Mutational and Putative Neoantigen Load Predict Clinical Benefit of Adoptive T Cell Therapy in Melanoma

Overview

Journal Nat Commun

Specialty Biology

Date 2017 Nov 25

PMID 29170503

Citations 256

Authors

Martin Lauss

Marco Donia

Katja Harbst

Rikke Andersen

Shamik Mitra

Frida Rosengren

Maryem Salim

Johan Vallon-Christersson

Therese Torngren

Anders Kvist

Markus Ringner

Inge Marie Svane

Goran Jonsson

Affiliations

Soon will be listed here.

Abstract

Adoptive T-cell therapy (ACT) is a highly intensive immunotherapy regime that has yielded remarkable response rates and many durable responses in clinical trials in melanoma; however, 50-60% of the patients have no clinical benefit. Here, we searched for predictive biomarkers to ACT in melanoma. Whole exome- and transcriptome sequencing and neoantigen prediction were applied to pre-treatment samples from 27 patients recruited to a clinical phase I/II trial of ACT in stage IV melanoma. All patients had previously progressed on other immunotherapies. We report that clinical benefit is associated with significantly higher predicted neoantigen load. High mutation and predicted neoantigen load are significantly associated with improved progression-free and overall survival. Further, clinical benefit is associated with the expression of immune activation signatures including a high MHC-I antigen processing and presentation score. These results improve our understanding of mechanisms behind clinical benefit of ACT in melanoma.

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References

Sade-Feldman M, Kanterman J, Klieger Y, Ish-Shalom E, Olga M, Saragovi A . Clinical Significance of Circulating CD33+CD11b+HLA-DR- Myeloid Cells in Patients with Stage IV Melanoma Treated with Ipilimumab. Clin Cancer Res. 2016; 22(23):5661-5672. DOI: 10.1158/1078-0432.CCR-15-3104. View

Carter S, Cibulskis K, Helman E, McKenna A, Shen H, Zack T . Absolute quantification of somatic DNA alterations in human cancer. Nat Biotechnol. 2012; 30(5):413-21. PMC: 4383288. DOI: 10.1038/nbt.2203. View

Saal L, Vallon-Christersson J, Hakkinen J, Hegardt C, Grabau D, Winter C . The Sweden Cancerome Analysis Network - Breast (SCAN-B) Initiative: a large-scale multicenter infrastructure towards implementation of breast cancer genomic analyses in the clinical routine. Genome Med. 2015; 7(1):20. PMC: 4341872. DOI: 10.1186/s13073-015-0131-9. View

Hugo W, Zaretsky J, Sun L, Song C, Moreno B, Hu-Lieskovan S . Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma. Cell. 2016; 165(1):35-44. PMC: 4808437. DOI: 10.1016/j.cell.2016.02.065. View

Ellebaek E, Iversen T, Junker N, Donia M, Engell-Noerregaard L, Met O . Adoptive cell therapy with autologous tumor infiltrating lymphocytes and low-dose Interleukin-2 in metastatic melanoma patients. J Transl Med. 2012; 10:169. PMC: 3514199. DOI: 10.1186/1479-5876-10-169. View

Wang K, Li M, Hakonarson H . ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010; 38(16):e164. PMC: 2938201. DOI: 10.1093/nar/gkq603. View

McGranahan N, Furness A, Rosenthal R, Ramskov S, Lyngaa R, Saini S . Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science. 2016; 351(6280):1463-9. PMC: 4984254. DOI: 10.1126/science.aaf1490. View

Subramanian A, Tamayo P, Mootha V, Mukherjee S, Ebert B, Gillette M . Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005; 102(43):15545-50. PMC: 1239896. DOI: 10.1073/pnas.0506580102. View

Johannessen C, Johnson L, Piccioni F, Townes A, Frederick D, Donahue M . A melanocyte lineage program confers resistance to MAP kinase pathway inhibition. Nature. 2013; 504(7478):138-42. PMC: 4098832. DOI: 10.1038/nature12688. View

10.

Andersen R, Donia M, Ellebaek E, Holz Borch T, Kongsted P, Iversen T . Long-Lasting Complete Responses in Patients with Metastatic Melanoma after Adoptive Cell Therapy with Tumor-Infiltrating Lymphocytes and an Attenuated IL2 Regimen. Clin Cancer Res. 2016; 22(15):3734-45. DOI: 10.1158/1078-0432.CCR-15-1879. View

11.

Gubin M, Zhang X, Schuster H, Caron E, Ward J, Noguchi T . Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature. 2014; 515(7528):577-81. PMC: 4279952. DOI: 10.1038/nature13988. View

12.

Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky J, Desrichard A . Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2014; 371(23):2189-2199. PMC: 4315319. DOI: 10.1056/NEJMoa1406498. View

13.

Van Allen E, Miao D, Schilling B, Shukla S, Blank C, Zimmer L . Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015; 350(6257):207-211. PMC: 5054517. DOI: 10.1126/science.aad0095. View

14.

Rizvi N, Hellmann M, Snyder A, Kvistborg P, Makarov V, Havel J . Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015; 348(6230):124-8. PMC: 4993154. DOI: 10.1126/science.aaa1348. View

15.

Hupe P, Stransky N, Thiery J, Radvanyi F, Barillot E . Analysis of array CGH data: from signal ratio to gain and loss of DNA regions. Bioinformatics. 2004; 20(18):3413-22. DOI: 10.1093/bioinformatics/bth418. View

16.

Huang D, Sherman B, Lempicki R . Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009; 4(1):44-57. DOI: 10.1038/nprot.2008.211. View

17.

Cibulskis K, Lawrence M, Carter S, Sivachenko A, Jaffe D, Sougnez C . Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013; 31(3):213-9. PMC: 3833702. DOI: 10.1038/nbt.2514. View

18.

Rooney M, Shukla S, Wu C, Getz G, Hacohen N . Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 2015; 160(1-2):48-61. PMC: 4856474. DOI: 10.1016/j.cell.2014.12.033. View

19.

Jonsson G, Busch C, Knappskog S, Geisler J, Miletic H, Ringner M . Gene expression profiling-based identification of molecular subtypes in stage IV melanomas with different clinical outcome. Clin Cancer Res. 2010; 16(13):3356-67. DOI: 10.1158/1078-0432.CCR-09-2509. View

20.

Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley D . Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012; 7(3):562-78. PMC: 3334321. DOI: 10.1038/nprot.2012.016. View