Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies

Abstract

Identification of the primary tumor site in patients with metastatic cancer is clinically important, but remains a challenge. Hence, efforts have been made towards establishing new diagnostic tools. Molecular profiling is a promising diagnostic approach, but tissue heterogeneity and inadequacy may negatively affect the accuracy and usability of molecular classifiers. We have developed and validated a microRNA-based classifier, which predicts the primary tumor site of liver biopsies, containing a limited number of tumor cells. Concurrently we explored the influence of surrounding normal tissue on classification. MicroRNA profiling was performed using quantitative Real-Time PCR on formalin-fixed paraffin-embedded samples. 278 primary tumors and liver metastases, representing nine primary tumor classes, as well as normal liver samples were used as a training set. A statistical model was applied to adjust for normal liver tissue contamination. Performance was estimated by cross-validation, followed by independent validation on 55 liver core biopsies with a tumor content as low as 10%. A microRNA classifier developed, using the statistical contamination model, showed an overall classification accuracy of 74.5% upon independent validation. Two-thirds of the samples were classified with high-confidence, with an accuracy of 92% on high-confidence predictions. A classifier trained without adjusting for liver tissue contamination, showed a classification accuracy of 38.2%. Our results indicate that surrounding normal tissue from the biopsy site may critically influence molecular classification. A significant improvement in classification accuracy was obtained when the influence of normal tissue was limited by application of a statistical contamination model.

Genome-wide miRNA profiling reinforces the importance of miR-9 in human papillomavirus associated oral and oropharyngeal head and neck cancer

“…It appears that large sample heterogeneity, even in the independent sample sets such as TCGA, might in part explain the discrepancies in previous literature. It probably is interesting to note that classifiers based on miRNA expression might need further specialized statistical modeling to decrease the impact of “normal tissue contamination” on the classifier 29 ; however, this was beyond the scope of the current study.…”

“…In attempt to create a miRNA classifier from the NGS data, we used multinomial sparse group lasso method as implemented within msgl R package 28,29 . Normalized counts of our and TCGA data were imported to R as “reads per million miRNA mapped”.…”

MiR-141 Activates Nrf2-Dependent Antioxidant Pathway via Down-Regulating the Expression of Keap1 Conferring the Resistance of Hepatocellular Carcinoma Cells to 5-Fluorouracil

“…For the luciferase assay, cells, seeded at 1.5×10 5 per well in 24-well plates, and were co-transfected with 50 ng of luciferase reporter plasmids containing wild-type or mutated 3'UTR of Keap1 and miR-141 mimics or anti-miR-141 or negative control (100 nM) using lipofectamine 2000 (Invitrogen) according to the manufacturer's protocol. The pRL-TK vector (Promega) was used as an internal control.…”

Gene Expression Profiling for Differential Diagnosis of Liver Metastases: A Multicenter, Retrospective Cohort Study

“…Previous studies have shown that the gene expression patterns were sustained in LM compared to corresponding primary tumors, but normal liver tissue contamination in the surrounding must be considered as a potential cause of misclassification in gene expression analysis (27,28). Katharina et al reported a microRNA-based trained without contamination consideration showing a disappointing classification accuracy of 38.2%.…”