Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer Subtypes

Autores
Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Anibal
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background and objectives: Tumors are complex systems characterized by variations across genetic, transcriptomic, phenotypic, and microenvironmental levels. This study introduced a novel framework for quantifying cancer cell heterogeneity using single-cell RNA sequencing data. The framework comprised several scores aimed at uncovering the complexities of key cancer traits, such as metastasis, tumor progression, and recurrence. Methods: This study leveraged publicly available single-cell transcriptomic data from three human breast cancer subtypes: estrogen receptor-positive, human epidermal growth factor receptor 2-positive, and triple-negative. We employed a quantitative approach, analyzing copy number alterations (CNAs), entropy, transcriptomic heterogeneity, and diverse protein-protein interaction networks (PPINs) to explore critical concepts in cancer biology. Results: We found that entropy and PPIN activity related to the cell cycle could distinguish cell clusters with elevated mitotic activity, particularly in aggressive breast cancer subtypes. Additionally, CNA distributions varied across cancer subtypes. We also identified positive correlations between the CNA score, entropy, and the activities of PPINs associated with the cell cycle, as well as those linked to basal and mesenchymal cell lines. Conclusions: This study addresses a gap in the current understanding of breast cancer heterogeneity by presenting a novel quantitative approach that offers deeper insights into tumor biology, surpassing traditional marker-based methods.
Fil: Senra, Daniela. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Guisoni, Nara Cristina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Argentina de la Empresa; Argentina
Fil: Diambra, Luis Anibal. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Materia
Breast cancer
Tumor heterogeneity
scRNA-seq
Copy number alteration
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/267060
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/267060
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer SubtypesSenra, DanielaGuisoni, Nara CristinaDiambra, Luis AnibalBreast cancerTumor heterogeneityscRNA-seqCopy number alterationhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Background and objectives: Tumors are complex systems characterized by variations across genetic, transcriptomic, phenotypic, and microenvironmental levels. This study introduced a novel framework for quantifying cancer cell heterogeneity using single-cell RNA sequencing data. The framework comprised several scores aimed at uncovering the complexities of key cancer traits, such as metastasis, tumor progression, and recurrence. Methods: This study leveraged publicly available single-cell transcriptomic data from three human breast cancer subtypes: estrogen receptor-positive, human epidermal growth factor receptor 2-positive, and triple-negative. We employed a quantitative approach, analyzing copy number alterations (CNAs), entropy, transcriptomic heterogeneity, and diverse protein-protein interaction networks (PPINs) to explore critical concepts in cancer biology. Results: We found that entropy and PPIN activity related to the cell cycle could distinguish cell clusters with elevated mitotic activity, particularly in aggressive breast cancer subtypes. Additionally, CNA distributions varied across cancer subtypes. We also identified positive correlations between the CNA score, entropy, and the activities of PPINs associated with the cell cycle, as well as those linked to basal and mesenchymal cell lines. Conclusions: This study addresses a gap in the current understanding of breast cancer heterogeneity by presenting a novel quantitative approach that offers deeper insights into tumor biology, surpassing traditional marker-based methods.Fil: Senra, Daniela. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Guisoni, Nara Cristina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Argentina de la Empresa; ArgentinaFil: Diambra, Luis Anibal. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaCognizant Communication Corp2025-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/267060Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Anibal; Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer Subtypes; Cognizant Communication Corp; Gene Expression; 4-2025; 1-121052-2166CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.xiahepublishing.com/1555-3884/GE-2024-00071info:eu-repo/semantics/altIdentifier/doi/10.14218/GE.2024.00071info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:32:33Zoai:ri.conicet.gov.ar:11336/267060instacron: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 09:32:33.441CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer Subtypes
title Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer Subtypes
spellingShingle Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer Subtypes
Senra, Daniela
Breast cancer
Tumor heterogeneity
scRNA-seq
Copy number alteration
title_short Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer Subtypes
title_full Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer Subtypes
title_fullStr Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer Subtypes
title_full_unstemmed Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer Subtypes
title_sort Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer Subtypes
dc.creator.none.fl_str_mv Senra, Daniela
Guisoni, Nara Cristina
Diambra, Luis Anibal
author Senra, Daniela
author_facet Senra, Daniela
Guisoni, Nara Cristina
Diambra, Luis Anibal
author_role author
author2 Guisoni, Nara Cristina
Diambra, Luis Anibal
author2_role author
author
dc.subject.none.fl_str_mv Breast cancer
Tumor heterogeneity
scRNA-seq
Copy number alteration
topic Breast cancer
Tumor heterogeneity
scRNA-seq
Copy number alteration
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Background and objectives: Tumors are complex systems characterized by variations across genetic, transcriptomic, phenotypic, and microenvironmental levels. This study introduced a novel framework for quantifying cancer cell heterogeneity using single-cell RNA sequencing data. The framework comprised several scores aimed at uncovering the complexities of key cancer traits, such as metastasis, tumor progression, and recurrence. Methods: This study leveraged publicly available single-cell transcriptomic data from three human breast cancer subtypes: estrogen receptor-positive, human epidermal growth factor receptor 2-positive, and triple-negative. We employed a quantitative approach, analyzing copy number alterations (CNAs), entropy, transcriptomic heterogeneity, and diverse protein-protein interaction networks (PPINs) to explore critical concepts in cancer biology. Results: We found that entropy and PPIN activity related to the cell cycle could distinguish cell clusters with elevated mitotic activity, particularly in aggressive breast cancer subtypes. Additionally, CNA distributions varied across cancer subtypes. We also identified positive correlations between the CNA score, entropy, and the activities of PPINs associated with the cell cycle, as well as those linked to basal and mesenchymal cell lines. Conclusions: This study addresses a gap in the current understanding of breast cancer heterogeneity by presenting a novel quantitative approach that offers deeper insights into tumor biology, surpassing traditional marker-based methods.
Fil: Senra, Daniela. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Guisoni, Nara Cristina. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Argentina de la Empresa; Argentina
Fil: Diambra, Luis Anibal. Universidad Nacional de La Plata. Centro Regional de Estudios Genómicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
description Background and objectives: Tumors are complex systems characterized by variations across genetic, transcriptomic, phenotypic, and microenvironmental levels. This study introduced a novel framework for quantifying cancer cell heterogeneity using single-cell RNA sequencing data. The framework comprised several scores aimed at uncovering the complexities of key cancer traits, such as metastasis, tumor progression, and recurrence. Methods: This study leveraged publicly available single-cell transcriptomic data from three human breast cancer subtypes: estrogen receptor-positive, human epidermal growth factor receptor 2-positive, and triple-negative. We employed a quantitative approach, analyzing copy number alterations (CNAs), entropy, transcriptomic heterogeneity, and diverse protein-protein interaction networks (PPINs) to explore critical concepts in cancer biology. Results: We found that entropy and PPIN activity related to the cell cycle could distinguish cell clusters with elevated mitotic activity, particularly in aggressive breast cancer subtypes. Additionally, CNA distributions varied across cancer subtypes. We also identified positive correlations between the CNA score, entropy, and the activities of PPINs associated with the cell cycle, as well as those linked to basal and mesenchymal cell lines. Conclusions: This study addresses a gap in the current understanding of breast cancer heterogeneity by presenting a novel quantitative approach that offers deeper insights into tumor biology, surpassing traditional marker-based methods.
publishDate 2025
dc.date.none.fl_str_mv 2025-04
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/11336/267060
Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Anibal; Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer Subtypes; Cognizant Communication Corp; Gene Expression; 4-2025; 1-12
1052-2166
CONICET Digital
CONICET
url http://hdl.handle.net/11336/267060
identifier_str_mv Senra, Daniela; Guisoni, Nara Cristina; Diambra, Luis Anibal; Unraveling Tumor Heterogeneity: Quantitative Insights from Single-cell RNA Sequencing Analysis in Breast Cancer Subtypes; Cognizant Communication Corp; Gene Expression; 4-2025; 1-12
1052-2166
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://www.xiahepublishing.com/1555-3884/GE-2024-00071
info:eu-repo/semantics/altIdentifier/doi/10.14218/GE.2024.00071
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Cognizant Communication Corp
publisher.none.fl_str_mv Cognizant Communication Corp
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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