Purpose: 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.Experimental Design: We analyzed tumor samples from 58 patients with ES-SCLC enrolled in two multicenter single-arm phase IIIb studies evaluating frontline chemoimmunotherapy in Spain: n = 32 from the IMfirst trial and n = 26 from the CANTABRICO trial. We used the GeoMx Digital Spatial Profiler 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 found to be similar between bothcohorts, except for SCLC-P, which was not identified in the CANTABRICO_DSP cohort. A total of 44% of the patients in both cohorts had tumors with multiple coexisting transcriptional subtypes. Transcriptional subtypes or subtype heterogeneity was 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 ≥12 months) contained an IFNγ-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. Preexisting IFNγ-driven immunity and mitochondrial metabolism seem to be 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.

Background/Objectives: Anti–PD-1/PD-L1 blockers have revolutionized the treatment landscape of non–small cell lung cancer (NSCLC) lacking oncogene-addicted alterations, particularly in tumors with high PD-L1 expression (tumor proportion score [TPS] ≥ 50%). Cemiplimab is approved as first-line monotherapy in this setting. However, real-world data remain scarce. This study aimed to evaluate the efficacy and safety of single-agent cemiplimab in a multicenter Spanish cohort and compare outcomes with a historical pembrolizumab cohort. Methods: Cemi-SPA is a retrospective multicenter study including 150 patients with advanced NSCLC and PD-L1 ≥ 50% treated with cemiplimab as first-line monotherapy across 21 Spanish centers. Clinical outcomes were analyzed and compared with a historical cohort of 144 patients treated with pembrolizumab. Propensity score matching (PSM) was performed to adjust for baseline differences. Results: Median progression-free survival (PFS) and overall survival (OS) were 8.1 and 12.6 months, respectively. ECOG performance status ≥ 2 was independently associated with worse outcomes, whereas the development of immune-related adverse events correlated with improved PFS and OS. After PSM, no significant differences were observed between cemiplimab and pembrolizumab in terms of efficacy. Conclusions: Cemiplimab demonstrated comparable real-world efficacy and safety to pembrolizumab in patients with advanced NSCLC and PD-L1 ≥ 50%. ECOG performance status emerged as the strongest prognostic factor, highlighting the importance of patient selection in routine clinical practice.

BackgroundPathologic response (PR), expressed as the percentage of residual viable tumor (%RVT), has emerged as a surrogate endpoint after neoadjuvant chemoimmunotherapy in non–small cell lung cancer (NSCLC). However, PR assessment remains subject to interobserver variability and is insufficiently standardized across institutions and clinical trials. We hypothesized that artificial intelligence (AI)-based %RVT scoring would be concordant with expert manual assessment and consistent across real-world, multi-scanner cohorts.Materials and methodsFour retrospective NSCLC cohorts treated with neoadjuvant chemoimmunotherapy and surgery were analyzed. The internal cohort included 38 patients; three external cohorts from China, Germany, and Spain contributed 97 additional patients. Weighted consensus manual %RVT served as the reference standard for benchmarking an AI algorithm. Discordant cases were interrogated for algorithmic bias.ResultsOverall, 135 patients and 1,344 primary-tumor H&E slides were evaluable. Concordance between AI-based and manual assessment was at least moderate across all cohorts. For major pathologic response (MPR) classification, the AI algorithm achieved a pooled accuracy of 91% (95% CI, 85–95), sensitivity of 90% (82–95), specificity of 93% (81–99), and an area under the curve ≥0.95 in all cohorts. Discordant classifications occurred in 8.9% of cases and were mainly attributable to stromal underrepresentation and misclassification of non-neoplastic elements.ConclusionsAI-based %RVT scoring was concordant with expert pathologist consensus and consistent across real-world, multicenter, multi-scanner cohorts, with most algorithmic errors mirroring those encountered in human practice. By providing explainable %RVT grounded in international recommendations, our framework may facilitate harmonization of PR assessment for perioperative therapies.

Supplementary Data 2 from Spatially Preserved Multi-Region Transcriptomic Subtyping and Biomarkers of Chemoimmunotherapy Outcome in Extensive-Stage Small Cell Lung Cancer

Supplementary Data 2 from Spatially Preserved Multi-Region Transcriptomic Subtyping and Biomarkers of Chemoimmunotherapy Outcome in Extensive-Stage Small Cell Lung Cancer