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An integrative bioinformatics approach reveals coding and non-coding gene variants associated with gene expression profiles and outcome in breast cancer molecular subtypes.

ABSTRACT: BACKGROUND:Sequence variations in coding and non-coding regions of the genome can affect gene expression and signalling pathways, which in turn may influence disease outcome. METHODS:In this study, we integrated somatic mutations, gene expression and clinical data from 930 breast cancer patients included in the TCGA database. Genes associated with single mutations in molecular breast cancer subtypes were identified by the Mann-Whitney U-test and their prognostic value was evaluated by Kaplan-Meier and Cox regression analyses. Results were confirmed using gene expression profiles from the Metabric data set (n = 1988) and whole-genome sequencing data from the TCGA cohort (n = 117). RESULTS:The overall mutation rate in coding and non-coding regions were significantly higher in ER-negative/HER2-negative tumours (P = 2.8E-03 and P = 2.4E-07, respectively). Recurrent sequence variations were identified in non-coding regulatory regions of several cancer-associated genes, including NBPF1, PIK3CA and TP53. After multivariate regression analysis, gene signatures associated with three coding mutations (CDH1, MAP3K1 and TP53) and two non-coding variants (CRTC3 and STAG2) in cancer-related genes predicted prognosis in ER-positive/HER2-negative tumours. CONCLUSIONS:These findings demonstrate that sequence alterations influence gene expression and oncogenic pathways, possibly affecting the outcome of breast cancer patients. Our data provide potential opportunities to identify non-coding variations with functional and clinical relevance in breast cancer.

SUBMITTER: Gyorffy B

PROVIDER: S-EPMC5931099 | biostudies-other | 2018 Apr

REPOSITORIES: biostudies-other

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An integrative bioinformatics approach reveals coding and non-coding gene variants associated with gene expression profiles and outcome in breast cancer molecular subtypes.

Győrffy Balázs B Pongor Lőrinc L Bottai Giulia G Li Xiaotong X Budczies Jan J Szabó András A Hatzis Christos C Pusztai Lajos L Santarpia Libero L

British journal of cancer 20180321 8

<h4>Background</h4>Sequence variations in coding and non-coding regions of the genome can affect gene expression and signalling pathways, which in turn may influence disease outcome.<h4>Methods</h4>In this study, we integrated somatic mutations, gene expression and clinical data from 930 breast cancer patients included in the TCGA database. Genes associated with single mutations in molecular breast cancer subtypes were identified by the Mann-Whitney U-test and their prognostic value was evaluate ...[more]

PMID: 29559730

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Project description:Purpose HER2 gene amplification or protein overexpression (HER2+) defines a clinically challenging subgroup of breast cancer with variable prognosis and response to therapy. We aimed to investigate the heterogeneous biological appearance and clinical behavior of HER2+ tumors using molecular profiling. Materials and Methods Hierarchical clustering of gene expression data from 58 HER2-amplified tumors of various stage, histological grade and estrogen receptor (ER) status was used to construct a HER2-derived prognostic predictor that was further evaluated in several large independent breast cancer data sets. Results Unsupervised analysis identified three subtypes of HER2+ tumors with mixed stage, histological grade and ER-status. One subtype had a significantly worse clinical outcome. A prognostic predictor was created based on differentially expressed genes between the subtype with worse outcome and the other subtypes. The predictor was able to define patient groups with better and worse outcome in HER2+ breast cancer across multiple independent breast cancer data sets and identify a sizable HER2+ group with long disease-free survival and low mortality. Significant correlation to prognosis was also observed in basal-like, ER−, lymph node positive or high-grade tumors, irrespective of HER2-status. The predictor included genes associated to immune response, tumor invasion and metastasis. Conclusion The HER2-derived prognostic predictor provides further insight into the heterogeneous biology of HER2+ tumors and may become useful for improved selection of patients that need additional treatment with new drugs targeting the HER2 pathway. Array comparative genomic hybridization (aCGH) identified 58 breast tumors with amplification of HER2 from a larger cohort of approx 500 tumors breast. Global gene expression profiles were obtained using 70-mer oligonucleotide microarrays. Unsupervised hierarchical clustering of the 58 tumors, using Pearson correlation and complete linkage, identified three main clusters. One cluster showed significantly poorer clinical outcome. Significance of microarray (SAM) analysis was performed to identify 158 genes separating the poor outcome cluster compared to the other two clusters. Gene expression centroids, based on the 158 genes, were created for each cluster for validation in independent breast cancer data sets.

Dataset Information

An integrative bioinformatics approach reveals coding and non-coding gene variants associated with gene expression profiles and outcome in breast cancer molecular subtypes.

Publications

An integrative bioinformatics approach reveals coding and non-coding gene variants associated with gene expression profiles and outcome in breast cancer molecular subtypes.

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