MuPeXI: prediction of neo-epitopes from tumor sequencing data

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

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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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Correspondence to Anne-Mette Bjerregaard or Aron Charles Eklund.

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Bjerregaard, A., 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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Keywords

  • Neo-epitopes
  • Neo-antigens
  • Immunotherapy
  • Prediction
  • Mutation
  • Sequencing