Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape
The cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer patients. Based on the expression levels of co-expressed genes, GEP...
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doaj-b44622ede3d24eb58d5a2d0d31ebf5c12020-11-25T02:20:04ZengMDPI AGCancers2072-66942020-05-01121165116510.3390/cancers12051165Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic LandscapeSeokhyun Yoon0Hye Sung Won1Keunsoo Kang2Kexin Qiu3Woong June Park4Yoon Ho Ko5Department of Electronics Eng., College of Engineering, Dankook University, Yongin-si 16890, KoreaDepartment of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul 06591, KoreaDepartment of Microbiology, College of Natural Sciences, Dankook University, Cheonan-si 31116, KoreaDepartment of Electronics Eng., College of Engineering, Dankook University, Yongin-si 16890, KoreaDepartment of Molecular Biology, College of Natural Sciences, Dankook University, Cheonan-si 31116, KoreaDepartment of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul 06591, KoreaThe cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal breast cancer. We also confirmed that non-matching subgroup classification affected the survival of breast cancer patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.https://www.mdpi.com/2072-6694/12/5/1165breast cancerintrinsic subtypehormone receptor-status predictiongene expression profileLASSO regression |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Seokhyun Yoon Hye Sung Won Keunsoo Kang Kexin Qiu Woong June Park Yoon Ho Ko |
spellingShingle |
Seokhyun Yoon Hye Sung Won Keunsoo Kang Kexin Qiu Woong June Park Yoon Ho Ko Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape Cancers breast cancer intrinsic subtype hormone receptor-status prediction gene expression profile LASSO regression |
author_facet |
Seokhyun Yoon Hye Sung Won Keunsoo Kang Kexin Qiu Woong June Park Yoon Ho Ko |
author_sort |
Seokhyun Yoon |
title |
Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape |
title_short |
Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape |
title_full |
Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape |
title_fullStr |
Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape |
title_full_unstemmed |
Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape |
title_sort |
hormone receptor-status prediction in breast cancer using gene expression profiles and their macroscopic landscape |
publisher |
MDPI AG |
series |
Cancers |
issn |
2072-6694 |
publishDate |
2020-05-01 |
description |
The cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal breast cancer. We also confirmed that non-matching subgroup classification affected the survival of breast cancer patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients. |
topic |
breast cancer intrinsic subtype hormone receptor-status prediction gene expression profile LASSO regression |
url |
https://www.mdpi.com/2072-6694/12/5/1165 |
work_keys_str_mv |
AT seokhyunyoon hormonereceptorstatuspredictioninbreastcancerusinggeneexpressionprofilesandtheirmacroscopiclandscape AT hyesungwon hormonereceptorstatuspredictioninbreastcancerusinggeneexpressionprofilesandtheirmacroscopiclandscape AT keunsookang hormonereceptorstatuspredictioninbreastcancerusinggeneexpressionprofilesandtheirmacroscopiclandscape AT kexinqiu hormonereceptorstatuspredictioninbreastcancerusinggeneexpressionprofilesandtheirmacroscopiclandscape AT woongjunepark hormonereceptorstatuspredictioninbreastcancerusinggeneexpressionprofilesandtheirmacroscopiclandscape AT yoonhoko hormonereceptorstatuspredictioninbreastcancerusinggeneexpressionprofilesandtheirmacroscopiclandscape |
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