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...

Descripción completa

Detalles Bibliográficos
Autores principales: Feng, Jikun, Li, Jianxia, Huang, Xinjian, Yi, Jiarong, Wu, Haoming, Zou, Xuxiazi, Zhong, Wenjing, Wang, Xi
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