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

Matthew D. Wilkerson, Jason M. Schallheim, D. Neil Hayes*, Patrick J. Roberts, Roy R.L. Bastien, Michael Mullins, Xiaoying Yin, C. Ryan Miller, Leigh B. Thorne, Katherine B. Geiersbach, Kenneth L. Muldrew, William K. Funkhouser, Cheng Fan, Michele C. Hayward, Steven Bayer, Charles M. Perou, Philip S. Bernard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


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 κ = 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.

Original languageEnglish
Pages (from-to)485-497
Number of pages13
JournalJournal of Molecular Diagnostics
Issue number4
StatePublished - Jul 2013
Externally publishedYes


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