Hierarchical identification of a transcriptional panel for the histological diagnosis of lung neuroendocrine tumors - Zhang, Juxuan; Deng, Jiaxing; Feng, Xiao; Tan, Yilong; Li, Xin; Liu, Yixin; Li, Mengyue; Qi, Haitao; Tang, Lefan; Meng, Qingwei; Yan, Haidan; Qi, Lishuang (Frontiers in Genetics, 2022)
Background: Lung cancer is a complex disease composed of neuroendocrine (NE) and non-neuroendocrine (non-NE) tumors. The accurately diagnosis of lung cancer is essential in guiding therapeutic management. Several transcriptional signatures have been reported to distinguish between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) belonging to non-NE tumors. This study aims to identify a transcriptional panel that could distinguish the histological subtypes of NE tumors, to complement morphology-based classification of an individual. Methods: A public dataset with NE subtypes including 21 small-cell lung cancer (SCLC), 56 large-cell neuroendocrine carcinomas (LCNEC) and 24 carcinoids (CARCI), and non-NE subtypes including 85 ADC and 61 SCC, was used as a training set. In the training set, the consensus clustering was first used to filter out the samples whose expression patterns disagreed with their histological subtypes. Then, a rank-based method was proposed to develop a panel of transcriptional signatures for determining NE subtype for an individual, based on the within-sample relative gene expression orderings of gene pairs. Twenty-three public datasets with a total of 3,454 samples, derived from fresh-frozen, formalin-fixed paraffin-embedded, biopsies, and single-cells, were used for validation. Clinical feasibility was tested in 10 SCLC biopsies specimens collected from cancer hospital by bronchoscopy. Results: The NEsubtype-panel composed of three signatures that could distinguish NE from non-NE, CARCI from non-CARCI, and SCLC from LCNEC step by step, and ultimately determined the histological subtype for each NE sample. The three signatures achieved high average concordance rates with 97.31%, 98.11% and 90.63%, respectively, in the 23 public validation datasets. Notably, the 10 clinic-derived SCLC samples diagnosed by immunohistochemical staining were also accurately predicted by the NEsubtype-panel. Furthermore, the subtype-specific gene expression patterns and survival analyses provided evidences for the rationality of the re-classification by the NEsubtype-panel. Conclusions: The rank-based NEsubtype-panel could accurately distinguish lung NE from non-NE tumors and determine NE subtypes even in clinically challenging samples (such as biopsy). The panel together with our previous reported signature (KRT5-AGR2) for SCC and ADC would be an auxiliary test for the histological diagnosis of lung cancer.