Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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An Integrated Model of the Transcriptome Landscape of HER2-Positive Breast Cancer

ABSTRACT: The goal of our study is to build an integrated transcriptome landscape model for HER2 positive breast tumors and identify the crucial signaling pathways associated with HER2 tumors. Genomic features include, 685 genes that were differentially expressed only in HER2-positive tumors, 102 genes that were alternatively spliced in a pattern that is unique to HER2-positive tumors, and 303 genes that expressed single nucleotide sequence variants (eSNVs) that were unique to HER2-positive tumors. Network analysis was performed to integrate the genomic features into a transcriptome landscape model that identified 12 highly interconnected cellular processes that appear to be critical to the establishment and maintenance of HER2-positive tumors. We observed that integrin signaling was linked to lapatinib sensitivity in vitro and strongly associated with risk of relapse in the NCCTG N9831 adjuvant trastuzumab clinical trial dataset. We analyzed RNA-seq data from a survey panel consisting of 8 benign breast lesions, 8 ER+, 8 triple negative, and 8 HER2-positive primary breast tumors to identify genomic features that were uniquely associated with HER2-positive tumors

ORGANISM(S): Homo sapiens

SUBMITTER: Krishna Kalari

PROVIDER: E-GEOD-45419 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

An integrated model of the transcriptome of HER2-positive breast cancer.

Kalari Krishna R KR Necela Brian M BM Tang Xiaojia X Thompson Kevin J KJ Lau Melissa M Eckel-Passow Jeanette E JE Kachergus Jennifer M JM Anderson S Keith SK Sun Zhifu Z Baheti Saurabh S Carr Jennifer M JM Baker Tiffany R TR Barman Poulami P Radisky Derek C DC Joseph Richard W RW McLaughlin Sarah A SA Chai High-seng HS Camille Stephan S Rossell David D Asmann Yan W YW Thompson E Aubrey EA Perez Edith A EA

PloS one 20131101 11

Our goal in these analyses was to use genomic features from a test set of primary breast tumors to build an integrated transcriptome landscape model that makes relevant hypothetical predictions about the biological and/or clinical behavior of HER2-positive breast cancer. We interrogated RNA-Seq data from benign breast lesions, ER+, triple negative, and HER2-positive tumors to identify 685 differentially expressed genes, 102 alternatively spliced genes, and 303 genes that expressed single nucleot ...[more]

PMID: 24223926

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Project description:We used deep sequencinge technology to profile the transcriptome, gene copy number, and CpG island methylation status simultaneously in eight commonly used breast cell lines to develop a model for how these genomic features are integrated in estrogen receptor positive (ER+) and negative breast cancer. Total mRNA sequence, gene copy number, and genomic CpG island methylation were carried out using the Illumina Genome Analyzer. Sequences were mapped to the human genome to obtain digitized gene expression data, DNA copy number in reference to the non-tumor cell line (MCF10A), and methylation status of 21,570 CpG islands to identify differentially expressed genes that were correlated with methylation or copy number changes. These were evaluated in a dataset from 129 primary breast tumors. Gene expression in cell lines was dominated by ER-associated genes. ER+ and ER- cell lines formed two distinct, stable clusters, and 1,873 genes were differentially expressed in the two groups. Part of chromosome 8 was deleted in all ER- cells and part of chromosome 17 amplified in all ER+ cells. These loci encoded 30 genes that were overexpressed in ER+ cells; 9 of these genes were overexpressed in ER+ tumors. We identified 149 differentially expressed genes that exhibited differential methylation of one or more CpG islands within 5kb of the 5' end of the gene and for which mRNA abundance was inversely correlated with CpG island methylation status. In primary tumors we identified 84 genes that appear to be robust components of the methylation signature that we identified in ER+ cell lines. Our analyses reveal a global pattern of differential CpG island methylation that contributes to the transcriptome landscape of ER+ and ER- breast cancer cells and tumors. The role of gene amplification/deletion appears to more modest, although several potentially significant genes appear to be regulated by copy number aberrations. 8 commonly used breast cancer cell lines were sequenced for mRNA expression, CpG methylation and DNA copy number

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 Integrated Model of the Transcriptome Landscape of HER2-Positive Breast Cancer

Publications

An integrated model of the transcriptome of HER2-positive breast cancer.

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