A microRNA-based prediction algorithm for diagnosis of non-small lung cell carcinoma in minimal biopsy material

• Published in: British journal of cancer Vol. 109; no. 9; pp. 2404 - 2411
• Main Authors: Bediaga, N G, Davies, M P A, Acha-Sagredo, A, Hyde, R, Raji, O Y, Page, R, Walshaw, M, Gosney, J, Alfirevic, A, Field, J K, Liloglou, T
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.10.2013; Nature Publishing Group
• Subjects: 631/114/2413; 631/337/384/331; 692/699/67/1612/1350; 692/700/139; Aged; Algorithms; Biological and medical sciences; Biomarkers; Biomarkers, Tumor - genetics; Biomedical and Life Sciences; Biomedicine; Biopsy; Cancer Research; Carcinoma, Non-Small-Cell Lung - diagnosis; Carcinoma, Non-Small-Cell Lung - genetics; Carcinoma, Non-Small-Cell Lung - pathology; Decades; DNA Methylation; Drug Resistance; Epidemiology; Female; Gene Expression; Humans; Lung Neoplasms - diagnosis; Lung Neoplasms - genetics; Lung Neoplasms - pathology; Male; Medical sciences; MicroRNAs; MicroRNAs - genetics; Models, Biological; Models, Statistical; Molecular Diagnostics; Molecular Medicine; Multiple tumors. Solid tumors. Tumors in childhood (general aspects); Oncology; Paraffin Embedding; Pathology; Pneumology; Tumors; Tumors of the respiratory system and mediastinum; United Kingdom > UK; Spain; lung cancer; miRNA; FFPE; molecular diagnosis and prognosis; Lung disease; RNA interference; Prognosis; Respiratory disease; Lung cancer; Materials; Micro RNA; Prediction; Malignant tumor; non-small cell lung carcinoma; Algorithm; Gene silencing; Anatomic pathology; Cancerology; Biopsy; Bronchus disease; Diagnosis; Predictive factor; Cancer
• ISSN: 0007-0920; 1532-1827; 1532-1827
• DOI: 10.1038/bjc.2013.623

Background: Diagnosis is jeopardised when limited biopsy material is available or histological quality compromised. Here we developed and validated a prediction algorithm based on microRNA (miRNA) expression that can assist clinical diagnosis of lung cancer in minimal biopsy material to improve clinical management. Methods: Discovery utilised Taqman Low Density Arrays (754 miRNAs) in 20 non-small cell lung cancer (NSCLC) tumour/normal pairs. In an independent set of 40 NSCLC patients, 28 miRNA targets were validated using qRT–PCR. A prediction algorithm based on eight miRNA targets was validated blindly in a third independent set of 47 NSCLC patients. The panel was also tested in formalin-fixed paraffin-embedded (FFPE) specimens from 20 NSCLC patients. The genomic methylation status of highly deregulated miRNAs was investigated by pyrosequencing. Results: In the final, frozen validation set the panel had very high sensitivity (97.5%), specificity (96.3%) and ROC-AUC (0.99, P =10 −15 ). The panel provided 100% sensitivity and 95% specificity in FFPE tissue (ROC-AUC=0.97 ( P =10 −6 )). DNA methylation abnormalities contribute little to the deregulation of the miRNAs tested. Conclusion: The developed prediction algorithm is a valuable potential biomarker for assisting lung cancer diagnosis in minimal biopsy material. A prospective validation is required to measure the enhancement of diagnostic accuracy of our current clinical practice.