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A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients
BACKGROUND: Sentinel lymph node biopsy (SLNB) is the standard treatment for breast cancer patients with clinically negative axilla. However, axillary lymph node dissection (ALND) is still the standard care for sentinel lymph node (SLN) positive patients. Clinical data reveals about 40-75% of patient...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154525/ https://www.ncbi.nlm.nih.gov/pubmed/37152050 http://dx.doi.org/10.3389/fonc.2023.1028830 |
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author | Zhu, Liling Liu, Ke Bao, Baoshi Li, Fengyun Liang, Wentao Jiang, Zhaoyun Hao, Xiaopeng Wang, Jiandong |
author_facet | Zhu, Liling Liu, Ke Bao, Baoshi Li, Fengyun Liang, Wentao Jiang, Zhaoyun Hao, Xiaopeng Wang, Jiandong |
author_sort | Zhu, Liling |
collection | PubMed |
description | BACKGROUND: Sentinel lymph node biopsy (SLNB) is the standard treatment for breast cancer patients with clinically negative axilla. However, axillary lymph node dissection (ALND) is still the standard care for sentinel lymph node (SLN) positive patients. Clinical data reveals about 40-75% of patients without non-sentinel lymph node (NSLN) metastasis after ALND. Unnecessary ALND increases the risk of complications and detracts from quality of life. In this study, we expect to develop a nomogram based on genotypic and clinicopathologic factors to predict the risk of NSLN metastasis in SLN-positive Chinese women breast cancer patients. METHODS: This retrospective study collected data from 1,879 women breast cancer patients enrolled from multiple centers. Genotypic features contain 96 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, therapy and prognosis. SNP genotyping was identified by the quantitative PCR detection platform. The genetic features were divided into two clusters by the mutational stability. The normalized polygenic risk score (PRS) was used to evaluate the combined effect of each SNP cluster. Recursive feature elimination (RFE) based on linear discriminant analysis (LDA) was adopted to select the most useful predictive features, and RFE based on support vector machine (SVM) was used to reduce the number of SNPs. Multivariable logistic regression models (i.e., nomogram) were built for predicting NSLN metastasis. The predictive abilities of three types of model (based on only clinicopathologic information, the integrated clinicopathologic and all SNPs information, and integrated clinicopathologic and significant SNPs information) were compared. Internal and external validations were performed and the area under ROC curves (AUCs) as well as a series of evaluation indicators were assessed. RESULTS: 229 patients underwent SLNB followed by ALND and without any neo-adjuvant therapy, 79 among them (34%) had a positive axillary NSLN metastasis. The LDA-RFE identified the characteristics including lymphovascular invasion, number of positive SLNs, number of negative SLNs and two SNP clusters as significant predictors of NSLN metastasis. Furthermore, the SVM-RFE selected 29 significant SNPs in the prediction of NSLN metastasis. In internal validation, the median AUCs of the clinical and all SNPs combining model, the clinical and 29 significant SNPs combining model, and the clinical model were 0.837, 0.795 and 0.708 respectively. Meanwhile, in external validation, the AUCs of the three models were 0.817, 0.815 and 0.745 respectively. CONCLUSION: We present a new nomogram by combining genotypic and clinicopathologic factors to achieve higher sensitivity and specificity comparing with traditional clinicopathologic factors to predict NSLN metastasis in Chinese women breast cancer. It is recommended that more validations are required in prospective studies among different patient populations. |
format | Online Article Text |
id | pubmed-10154525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101545252023-05-04 A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients Zhu, Liling Liu, Ke Bao, Baoshi Li, Fengyun Liang, Wentao Jiang, Zhaoyun Hao, Xiaopeng Wang, Jiandong Front Oncol Oncology BACKGROUND: Sentinel lymph node biopsy (SLNB) is the standard treatment for breast cancer patients with clinically negative axilla. However, axillary lymph node dissection (ALND) is still the standard care for sentinel lymph node (SLN) positive patients. Clinical data reveals about 40-75% of patients without non-sentinel lymph node (NSLN) metastasis after ALND. Unnecessary ALND increases the risk of complications and detracts from quality of life. In this study, we expect to develop a nomogram based on genotypic and clinicopathologic factors to predict the risk of NSLN metastasis in SLN-positive Chinese women breast cancer patients. METHODS: This retrospective study collected data from 1,879 women breast cancer patients enrolled from multiple centers. Genotypic features contain 96 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, therapy and prognosis. SNP genotyping was identified by the quantitative PCR detection platform. The genetic features were divided into two clusters by the mutational stability. The normalized polygenic risk score (PRS) was used to evaluate the combined effect of each SNP cluster. Recursive feature elimination (RFE) based on linear discriminant analysis (LDA) was adopted to select the most useful predictive features, and RFE based on support vector machine (SVM) was used to reduce the number of SNPs. Multivariable logistic regression models (i.e., nomogram) were built for predicting NSLN metastasis. The predictive abilities of three types of model (based on only clinicopathologic information, the integrated clinicopathologic and all SNPs information, and integrated clinicopathologic and significant SNPs information) were compared. Internal and external validations were performed and the area under ROC curves (AUCs) as well as a series of evaluation indicators were assessed. RESULTS: 229 patients underwent SLNB followed by ALND and without any neo-adjuvant therapy, 79 among them (34%) had a positive axillary NSLN metastasis. The LDA-RFE identified the characteristics including lymphovascular invasion, number of positive SLNs, number of negative SLNs and two SNP clusters as significant predictors of NSLN metastasis. Furthermore, the SVM-RFE selected 29 significant SNPs in the prediction of NSLN metastasis. In internal validation, the median AUCs of the clinical and all SNPs combining model, the clinical and 29 significant SNPs combining model, and the clinical model were 0.837, 0.795 and 0.708 respectively. Meanwhile, in external validation, the AUCs of the three models were 0.817, 0.815 and 0.745 respectively. CONCLUSION: We present a new nomogram by combining genotypic and clinicopathologic factors to achieve higher sensitivity and specificity comparing with traditional clinicopathologic factors to predict NSLN metastasis in Chinese women breast cancer. It is recommended that more validations are required in prospective studies among different patient populations. Frontiers Media S.A. 2023-04-19 /pmc/articles/PMC10154525/ /pubmed/37152050 http://dx.doi.org/10.3389/fonc.2023.1028830 Text en Copyright © 2023 Zhu, Liu, Bao, Li, Liang, Jiang, Hao 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 | Oncology Zhu, Liling Liu, Ke Bao, Baoshi Li, Fengyun Liang, Wentao Jiang, Zhaoyun Hao, Xiaopeng Wang, Jiandong A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients |
title | A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients |
title_full | A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients |
title_fullStr | A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients |
title_full_unstemmed | A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients |
title_short | A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients |
title_sort | nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in chinese women breast cancer patients |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154525/ https://www.ncbi.nlm.nih.gov/pubmed/37152050 http://dx.doi.org/10.3389/fonc.2023.1028830 |
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