Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

A microRNA-based primary tumor site classification of liver core biopsies

ABSTRACT: To develop a classifier based on microRNAs for primary tumor site identification of liver core biopsies and to explore the influence of surrounding normal liver tissue on classification. Tissue samples from 333 patients, corresponding to one of the following ten assay classes, were obtained from archives of the pathology department, Copenhagen University Hospital, Rigshospitalet, Denmark: Lung cancer, breast cancer, gastric/cardia cancer, colorectal cancer, bladder cancer, pancreatic cancer, hepatocellular carcinoma, cholangiocarcinoma, squamous cell cancers of different origin, and normal liver tissue

ORGANISM(S): Homo sapiens

SUBMITTER: Katharina Perell

PROVIDER: E-GEOD-51429 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Modeling tissue contamination to improve molecular identification of the primary tumor site of metastases.

Vincent Martin M Perell Katharina K Nielsen Finn Cilius FC Daugaard Gedske G Hansen Niels Richard NR

Bioinformatics (Oxford, England) 20140124 10

<h4>Motivation</h4>Contamination of a cancer tissue by the surrounding benign (non-cancerous) tissue is a concern for molecular cancer diagnostics. This is because an observed molecular signature will be distorted by the surrounding benign tissue, possibly leading to an incorrect diagnosis. One example is molecular identification of the primary tumor site of metastases because biopsies of metastases typically contain a significant amount of benign tissue.<h4>Results</h4>A model of tissue contami ...[more]

PMID: 24463184

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Project description:Transcriptomic profiling Background Cholangiocarcinoma accounts for 5-10% of primary hepatic cancers. The etiology is unclear and patients are often diagnosed without risk factors. Resection is the only curative treatment although patients frequently remain undiagnosed until advanced stage of disease. Methods To construct molecular classification of cholangiocarcinoma, we profiled the transcriptomes of 104 freshly-frozen tumors and 59 matched non-cancerous livers obtained from Australia, Europe and the United States. We also performed mutational analysis of KRAS, EGFR and BRAF, and used laser-capture microdissection to obtain independent gene expression profiles for epithelial and stromal compartments in a subset of tumors. The selected target genes were validated by western blotting and immunohistochemistry. Results Transcriptomic profiling classified cholangiocarcinoma into two distinct subclasses defined by survival (P<0.0007) and early recurrence (P<0.001). Applying leave-one-out cross-validation, we optimized the prognostic classifier to 238 genes which were positively enriched in the epithelial tumor compartment. A deregulated HER2 network was associated with the epithelial compartment which also showed a frequent overexpression of Ki67, EGFR, MET and pRPS6 whereas inflammatory cytokines were enriched in tumor stroma specifically in patients with poor prognosis. KRAS mutations were found in 24.6% of patients with poor disease outcome. Conclusion Our study presents new insights into pathogenesis of cholangiocarcinoma and stratification of the patients according to survival and recurrence. Identification of a subgroup of patients among the poor prognostic cohort characterized by KRAS mutations and oncogenic-addiction may provide a novel therapeutic opportunity for this treatment-refractory malignancy. Profiling of individual cholangiocarcinomas and non-cancerous matched surrounding livers using normal bile ducts as reference

Dataset Information

A microRNA-based primary tumor site classification of liver core biopsies

Publications

Modeling tissue contamination to improve molecular identification of the primary tumor site of metastases.

Similar Datasets

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