Author | Azevedo, Alexandre Luiz Korte | |
Author | Gomig, Talita Helen Bombardelli | |
Author | Batista, Michel | |
Author | Marchini, Fabricio Klerynton | |
Author | Spautz, Cleverton C´esar | |
Author | Rabinovich, Iris | |
Author | Sebastião, Ana Paula Martins | |
Author | Oliveira, Jaqueline Carvalho | |
Author | Gradia, Daniela Fiori | |
Author | Cavalli, Iglenir João | |
Author | Ribeiro, Enilze Maria de Souza Fonseca | |
Access date | 2023-07-21T15:59:29Z | |
Available date | 2023-07-21T15:59:29Z | |
Document date | 2023 | |
Citation | AZEVEDO, Alexandre Luiz Korte et al. High-throughput proteomics of breast cancer subtypes: Biological characterization and multiple candidate biomarker panels to patients' stratification. Viruses, v. 285, n. 104955, p. 1-13, 2023. | en_US |
ISSN | 1874-3919 | en_US |
URI | https://www.arca.fiocruz.br/handle/icict/59712 | |
Language | por | en_US |
Publisher | Elsevier | en_US |
Rights | open access | en_US |
Title | High-throughput proteomics of breast cancer subtypes: biological characterization and multiple candidate biomarker panels to patients’ stratification | en_US |
Type | Article | en_US |
Abstract | Background and aims: The actual classification of breast tumors in subtypes represents an attempt to stratify patients into clinically cohesive groups, nevertheless, clinicians still lack reproducible and reliable protein biomarkers for breast cancer subtype discrimination. In this study, we aimed to access the differentially expressed proteins between these tumors and its biological implications, contributing to the subtype’s biological and clinical characterization, and with protein panels for subtype discrimination. Methods: In our study, we applied high-throughput mass spectrometry, bioinformatic, and machine learning approaches to investigate the proteome of different breast cancer subtypes. Results: We identified that each subtype depends on different protein expression patterns to sustain its malignancy, and also alterations in pathways and processes that can be associated with each subtype and its biological and clinical behaviors. Regarding subtype biomarkers, our panels achieved performances with at least 75% of sensibility and 92% of specificity. In the validation cohort, the panels obtained acceptable to outstanding performances (AUC = 0.740 to 1.00). Conclusions: In general, our results expand the accuracy of breast cancer subtypes’ proteomic landscape and improve the understanding of its biological heterogeneity. In addition, we identified potential protein biomarkers for the stratification of breast cancer patients, improving the repertoire of reliable protein biomarkers. Significance: Breast cancer is the most diagnosed cancer type worldwide and the most lethal cancer in women. As a heterogeneous disease, breast cancer tumors can be classified into four major subtypes, each presenting particular molecular alterations, clinical behaviors, and treatment responses. Thus, a pivotal step in patient management and clinical decisions is accurately classifying breast tumor subtypes. Currently, this classification is made by the immunohistochemical detection of four classical markers (estrogen receptor, progesterone receptor, HER2 receptor, and the Ki-67 index); however, it is known that these markers alone do not fully discriminate the breast tumor subtypes. Also, the poor understanding of the molecular alterations of each subtype leads to a challenging decision-making process regarding treatment choice and prognostic determination. This study, through high-throughput label-free mass-spectrometry data acquisition and downstream bioinformatic analysis, advances in the proteomic discrimination of breast tumors and achieves an in-depth characterization of the subtype’s proteomes. Here, we indicate how the variations in the subtype’s proteome can influence the tumor’s biological and clinical differences, highlighting the variation in the expression pattern of oncoproteins and tumor. | en_US |
Affilliation | Universidade Federal do Paraná. Departamento de Genética. Programa de Pós-Graduação em Genética. Curitiba, PR, Brasil. | en_US |
Affilliation | Instituto de Pesquisa Pelé Pequeno Príncipe. Curitiba, PR, Brasil. | en_US |
Affilliation | Fundação Oswaldo Cruz. Instituto Carlos Chagas. Laboratório de Ciencias e Tecnologias Aplicadas em Saúde. Curitiba, PR, Brasil. / Fundação Oswaldo Cruz. Instituto Carlos Chagas. Mass Spectrometry Facility - RPT02H. Curitiba, PR, Brasil. | en_US |
Affilliation | Fundação Oswaldo Cruz. Instituto Carlos Chagas. Laboratório de Ciencias e Tecnologias Aplicadas em Saúde. Curitiba, PR, Brasil. / Fundação Oswaldo Cruz. Instituto Carlos Chagas. Mass Spectrometry Facility - RPT02H. Curitiba, PR, Brasil. | en_US |
Affilliation | Hospital Nossa Senhora das Graças. Centro de Doenças da Mama. Curitiba, PR, Brasil. | en_US |
Affilliation | Hospital Nossa Senhora das Graças. Centro de Doenças da Mama. Curitiba, PR, Brasil. | en_US |
Affilliation | Universidade Federal do Paraná. Departamento de Patologia Médica. Curitiba, PR, Brasil. / Hospital Nossa Senhora das Graças. Centro de Patologia. Curitiba, PR, Brasil. | en_US |
Affilliation | Universidade Federal do Paraná. Departamento de Genética. Programa de Pós-Graduação em Genética. Curitiba, PR, Brasil. | en_US |
Affilliation | Universidade Federal do Paraná. Departamento de Genética. Programa de Pós-Graduação em Genética. Curitiba, PR, Brasil. | en_US |
Affilliation | Universidade Federal do Paraná. Departamento de Genética. Programa de Pós-Graduação em Genética. Curitiba, PR, Brasil. | en_US |
Affilliation | Universidade Federal do Paraná. Departamento de Genética. Programa de Pós-Graduação em Genética. Curitiba, PR, Brasil. | en_US |
Subject | Breast Cancer | en_US |
Subject | Machine Learning | en_US |
Subject | Mass Spectrometry | en_US |
Subject | Proteome | en_US |
Subject | Support Vector Machine | en_US |
Subject in Spanish | Neoplasias de la Mama | en_US |
Subject in Spanish | Aprendizaje Automático | en_US |
Subject in Spanish | Espectrometría de Masas | en_US |
Subject in Spanish | Máquina de Vectores de Soporte | en_US |
Subject in French | Tumeurs du sein | en_US |
Subject in French | Apprentissage machine | en_US |
Subject in French | Spectrométrie de masse | en_US |
Subject in French | Protéome | en_US |
Subject in French | Machine à vecteur de support | en_US |
DeCS | Neoplasias da Mama | en_US |
DeCS | Aprendizado de Máquina | en_US |
DeCS | Espectrometria de Massas | en_US |
DeCS | Proteoma | en_US |
DeCS | Máquina de Vetores de Suporte | en_US |
xmlui.metadata.dc.subject.ods | 04 Educação de qualidade | |