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

Girard, Luc, Rodriguez-Canales, Jaime, Behrens, Carmen, Thompson, Debrah M., Botros, Ihab W., Tang, Hao, Xie, Yang, Rekhtman, Natasha, Travis, William D., Wistuba, Ignacio I., Minna, John D., Gazdar, Adi F.·2016·Clinical Cancer Research
DOI 10.1158/1078-0432.ccr-15-2900PMC PMC5492382

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Abstract

Purpose: Most non–small cell lung cancers (NSCLC) 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 histopathologic 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 nonmalignant lung was used as the learning set for a tumor versus 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 nonmalignant lung from lung cancer. Testing of the TCGA and other public datasets resulted in high prediction accuracies (93%–95%). In addition, 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 pathologic diagnosis of lung cancer. Clin Cancer Res; 22(19); 4880–9. ©2016 AACR.

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  • The 88 genes differentially expressed in both cohorts included known EGFRMUT-associated genes ETV5 and NKX2-1.

    “The 88 genes showed clear differential expression across EGFRMUT and EGFRWT tumors and contained known EGFRMUT associated genes such as ETV5 and NKX2-118, 19.”

    From Integrative Genomic Analyses Identifies GGA2 as a Cooperative Driver of EGFR-Mediated Lung Tumorigenesis (2019) · cites this paper as [18]

    The Results describe a 62-gene signature for histologic classification, and the Discussion identifies NKX2-1 as an adenocarcinoma-associated classification gene. Neither passage establishes that 88 genes differed between EGFR-mutant and EGFR-wild-type tumors or that such a set included ETV5 and NKX2-1. Searches for ETV5, EGFR, and “88 genes” did not locate that finding in our copy. (LLM, conf=0.76)

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Research works printed but not linked: 35

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Metadata

Authors
Girard, L., Rodriguez-Canales, J., Behrens, C., Thompson, D. M., Botros, I. W., Tang, H., Xie, Y., Rekhtman, N., Travis, W. D., Wistuba, I. I., Minna, J. D., & Gazdar, A. F.
Affiliations
  • 1Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas.
  • 3Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas.
  • 4Department of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas.
  • 5HTG Molecular Diagnostics, Tucson, Arizona.
  • 6Department of Clinical Science, University of Texas Southwestern Medical Center, Dallas, Texas.
  • 7Department of Thoracic Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.
Venue
Clinical Cancer Research
Publisher
American Association for Cancer Research (AACR)
Published
2016-10-01
Volume
22
Issue
19
Pages
4880-4889
ISSN
  • 1078-0432
  • 1557-3265
DOI
10.1158/1078-0432.ccr-15-2900
PMC
PMC5492382
Cited by
154
References
3
Language
en
Funders
  • NCI (P50CA70907)
  • Lungevity Foundation Canary Foundation (U01CA086402)
  • NIH (R44HG005949)

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