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dc.contributor.author
Bjerregaard, Anne Mette  
dc.contributor.author
Nielsen, Morten  
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Hadrup, Sine Reker  
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Szallasi, Zoltan  
dc.contributor.author
Eklund, Aron Charles  
dc.date.available
2018-06-14T16:16:23Z  
dc.date.issued
2017-09  
dc.identifier.citation
Bjerregaard, Anne Mette; Nielsen, Morten; Hadrup, Sine Reker; Szallasi, Zoltan; Eklund, Aron Charles; MuPeXI: prediction of neo-epitopes from tumor sequencing data; Springer; Cancer Immunology Immunotherapy; 66; 9; 9-2017; 1123-1130  
dc.identifier.issn
0340-7004  
dc.identifier.uri
http://hdl.handle.net/11336/48652  
dc.description.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.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Springer  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
Immunotherapy  
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Mutation  
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Neo-Antigens  
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Neo-Epitopes  
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Prediction  
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Sequencing  
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Otras Ciencias de la Salud  
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Ciencias de la Salud  
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CIENCIAS MÉDICAS Y DE LA SALUD  
dc.title
MuPeXI: prediction of neo-epitopes from tumor sequencing data  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2018-06-13T14:58:17Z  
dc.identifier.eissn
1432-0851  
dc.journal.volume
66  
dc.journal.number
9  
dc.journal.pagination
1123-1130  
dc.journal.pais
Alemania  
dc.description.fil
Fil: Bjerregaard, Anne Mette. Technical University of Denmark; Dinamarca  
dc.description.fil
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina  
dc.description.fil
Fil: Hadrup, Sine Reker. Technical University of Denmark; Dinamarca  
dc.description.fil
Fil: Szallasi, Zoltan. Technical University of Denmark; Dinamarca. Harvard Medical School; Estados Unidos  
dc.description.fil
Fil: Eklund, Aron Charles. Technical University of Denmark; Dinamarca  
dc.journal.title
Cancer Immunology Immunotherapy  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1007/s00262-017-2001-3  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs00262-017-2001-3