Dataset Information

Prediction of lung cancer histological types by RT-qPCR gene expression in FFPE specimens.

ABSTRACT: Lung cancer histologic diagnosis is clinically relevant because there are histology-specific treatment indications and contraindications. Histologic diagnosis can be challenging owing to tumor characteristics, and it has been shown to have less-than-ideal agreement among pathologists reviewing the same specimens. Microarray profiling studies using frozen specimens have shown that histologies exhibit different gene expression trends; however, frozen specimens are not amenable to routine clinical application. Herein, we developed a gene expression-based predictor of lung cancer histology for FFPE specimens, which are routinely available in clinical settings. Genes predictive of lung cancer histologies were derived from published cohorts that had been profiled by microarrays. Expression of these genes was measured by quantitative RT-PCR (RT-qPCR) in a cohort of patients with FFPE lung cancer. A histology expression predictor (HEP) was developed using RT-qPCR expression data for adenocarcinoma, carcinoid, small cell carcinoma, and squamous cell carcinoma. In cross-validation, the HEP exhibited mean accuracy of 84% and ? = 0.77. In separate independent validation sets, the HEP was compared with pathologist diagnoses on the same tumor block specimens, and the HEP yielded similar accuracy and precision as the pathologists. The HEP also exhibited good performance in specimens with low tumor cellularity. Therefore, RT-qPCR gene expression from FFPE specimens can be effectively used to predict lung cancer histology.

SUBMITTER: Wilkerson MD

PROVIDER: S-EPMC3699698 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Prediction of lung cancer histological types by RT-qPCR gene expression in FFPE specimens.

Wilkerson Matthew D MD Schallheim Jason M JM Hayes D Neil DN Roberts Patrick J PJ Bastien Roy R L RR Mullins Michael M Yin Xiaoying X Miller C Ryan CR Thorne Leigh B LB Geiersbach Katherine B KB Muldrew Kenneth L KL Funkhouser William K WK Fan Cheng C Hayward Michele C MC Bayer Steven S Perou Charles M CM Bernard Philip S PS

The Journal of molecular diagnostics : JMD 20130520 4

Lung cancer histologic diagnosis is clinically relevant because there are histology-specific treatment indications and contraindications. Histologic diagnosis can be challenging owing to tumor characteristics, and it has been shown to have less-than-ideal agreement among pathologists reviewing the same specimens. Microarray profiling studies using frozen specimens have shown that histologies exhibit different gene expression trends; however, frozen specimens are not amenable to routine clinical ...[more]

PMID: 23701907

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Project description:Accurate histological classification and identification of fusion genes represent two cornerstones of clinical diagnostics in non-small cell lung cancer (NSCLC). Here, we present a NanoString gene expression platform and a novel platform-independent, single sample predictor (SSP) of NSCLC histology for combined, simultaneous, histological classification and fusion gene detection in minimal formalin fixed paraffin embedded (FFPE) tissue. The SSP was developed in 68 NSCLC tumors of adenocarcinoma (AC), squamous cell carcinoma (SqCC) and large-cell neuroendocrine carcinoma (LCNEC) histology, based on NanoString expression of 11 (CHGA, SYP, CD56, SFTPG, NAPSA, TTF-1, TP73L, KRT6A, KRT5, KRT40, KRT16) relevant genes for IHC-based NSCLC histology classification. The SSP was combined with a gene fusion detection module (analyzing ALK, RET, ROS1, MET, NRG1, and NTRK1) into a multicomponent NanoString assay. The histological SSP was validated in six cohorts varying in size (n = 11-199), tissue origin (early or advanced disease), histological composition (including undifferentiated cancer), and gene expression platform. Fusion gene detection revealed five EML4-ALK fusions, four KIF5B-RET fusions, two CD74-NRG1 fusion and three MET exon 14 skipping events among 131 tested cases. The histological SSP was successfully trained and tested in the development cohort (mean AUC = 0.96 in iterated test sets). The SSP proved successful in predicting histology of NSCLC tumors of well-defined subgroups and difficult undifferentiated morphology irrespective of gene expression data platform. Discrepancies between gene expression prediction and histologic diagnosis included cases with mixed histologies, true large cell carcinomas, or poorly differentiated adenocarcinomas with mucin expression. In summary, we present a proof-of-concept multicomponent assay for parallel histological classification and multiplexed fusion gene detection in archival tissue, including a novel platform-independent histological SSP classifier. The assay and SSP could serve as a promising complement in the routine evaluation of diagnostic lung cancer biopsies.

Dataset Information

Prediction of lung cancer histological types by RT-qPCR gene expression in FFPE specimens.

Publications

Prediction of lung cancer histological types by RT-qPCR gene expression in FFPE specimens.

Similar Datasets

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