Panels and models for accurate prediction of tumor mutation burden in tumor samples

Autores
Martinez Perez, Elizabeth; Molina Vila, Miguel Angel; Marino, Cristina Ester
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Immune checkpoint blockade (ICB) is becoming standard-of-care in many types of human malignancies, but patient selection is still imperfect. Tumor mutation burden (TMB) is being evaluated as a biomarker for ICB in clinical trials, but most of the sequencing panels used to estimate it are inadequately designed. Here, we present a bioinformatics-based method to select panels and mathematical models for accurate TMB prediction. Our method is based on tumor-specific, forward-step selection of genes, generation of panels using a linear regression algorithm, and rigorous internal and external validation comparing predicted with experimental TMB. As a result, we propose cancer-specific panels for 14 malignancies which can offer reliable, clinically relevant estimates of TMBs. Our work facilitates a better prediction of TMB that can improve the selection of patients for ICB therapy.
Fil: Martinez Perez, Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: Molina Vila, Miguel Angel. Hospital Universitario Quirón Dexeus; España
Fil: Marino, Cristina Ester. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Materia
CANCER
TUMOR MUTATIONAL BURDEN (TMB)
PREDICTION
IMMUNOTHERAPY
CLINICAL ONCOLOGY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/142215
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Panels and models for accurate prediction of tumor mutation burden in tumor samplesMartinez Perez, ElizabethMolina Vila, Miguel AngelMarino, Cristina EsterCANCERTUMOR MUTATIONAL BURDEN (TMB)PREDICTIONIMMUNOTHERAPYCLINICAL ONCOLOGYhttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1Immune checkpoint blockade (ICB) is becoming standard-of-care in many types of human malignancies, but patient selection is still imperfect. Tumor mutation burden (TMB) is being evaluated as a biomarker for ICB in clinical trials, but most of the sequencing panels used to estimate it are inadequately designed. Here, we present a bioinformatics-based method to select panels and mathematical models for accurate TMB prediction. Our method is based on tumor-specific, forward-step selection of genes, generation of panels using a linear regression algorithm, and rigorous internal and external validation comparing predicted with experimental TMB. As a result, we propose cancer-specific panels for 14 malignancies which can offer reliable, clinically relevant estimates of TMBs. Our work facilitates a better prediction of TMB that can improve the selection of patients for ICB therapy.Fil: Martinez Perez, Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Molina Vila, Miguel Angel. Hospital Universitario Quirón Dexeus; EspañaFil: Marino, Cristina Ester. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaSpringer2021-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/142215Martinez Perez, Elizabeth; Molina Vila, Miguel Angel; Marino, Cristina Ester; Panels and models for accurate prediction of tumor mutation burden in tumor samples; Springer; npj Precision Oncology; 5; 1; 4-2021; 1-82397-768XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.nature.com/articles/s41698-021-00169-0info:eu-repo/semantics/altIdentifier/doi/10.1038/s41698-021-00169-0info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:51:02Zoai:ri.conicet.gov.ar:11336/142215instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:51:03.171CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Panels and models for accurate prediction of tumor mutation burden in tumor samples
title Panels and models for accurate prediction of tumor mutation burden in tumor samples
spellingShingle Panels and models for accurate prediction of tumor mutation burden in tumor samples
Martinez Perez, Elizabeth
CANCER
TUMOR MUTATIONAL BURDEN (TMB)
PREDICTION
IMMUNOTHERAPY
CLINICAL ONCOLOGY
title_short Panels and models for accurate prediction of tumor mutation burden in tumor samples
title_full Panels and models for accurate prediction of tumor mutation burden in tumor samples
title_fullStr Panels and models for accurate prediction of tumor mutation burden in tumor samples
title_full_unstemmed Panels and models for accurate prediction of tumor mutation burden in tumor samples
title_sort Panels and models for accurate prediction of tumor mutation burden in tumor samples
dc.creator.none.fl_str_mv Martinez Perez, Elizabeth
Molina Vila, Miguel Angel
Marino, Cristina Ester
author Martinez Perez, Elizabeth
author_facet Martinez Perez, Elizabeth
Molina Vila, Miguel Angel
Marino, Cristina Ester
author_role author
author2 Molina Vila, Miguel Angel
Marino, Cristina Ester
author2_role author
author
dc.subject.none.fl_str_mv CANCER
TUMOR MUTATIONAL BURDEN (TMB)
PREDICTION
IMMUNOTHERAPY
CLINICAL ONCOLOGY
topic CANCER
TUMOR MUTATIONAL BURDEN (TMB)
PREDICTION
IMMUNOTHERAPY
CLINICAL ONCOLOGY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Immune checkpoint blockade (ICB) is becoming standard-of-care in many types of human malignancies, but patient selection is still imperfect. Tumor mutation burden (TMB) is being evaluated as a biomarker for ICB in clinical trials, but most of the sequencing panels used to estimate it are inadequately designed. Here, we present a bioinformatics-based method to select panels and mathematical models for accurate TMB prediction. Our method is based on tumor-specific, forward-step selection of genes, generation of panels using a linear regression algorithm, and rigorous internal and external validation comparing predicted with experimental TMB. As a result, we propose cancer-specific panels for 14 malignancies which can offer reliable, clinically relevant estimates of TMBs. Our work facilitates a better prediction of TMB that can improve the selection of patients for ICB therapy.
Fil: Martinez Perez, Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Fil: Molina Vila, Miguel Angel. Hospital Universitario Quirón Dexeus; España
Fil: Marino, Cristina Ester. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
description Immune checkpoint blockade (ICB) is becoming standard-of-care in many types of human malignancies, but patient selection is still imperfect. Tumor mutation burden (TMB) is being evaluated as a biomarker for ICB in clinical trials, but most of the sequencing panels used to estimate it are inadequately designed. Here, we present a bioinformatics-based method to select panels and mathematical models for accurate TMB prediction. Our method is based on tumor-specific, forward-step selection of genes, generation of panels using a linear regression algorithm, and rigorous internal and external validation comparing predicted with experimental TMB. As a result, we propose cancer-specific panels for 14 malignancies which can offer reliable, clinically relevant estimates of TMBs. Our work facilitates a better prediction of TMB that can improve the selection of patients for ICB therapy.
publishDate 2021
dc.date.none.fl_str_mv 2021-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/142215
Martinez Perez, Elizabeth; Molina Vila, Miguel Angel; Marino, Cristina Ester; Panels and models for accurate prediction of tumor mutation burden in tumor samples; Springer; npj Precision Oncology; 5; 1; 4-2021; 1-8
2397-768X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/142215
identifier_str_mv Martinez Perez, Elizabeth; Molina Vila, Miguel Angel; Marino, Cristina Ester; Panels and models for accurate prediction of tumor mutation burden in tumor samples; Springer; npj Precision Oncology; 5; 1; 4-2021; 1-8
2397-768X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.nature.com/articles/s41698-021-00169-0
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41698-021-00169-0
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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