New MicroRNA-Based Diagnostic Test for Lung Cancer Classification
Mats Olof Sanden, Hadas Gibori, Michal Kushnir, Gila Lithwick-Yanai, Hila Benjamin, Marluce Bibbo, Craig Thurm, Laurie Horowitz, Yajue Huang, Meora Feinmesser, Iris Barshack, J Steve Hou, Tina B Edmonston, Shlomit Gilad, Sima Benjamin, Ayelet Chajut. Rosetta Genomics Inc., Philadelphia; Rosetta Genomics Ltd., Rehovot, Israel; Thomas Jefferson University Hospital, Philadelphia; Jamaica Hospital Medical Center, Jamaica, NY; Temple University Hospital, Philadelphia; Rabin Medical Center, Petah Tikva, Israel; Tel Aviv University, Tel Aviv, Israel; Sheba Medical Center, Tel-Hashomer, Israel; Drexel University College of Medicine, Philadelphia; Cooper University Hospital, Camden
Background: Lung cancer is the leading cause of cancer deaths in the USA. With the advent of therapies showing varying response rates for different lung cancer types, a growing need exists for a standardized and highly accurate diagnostic classification tool. Furthermore, in 20%-30% of pre-operative biopsies significant limitations of tumor quantity and quality prevent full classification of the tumor using traditional diagnostic methods. We have previously described a microRNA-based assay which accurately differentiates between squamous and non-squamous non-small cell lung cancer (NSCLC). Here, we present the development and clinical validation of a new microRNA-based qRT-PCR assay that differentiates primary lung cancers into four types: squamous cell carcinoma, non-squamous NSCLC, carcinoid and small cell carcinoma.
Design: Over 750 primary tumor samples representing different lung cancer histological types were collected. Samples included formalin-fixed, paraffin-embedded (FFPE) blocks from resection or biopsies and cell blocks from cytological procedures. Expression levels of potential microRNA biomarkers were profiled using a microRNA microarray followed by a sensitive and specific qRT-PCR platform. A classifier that identifies the lung tumor types was developed and a diagnostic assay was defined and validated on an independent, blinded set of 451 samples.
Results: Using the expression levels of eight microRNAs, accurate classification of the lung tumors into the four above-mentioned categories was obtained. The microRNA-based assay that was developed reached an accuracy of 93.7% in an extensive independent, blinded validation set. Cytological samples, which constituted over 50% of the validation set, reached an accuracy of 95%.
Conclusions: We present here a novel microRNA-based assay for the classification of the four main types of lung cancer based on the expression of a small set of microRNAs. This assay displays high levels of accuracy in both pathological and cytological samples. The assay is a standardized, well-tested tool which can assist caregivers in the diagnosis of lung cancer.
Tuesday, March 20, 2012 9:30 AM
Poster Session III # 312, Tuesday Morning