteins confer differential sensitivity to CTLA-4 and PD-1 blockade in untreated metastatic melanoma. Sci. Transl. Med. 10, eaar3342 (2018).
doi: 10.1126/scitranslmed.aar3342 pubmed: 30021886
Gocher, A. M., Workman, C. J. & Vignali, D. A. A. Interferon-γ: teammate or opponent in the tumour microenvironment? Nat. Rev. Immunol. 22, 158–172 (2021).
doi: 10.1038/s41577-021-00566-3 pubmed: 34155388 pmcid: 8688586
Claeys, A. & Van den Eynden, J. CCGGlab/mhc_immunotherapy. https://doi.org/10.5281/zenodo.12517305 (2024).

Auteurs

Arne Claeys (A)

Department of Human Structure and Repair, Unit of Anatomy and Embryology, Ghent University, Ghent, Belgium.
Cancer Research Institute Ghent, Ghent University, Ghent, Belgium.

Jimmy Van den Eynden (J)

Department of Human Structure and Repair, Unit of Anatomy and Embryology, Ghent University, Ghent, Belgium. jimmy.vandeneynden@ugent.be.
Cancer Research Institute Ghent, Ghent University, Ghent, Belgium. jimmy.vandeneynden@ugent.be.

Classifications MeSH