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Preoperative Breast Immune Prognostic Index as Prognostic Factor Predicts the Clinical Outcomes of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

OBJECTIVE: This study aims at investigating the potential prognostic significance of the breast immune prognostic index (BIPI) in breast cancer patients who received neoadjuvant chemotherapy (NACT). METHODS: The optimal cutoff value was calculated through the receiver operating characteristic curve...

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Autores principales: Chen, Li, Kong, Xiangyi, Huang, Shaolong, Su, Zhaohui, Zhu, Mengliu, Fang, Yi, Zhang, Lin, Li, Xingrui, Wang, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8937039/
https://www.ncbi.nlm.nih.gov/pubmed/35320931
http://dx.doi.org/10.3389/fimmu.2022.831848
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author Chen, Li
Kong, Xiangyi
Huang, Shaolong
Su, Zhaohui
Zhu, Mengliu
Fang, Yi
Zhang, Lin
Li, Xingrui
Wang, Jing
author_facet Chen, Li
Kong, Xiangyi
Huang, Shaolong
Su, Zhaohui
Zhu, Mengliu
Fang, Yi
Zhang, Lin
Li, Xingrui
Wang, Jing
author_sort Chen, Li
collection PubMed
description OBJECTIVE: This study aims at investigating the potential prognostic significance of the breast immune prognostic index (BIPI) in breast cancer patients who received neoadjuvant chemotherapy (NACT). METHODS: The optimal cutoff value was calculated through the receiver operating characteristic curve (ROC). The correlations between BIPI and clinicopathologic characteristics were determined by the chi-square test or Fisher’s exact test. The Kaplan–Meier method was used to estimate the survival probability, and the log-rank test was used to analyze the differences in the survival probability among patients. The univariate and multivariate Cox proportional hazard regression model was used to screen the independent prognostic factors. A prognostic nomogram for disease-free survival (DFS) and overall survival (OS) was built on the basis of the multivariate analyses. Furthermore, the calibration curve and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram. RESULTS: All enrolled patients were split into three subgroups based on the BIPI score. The mean DFS and OS of the BIPI score 0 group and BIPI score 1 group were significantly longer than those of the BIPI score 2 group (42.02 vs. 38.61 vs. 26.01 months, 77.61 vs. 71.83 vs. 53.15 months; p < 0.05). Univariate and multivariate analyses indicated that BIPI was an independent prognostic factor for patients’ DFS and OS (DFS, hazard ratio (HR): 6.720, 95% confidence interval (CI): 1.629–27.717; OS, HR: 8.006, 95% CI: 1.638–39.119). A nomogram with a C-index of 0.873 (95% CI: 0.779–0.966) and 0.801 (95% CI: 0.702–0.901) had a favorable performance for predicting DFS and OS survival rates for clinical use by combining immune scores with other clinical features. The calibration curves at 1-, 3-, and 5-year survival suggested a good consistency between the predicted and actual DFS and OS probability. The DCA demonstrated that the constructed nomogram had better clinical predictive usefulness than only BIPI in predictive clinical applications of 5-year DFS and OS prognostic assessments. CONCLUSIONS: The patients with low BIPI score have better prognoses and longer DFS and OS. Furthermore, the BIPI-based nomogram may serve as a convenient prognostic tool for breast cancer and help in clinical decision-making.
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spelling pubmed-89370392022-03-22 Preoperative Breast Immune Prognostic Index as Prognostic Factor Predicts the Clinical Outcomes of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy Chen, Li Kong, Xiangyi Huang, Shaolong Su, Zhaohui Zhu, Mengliu Fang, Yi Zhang, Lin Li, Xingrui Wang, Jing Front Immunol Immunology OBJECTIVE: This study aims at investigating the potential prognostic significance of the breast immune prognostic index (BIPI) in breast cancer patients who received neoadjuvant chemotherapy (NACT). METHODS: The optimal cutoff value was calculated through the receiver operating characteristic curve (ROC). The correlations between BIPI and clinicopathologic characteristics were determined by the chi-square test or Fisher’s exact test. The Kaplan–Meier method was used to estimate the survival probability, and the log-rank test was used to analyze the differences in the survival probability among patients. The univariate and multivariate Cox proportional hazard regression model was used to screen the independent prognostic factors. A prognostic nomogram for disease-free survival (DFS) and overall survival (OS) was built on the basis of the multivariate analyses. Furthermore, the calibration curve and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram. RESULTS: All enrolled patients were split into three subgroups based on the BIPI score. The mean DFS and OS of the BIPI score 0 group and BIPI score 1 group were significantly longer than those of the BIPI score 2 group (42.02 vs. 38.61 vs. 26.01 months, 77.61 vs. 71.83 vs. 53.15 months; p < 0.05). Univariate and multivariate analyses indicated that BIPI was an independent prognostic factor for patients’ DFS and OS (DFS, hazard ratio (HR): 6.720, 95% confidence interval (CI): 1.629–27.717; OS, HR: 8.006, 95% CI: 1.638–39.119). A nomogram with a C-index of 0.873 (95% CI: 0.779–0.966) and 0.801 (95% CI: 0.702–0.901) had a favorable performance for predicting DFS and OS survival rates for clinical use by combining immune scores with other clinical features. The calibration curves at 1-, 3-, and 5-year survival suggested a good consistency between the predicted and actual DFS and OS probability. The DCA demonstrated that the constructed nomogram had better clinical predictive usefulness than only BIPI in predictive clinical applications of 5-year DFS and OS prognostic assessments. CONCLUSIONS: The patients with low BIPI score have better prognoses and longer DFS and OS. Furthermore, the BIPI-based nomogram may serve as a convenient prognostic tool for breast cancer and help in clinical decision-making. Frontiers Media S.A. 2022-03-07 /pmc/articles/PMC8937039/ /pubmed/35320931 http://dx.doi.org/10.3389/fimmu.2022.831848 Text en Copyright © 2022 Chen, Kong, Huang, Su, Zhu, Fang, Zhang, Li and Wang 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, Li
Kong, Xiangyi
Huang, Shaolong
Su, Zhaohui
Zhu, Mengliu
Fang, Yi
Zhang, Lin
Li, Xingrui
Wang, Jing
Preoperative Breast Immune Prognostic Index as Prognostic Factor Predicts the Clinical Outcomes of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy
title Preoperative Breast Immune Prognostic Index as Prognostic Factor Predicts the Clinical Outcomes of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy
title_full Preoperative Breast Immune Prognostic Index as Prognostic Factor Predicts the Clinical Outcomes of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy
title_fullStr Preoperative Breast Immune Prognostic Index as Prognostic Factor Predicts the Clinical Outcomes of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy
title_full_unstemmed Preoperative Breast Immune Prognostic Index as Prognostic Factor Predicts the Clinical Outcomes of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy
title_short Preoperative Breast Immune Prognostic Index as Prognostic Factor Predicts the Clinical Outcomes of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy
title_sort preoperative breast immune prognostic index as prognostic factor predicts the clinical outcomes of breast cancer patients receiving neoadjuvant chemotherapy
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8937039/
https://www.ncbi.nlm.nih.gov/pubmed/35320931
http://dx.doi.org/10.3389/fimmu.2022.831848
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