Spatially preserved multi-region transcriptomic subtyping and biomarkers of chemoimmunotherapy outcome in extensive-stage small cell lung cancer

Abstract Background: Transcriptomic subtyping holds promise for personalized therapy in extensive-stage small-cell lung cancer (ES-SCLC). In this study, we aimed to assess intratumoral transcriptomic subtype diversity and to identify biomarkers of long-term chemoimmunotherapy benefit in human ES-SCLC. Patients and methods: We analyzed tumor samples from 58 ES-SCLC patients enrolled in two multicenter single-arm phase IIIb studies evaluating front-line chemoimmunotherapy in Spain: n=32 from the IMfirst trial, and n=26 from the CANTABRICO trial. We utilized the GeoMxTM DSP system to perform multi-region transcriptomic analysis. For subtype classification, we performed hierarchical clustering using the relative expression of ASCL1 (SCLC-A), NEUROD1 (SCLC-N), POU2F3 (SCLC-P), and YAP1 (SCLC-Y). Results: Subtype distribution was similar between both cohorts, except for SCLC-P, not identified in the CANTABRICO_DSP cohort. A total of 44% of the patients in both cohorts had tumors with multiple co-existing transcriptional subtypes. Transcriptional subtypes or subtype heterogeneity were not associated with outcomes. Most potential targets did not show subtype-specific expression. Consistently in both cohorts, tumors from patients with long-term benefit (time to progression ³12 months) contained an IFNg-dominated mRNA profile, including enhanced capacity for antigen presentation. Hypoxia and glycolytic pathways were associated with resistance to chemoimmunotherapy. Conclusions: This work suggests that intratumoral heterogeneity, inconsistent association with outcome, and unclear subtype-specific target expression might be significant challenges for subtype-based precision oncology in SCLC. Pre-existing IFNg-driven immunity and mitochondrial metabolism seem correlates of long-term efficacy in this study, although the absence of a chemotherapy control arm precludes concluding that these are predictive features specific for immunotherapy.