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

N. G. Bediaga, M. P.A. Davies, A. Acha-Sagredo, R. Hyde, O. Y. Raji, R. Page, M. Walshaw, J. Gosney, A. Alfirevic, J. K. Field, T. Liloglou

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)2404-2411
Number of pages8
JournalBritish Journal of Cancer
Volume109
Issue number9
DOIs
StatePublished - Oct 29 2013

Keywords

  • FFPE
  • lung cancer
  • miRNA
  • molecular diagnosis and prognosis

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