Cargando…
Nomogram to Predict Tumor-Infiltrating Lymphocytes in Breast Cancer Patients
Background: Tumor-infiltrating lymphocytes (TILs) play important roles in the prediction of prognosis and neoadjuvant therapy (NAT) efficacy in breast cancer (BRCA) patients, in this study, we identified clinicopathological factors related to BRCA TILs, then to construct and validate nomogram to pre...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8662984/ https://www.ncbi.nlm.nih.gov/pubmed/34901155 http://dx.doi.org/10.3389/fmolb.2021.761163 |
_version_ | 1784613548254560256 |
---|---|
author | Feng, Jikun Li, Jianxia Huang, Xinjian Yi, Jiarong Wu, Haoming Zou, Xuxiazi Zhong, Wenjing Wang, Xi |
author_facet | Feng, Jikun Li, Jianxia Huang, Xinjian Yi, Jiarong Wu, Haoming Zou, Xuxiazi Zhong, Wenjing Wang, Xi |
author_sort | Feng, Jikun |
collection | PubMed |
description | Background: Tumor-infiltrating lymphocytes (TILs) play important roles in the prediction of prognosis and neoadjuvant therapy (NAT) efficacy in breast cancer (BRCA) patients, in this study, we identified clinicopathological factors related to BRCA TILs, then to construct and validate nomogram to predict high density of TILs. Methods: A total of 826 patients diagnosed with BRCA in Sun Yat-Sen University cancer center were enrolled in nomogram cohort. TILs were assessed using hematoxylin-eosin (H&E) staining by two pathologists. Complete clinical data were collected for analysis. Then the enrolled patients were split into a training set and validation set at a ratio of 8:2. and the backward multivariate binary logistic regression model was used to establish nomogram for predicting BRCA TILs, which were further evaluated and validated using the C-index, receiver operating characteristic (ROC) curves and calibration curves. Then another independent NAT cohort of 106 patients was established for verifying this nomogram in NAT efficacy prediction. Results: TILs were significantly correlated with body mass index (BMI), tumor differentiation, ER, PR, HER2 expression, Ki67, blood biochemical indicators including total bilirubin (TBIL), indirect bilirubin (IBIL), total protein (TP), Globulin (GLOB), inorganic phosphorus (IP), calcium (Ca). In which ER expression level [OR = 0.987, 95%CI (0.982–0.992), p < 0.001], IP [OR = 4.462, 95%CI (1.171∼17.289), p = 0.029], IBIL [OR = 0.906, 95%CI (0.845–0.966), p = 0.004] and TP [OR = 1.053, 95%CI (1.010–1.098, p = 0.016)] were independent predictors of TILs. Then nomogram was established, for which calibration curves (C-index = 0.759) and ROC curve (AUC = 0.759, 95%CI 0.717–0.801) in training sets, calibration curves (C-index = 0.708) and ROC curve (AUC = 0.708, 95%CI 0.617–0.800) in validation sets demonstrated great evaluation efficiency. Besides, independent NAT cohort verified this nomogram can distinguish patients with greater NAT efficacy (p = 0.041). Conclusion: The finds of clinicopathological factors associated with TILs could help clinicians to understand the tumor immunity of BRCA and improve treatment system for patients, and the established nomogram with high evaluation efficiency may be used as a complement tool for distinguishing patients with better NAT efficacy. |
format | Online Article Text |
id | pubmed-8662984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86629842021-12-11 Nomogram to Predict Tumor-Infiltrating Lymphocytes in Breast Cancer Patients Feng, Jikun Li, Jianxia Huang, Xinjian Yi, Jiarong Wu, Haoming Zou, Xuxiazi Zhong, Wenjing Wang, Xi Front Mol Biosci Molecular Biosciences Background: Tumor-infiltrating lymphocytes (TILs) play important roles in the prediction of prognosis and neoadjuvant therapy (NAT) efficacy in breast cancer (BRCA) patients, in this study, we identified clinicopathological factors related to BRCA TILs, then to construct and validate nomogram to predict high density of TILs. Methods: A total of 826 patients diagnosed with BRCA in Sun Yat-Sen University cancer center were enrolled in nomogram cohort. TILs were assessed using hematoxylin-eosin (H&E) staining by two pathologists. Complete clinical data were collected for analysis. Then the enrolled patients were split into a training set and validation set at a ratio of 8:2. and the backward multivariate binary logistic regression model was used to establish nomogram for predicting BRCA TILs, which were further evaluated and validated using the C-index, receiver operating characteristic (ROC) curves and calibration curves. Then another independent NAT cohort of 106 patients was established for verifying this nomogram in NAT efficacy prediction. Results: TILs were significantly correlated with body mass index (BMI), tumor differentiation, ER, PR, HER2 expression, Ki67, blood biochemical indicators including total bilirubin (TBIL), indirect bilirubin (IBIL), total protein (TP), Globulin (GLOB), inorganic phosphorus (IP), calcium (Ca). In which ER expression level [OR = 0.987, 95%CI (0.982–0.992), p < 0.001], IP [OR = 4.462, 95%CI (1.171∼17.289), p = 0.029], IBIL [OR = 0.906, 95%CI (0.845–0.966), p = 0.004] and TP [OR = 1.053, 95%CI (1.010–1.098, p = 0.016)] were independent predictors of TILs. Then nomogram was established, for which calibration curves (C-index = 0.759) and ROC curve (AUC = 0.759, 95%CI 0.717–0.801) in training sets, calibration curves (C-index = 0.708) and ROC curve (AUC = 0.708, 95%CI 0.617–0.800) in validation sets demonstrated great evaluation efficiency. Besides, independent NAT cohort verified this nomogram can distinguish patients with greater NAT efficacy (p = 0.041). Conclusion: The finds of clinicopathological factors associated with TILs could help clinicians to understand the tumor immunity of BRCA and improve treatment system for patients, and the established nomogram with high evaluation efficiency may be used as a complement tool for distinguishing patients with better NAT efficacy. Frontiers Media S.A. 2021-11-26 /pmc/articles/PMC8662984/ /pubmed/34901155 http://dx.doi.org/10.3389/fmolb.2021.761163 Text en Copyright © 2021 Feng, Li, Huang, Yi, Wu, Zou, Zhong 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 | Molecular Biosciences Feng, Jikun Li, Jianxia Huang, Xinjian Yi, Jiarong Wu, Haoming Zou, Xuxiazi Zhong, Wenjing Wang, Xi Nomogram to Predict Tumor-Infiltrating Lymphocytes in Breast Cancer Patients |
title | Nomogram to Predict Tumor-Infiltrating Lymphocytes in Breast Cancer Patients |
title_full | Nomogram to Predict Tumor-Infiltrating Lymphocytes in Breast Cancer Patients |
title_fullStr | Nomogram to Predict Tumor-Infiltrating Lymphocytes in Breast Cancer Patients |
title_full_unstemmed | Nomogram to Predict Tumor-Infiltrating Lymphocytes in Breast Cancer Patients |
title_short | Nomogram to Predict Tumor-Infiltrating Lymphocytes in Breast Cancer Patients |
title_sort | nomogram to predict tumor-infiltrating lymphocytes in breast cancer patients |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8662984/ https://www.ncbi.nlm.nih.gov/pubmed/34901155 http://dx.doi.org/10.3389/fmolb.2021.761163 |
work_keys_str_mv | AT fengjikun nomogramtopredicttumorinfiltratinglymphocytesinbreastcancerpatients AT lijianxia nomogramtopredicttumorinfiltratinglymphocytesinbreastcancerpatients AT huangxinjian nomogramtopredicttumorinfiltratinglymphocytesinbreastcancerpatients AT yijiarong nomogramtopredicttumorinfiltratinglymphocytesinbreastcancerpatients AT wuhaoming nomogramtopredicttumorinfiltratinglymphocytesinbreastcancerpatients AT zouxuxiazi nomogramtopredicttumorinfiltratinglymphocytesinbreastcancerpatients AT zhongwenjing nomogramtopredicttumorinfiltratinglymphocytesinbreastcancerpatients AT wangxi nomogramtopredicttumorinfiltratinglymphocytesinbreastcancerpatients |