ArticleThe Journal of molecular diagnostics : JMD2013
Prediction of lung cancer histological types by RT-qPCR gene expression in FFPE specimens.
Article in The Journal of molecular diagnostics : JMD, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Assessing the benefits and safety profile of incorporating poly ADP-ribose polymerase (PARP) inhibitors in the treatment of advanced lung cancer: a thorough systematic review and meta-analysis.Frontiers in pharmacology · 2024Pooled it
- Veliparib in Combination With Platinum-Based Chemotherapy for First-Line Treatment of Advanced Squamous Cell Lung Cancer: A Randomized, Multicenter Phase III Study.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2021Trial
- A combined gene expression tool for parallel histological prediction and gene fusion detection in non-small cell lung cancer.Scientific reports · 2019Trial
- Trial
- Six-Gene Signature for Differential Diagnosis and Therapeutic Decisions in Non-Small-Cell Lung Cancer-A Validation Study.International journal of molecular sciences · 2024Article
- A comprehensive in silico analysis and experimental validation of miRNAs capable of discriminating between lung adenocarcinoma and squamous cell carcinoma.Frontiers in genetics · 2024Article
- Review
- Effects of exposure to environmentally relevant concentrations of lead (Pb) on expression of stress and immune-related genes, and microRNAs in shorthorn sculpins (Myoxocephalus scorpius).Ecotoxicology (London, England) · 2022Article
- Curcumin Restrains Oxidative Stress of After Intracerebral Hemorrhage in Rat by Activating the Nrf2/HO-1 Pathway.Frontiers in pharmacology · 2022Article
- Profiling the expression of pro-metastatic genes in association with the clinicopathological features of primary breast cancer.Cancer cell international · 2021Article
- Prediction of Non-small Cell Lung Cancer Histology by a Deep Ensemble of Convolutional and Bidirectional Recurrent Neural Network.Journal of digital imaging · 2020Article
- Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC.Translational oncology · 2017Article
- An Expression Signature as an Aid to the Histologic Classification of Non-Small Cell Lung Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2016Article
- Lung Cancer Gene Signatures and Clinical Perspectives.Microarrays (Basel, Switzerland) · 2013Review
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17 authors.
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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 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.
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