Literature DB >> 22749746

Classification of the four main types of lung cancer using a microRNA-based diagnostic assay.

Shlomit Gilad1, Gila Lithwick-Yanai, Iris Barshack, Sima Benjamin, Irit Krivitsky, Tina Bocker Edmonston, Marluce Bibbo, Craig Thurm, Laurie Horowitz, Yajue Huang, Meora Feinmesser, J Steve Hou, Brianna St Cyr, Ilanit Burnstein, Hadas Gibori, Nir Dromi, Mats Sanden, Michal Kushnir, Ranit Aharonov.   

Abstract

For patients with primary lung cancer, accurate determination of the tumor type significantly influences treatment decisions. However, techniques and methods for lung cancer typing lack standardization. In particular, owing to limited tumor sample amounts and the poor quality of some samples, the classification of primary lung cancers using small preoperative biopsy specimens presents a diagnostic challenge using current tools. We previously described a microRNA-based assay (miRview squamous; Rosetta Genomics Ltd., Rehovot, Israel) that accurately differentiates between squamous and nonsquamous non-small cell lung cancer. Herein, we describe the development and validation of an assay that differentiates between the four main types of lung cancer: squamous cell carcinoma, nonsquamous non-small cell lung cancer, carcinoid, and small cell carcinoma. The assay, miRview lung (Rosetta Genomics Ltd.), is based on the expression levels of eight microRNAs, measured using a sensitive quantitative RT-PCR platform. It was validated on an independent set of 451 samples, more than half of which were preoperative cytologic samples (fine-needle aspiration and bronchial brushing and washing). The assay returned a result for more than 90% of the samples with overall accuracy of 94% (95% CI, 91% to 96%), with similar performance observed in pathologic and cytologic samples. Thus, miRview lung is a simple and reliable diagnostic assay that offers an accurate and standardized classification tool for primary lung cancer using pathologic and cytologic samples.
Copyright © 2012 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22749746     DOI: 10.1016/j.jmoldx.2012.03.004

Source DB:  PubMed          Journal:  J Mol Diagn        ISSN: 1525-1578            Impact factor:   5.568


  45 in total

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