Guo Junhong, Hou Likun, Zhang Wei, Dong Zhengwei, Zhang Lei, Wu Chunyan
Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, PR China.
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, PR China.
Transl Oncol. 2021 Dec;14(12):101222. doi: 10.1016/j.tranon.2021.101222. Epub 2021 Sep 14.
Accurately differentiating between pulmonary large cell neuroendocrine carcinomas (LCNEC) and small cell lung cancer (SCLC) is crucial to make appropriate therapeutic decisions. Here, a classifier was constructed based on transcriptome data to improve the diagnostic accuracy for LCNEC and SCLC.
13,959 genes mapped to 186 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were included. Gene Set Variation Analysis (GSVA) algorithm was used to enrich and score each KEGG pathway from RNA-sequencing data of each sample. A prediction model based on GSVA score was constructed and trained via ridge regression based on RNA-sequencing datasets from 3 published studies. It was validated by another independent RNA-sequencing dataset. Clinical feasibility was tested by comparing model predicated result using RNA-sequencing data derived from hard-to-diagnose samples of lung neuroendocrine cancer to conventional histology-based diagnosis.
This model achieved a ROC-AUC of 0.949 and a concordance rate of 0.75 for the entire prediction efficiency. Of the 27 borderline samples, 17/27 (63.0%) were predicted as LCNEC, 7/27 were predicted as SCLC, and the remainder was NSCLC. Only 8 cases (29.6%) with LCNEC were diagnosed by pathologists, which was significantly lower than the results predicted by the model. Furthermore, cases with predicted LCNEC by the model had a significant longer disease-free survival than those where the model predicted SCLC (P = 0.0043).
This model was able to give an accurate prediction of LCNEC and SCLC. It may assist clinicians to make the optimal decision for patients with pulmonary neuroendocrine tumors in choosing appropriate treatment.
准确区分肺大细胞神经内分泌癌（LCNEC）和小细胞肺癌（SCLC）对于做出恰当的治疗决策至关重要。在此，基于转录组数据构建了一个分类器，以提高LCNEC和SCLC的诊断准确性。
纳入了映射到186条京都基因与基因组百科全书（KEGG）通路的13959个基因。使用基因集变异分析（GSVA）算法从每个样本的RNA测序数据中富集并评分每条KEGG通路。基于GSVA评分构建预测模型，并通过基于3项已发表研究的RNA测序数据集的岭回归进行训练。通过另一个独立的RNA测序数据集对其进行验证。通过比较使用来自肺神经内分泌癌难以诊断样本的RNA测序数据的模型预测结果与传统组织学诊断结果，来测试临床可行性。
该模型在整体预测效率方面的受试者工作特征曲线下面积（ROC-AUC）为0.949，一致性率为0.75。在27个临界样本中，17/27（63.0%）被预测为LCNEC，7/27被预测为SCLC，其余为非小细胞肺癌（NSCLC）。病理学家仅诊断出8例（29.6%）LCNEC，显著低于模型预测结果。此外，模型预测为LCNEC的病例的无病生存期明显长于模型预测为SCLC的病例（P = 0.0043）。
该模型能够准确预测LCNEC和SCLC。它可能有助于临床医生为肺神经内分泌肿瘤患者做出选择合适治疗的最佳决策。
