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A multifactorial ‘Consensus Signature’ by in silico analysis to predict response to neoadjuvant anthracycline-based chemotherapy in triple-negative breast cancer
BACKGROUND: Owing to the complex processes required for anthracycline-induced cytotoxicity, a prospectively defined multifactorial Consensus Signature (ConSig) might improve prediction of anthracycline response in triple-negative breast cancer (TNBC) patients, whose only standard systemic treatment...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5515202/ https://www.ncbi.nlm.nih.gov/pubmed/28721363 http://dx.doi.org/10.1038/npjbcancer.2015.3 |
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author | Turner, Natalie Forcato, Mattia Nuzzo, Simona Malorni, Luca Bicciato, Silvio Di Leo, Angelo |
author_facet | Turner, Natalie Forcato, Mattia Nuzzo, Simona Malorni, Luca Bicciato, Silvio Di Leo, Angelo |
author_sort | Turner, Natalie |
collection | PubMed |
description | BACKGROUND: Owing to the complex processes required for anthracycline-induced cytotoxicity, a prospectively defined multifactorial Consensus Signature (ConSig) might improve prediction of anthracycline response in triple-negative breast cancer (TNBC) patients, whose only standard systemic treatment option is chemotherapy. AIMS: We aimed to construct and evaluate a multifactorial signature, comprising measures of each function required for anthracycline sensitivity in TNBC. METHODS: ConSigs were constructed based on five steps required for anthracycline function: drug penetration, nuclear topoisomerase IIα (topoIIα) protein location, increased topoIIα messenger RNA (mRNA) expression, apoptosis induction, and immune activation measured by, respectively, HIF1α or SHARP1 signature, LAPTM4B mRNA, topoIIα mRNA, Minimal Gene signature or YWHAZ mRNA, and STAT1 signature. TNBC patients treated with neoadjuvant anthracycline-based chemotherapy without taxane were identified from publicly available gene expression data derived with Affymetrix HG-U133 arrays (training set). In silico analyses of correlation between gene expression data and pathological complete response (pCR) were performed using receiver-operating characteristic curves. To determine anthracycline specificity, ConSigs were assessed in patients treated with anthracycline plus taxane. Specificity, sensitivity, positive and negative predictive value, and odds ratio (OR) were calculated for ConSigs. Analyses were repeated in two validation gene expression data sets derived using different microarray platforms. RESULTS: In the training set, 29 of 147 patients had pCR after anthracycline-based chemotherapy. Various combinations of components were evaluated, with the most powerful anthracycline response predictors being ConSig1: (STAT1+topoIIα mRNA+LAPTM4B) and ConSig2: (STAT1+topoIIα mRNA+HIF1α). ConSig1 demonstrated high negative predictive value (85%) and high OR for no pCR (3.18) and outperformed ConSig2 in validation sets for anthracycline specificity. CONCLUSIONS: With further validation, ConSig1 may help refine selection of TNBC patients for anthracycline chemotherapy. |
format | Online Article Text |
id | pubmed-5515202 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-55152022017-07-18 A multifactorial ‘Consensus Signature’ by in silico analysis to predict response to neoadjuvant anthracycline-based chemotherapy in triple-negative breast cancer Turner, Natalie Forcato, Mattia Nuzzo, Simona Malorni, Luca Bicciato, Silvio Di Leo, Angelo NPJ Breast Cancer Article BACKGROUND: Owing to the complex processes required for anthracycline-induced cytotoxicity, a prospectively defined multifactorial Consensus Signature (ConSig) might improve prediction of anthracycline response in triple-negative breast cancer (TNBC) patients, whose only standard systemic treatment option is chemotherapy. AIMS: We aimed to construct and evaluate a multifactorial signature, comprising measures of each function required for anthracycline sensitivity in TNBC. METHODS: ConSigs were constructed based on five steps required for anthracycline function: drug penetration, nuclear topoisomerase IIα (topoIIα) protein location, increased topoIIα messenger RNA (mRNA) expression, apoptosis induction, and immune activation measured by, respectively, HIF1α or SHARP1 signature, LAPTM4B mRNA, topoIIα mRNA, Minimal Gene signature or YWHAZ mRNA, and STAT1 signature. TNBC patients treated with neoadjuvant anthracycline-based chemotherapy without taxane were identified from publicly available gene expression data derived with Affymetrix HG-U133 arrays (training set). In silico analyses of correlation between gene expression data and pathological complete response (pCR) were performed using receiver-operating characteristic curves. To determine anthracycline specificity, ConSigs were assessed in patients treated with anthracycline plus taxane. Specificity, sensitivity, positive and negative predictive value, and odds ratio (OR) were calculated for ConSigs. Analyses were repeated in two validation gene expression data sets derived using different microarray platforms. RESULTS: In the training set, 29 of 147 patients had pCR after anthracycline-based chemotherapy. Various combinations of components were evaluated, with the most powerful anthracycline response predictors being ConSig1: (STAT1+topoIIα mRNA+LAPTM4B) and ConSig2: (STAT1+topoIIα mRNA+HIF1α). ConSig1 demonstrated high negative predictive value (85%) and high OR for no pCR (3.18) and outperformed ConSig2 in validation sets for anthracycline specificity. CONCLUSIONS: With further validation, ConSig1 may help refine selection of TNBC patients for anthracycline chemotherapy. Nature Publishing Group 2015-06-02 /pmc/articles/PMC5515202/ /pubmed/28721363 http://dx.doi.org/10.1038/npjbcancer.2015.3 Text en Copyright © 2015 Breast Cancer Research Foundation/Macmillan Publishers Limited http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ |
spellingShingle | Article Turner, Natalie Forcato, Mattia Nuzzo, Simona Malorni, Luca Bicciato, Silvio Di Leo, Angelo A multifactorial ‘Consensus Signature’ by in silico analysis to predict response to neoadjuvant anthracycline-based chemotherapy in triple-negative breast cancer |
title | A multifactorial ‘Consensus Signature’ by in silico analysis to predict response to neoadjuvant anthracycline-based chemotherapy in triple-negative breast cancer |
title_full | A multifactorial ‘Consensus Signature’ by in silico analysis to predict response to neoadjuvant anthracycline-based chemotherapy in triple-negative breast cancer |
title_fullStr | A multifactorial ‘Consensus Signature’ by in silico analysis to predict response to neoadjuvant anthracycline-based chemotherapy in triple-negative breast cancer |
title_full_unstemmed | A multifactorial ‘Consensus Signature’ by in silico analysis to predict response to neoadjuvant anthracycline-based chemotherapy in triple-negative breast cancer |
title_short | A multifactorial ‘Consensus Signature’ by in silico analysis to predict response to neoadjuvant anthracycline-based chemotherapy in triple-negative breast cancer |
title_sort | multifactorial ‘consensus signature’ by in silico analysis to predict response to neoadjuvant anthracycline-based chemotherapy in triple-negative breast cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5515202/ https://www.ncbi.nlm.nih.gov/pubmed/28721363 http://dx.doi.org/10.1038/npjbcancer.2015.3 |
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