Dataset Information

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

ABSTRACT: 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 nucleotide sequence variants (eSNVs) that were associated with the HER2-positive tumors in our survey panel. These features were integrated into a transcriptome landscape model that identified 12 highly interconnected genomic modules, each of which represents a cellular processes pathway that appears to define the genomic architecture of the HER2-positive tumors in our test set. The generality of the model was confirmed by the observation that several key pathways were enriched in HER2-positive TCGA breast tumors. The ability of this model to make relevant predictions about the biology of breast cancer cells was established by the observation 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. Additional modules from the HER2 transcriptome model, including ubiquitin-mediated proteolysis, TGF-beta signaling, RHO-family GTPase signaling, and M-phase progression, were linked to response to lapatinib and paclitaxel in vitro and/or risk of relapse in the N9831 dataset. These data indicate that an integrated transcriptome landscape model derived from a test set of HER2-positive breast tumors has potential for predicting outcome and for identifying novel potential therapeutic strategies for this breast cancer subtype.

SUBMITTER: Kalari KR

PROVIDER: S-EPMC3815156 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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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:Estrogen receptor-positive and human epidermal growth factor receptor 2-negative (ER+ HER2- ) breast cancer accounts for ~ 60-70% of all cases of invasive breast carcinoma. High-grade ER+ HER2- tumors respond poorly to endocrine therapy. In this study, we systematically analyzed clinical and multi-omics data to find potential strategies for personalized therapy of patients with high-grade ER+ HER2- disease. Six different cohorts were analyzed, for which multi-omics data were available. Grade III ER+ HER2- cases harbored higher proportions of large tumor size (> 5 cm), lymph node metastasis, chemotherapy use, and luminal B subtypes defined by PAM50, as compared with grade I/II tumors. DNA methylation (HM450) data and methylation-specific PCR indicated that the cg18629132 locus in the MKI67 promoter was hypermethylated in grade I/II cases and normal tissue, but hypomethylated in grade III cases or triple-negative breast cancer, resulting in higher expression of MKI67. Mutations in ESR1 and TP53 were detected in post-endocrine treatment metastatic samples at a higher rate than in treatment-naive tumors in grade III cases. We identified 42 and 20 focal copy number events in nonmetastatic and metastatic high-grade ER+ HER2- cases, respectively, with either MYC or MDM2 amplification representing an independent prognostic event in grade III cases. Transcriptional profiling within grade III tumors highlighted ER signaling downregulation and upregulation of immune-related pathways in non-luminal-like tumors defined by PAM50. Recursive partitioning analysis was employed to construct a decision tree of an endocrine-resistant subgroup (GATA3-negative and AGR-negative) of two genes that was validated by immunohistochemistry in a Chinese cohort. All together, these data suggest that grade III ER+ HER2- tumors have distinct clinical and molecular characteristics compared with low-grade tumors, particularly in cases with non-luminal-like biology. Due to the dismal prognosis in this group, clinical trials are warranted to test the efficacy of potential novel therapies.

Project description:BackgroundTreatment strategies for various subtypes of breast cancer (BC) are different based on their distinct molecular characteristics. Therefore, it is very important to identify key differentially expressed genes (DEGs) between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.MethodsGene expression profiles of GSE22093 and GSE23988 were obtained from the Gene Expression Omnibus database. There were 74 ER-positive/HER2-negative BC tissues and 85 ER-negative/HER2-negative BC tissues in the two profile datasets. DEGs between ER-positive/HER2-negative tissues and ER-negative/HER2-negative BC tissues were identified by the GEO2R tool. The common DEGs among the two datasets were detected with Venn software online. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery to analyze enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathways and gene ontology terms. Then, the protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with the Search Tool for the Retrieval of Interacting Genes. Of the proteins in the PPI network, Molecular Complex Detection plug-in analysis identified nine core upregulated genes and one core downregulated gene. UALCAN and Gene Expression Profiling Interactive Analysis were applied to determine the expression of these 10 genes in BC. Furthermore, for the analysis of overall survival among those genes, the Kaplan-Meier method was implemented.ResultsNinety-three common DEGs (63 upregulated and 30 downregulated) were identified. KEGG pathway enrichment analysis showed that upregulated DEGs were particularly enriched in the progesterone-mediated oocyte maturation pathway. In addition, PGR might be a prognostic biomarker for ER-positive/HER2-negative BC. CCND1 is a poor prognostic biomarker for ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Moreover, TFF1, AGR2 and EGFR might be predictive biomarkers of node metastasis in ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.ConclusionsCCND1, AGR2, PGR, TFF1 and EGFR are the key DEGs between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Further studies are required to confirm the functions of the identified genes.

Dataset Information

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

Publications

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

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