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Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape.

ABSTRACT: 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.

SUBMITTER: Yoon S

PROVIDER: S-EPMC7281553 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape.

Yoon Seokhyun S Won Hye Sung HS Kang Keunsoo K Qiu Kexin K Park Woong June WJ Ko Yoon Ho YH

Cancers 20200505 5

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 ...[more]

PMID: 32380759

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Project description:The occurrence of large publically available repositories of human breast tumor gene expression profiles provides an important resource to discover new breast cancer biomarkers and therapeutic targets. For example, knowledge of the expression of the estrogen and progesterone hormone receptors (ER and PR), and that of the ERBB2 in breast tumor samples enables choice of therapies for the breast cancer patients that express these proteins. Identifying new biomarkers and therapeutic agents affecting the activity of signaling pathways regulated by the hormone receptors or ERBB2 might be accelerated by knowledge of their expression levels in large gene expression profiling data sets. Unfortunately, the status of these receptors is not invariably reported in public databases of breast tumor gene expression profiles. Attempts have been made to employ a single probe set to identify ER, PR and ERBB2 status, but the specificity or sensitivity of their prediction is low. We enquired whether estimation of ER, PR and ERBB2 status of profiled tumor samples could be improved by using multiple probe sets representing these three genes and others with related expression.We used 8 independent datasets of human breast tumor samples to define gene expression signatures comprising 24, 51 and 14 genes predictive of ER, PR and ERBB2 status respectively. These signatures, as demonstrated by sensitivity and specificity measures, reliably identified hormone receptor and ERBB2 expression in breast tumors that had been previously determined using protein and DNA based assays. Our findings demonstrate that gene signatures can be identified which reliably predict the expression status of the estrogen and progesterone hormone receptors and that of ERBB2 in publically available gene expression profiles of breast tumor samples. Using these signatures to query transcript profiles of breast tumor specimens may enable discovery of new biomarkers and therapeutic targets for particular subtypes of breast cancer.

Project description:IntroductionAberrant DNA methylation has been found frequently in human breast cancers, associated with the loss of expression of a number of regulatory genes for growth and correlated to clinical outcomes. The present study was undertaken to determine whether methylation of a set of growth-suppressor genes would correlate to the expression of estrogen receptors (ERs) and progesterone receptors (PRs).MethodsWe used a pyrosequencing methylation analysis to study the methylation of 12 known growth-suppressor genes in 90 pairs of malignant/normal breast tissues. We also examined the expression of ERs and PRs in those specimens by immunohistochemistry. Mutations of p53 in tumor cells were detected by direct sequencing.ResultsTwelve tumor-suppressor genes: ARHI, RASSF1A, HIN-1, RARbeta2, hMLH1, 14-3-3 sigma, RIZ1, p16, E-cadherin, RIL, CDH13, and NKD2 were selected for this methylation study. Five of them (RIL, HIN-1, RASSF1A, CDH13, and RARbeta2) were frequently methylated in breast cancers (57%, 49%, 58%, 44%, and 17%, respectively) but not the normal breast (0-4%). Two panels of methylation profiles were defined. The methylation of the HIN-1/RASSFIA panel strongly correlated to the expression of ERs, PRs, and hormone receptors (HRs; which were defined as 'positive' if ERs and/or PRs were positive; p < 0.001). Conversely, the methylation of the RIL/CDH13 panel strongly correlated to negative ER, PR, and HR expression (p = 0.001, 0.025, and 0.001, respectively). The subset of triple-negative breast cancers (in other words, those with negative ER, PR, and HER-2/neu status) was positively associated with the methylation of the RIL/CDH13 panel and negatively associated with the HIN-1/RASSF1A panel. Mutations of p53 were found in nine breast tumors (11%), seven of which lacked methylation in both panels.ConclusionWe have defined two panels (HIN-1/RASSFIA, and RIL/CDH13) of methylation profiles, which correlated, either positively or negatively, to HR status.

Project description:We examined whether differences in tumor DNA methylation were associated with more aggressive hormone receptor-negative breast cancer in an ethnically diverse group of patients in the Breast Cancer Care in Chicago (BCCC) study and using data from The Cancer Genome Atlas (TCGA).DNA was extracted from formalin-fixed, paraffin-embedded samples on 75 patients (21 White, 31 African-American, and 23 Hispanic) (training dataset) enrolled in the BCCC. Hormone receptor status was defined as negative if tumors were negative for both estrogen and progesterone (ER/PR) receptors (N?=?22/75). DNA methylation was analyzed at 1505 CpG sites within 807 gene promoters using the Illumina GoldenGate assay. Differential DNA methylation as a predictor of hormone receptor status was tested while controlling for false discovery rate and assigned to the gene closest to the respective CpG site. Next, those genes that predicted ER/PR status were validated using TCGA data with respect to DNA methylation (validation dataset), and correlations between CpG methylation and gene expression were examined. In the training dataset, 5.7 % of promoter mean methylation values (46/807) were associated with receptor status at P?<?0.05; for 88 % of these (38/46), hypermethylation was associated with receptor-positive disease. Hypermethylation for FZD9, MME, BCAP31, HDAC9, PAX6, SCGB3A1, PDGFRA, IGFBP3, and PTGS2 genes most strongly predicted receptor-positive disease. Twenty-one of 24 predictor genes from the training dataset were confirmed in the validation dataset. The level of DNA methylation at 19 out 22 genes, for which gene expression data were available, was associated with gene activity.Higher levels of promoter methylation strongly correlate with hormone receptor positive status of breast tumors. For most of the genes identified in our training dataset as ER/PR receptor status predictors, DNA methylation correlated with stable gene expression level. The predictors performed well when evaluated on independent set of samples, with different racioethnic distribution, thus providing evidence that this set of DNA methylation biomarkers will likely generalize to prospective patient samples.

Dataset Information

Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape.

Publications

Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape.

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