Improving differential diagnosis of pulmonary large cell neuroendocrine carcinoma and small cell lung cancer via a transcriptomic, biological pathway-based machine learning model

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.