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
Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas. Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas. Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas.
Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas.
Clin Cancer Res. 2016 Oct 1;22(19):4880-4889. doi: 10.1158/1078-0432.CCR-15-2900. Epub 2016 Jun 28.
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.
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.
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.
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.
目前大多数非小细胞肺癌（NSCLC）是通过小标本诊断的，使用标准病理方法进行分类可能会有困难。这具有临床相关性，因为许多治疗方案和临床试验都依赖于组织学类型。本研究的目的是开发一种mRNA表达特征作为NSCLC常规组织病理学分类的辅助检测方法。
将切除的腺癌（ADC）和鳞状细胞癌（SCC）的微阵列数据集用作ADC-SCC特征的学习集。癌症基因组图谱（TCGA）肺RNA测序数据集用于验证。另一个ADC和匹配的非恶性肺组织的微阵列数据集用作肿瘤与非恶性特征的学习集。选择差异表达最显著的基因作为分类器，并通过最近距离方法确定样本分类。
我们开发了一种包含62个基因的表达特征，其中包含许多用于NSCLC分型免疫染色的基因。它包括42个区分ADC和SCC的基因以及20个区分非恶性肺组织和肺癌的基因。对TCGA和其他公共数据集的测试产生了较高的预测准确率（93%-95%）。此外，还得出了一个与组织学分级和预后相关的预测分数。我们使用基于HTG EdgeSeq核酸酶保护技术结合下一代测序开发了一种实用版的分类器，可应用于福尔马林固定石蜡包埋（FFPE）组织和小活检标本。
我们的RNA分类器提供了一种客观、定量的方法来辅助肺癌的病理诊断。《临床癌症研究》；22(19)；4880-4889。©2016美国癌症研究协会。
