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

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.