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

Katharina Perell, Martin Vincent, Ben Vainer, Bodil Laub Petersen, Birgitte Federspiel, Anne Kirstine Hundahl Møller, Mette Madsen, Niels Richard Hansen, Lennart Friis-Hansen, Finn Cilius Nielsen, Gedske Daugaard

10 Citations (Scopus)

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.

Original languageEnglish
JournalMolecular Oncology
Volume9
Issue number1
Pages (from-to)68-77
Number of pages10
ISSN1574-7891
DOIs
Publication statusPublished - Jan 2015

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