Genetic profile analysis of tumor stem cells in locally advanced breast cancer

Detalhes bibliográficos
Autor(a) principal: Willian Abraham da Silveira
Data de Publicação: 2015
Tipo de documento: Tese
Idioma: por
Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
Texto Completo: https://doi.org/10.11606/T.17.2017.tde-05012016-144854
Resumo: INTRODUCTION: Breast cancer is the most common cancer in women worldwide and metastatic dissemination is the principal factor related to death by this disease. Breast cancer stem cells (bCSC), defined in this work as the ALDH1high/LIN-/ESA+ population, are thought to be responsible for metastasis and chemoresistance. The objective of this work is to find gene master regulators, in particular transcription factors (TFs), which are controlling the bCSC phenotype. METHODS: We used in this work two groups of datasets with transcriptome data, the discovery dataset group contains one dataset obtained by ourselves containing three paired samples comparing the bCSC and the bulk of the tumor (My Data - bCSC/Bulk dataset), a dataset with eight paired samples comparing the bCSC and cancer cells (Wicha - bCSC/CC dataset) and a dataset with 115 samples of breast cancer tissue (clinical response dataset). The second group, validation datasets, contains the BRCA-TCGA dataset with information of 621 samples, 4142 breast cancer samples of the Kmplot tool, 17 primary samples of BasL subtype and their information of grafting in patient derived xenografts and analyzes of cell lines (MF10A and HMLE). For the analyzes we used the paired t-test in the Limma R package, the ARACNE algorithm for the inference of regulons in the clinical response dataset, MRA-FET to define the master regulators of the bCSC phenotype, and GSEA to identify the biological meaning of the findings in the different datasets. RESULTS: We identified 12 TFs as master regulators of the bCSC phenotype, with nine of them forming two highly interconnected networks, one positively related with the bCSC phenotype formed by SNAI2, TWIST, PRRX1, BNC2 and TBX5 with its regulons, defined here as the mesenchymal transcription network and one negative correlated to the phenotype formed by SCML4, ZNF831, SP140 and IKZF3, defined as the immune response transcription network, totally unknown in the context of breast cancer in the literature. Although still with weak evidence, ZEB1 seems to control the two networks and can be responsible for the expression of ALDH1 and of the three remaining TFs: ID4, HOXA5 and TEAD1. As their names portray, our data showed in the different datasets, and independently of the molecular subtype and of the platform used, that the mesenchymal transcription network seems to be responsible for the bCSC phenotype and the immune response transcription network to the adaptive immune response in the tumor and a better prognosis for the patients. We also defined 10 membrane proteins as new markers and/or therapeutic targets of the bCSC. CONCLUSION: We found and described two TF networks that seem to control the bCSC phenotype, one of them totally unknown until now and correlated to a good prognosis. Our findings have a clear potential for clinical use.
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spelling info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis Genetic profile analysis of tumor stem cells in locally advanced breast cancer Análise do perfil genético de células tronco tumorais no câncer de mama localmente avançado 2015-10-26Daniel Guimarães TiezziStanislas du ManoirHoutan NoushmehrSergio Akira UyemuraWillian Abraham da SilveiraUniversidade de São PauloMedicina (Ginecologia e Obstetrícia)USPBR Biologia Sistêmica Breast cancer Cancêr de Mama Célula-Tronco stem cell System Biology Transcriptoma transcriptome INTRODUCTION: Breast cancer is the most common cancer in women worldwide and metastatic dissemination is the principal factor related to death by this disease. Breast cancer stem cells (bCSC), defined in this work as the ALDH1high/LIN-/ESA+ population, are thought to be responsible for metastasis and chemoresistance. The objective of this work is to find gene master regulators, in particular transcription factors (TFs), which are controlling the bCSC phenotype. METHODS: We used in this work two groups of datasets with transcriptome data, the discovery dataset group contains one dataset obtained by ourselves containing three paired samples comparing the bCSC and the bulk of the tumor (My Data - bCSC/Bulk dataset), a dataset with eight paired samples comparing the bCSC and cancer cells (Wicha - bCSC/CC dataset) and a dataset with 115 samples of breast cancer tissue (clinical response dataset). The second group, validation datasets, contains the BRCA-TCGA dataset with information of 621 samples, 4142 breast cancer samples of the Kmplot tool, 17 primary samples of BasL subtype and their information of grafting in patient derived xenografts and analyzes of cell lines (MF10A and HMLE). For the analyzes we used the paired t-test in the Limma R package, the ARACNE algorithm for the inference of regulons in the clinical response dataset, MRA-FET to define the master regulators of the bCSC phenotype, and GSEA to identify the biological meaning of the findings in the different datasets. RESULTS: We identified 12 TFs as master regulators of the bCSC phenotype, with nine of them forming two highly interconnected networks, one positively related with the bCSC phenotype formed by SNAI2, TWIST, PRRX1, BNC2 and TBX5 with its regulons, defined here as the mesenchymal transcription network and one negative correlated to the phenotype formed by SCML4, ZNF831, SP140 and IKZF3, defined as the immune response transcription network, totally unknown in the context of breast cancer in the literature. Although still with weak evidence, ZEB1 seems to control the two networks and can be responsible for the expression of ALDH1 and of the three remaining TFs: ID4, HOXA5 and TEAD1. As their names portray, our data showed in the different datasets, and independently of the molecular subtype and of the platform used, that the mesenchymal transcription network seems to be responsible for the bCSC phenotype and the immune response transcription network to the adaptive immune response in the tumor and a better prognosis for the patients. We also defined 10 membrane proteins as new markers and/or therapeutic targets of the bCSC. CONCLUSION: We found and described two TF networks that seem to control the bCSC phenotype, one of them totally unknown until now and correlated to a good prognosis. Our findings have a clear potential for clinical use. INTRODUÇÃO: O cancer de mama é no mundo o câncer mais comum em mulheres e a disseminação metastática é o principal fator relacionado com a morte pela doença. Acreditasse que as células tronco do câncer de mama - bCSC, na sigla em inglês e definida neste trabalho com a população ALDH1high/LIN-/ESA+ - é responsável pela metástase e pela quimioresistência. O objetivo deste trabalho é encontrar genes que são essenciais para o controle do fenótipo das bCSC, em particular fatores de transcrição. MATERIAIS E MÉTODOS: Nesse trabalho nós utlizamos dois grupos de datasets com dados do transcriptoma, o grupo de datasets de descoberta contém um dataset gerado por nós com 3 amostras pareadas comparando as bCSC com o tumor total (My Data - bCSC/Bulk dataset), um dataset com 8 amostras pareadas comparando as bCSC com as células cancerígenas (Wicha - bCSC/CC dataset) e um dataset com 115 amostras de tecido de câncer de mama (Clinical Response dataset). O segundo grupo, grupo de validação, contém o dataset BRCA-TCGA com 621 amostras, as 4142 amostras de câncer de mama da ferramenta Kmplot, as 17 amostras humanas primárias do subtipo BasL e sua informação sobre a geração, ou não, de tumores em camundongos imunosuprimidos e a análise de linhagens celulares (MF10A e HMLE). Para a análise dos dataset utilizamos o test-t pareado no pacote Limma da liguagem R, o algoritmo ARACNE para a inferência de regulons no dataset Clinical Response, a análise MRA-FET para definir os Reguladores Mestres para o fenótipo das bCSC e a análise GSEA para identificar o significado biológico de nosso achados nos diferentes datasets. RESULTADOS E DISCUSSÃO: Nós identificamos 12 TFs como reguladores mestres, com 9 deles formando duas redes altamente conectadas, uma positivamente relacionada ao fenótipo bCSC formada por SNAI2, TWIST, PRRX1, BNC2 e TBX5 com seus regulons, e definida aqui como a rede de transcrição mesenquimal, e uma rede correlacionada negativamente, formada por SCML4, ZNF831, SP140 e IKZF3, definida aqui como a rede de transcrição da resposta imune e totalmente desconhecida da literatura no contexto do câncer de mama. Embora ainda com fraca evidencia, ZEB1 para controlar as duas redes e ser responsável pela expressão de ALDH1 e dos 3 TFs restantes: ID4, HOXA5 e TEAD1. Como mostram seus nomes, e independente do dataset, do subtipo molecular ou da plataforma utilizada, a rede de transcrição mesenquimal, parece ser responsável pela manutenção do fenótipo de células tronco cancerígenas e a rede de transcrição da resposta imune pela resposta imune adaptativa ao tumor e a um bom prognóstico para as pacientes. CONCLUSÃO: Nós encontramos e descrevemos duas redes de fatores de transcrição que parecem controlar o fenótipo das bCSC, uma delas totalmente desconhecida até agora e relacionada a um bom prognóstico. Nosso achados possuem um claro potencial para uso clínico. https://doi.org/10.11606/T.17.2017.tde-05012016-144854info:eu-repo/semantics/openAccessporreponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USP2023-12-21T18:14:13Zoai:teses.usp.br:tde-05012016-144854Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212023-12-22T12:08:42.879573Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false
dc.title.pt.fl_str_mv Genetic profile analysis of tumor stem cells in locally advanced breast cancer
dc.title.alternative.en.fl_str_mv Análise do perfil genético de células tronco tumorais no câncer de mama localmente avançado
title Genetic profile analysis of tumor stem cells in locally advanced breast cancer
spellingShingle Genetic profile analysis of tumor stem cells in locally advanced breast cancer
Willian Abraham da Silveira
title_short Genetic profile analysis of tumor stem cells in locally advanced breast cancer
title_full Genetic profile analysis of tumor stem cells in locally advanced breast cancer
title_fullStr Genetic profile analysis of tumor stem cells in locally advanced breast cancer
title_full_unstemmed Genetic profile analysis of tumor stem cells in locally advanced breast cancer
title_sort Genetic profile analysis of tumor stem cells in locally advanced breast cancer
author Willian Abraham da Silveira
author_facet Willian Abraham da Silveira
author_role author
dc.contributor.advisor1.fl_str_mv Daniel Guimarães Tiezzi
dc.contributor.referee1.fl_str_mv Stanislas du Manoir
dc.contributor.referee2.fl_str_mv Houtan Noushmehr
dc.contributor.referee3.fl_str_mv Sergio Akira Uyemura
dc.contributor.author.fl_str_mv Willian Abraham da Silveira
contributor_str_mv Daniel Guimarães Tiezzi
Stanislas du Manoir
Houtan Noushmehr
Sergio Akira Uyemura
description INTRODUCTION: Breast cancer is the most common cancer in women worldwide and metastatic dissemination is the principal factor related to death by this disease. Breast cancer stem cells (bCSC), defined in this work as the ALDH1high/LIN-/ESA+ population, are thought to be responsible for metastasis and chemoresistance. The objective of this work is to find gene master regulators, in particular transcription factors (TFs), which are controlling the bCSC phenotype. METHODS: We used in this work two groups of datasets with transcriptome data, the discovery dataset group contains one dataset obtained by ourselves containing three paired samples comparing the bCSC and the bulk of the tumor (My Data - bCSC/Bulk dataset), a dataset with eight paired samples comparing the bCSC and cancer cells (Wicha - bCSC/CC dataset) and a dataset with 115 samples of breast cancer tissue (clinical response dataset). The second group, validation datasets, contains the BRCA-TCGA dataset with information of 621 samples, 4142 breast cancer samples of the Kmplot tool, 17 primary samples of BasL subtype and their information of grafting in patient derived xenografts and analyzes of cell lines (MF10A and HMLE). For the analyzes we used the paired t-test in the Limma R package, the ARACNE algorithm for the inference of regulons in the clinical response dataset, MRA-FET to define the master regulators of the bCSC phenotype, and GSEA to identify the biological meaning of the findings in the different datasets. RESULTS: We identified 12 TFs as master regulators of the bCSC phenotype, with nine of them forming two highly interconnected networks, one positively related with the bCSC phenotype formed by SNAI2, TWIST, PRRX1, BNC2 and TBX5 with its regulons, defined here as the mesenchymal transcription network and one negative correlated to the phenotype formed by SCML4, ZNF831, SP140 and IKZF3, defined as the immune response transcription network, totally unknown in the context of breast cancer in the literature. Although still with weak evidence, ZEB1 seems to control the two networks and can be responsible for the expression of ALDH1 and of the three remaining TFs: ID4, HOXA5 and TEAD1. As their names portray, our data showed in the different datasets, and independently of the molecular subtype and of the platform used, that the mesenchymal transcription network seems to be responsible for the bCSC phenotype and the immune response transcription network to the adaptive immune response in the tumor and a better prognosis for the patients. We also defined 10 membrane proteins as new markers and/or therapeutic targets of the bCSC. CONCLUSION: We found and described two TF networks that seem to control the bCSC phenotype, one of them totally unknown until now and correlated to a good prognosis. Our findings have a clear potential for clinical use.
publishDate 2015
dc.date.issued.fl_str_mv 2015-10-26
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
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dc.identifier.uri.fl_str_mv https://doi.org/10.11606/T.17.2017.tde-05012016-144854
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dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.publisher.none.fl_str_mv Universidade de São Paulo
dc.publisher.program.fl_str_mv Medicina (Ginecologia e Obstetrícia)
dc.publisher.initials.fl_str_mv USP
dc.publisher.country.fl_str_mv BR
publisher.none.fl_str_mv Universidade de São Paulo
dc.source.none.fl_str_mv reponame:Biblioteca Digital de Teses e Dissertações da USP
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
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reponame_str Biblioteca Digital de Teses e Dissertações da USP
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repository.name.fl_str_mv Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)
repository.mail.fl_str_mv virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br
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