Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies. Issue 1 (1st August 2014)

• Record Type: Journal Article
• Authors: Perell, Katharina; Vincent, Martin; Vainer, Ben; Petersen, Bodil Laub; Federspiel, Birgitte; Møller, Anne Kirstine; Madsen, Mette; Hansen, Niels Richard; Friis-Hansen, Lennart; Nielsen, Finn Cilius; Daugaard, Gedske
• Is Part Of: Molecular oncology. Volume 9:Issue 1(2015:Jan.)
• Page Start: 68
• Page End: 77
• Publication Date: 2014-08-01
• Subjects: microRNA -- Classification -- Liver biopsy -- Metastases -- Surrounding tissue -- Tissue contamination; Cancer -- Molecular aspects -- Periodicals; 616.994005
• DOI: 10.1016/j.molonc.2014.07.015
• Languages: English
• ISSNs: 1574-7891

Abstract

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. Highlights: Metastatic core biopsies contain a mixture of malignant‐ and non‐malignant cells. We explore the impact of non‐malignant cells on tissue of origin classification. Non‐malignant cells significantly hamper correct tissue of origin classification. A statistical model adjusts for the signal provided by non‐malignant cells. Applying this model to a microRNA tissue of origin test improves classification.