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Cancer Immunology, Immunotherapy

, Volume 66, Issue 9, pp 1123–1130 | Cite as

MuPeXI: prediction of neo-epitopes from tumor sequencing data

  • Anne-Mette Bjerregaard
  • Morten Nielsen
  • Sine Reker Hadrup
  • Zoltan Szallasi
  • Aron Charles Eklund
Original Article

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.

Keywords

Neo-epitopes Neo-antigens Immunotherapy Prediction Mutation Sequencing 

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

Notes

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).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


Supplementary material

262_2017_2001_MOESM1_ESM.pdf (206 kb)
Supplementary material 1 (PDF 205 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Bio and Health InformaticsTechnical University of DenmarkLyngbyDenmark
  2. 2.Instituto de Investigaciones BiotecnológicasUniversidad Nacional de San MartínBuenos AiresArgentina
  3. 3.Section for Immunology and Vaccinology, National Veterinary InstituteTechnical University of DenmarkCopenhagenDenmark
  4. 4.Computational Health Informatics Program, Boston Children’s Hospital, USAHarvard Medical SchoolBostonUSA

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