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Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients

Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predi...

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Autores principales: Chen, Jian, Hao, Li, Qian, Xiaojun, Lin, Lin, Pan, Yueyin, Han, Xinghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352856/
https://www.ncbi.nlm.nih.gov/pubmed/35935976
http://dx.doi.org/10.3389/fimmu.2022.948601
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author Chen, Jian
Hao, Li
Qian, Xiaojun
Lin, Lin
Pan, Yueyin
Han, Xinghua
author_facet Chen, Jian
Hao, Li
Qian, Xiaojun
Lin, Lin
Pan, Yueyin
Han, Xinghua
author_sort Chen, Jian
collection PubMed
description Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine learning algorithms and a model stacking strategy to build an immunological gene based model (Ipredictor model) and an immunological gene and receptor status based model (ICpredictor model) in the RNA-seq dataset. The areas under the receiver operator characteristic curves for the Ipredictor model and ICpredictor models were 0.745 and 0.769 in an independent external test set based on the RNA-seq platform, and were 0.716 and 0.752 in another independent external test set based on the microarray platform. Furthermore, we found that the predictive score of the Ipredictor model was correlated with immune microenvironment and genomic aberration markers. These results demonstrated that the models can accurately predict the response to NAT for BC patients and will contribute to individualized therapy.
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spelling pubmed-93528562022-08-06 Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients Chen, Jian Hao, Li Qian, Xiaojun Lin, Lin Pan, Yueyin Han, Xinghua Front Immunol Immunology Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine learning algorithms and a model stacking strategy to build an immunological gene based model (Ipredictor model) and an immunological gene and receptor status based model (ICpredictor model) in the RNA-seq dataset. The areas under the receiver operator characteristic curves for the Ipredictor model and ICpredictor models were 0.745 and 0.769 in an independent external test set based on the RNA-seq platform, and were 0.716 and 0.752 in another independent external test set based on the microarray platform. Furthermore, we found that the predictive score of the Ipredictor model was correlated with immune microenvironment and genomic aberration markers. These results demonstrated that the models can accurately predict the response to NAT for BC patients and will contribute to individualized therapy. Frontiers Media S.A. 2022-07-22 /pmc/articles/PMC9352856/ /pubmed/35935976 http://dx.doi.org/10.3389/fimmu.2022.948601 Text en Copyright © 2022 Chen, Hao, Qian, Lin, Pan and Han https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Chen, Jian
Hao, Li
Qian, Xiaojun
Lin, Lin
Pan, Yueyin
Han, Xinghua
Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients
title Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients
title_full Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients
title_fullStr Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients
title_full_unstemmed Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients
title_short Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients
title_sort machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352856/
https://www.ncbi.nlm.nih.gov/pubmed/35935976
http://dx.doi.org/10.3389/fimmu.2022.948601
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