Background

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.

Methods

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.

Results

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).

Conclusion

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.

中文翻译：

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通过转录组学、基于生物通路的机器学习模型改进肺大细胞神经内分泌癌和小细胞肺癌的鉴别诊断

背景

准确区分肺大细胞神经内分泌癌 (LCNEC) 和小细胞肺癌 (SCLC) 对于做出适当的治疗决策至关重要。在这里，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。

方法

包括映射到 186 条京都基因和基因组百科全书 (KEGG) 途径的 13,959 个基因。使用基因集变异分析 (GSVA) 算法从每个样本的 RNA 测序数据中对每个 KEGG 通路进行富集和评分。基于 GSVA 评分的预测模型是基于 3 项已发表研究的 RNA 测序数据集，通过岭回归构建和训练的。它由另一个独立的 RNA 测序数据集验证。通过比较使用源自难以诊断的肺神经内分泌癌样本的 RNA 测序数据的模型预测结果与传统的基于组织学的诊断来测试临床可行性。

结果

该模型实现了 0.949 的 ROC-AUC 和 0.75 的整体预测效率的一致率。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 NSCLC。LCNEC 仅 8 例（29.6%）被病理学家诊断，显着低于模型预测的结果。此外，模型预测的 LCNEC 病例的无病生存期显着长于模型预测 SCLC 的病例（P  = 0.0043）。

结论

该模型能够准确预测 LCNEC 和 SCLC。它可以帮助临床医生为肺神经内分泌肿瘤患者选择合适的治疗方法做出最佳决策。