Cancer Immunology, Immunotherapy

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

MuPeXI: prediction of neo-epitopes from tumor sequencing data

  • Anne-Mette BjerregaardEmail author
  • Morten Nielsen
  • Sine Reker Hadrup
  • Zoltan Szallasi
  • Aron Charles EklundEmail author
Original Article


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


Neo-epitopes Neo-antigens Immunotherapy Prediction Mutation Sequencing 



Area under the curve


Mutant peptide extractor and informer


Next generation sequencing


Non-small cell lung cancer


RNA sequencing


Receiver operator characteristic


Single nucleotide variant


Variant call format


Variant effect predictor


Whole exome sequencing



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.


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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