A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithm

Autores
Fresno Rodríguez, Cristóbal; Gonzalez, Germán Alexis; Merino, Gabriela Alejandra; Flesia, Ana Georgina; Podhajcer, Osvaldo Luis; Llera, Andrea Sabina; Fernandez, Elmer Andres
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The PAM50 classifier is used to assign patients to the highest correlated breast cancer subtype irrespectively of the obtained value. Nonetheless, all subtype correlations are required to build the risk of recurrence (ROR) score, currently used in therapeutic decisions. Present subtype uncertainty estimations are not accurate, seldom considered or require a population-based approach for this context. Results: Here we present a novel single-subject non-parametric uncertainty estimation based on PAM50's gene label permutations. Simulations results (n = 5228) showed that only 61% subjects can be reliably 'Assigned' to the PAM50 subtype, whereas 33% should be 'Not Assigned' (NA), leaving the rest to tight 'Ambiguous' correlations between subtypes. The NA subjects exclusion from the analysis improved survival subtype curves discrimination yielding a higher proportion of low and high ROR values. Conversely, all NA subjects showed similar survival behaviour regardless of the original PAM50 assignment. We propose to incorporate our PAM50 uncertainty estimation to support therapeutic decisions.
Fil: Fresno Rodríguez, Cristóbal. Area de Cs. Agrarias, Ingeniería, Cs. Biológicas y de la Salud de la Universidad Catolica de Córdoba; Argentina
Fil: Gonzalez, Germán Alexis. Area de Cs. Agrarias, Ingeniería, Cs. Biológicas y de la Salud de la Universidad Catolica de Córdoba; Argentina
Fil: Merino, Gabriela Alejandra. Area de Cs. Agrarias, Ingeniería, Cs. Biológicas y de la Salud de la Universidad Catolica de Córdoba; Argentina
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; Argentina
Fil: Podhajcer, Osvaldo Luis. 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: Llera, Andrea Sabina. 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: Fernandez, Elmer Andres. Area de Cs. Agrarias, Ingeniería, Cs. Biológicas y de la Salud de la Universidad Catolica de Córdoba; Argentina
Materia
classsificationCLASSIFICATION
REJECTION OPTION
PAM50
MOLECULAR MAKER
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/60239
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/60239
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithmFresno Rodríguez, CristóbalGonzalez, Germán AlexisMerino, Gabriela AlejandraFlesia, Ana GeorginaPodhajcer, Osvaldo LuisLlera, Andrea SabinaFernandez, Elmer AndresclasssificationCLASSIFICATIONREJECTION OPTIONPAM50MOLECULAR MAKERhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The PAM50 classifier is used to assign patients to the highest correlated breast cancer subtype irrespectively of the obtained value. Nonetheless, all subtype correlations are required to build the risk of recurrence (ROR) score, currently used in therapeutic decisions. Present subtype uncertainty estimations are not accurate, seldom considered or require a population-based approach for this context. Results: Here we present a novel single-subject non-parametric uncertainty estimation based on PAM50's gene label permutations. Simulations results (n = 5228) showed that only 61% subjects can be reliably 'Assigned' to the PAM50 subtype, whereas 33% should be 'Not Assigned' (NA), leaving the rest to tight 'Ambiguous' correlations between subtypes. The NA subjects exclusion from the analysis improved survival subtype curves discrimination yielding a higher proportion of low and high ROR values. Conversely, all NA subjects showed similar survival behaviour regardless of the original PAM50 assignment. We propose to incorporate our PAM50 uncertainty estimation to support therapeutic decisions.Fil: Fresno Rodríguez, Cristóbal. Area de Cs. Agrarias, Ingeniería, Cs. Biológicas y de la Salud de la Universidad Catolica de Córdoba; ArgentinaFil: Gonzalez, Germán Alexis. Area de Cs. Agrarias, Ingeniería, Cs. Biológicas y de la Salud de la Universidad Catolica de Córdoba; ArgentinaFil: Merino, Gabriela Alejandra. Area de Cs. Agrarias, Ingeniería, Cs. Biológicas y de la Salud de la Universidad Catolica de Córdoba; ArgentinaFil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; ArgentinaFil: Podhajcer, Osvaldo Luis. 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: Llera, Andrea Sabina. 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: Fernandez, Elmer Andres. Area de Cs. Agrarias, Ingeniería, Cs. Biológicas y de la Salud de la Universidad Catolica de Córdoba; ArgentinaOxford University Press2017-03info: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/11336/60239Fresno Rodríguez, Cristóbal; Gonzalez, Germán Alexis; Merino, Gabriela Alejandra; Flesia, Ana Georgina; Podhajcer, Osvaldo Luis; et al.; A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithm; Oxford University Press; Bioinformatics (Oxford, England); 33; 5; 3-2017; 693-7001367-48031460-2059CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/bioinformatics/article/33/5/693/2849457info:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btw704info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:55:17Zoai:ri.conicet.gov.ar:11336/60239instacron: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 09:55:17.621CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithm
title A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithm
spellingShingle A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithm
Fresno Rodríguez, Cristóbal
classsificationCLASSIFICATION
REJECTION OPTION
PAM50
MOLECULAR MAKER
title_short A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithm
title_full A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithm
title_fullStr A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithm
title_full_unstemmed A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithm
title_sort A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithm
dc.creator.none.fl_str_mv Fresno Rodríguez, Cristóbal
Gonzalez, Germán Alexis
Merino, Gabriela Alejandra
Flesia, Ana Georgina
Podhajcer, Osvaldo Luis
Llera, Andrea Sabina
Fernandez, Elmer Andres
author Fresno Rodríguez, Cristóbal
author_facet Fresno Rodríguez, Cristóbal
Gonzalez, Germán Alexis
Merino, Gabriela Alejandra
Flesia, Ana Georgina
Podhajcer, Osvaldo Luis
Llera, Andrea Sabina
Fernandez, Elmer Andres
author_role author
author2 Gonzalez, Germán Alexis
Merino, Gabriela Alejandra
Flesia, Ana Georgina
Podhajcer, Osvaldo Luis
Llera, Andrea Sabina
Fernandez, Elmer Andres
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv classsificationCLASSIFICATION
REJECTION OPTION
PAM50
MOLECULAR MAKER
topic classsificationCLASSIFICATION
REJECTION OPTION
PAM50
MOLECULAR MAKER
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The PAM50 classifier is used to assign patients to the highest correlated breast cancer subtype irrespectively of the obtained value. Nonetheless, all subtype correlations are required to build the risk of recurrence (ROR) score, currently used in therapeutic decisions. Present subtype uncertainty estimations are not accurate, seldom considered or require a population-based approach for this context. Results: Here we present a novel single-subject non-parametric uncertainty estimation based on PAM50's gene label permutations. Simulations results (n = 5228) showed that only 61% subjects can be reliably 'Assigned' to the PAM50 subtype, whereas 33% should be 'Not Assigned' (NA), leaving the rest to tight 'Ambiguous' correlations between subtypes. The NA subjects exclusion from the analysis improved survival subtype curves discrimination yielding a higher proportion of low and high ROR values. Conversely, all NA subjects showed similar survival behaviour regardless of the original PAM50 assignment. We propose to incorporate our PAM50 uncertainty estimation to support therapeutic decisions.
Fil: Fresno Rodríguez, Cristóbal. Area de Cs. Agrarias, Ingeniería, Cs. Biológicas y de la Salud de la Universidad Catolica de Córdoba; Argentina
Fil: Gonzalez, Germán Alexis. Area de Cs. Agrarias, Ingeniería, Cs. Biológicas y de la Salud de la Universidad Catolica de Córdoba; Argentina
Fil: Merino, Gabriela Alejandra. Area de Cs. Agrarias, Ingeniería, Cs. Biológicas y de la Salud de la Universidad Catolica de Córdoba; Argentina
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; Argentina
Fil: Podhajcer, Osvaldo Luis. 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: Llera, Andrea Sabina. 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: Fernandez, Elmer Andres. Area de Cs. Agrarias, Ingeniería, Cs. Biológicas y de la Salud de la Universidad Catolica de Córdoba; Argentina
description The PAM50 classifier is used to assign patients to the highest correlated breast cancer subtype irrespectively of the obtained value. Nonetheless, all subtype correlations are required to build the risk of recurrence (ROR) score, currently used in therapeutic decisions. Present subtype uncertainty estimations are not accurate, seldom considered or require a population-based approach for this context. Results: Here we present a novel single-subject non-parametric uncertainty estimation based on PAM50's gene label permutations. Simulations results (n = 5228) showed that only 61% subjects can be reliably 'Assigned' to the PAM50 subtype, whereas 33% should be 'Not Assigned' (NA), leaving the rest to tight 'Ambiguous' correlations between subtypes. The NA subjects exclusion from the analysis improved survival subtype curves discrimination yielding a higher proportion of low and high ROR values. Conversely, all NA subjects showed similar survival behaviour regardless of the original PAM50 assignment. We propose to incorporate our PAM50 uncertainty estimation to support therapeutic decisions.
publishDate 2017
dc.date.none.fl_str_mv 2017-03
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/11336/60239
Fresno Rodríguez, Cristóbal; Gonzalez, Germán Alexis; Merino, Gabriela Alejandra; Flesia, Ana Georgina; Podhajcer, Osvaldo Luis; et al.; A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithm; Oxford University Press; Bioinformatics (Oxford, England); 33; 5; 3-2017; 693-700
1367-4803
1460-2059
CONICET Digital
CONICET
url http://hdl.handle.net/11336/60239
identifier_str_mv Fresno Rodríguez, Cristóbal; Gonzalez, Germán Alexis; Merino, Gabriela Alejandra; Flesia, Ana Georgina; Podhajcer, Osvaldo Luis; et al.; A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: Its application on PAM50 algorithm; Oxford University Press; Bioinformatics (Oxford, England); 33; 5; 3-2017; 693-700
1367-4803
1460-2059
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/bioinformatics/article/33/5/693/2849457
info:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btw704
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
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 13.070432

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