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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/142215
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:55:58Zoai: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:34982025-09-29 09:55:59.153CONICET 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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1844613684656078848 |
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13.070432 |