Cargando…

Development and validation of nomograms integrating immune‐related genomic signatures with clinicopathologic features to improve prognosis and predictive value of triple‐negative breast cancer: A gene expression‐based retrospective study

PURPOSE: Accumulating evidence indicated that triple‐negative breast cancer (TNBC) can stimulate stronger immune responses than other subtypes of breast cancer. We hypothesized that integrating immune‐related genomic signatures with clinicopathologic factors may yield a predictive accuracy exceeding...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Kang, Li, Hai‐Lin, Xiong, Yong‐Fu, Shi, Yang, Li, Zhu‐Yue, Li, Jie, Zhang, Xiang, Li, Hong‐Yuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382728/
https://www.ncbi.nlm.nih.gov/pubmed/30677255
http://dx.doi.org/10.1002/cam4.1880
_version_ 1783396702897569792
author Wang, Kang
Li, Hai‐Lin
Xiong, Yong‐Fu
Shi, Yang
Li, Zhu‐Yue
Li, Jie
Zhang, Xiang
Li, Hong‐Yuan
author_facet Wang, Kang
Li, Hai‐Lin
Xiong, Yong‐Fu
Shi, Yang
Li, Zhu‐Yue
Li, Jie
Zhang, Xiang
Li, Hong‐Yuan
author_sort Wang, Kang
collection PubMed
description PURPOSE: Accumulating evidence indicated that triple‐negative breast cancer (TNBC) can stimulate stronger immune responses than other subtypes of breast cancer. We hypothesized that integrating immune‐related genomic signatures with clinicopathologic factors may yield a predictive accuracy exceeding that of the currently available system. METHODS: Ten signatures that reflect specific immunogenic or immune microenvironmental features of TNBC were identified and re‐analyzed using bioinformatic methods. Then, clinically annotated TNBC (n = 711) with the corresponding expression profiles, which predicted a patient's probability of disease‐free survival (DFS) and overall survival (OS), was pooled to evaluate their prognostic values and establish a clinicopathologic‐genomic nomogram. Three and two immune features were, respectively, selected out of 10 immune features to construct nomogram for DFS and OS prediction based on multivariate backward stepwise Cox regression analyses. RESULTS: By integrating the above immune expression signatures with prognostic clinicopathologic features, clinicopathologic‐genomic nomograms were cautiously constructed, which showed reasonable prediction accuracies (DFS: HR, 1.79; 95% CI, 1.46‐2.18, P < 0.001; AUC, 0.71; OS: HR, 1.96; 95% CI, 1.54‐2.49; P < 0.001; AUC, 0.73). The nomogram showed low‐risk subgroup had higher immune checkpoint molecules (PD‐L1, PD‐1, CTLA‐4, LAG‐3) expression and benefited from radiotherapy (HR, 0.2, 95% CI, 0.05‐0.89; P = 0.034) rather than chemotherapy (HR, 1.26, 95% CI, 0.66‐2.43; P = 0.485). CONCLUSIONS: These findings offer evidence that immune‐related genomic data provide independent and complementary prognostic information for TNBC, and the nomogram might be a practical predictive tool to identify TNBC patients who would benefit from chemotherapy, radiotherapy, and upcoming popularity of immunotherapy.
format Online
Article
Text
id pubmed-6382728
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-63827282019-03-01 Development and validation of nomograms integrating immune‐related genomic signatures with clinicopathologic features to improve prognosis and predictive value of triple‐negative breast cancer: A gene expression‐based retrospective study Wang, Kang Li, Hai‐Lin Xiong, Yong‐Fu Shi, Yang Li, Zhu‐Yue Li, Jie Zhang, Xiang Li, Hong‐Yuan Cancer Med Clinical Cancer Research PURPOSE: Accumulating evidence indicated that triple‐negative breast cancer (TNBC) can stimulate stronger immune responses than other subtypes of breast cancer. We hypothesized that integrating immune‐related genomic signatures with clinicopathologic factors may yield a predictive accuracy exceeding that of the currently available system. METHODS: Ten signatures that reflect specific immunogenic or immune microenvironmental features of TNBC were identified and re‐analyzed using bioinformatic methods. Then, clinically annotated TNBC (n = 711) with the corresponding expression profiles, which predicted a patient's probability of disease‐free survival (DFS) and overall survival (OS), was pooled to evaluate their prognostic values and establish a clinicopathologic‐genomic nomogram. Three and two immune features were, respectively, selected out of 10 immune features to construct nomogram for DFS and OS prediction based on multivariate backward stepwise Cox regression analyses. RESULTS: By integrating the above immune expression signatures with prognostic clinicopathologic features, clinicopathologic‐genomic nomograms were cautiously constructed, which showed reasonable prediction accuracies (DFS: HR, 1.79; 95% CI, 1.46‐2.18, P < 0.001; AUC, 0.71; OS: HR, 1.96; 95% CI, 1.54‐2.49; P < 0.001; AUC, 0.73). The nomogram showed low‐risk subgroup had higher immune checkpoint molecules (PD‐L1, PD‐1, CTLA‐4, LAG‐3) expression and benefited from radiotherapy (HR, 0.2, 95% CI, 0.05‐0.89; P = 0.034) rather than chemotherapy (HR, 1.26, 95% CI, 0.66‐2.43; P = 0.485). CONCLUSIONS: These findings offer evidence that immune‐related genomic data provide independent and complementary prognostic information for TNBC, and the nomogram might be a practical predictive tool to identify TNBC patients who would benefit from chemotherapy, radiotherapy, and upcoming popularity of immunotherapy. John Wiley and Sons Inc. 2019-01-24 /pmc/articles/PMC6382728/ /pubmed/30677255 http://dx.doi.org/10.1002/cam4.1880 Text en © 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Cancer Research
Wang, Kang
Li, Hai‐Lin
Xiong, Yong‐Fu
Shi, Yang
Li, Zhu‐Yue
Li, Jie
Zhang, Xiang
Li, Hong‐Yuan
Development and validation of nomograms integrating immune‐related genomic signatures with clinicopathologic features to improve prognosis and predictive value of triple‐negative breast cancer: A gene expression‐based retrospective study
title Development and validation of nomograms integrating immune‐related genomic signatures with clinicopathologic features to improve prognosis and predictive value of triple‐negative breast cancer: A gene expression‐based retrospective study
title_full Development and validation of nomograms integrating immune‐related genomic signatures with clinicopathologic features to improve prognosis and predictive value of triple‐negative breast cancer: A gene expression‐based retrospective study
title_fullStr Development and validation of nomograms integrating immune‐related genomic signatures with clinicopathologic features to improve prognosis and predictive value of triple‐negative breast cancer: A gene expression‐based retrospective study
title_full_unstemmed Development and validation of nomograms integrating immune‐related genomic signatures with clinicopathologic features to improve prognosis and predictive value of triple‐negative breast cancer: A gene expression‐based retrospective study
title_short Development and validation of nomograms integrating immune‐related genomic signatures with clinicopathologic features to improve prognosis and predictive value of triple‐negative breast cancer: A gene expression‐based retrospective study
title_sort development and validation of nomograms integrating immune‐related genomic signatures with clinicopathologic features to improve prognosis and predictive value of triple‐negative breast cancer: a gene expression‐based retrospective study
topic Clinical Cancer Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382728/
https://www.ncbi.nlm.nih.gov/pubmed/30677255
http://dx.doi.org/10.1002/cam4.1880
work_keys_str_mv AT wangkang developmentandvalidationofnomogramsintegratingimmunerelatedgenomicsignatureswithclinicopathologicfeaturestoimproveprognosisandpredictivevalueoftriplenegativebreastcancerageneexpressionbasedretrospectivestudy
AT lihailin developmentandvalidationofnomogramsintegratingimmunerelatedgenomicsignatureswithclinicopathologicfeaturestoimproveprognosisandpredictivevalueoftriplenegativebreastcancerageneexpressionbasedretrospectivestudy
AT xiongyongfu developmentandvalidationofnomogramsintegratingimmunerelatedgenomicsignatureswithclinicopathologicfeaturestoimproveprognosisandpredictivevalueoftriplenegativebreastcancerageneexpressionbasedretrospectivestudy
AT shiyang developmentandvalidationofnomogramsintegratingimmunerelatedgenomicsignatureswithclinicopathologicfeaturestoimproveprognosisandpredictivevalueoftriplenegativebreastcancerageneexpressionbasedretrospectivestudy
AT lizhuyue developmentandvalidationofnomogramsintegratingimmunerelatedgenomicsignatureswithclinicopathologicfeaturestoimproveprognosisandpredictivevalueoftriplenegativebreastcancerageneexpressionbasedretrospectivestudy
AT lijie developmentandvalidationofnomogramsintegratingimmunerelatedgenomicsignatureswithclinicopathologicfeaturestoimproveprognosisandpredictivevalueoftriplenegativebreastcancerageneexpressionbasedretrospectivestudy
AT zhangxiang developmentandvalidationofnomogramsintegratingimmunerelatedgenomicsignatureswithclinicopathologicfeaturestoimproveprognosisandpredictivevalueoftriplenegativebreastcancerageneexpressionbasedretrospectivestudy
AT lihongyuan developmentandvalidationofnomogramsintegratingimmunerelatedgenomicsignatureswithclinicopathologicfeaturestoimproveprognosisandpredictivevalueoftriplenegativebreastcancerageneexpressionbasedretrospectivestudy