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...
Autores principales: | , , , , , , , |
---|---|
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 |