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

Kang Wang, Hai Lin Li, Yong Fu Xiong, Yang Shi, Zhu Yue Li, Jie Li, Xiang Zhang, Hong Yuan Li

Research output: Contribution to journalArticle

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Abstract

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.

Original languageEnglish (US)
Pages (from-to)686-700
Number of pages15
JournalCancer Medicine
Volume8
Issue number2
DOIs
StatePublished - Feb 1 2019

Fingerprint

Triple Negative Breast Neoplasms
Nomograms
Neoplasm Genes
Retrospective Studies
Gene Expression
Disease-Free Survival
Area Under Curve
Survival
Radiotherapy
Drug Therapy
Computational Biology
Immunotherapy
Regression Analysis
Breast Neoplasms

Keywords

  • immune-related genomic signatures
  • nomogram
  • triple-negative breast

ASJC Scopus subject areas

  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Cancer Research

Cite this

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.

In: Cancer Medicine, Vol. 8, No. 2, 01.02.2019, p. 686-700.

Research output: Contribution to journalArticle

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abstract = "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.",
keywords = "immune-related genomic signatures, nomogram, triple-negative breast",
author = "Kang Wang and Li, {Hai Lin} and Xiong, {Yong Fu} and Yang Shi and Li, {Zhu Yue} and Jie Li and Xiang Zhang and Li, {Hong Yuan}",
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T1 - Development and validation of nomograms integrating immune-related genomic signatures with clinicopathologic features to improve prognosis and predictive value of triple-negative breast cancer

T2 - A gene expression-based retrospective study

AU - Wang, Kang

AU - Li, Hai Lin

AU - Xiong, Yong Fu

AU - Shi, Yang

AU - Li, Zhu Yue

AU - Li, Jie

AU - Zhang, Xiang

AU - Li, Hong Yuan

PY - 2019/2/1

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N2 - 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.

AB - 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.

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