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dc.contributor.author
Bjerregaard, Anne Mette
dc.contributor.author
Nielsen, Morten
dc.contributor.author
Hadrup, Sine Reker
dc.contributor.author
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
dc.subject
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
dc.subject.classification
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
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