Subclassification of Small Cell Lung Cancer Based on Gene Expression Signatures and Machine Learning

Based on analysis of our novel real-word SCLC cohort, we extend the methods space for SCLC diagnosis by developing four downstream transcriptional programs linked to the key TFs that we used for ML-based NAPY classification. By combining evidence from TFs and their downstream signatures, we add functional robustness to current classification schemes. Furthermore, we describe distinct molecular and clinical patters observed across our NAPY subtypes.