Abstract
The worldwide incidence of pulmonary carcinoids is increasing, but little is known about their molecular characteristics. Through machine learning and multi-omics factor analysis, we compare and contrast the genomic profiles of 116 pulmonary carcinoids (including 35 atypical), 75 large-cell neuroendocrine carcinomas (LCNEC), and 66 small-cell lung cancers. Here we report that the integrative analyses on 257 lung neuroendocrine neoplasms stratify atypical carcinoids into two prognostic groups with a 10-year overall survival of 88% and 27%, respectively. We identify therapeutically relevant molecular groups of pulmonary carcinoids, suggesting DLL3 and the immune system as candidate therapeutic targets; we confirm the value of OTP expression levels for the prognosis and diagnosis of these diseases, and we unveil the group of supra-carcinoids. This group comprises samples with carcinoid-like morphology yet the molecular and clinical features of the deadly LCNEC, further supporting the previously proposed molecular link between the low- and high-grade lung neuroendocrine neoplasms.
Highlights
One patient (S02236) that was originally classified as male was switched to female based on its concordant wholeexome, transcriptome, and methylome data; and one patient (LNEN028) for whom no sex information was available was classified as male based on its methylation data (Supplementary Fig. 28; see details of the methods used in the DNA sequencing, expression, and methylation sections of the methods), because we had no other data type for this sample
Compared to the Principal component analysis (PCA) of the 5% most variable CpGs with normalisation (Supplementary Fig. 33A), we find that the chip position is significantly associated with PC10, and that PC2 is not associated with histopathology
We find a significant association between PC1, histopathological type, age, and smoking status, with LCNEC, smokers, and larger age classes located at higher PC1 coordinates (Supplementary Fig. 33A); these associations are expected, given that the difference between LCNEC and carcinoids is expected to be the main driver of variation in methylation, and given known the aetiology of the diseases[8]
Summary
We explored the molecular characteristics of each cluster from the MOFA LNET based on their core differentially expressed coding genes (coreDEGs, the expression levels of which defined a given group of samples), corresponding promoter methylation profiles (Fig. 5a and Supplementary Data 10), and their somatic mutational patterns
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