Prediction of Lung Cancer Histological Types by RT-qPCR Gene Expression in FFPE Specimens

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

doi:10.1016/j.jmoldx.2013.03.007

Cited by 19 publications

(29 citation statements)

References 32 publications

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“…Similar results were obtained by Wilkerson and colleagues, who demonstrated a predictor comprising 51 unique genes [34]. They reported the prediction accuracy to stand at the level of 84%, as estimated by the Monte Carlo cross-validation procedure.…”

Section: Discussionsupporting

confidence: 84%

Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC

Charkiewicz

Nikliński

Claesen

et al. 2017

Translational Oncology

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Advances in molecular analyses based on high-throughput technologies can contribute to a more accurate classification of non–small cell lung cancer (NSCLC), as well as a better prediction of both the disease course and the efficacy of targeted therapies. Here we set out to analyze whether global gene expression profiling performed in a group of early-stage NSCLC patients can contribute to classifying tumor subtypes and predicting the disease prognosis. Gene expression profiling was performed with the use of the microarray technology in a training set of 108 NSCLC samples. Subsequently, the recorded findings were validated further in an independent cohort of 44 samples. We demonstrated that the specific gene patterns differed significantly between lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) samples. Furthermore, we developed and validated a novel 53-gene signature distinguishing SCC from AC with 93% accuracy. Evaluation of the classifier performance in the validation set showed that our predictor classified the AC patients with 100% sensitivity and 88% specificity. We revealed that gene expression patterns observed in the early stages of NSCLC may help elucidate the histological distinctions of tumors through identification of different gene-mediated biological processes involved in the pathogenesis of histologically distinct tumors. However, we showed here that the gene expression profiles did not provide additional value in predicting the progression status of the early-stage NSCLC. Nevertheless, the gene expression signature analysis enabled us to perform a reliable subclassification of NSCLC tumors, and it can therefore become a useful diagnostic tool for a more accurate selection of patients for targeted therapies.

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Section: Discussionsupporting

confidence: 84%

Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC

Charkiewicz

Nikliński

Claesen

et al. 2017

Translational Oncology

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“…Previous ADC-SCC gene signatures have been reported (41–46) and about 10–45% of the genes in these signatures overlap with ours. Two of these signatures were formally developed as classifiers, with external tumor set validation.…”

Section: Discussionsupporting

confidence: 82%

“…The resulting prediction had an accuracy of 92% (sensitivity: 99%, specificity: 84%) while our Classifier showed 95% accuracy (97% sensitivity, 93% specificity). The second study, from Wilkerson et al (46), had 15 genes (4 overlapping with our signature) and a reported prediction accuracy of 81% in external validation. Using the TCGA validation test, this corresponded to an accuracy of 92% (sensitivity: 90%, specificity 95%).…”

Section: Discussionmentioning

confidence: 98%

An Expression Signature as an Aid to the Histologic Classification of Non–Small Cell Lung Cancer

Girard

Rodriguez‐Canales

Behrens

et al. 2016

Clinical Cancer Research

150

156

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Purpose Most non-small cell lung cancers (NSCLCs) are now diagnosed from small specimens, and classification using standard pathology methods can be difficult. This is of clinical relevance as many therapy regimens and clinical trials are histology dependent. The purpose of this study was to develop an mRNA expression signature as an adjunct test for routine histo-pathological classification of NSCLCs. Experimental Design A microarray dataset of resected adenocarcinomas (ADC) and squamous cell carcinomas (SCC) was used as the learning set for an ADC-SCC signature. The Cancer Genome Atlas (TCGA) lung RNAseq dataset was used for validation. Another microarray dataset of ADCs and matched non-malignant lung was used as the learning set for a Tumor vs. Nonmalignant signature. The classifiers were selected as the most differentially expressed genes and sample classification was determined by a nearest distance approach. Results We developed a 62-gene expression signature that contained many genes used in immunostains for NSCLC typing. It includes 42 genes that distinguish ADC from SCC and 20 genes differentiating non-malignant lung from lung cancer. Testing of the TCGA and other public datasets resulted in high prediction accuracies (93–95%). Additionally, a prediction score was derived that correlates both with histologic grading and prognosis. We developed a practical version of the Classifier using the HTG EdgeSeq nuclease protection-based technology in combination with next-generation sequencing that can be applied to formalin-fixed paraffin-embedded (FFPE) tissues and small biopsies. Conclusions Our RNA classifier provides an objective, quantitative method to aid in the pathological diagnosis of lung cancer.

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“…Multiple data sets, comprising 2177 samples, were assembled to evaluate a previously published 57-gene LSP gene-expression classifier. 21 The data sets included several publically available lung cancer gene-expression data sets, including 2099 FF lung cancer samples (The Cancer Genome Atlas [TCGA, Bethesda, Maryland], 15,16 University of North Carolina [Chapel Hill], 19,20 National Cancer Institute (NCI, Bethesda, Maryland), 22 25 and Duke University [Durham, North Carolina] 26 ), as well as newly collected geneexpression data from 78 FFPE samples. 27 Data sources are provided in Table 1, including normalization methods applied to each data set.…”

Section: Methodsmentioning

confidence: 99%

“…This method can thus be used to assist the pathologist in classifying lung tumors. 21 In this study, the ability of the LSP gene signature to reliably subtype lung tumor samples using gene expression data from any one of multiple platforms, including Affymetrix (Santa Clara, California) and Agilent (Santa Clara, California) DNA microarrays, RNA sequencing, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR), was investigated.…”

mentioning

confidence: 99%

Validation of the Lung Subtyping Panel in Multiple Fresh-Frozen and Formalin-Fixed, Paraffin-Embedded Lung Tumor Gene Expression Data Sets

Faruki¹,

Mayhew²,

Fan³

et al. 2015

Archives of Pathology &Amp; Laboratory Medicine

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Context .- A histologic classification of lung cancer subtypes is essential in guiding therapeutic management. Objective .- To complement morphology-based classification of lung tumors, a previously developed lung subtyping panel (LSP) of 57 genes was tested using multiple public fresh-frozen gene-expression data sets and a prospectively collected set of formalin-fixed, paraffin-embedded lung tumor samples. Design .- The LSP gene-expression signature was evaluated in multiple lung cancer gene-expression data sets totaling 2177 patients collected from 4 platforms: Illumina RNAseq (San Diego, California), Agilent (Santa Clara, California) and Affymetrix (Santa Clara) microarrays, and quantitative reverse transcription-polymerase chain reaction. Gene centroids were calculated for each of 3 genomic-defined subtypes: adenocarcinoma, squamous cell carcinoma, and neuroendocrine, the latter of which encompassed both small cell carcinoma and carcinoid. Classification by LSP into 3 subtypes was evaluated in both fresh-frozen and formalin-fixed, paraffin-embedded tumor samples, and agreement with the original morphology-based diagnosis was determined. Results .- The LSP-based classifications demonstrated overall agreement with the original clinical diagnosis ranging from 78% (251 of 322) to 91% (492 of 538 and 869 of 951) in the fresh-frozen public data sets and 84% (65 of 77) in the formalin-fixed, paraffin-embedded data set. The LSP performance was independent of tissue-preservation method and gene-expression platform. Secondary, blinded pathology review of formalin-fixed, paraffin-embedded samples demonstrated concordance of 82% (63 of 77) with the original morphology diagnosis. Conclusions .- The LSP gene-expression signature is a reproducible and objective method for classifying lung tumors and demonstrates good concordance with morphology-based classification across multiple data sets. The LSP panel can supplement morphologic assessment of lung cancers, particularly when classification by standard methods is challenging.

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Prediction of Lung Cancer Histological Types by RT-qPCR Gene Expression in FFPE Specimens

Cited by 19 publications

References 32 publications

Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC

Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC

An Expression Signature as an Aid to the Histologic Classification of Non–Small Cell Lung Cancer

Validation of the Lung Subtyping Panel in Multiple Fresh-Frozen and Formalin-Fixed, Paraffin-Embedded Lung Tumor Gene Expression Data Sets

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