Literature DB >> 28429069

MuPeXI: prediction of neo-epitopes from tumor sequencing data.

Anne-Mette Bjerregaard1, Morten Nielsen2,3, Sine Reker Hadrup4, Zoltan Szallasi2,5, Aron Charles Eklund6.   

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 .

Entities:  

Keywords:  Immunotherapy; Mutation; Neo-antigens; Neo-epitopes; Prediction; Sequencing

Mesh:

Substances:

Year:  2017        PMID: 28429069     DOI: 10.1007/s00262-017-2001-3

Source DB:  PubMed          Journal:  Cancer Immunol Immunother        ISSN: 0340-7004            Impact factor:   6.968


  64 in total

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2.  Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy.

Authors:  Elham Sherafat; Jordan Force; Ion I Măndoiu
Journal:  BMC Bioinformatics       Date:  2020-12-30       Impact factor: 3.169

Review 3.  Antitumour dendritic cell vaccination in a priming and boosting approach.

Authors:  Alexandre Harari; Michele Graciotti; Michal Bassani-Sternberg; Lana E Kandalaft
Journal:  Nat Rev Drug Discov       Date:  2020-08-06       Impact factor: 84.694

4.  NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

Authors:  Vanessa Jurtz; Sinu Paul; Massimo Andreatta; Paolo Marcatili; Bjoern Peters; Morten Nielsen
Journal:  J Immunol       Date:  2017-10-04       Impact factor: 5.422

Review 5.  Neoantigen prediction and computational perspectives towards clinical benefit: recommendations from the ESMO Precision Medicine Working Group.

Authors:  L De Mattos-Arruda; M Vazquez; F Finotello; R Lepore; E Porta; J Hundal; P Amengual-Rigo; C K Y Ng; A Valencia; J Carrillo; T A Chan; V Guallar; N McGranahan; J Blanco; M Griffith
Journal:  Ann Oncol       Date:  2020-06-28       Impact factor: 32.976

Review 6.  An overview of immunoinformatics approaches and databases linking T cell receptor repertoires to their antigen specificity.

Authors:  Ivan V Zvyagin; Vasily O Tsvetkov; Dmitry M Chudakov; Mikhail Shugay
Journal:  Immunogenetics       Date:  2019-11-18       Impact factor: 2.846

7.  Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide-MHC Binding Predictions.

Authors:  Michelle P Aranha; Yead S M Jewel; Robert A Beckman; Louis M Weiner; Julie C Mitchell; Jerry M Parks; Jeremy C Smith
Journal:  J Immunol       Date:  2020-09-02       Impact factor: 5.422

8.  Structural dissimilarity from self drives neoepitope escape from immune tolerance.

Authors:  Jason R Devlin; Jesus A Alonso; Cory M Ayres; Grant L J Keller; Sara Bobisse; Craig W Vander Kooi; George Coukos; David Gfeller; Alexandre Harari; Brian M Baker
Journal:  Nat Chem Biol       Date:  2020-08-17       Impact factor: 15.040

Review 9.  Cancer systems immunology.

Authors:  Nathan E Reticker-Flynn; Edgar G Engleman
Journal:  Elife       Date:  2020-07-13       Impact factor: 8.140

10.  pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens.

Authors:  Jasreet Hundal; Susanna Kiwala; Joshua McMichael; Christopher A Miller; Huiming Xia; Alexander T Wollam; Connor J Liu; Sidi Zhao; Yang-Yang Feng; Aaron P Graubert; Amber Z Wollam; Jonas Neichin; Megan Neveau; Jason Walker; William E Gillanders; Elaine R Mardis; Obi L Griffith; Malachi Griffith
Journal:  Cancer Immunol Res       Date:  2020-01-06       Impact factor: 11.151

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