Abstract
Personalization of immunotherapies such as cancer vaccines and adoptive T cell therapy depends on identification of patient-specific neo-epitopes that can be specifically targeted. MuPeXI, the mutant peptide extractor and informer, is a program to identify tumor-specific peptides and assess their potential to be neo-epitopes. The program input is a file with somatic mutation calls, a list of HLA types, and optionally a gene expression profile. The output is a table with all tumor-specific peptides derived from nucleotide substitutions, insertions, and deletions, along with comprehensive annotation, including HLA binding and similarity to normal peptides. The peptides are sorted according to a priority score which is intended to roughly predict immunogenicity. We applied MuPeXI to three tumors for which predicted MHC-binding peptides had been screened for T cell reactivity, and found that MuPeXI was able to prioritize immunogenic peptides with an area under the curve of 0.63. Compared to other available tools, MuPeXI provides more information and is easier to use. MuPeXI is available as stand-alone software and as a web server at http://www.cbs.dtu.dk/services/MuPeXI.
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Abbreviations
- AUC:
-
Area under the curve
- MuPeXI:
-
Mutant peptide extractor and informer
- NGS:
-
Next generation sequencing
- NSCLC:
-
Non-small cell lung cancer
- RNA-seq:
-
RNA sequencing
- ROC:
-
Receiver operator characteristic
- SNV:
-
Single nucleotide variant
- VCF:
-
Variant call format
- VEP:
-
Variant effect predictor
- WXS:
-
Whole exome sequencing
References
Vormehr M, Diken M, Boegel S et al (2015) Mutanome directed cancer immunotherapy. Curr Opin Immunol 39:14–22. doi:10.1016/j.coi.2015.12.001
Schumacher TN, Schreiber RD (2015) Neoantigens in cancer immunotherapy. Science 348:69–74. doi:10.1126/science.aaa4971
Rizvi NA, Hellmann MD, Snyder A et al (2015) Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348:124–128. doi:10.1126/science.aaa1348
McGranahan N, Furness AJS, Rosenthal R et al (2016) Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351:1463–1469. doi:10.1126/science.aaf1490
Snyder A, Makarov V, Merghoub T et al (2014) Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 371:2189–2199. doi:10.1056/NEJMoa1406498
Hugo W, Zaretsky JM, Sun L et al (2016) Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165:35–44. doi:10.1016/j.cell.2016.02.065
Olsen LR, Campos B, Barnkob MS et al (2014) Bioinformatics for cancer immunotherapy target discovery. Cancer Immunol Immunother 63:1235–1249. doi:10.1007/s00262-014-1627-7
Rajasagi M, Shukla S, Fritsch EF et al (2014) Systematic identification of personal tumor-specific neoantigens in chronic lymphocytic leukemia. Blood 124:453–462. doi:10.1182/blood-2014-04-567933
Schubert B, Brachvogel H-P, Jurges C, Kohlbacher O (2015) EpiToolKit–a web-based workbench for vaccine design. Bioinformatics 31:2211–2213. doi:10.1093/bioinformatics/btv116
Duan F, Duitama J, Al Seesi S et al (2014) Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. J Exp Med 211:2231–2248. doi:10.1084/jem.20141308
Hundal J, Carreno BM, Petti AA et al (2016) pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens. Genome Med 8:11. doi:10.1186/s13073-016-0264-5
Bentzen AK, Marquard AM, Lyngaa R et al (2016) Large-scale detection of antigen-specific T cells using peptide-MHC-I multimers labeled with DNA barcodes. Nat Biotechnol 34:1037–1045. doi:10.1038/nbt.3662
Krueger F Trim Galore (2016) http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/. Accessed 19 Sep 2016
Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17:10–12. doi:10.14806/ej.17.1.200
Andrews S FastQC (2016) http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 19 Sep 2016
Van der Auwera GA, Carneiro MO, Hartl C et al (2013) From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinforma 43:11. doi:10.1002/0471250953.bi1110s43
Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760. doi:10.1093/bioinformatics/btp324
Cibulskis K, Lawrence MS, Carter SL et al (2013) Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 31:213–219. doi:10.1038/nbt.2514
Szolek A, Schubert B, Mohr C et al (2014) OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics 30:3310–3316. doi:10.1093/bioinformatics/btu548
Weese D, Holtgrewe M, Reinert K (2012) RazerS 3: faster, fully sensitive read mapping. Bioinformatics 28:2592–2599. doi:10.1093/bioinformatics/bts505
McLaren W, Gil L, Hunt SE et al (2016) The ensembl variant effect predictor. Genome Biol 17:122. doi:10.1186/s13059-016-0974-4
Gubin MM, Artyomov MN, Mardis ER, Schreiber RD (2015) Tumor neoantigens: building a framework for personalized cancer immunotherapy. J Clin Invest 125(9):3413–3421
Shukla S, Rooney MS, Rajasagi M et al (2015) Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat Biotechnol 33:1152–1158. doi:10.1038/nbt.3344
Nielsen M, Andreatta M (2016) 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. doi:10.1186/s13073-016-0288-x
Hoof I, Pérez CL, Buggert M et al (2010) Interdisciplinary analysis of HIV-specific CD8+ T cell responses against variant epitopes reveals restricted TCR promiscuity. J Immunol 184:5383–5391. doi:10.4049/jimmunol.0903516
Schubert B, Walzer M, Brachvogel H-P et al (2016) FRED 2: an immunoinformatics framework for Python. Bioinformatics 32:2044–2046. doi:10.1093/bioinformatics/btw113
Acknowledgements
We thank Charles Swanton and Nicholas McGranahan for providing the raw data from the two NSCLC studies; Sofie Ramskov, Rikke Lyngaa and Sunil Kumar Saini for their experimental work in these studies; Amalie Kai Bentzen for her contribution to methods development; and Thomas Trolle, Andrea Marquard and Marcin Krzystanek for helpful discussions.
Funding
This work was supported by the Danish Cancer Society under grant R72-A4618 (Aron Charles Eklund); the Novo Nordisk Foundation under Grant 16,854 (Zoltan Szallasi); the Breast Cancer Research Foundation (Zoltan Szallasi); and the Danish Council for Independent Research under Grant 1331-00283 (Sine Reker Hadrup, Zoltan Szallasi).
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Bjerregaard, AM., Nielsen, M., Hadrup, S.R. et al. MuPeXI: prediction of neo-epitopes from tumor sequencing data. Cancer Immunol Immunother 66, 1123–1130 (2017). https://doi.org/10.1007/s00262-017-2001-3
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DOI: https://doi.org/10.1007/s00262-017-2001-3