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A 3-gene biomarker signature to predict response to taxane-based neoadjuvant chemotherapy in breast cancer

Breast cancer is the most common cancer in women worldwide, affecting one in eight women in their lifetime. Taxane-based chemotherapy is routinely used in the treatment of breast cancer. The purpose of this study was to develop and validate a predictive biomarker to improve the benefit/risk ratio fo...

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Autores principales: Kallarackal, Jim, Burger, Florian, Bianco, Stefano, Romualdi, Alessandro, Schad, Martina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083332/
https://www.ncbi.nlm.nih.gov/pubmed/32196521
http://dx.doi.org/10.1371/journal.pone.0230313
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author Kallarackal, Jim
Burger, Florian
Bianco, Stefano
Romualdi, Alessandro
Schad, Martina
author_facet Kallarackal, Jim
Burger, Florian
Bianco, Stefano
Romualdi, Alessandro
Schad, Martina
author_sort Kallarackal, Jim
collection PubMed
description Breast cancer is the most common cancer in women worldwide, affecting one in eight women in their lifetime. Taxane-based chemotherapy is routinely used in the treatment of breast cancer. The purpose of this study was to develop and validate a predictive biomarker to improve the benefit/risk ratio for that cytotoxic chemotherapy. We explicitly strived for a biomarker that enables secure translation into clinical practice. We used genome-wide gene expression data of the Hatzis et al. discovery cohort of 310 patients for biomarker development and three independent cohorts with a total of 567 breast cancer patients for validation. We were able to develop a biomarker signature that consists of just the three gene products ELF5, SCUBE2 and NFIB, measured on RNA level. Compared to Hatzis et al., we achieved a significant improvement in predicting responders and non-responders in the Hatzis et al. validation cohort with an area under the receiver operating characteristics curve of 0.73 [95% CI, 69%—77%]. Moreover, we could confirm the performance of our biomarker on two further independent validation cohorts. The overall performance on all three validation cohorts expressed as area under the receiver operating characteristics curve was 0.75 [95% CI, 70%—80%]. At the clinically relevant classifier’s operation point to optimize the exclusion of non-responders, the biomarker correctly predicts three out of four patients not responding to neoadjuvant taxane-based chemotherapy, independent of the breast cancer subtype. At the same time, the response rate in the group of predicted responders increased to 42% compared to 23% response rate in all patients of the validation cohorts.
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spelling pubmed-70833322020-03-30 A 3-gene biomarker signature to predict response to taxane-based neoadjuvant chemotherapy in breast cancer Kallarackal, Jim Burger, Florian Bianco, Stefano Romualdi, Alessandro Schad, Martina PLoS One Research Article Breast cancer is the most common cancer in women worldwide, affecting one in eight women in their lifetime. Taxane-based chemotherapy is routinely used in the treatment of breast cancer. The purpose of this study was to develop and validate a predictive biomarker to improve the benefit/risk ratio for that cytotoxic chemotherapy. We explicitly strived for a biomarker that enables secure translation into clinical practice. We used genome-wide gene expression data of the Hatzis et al. discovery cohort of 310 patients for biomarker development and three independent cohorts with a total of 567 breast cancer patients for validation. We were able to develop a biomarker signature that consists of just the three gene products ELF5, SCUBE2 and NFIB, measured on RNA level. Compared to Hatzis et al., we achieved a significant improvement in predicting responders and non-responders in the Hatzis et al. validation cohort with an area under the receiver operating characteristics curve of 0.73 [95% CI, 69%—77%]. Moreover, we could confirm the performance of our biomarker on two further independent validation cohorts. The overall performance on all three validation cohorts expressed as area under the receiver operating characteristics curve was 0.75 [95% CI, 70%—80%]. At the clinically relevant classifier’s operation point to optimize the exclusion of non-responders, the biomarker correctly predicts three out of four patients not responding to neoadjuvant taxane-based chemotherapy, independent of the breast cancer subtype. At the same time, the response rate in the group of predicted responders increased to 42% compared to 23% response rate in all patients of the validation cohorts. Public Library of Science 2020-03-20 /pmc/articles/PMC7083332/ /pubmed/32196521 http://dx.doi.org/10.1371/journal.pone.0230313 Text en © 2020 Kallarackal et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kallarackal, Jim
Burger, Florian
Bianco, Stefano
Romualdi, Alessandro
Schad, Martina
A 3-gene biomarker signature to predict response to taxane-based neoadjuvant chemotherapy in breast cancer
title A 3-gene biomarker signature to predict response to taxane-based neoadjuvant chemotherapy in breast cancer
title_full A 3-gene biomarker signature to predict response to taxane-based neoadjuvant chemotherapy in breast cancer
title_fullStr A 3-gene biomarker signature to predict response to taxane-based neoadjuvant chemotherapy in breast cancer
title_full_unstemmed A 3-gene biomarker signature to predict response to taxane-based neoadjuvant chemotherapy in breast cancer
title_short A 3-gene biomarker signature to predict response to taxane-based neoadjuvant chemotherapy in breast cancer
title_sort 3-gene biomarker signature to predict response to taxane-based neoadjuvant chemotherapy in breast cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083332/
https://www.ncbi.nlm.nih.gov/pubmed/32196521
http://dx.doi.org/10.1371/journal.pone.0230313
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