Integrative analysis of multi-omics data reveals the heterogeneity and signatures of immune therapy for small cell lung cancer

Abstract Small cell lung cancer (SCLC) is highly invasive and lethal. Genomic studies are beginning to characterize its genetic determinants and heterogeneity. Here we performed RNA-Seq and whoe exome sequencing (WES) in 19 Chinese SCLC clinical tumor specimens. Integration with other two public cohorts (n = 129 for RNA-seq, n = 171 for WES), we carried out gene co-expression network analysis and classified into four subgroups: ASCL1-high, NEUROD1-high, a novel variant subtype with CCSP/CC10-high, and a fourth subtype with high expression of NOTCH family and inflammatory genes. We further found that this fourth subtype was characterized by overexpression of immune-related pathways and immunosuppressive factors, referred to as immune subtype. In addition to transcriptomic signatures, specific genomic alterations were also significantly enriched in each subtype. We successfully built a machine learning model to predict the subtypes of newly coming specimens based on transcriptomic data. In particular, we found that POU2F3 protein was effective in distinguishing immunotherapy patients, i.e. the immune subtype, which was further validated by an independent data of chemo-resistant patients received 2nd -line immunotherapy. Collectively, our work systematically studied the transcriptomic and genomic heterogeneity of SCLC, and characterized an immune subtype that displayed features of sensitivity to immune checkpoint-based therapies.