RESUMO
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.
Assuntos
Biomarcadores Tumorais , Perfilação da Expressão Gênica , Imunoterapia , Neoplasias Pulmonares , Carcinoma de Pequenas Células do Pulmão , Transcriptoma , Humanos , Carcinoma de Pequenas Células do Pulmão/genética , Carcinoma de Pequenas Células do Pulmão/tratamento farmacológico , Carcinoma de Pequenas Células do Pulmão/patologia , Carcinoma de Pequenas Células do Pulmão/terapia , Biomarcadores Tumorais/genética , Masculino , Feminino , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/patologia , Neoplasias Pulmonares/terapia , Idoso , Imunoterapia/métodos , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Resultado do Tratamento , Regulação Neoplásica da Expressão Gênica , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Fatores de Transcrição Hélice-Alça-Hélice Básicos/genética , PrognósticoRESUMO
Several factors have been associated with the occurrence of immune-related adverse events (irAEs) induced by immune checkpoint inhibitor (ICI) therapy. Despite their availability, the predictive value of circulating blood cell parameters remains underexplored. Our aim was to investigate whether baseline values of and early changes in absolute neutrophil count (ANC), absolute lymphocyte count (ALC), other blood cell counts, and lymphocyte-related ratios can predict irAEs and whether sex may differentially influence this potential predictive ability. Of the 145 patients included, 52 patients (35.8%) experienced at least one irAE, with a 1-year cumulative incidence of 41.6%. Using Fine and Gray competing risk models, we identified female sex (hazard ratio (HR) = 2.17, 95% confidence interval (CI) = 1.20-3.85), high ALC before ICI initiation (HR = 1.63, 95% CI = 1.09-2.45), and low ANC after ICI initiation (HR = 0.81, 95% CI = 0.69-0.96) as predictors of irAEs. However, ALC and ANC may only have an impact on the risk of irAEs in women (stratified for female sex, ALC-related HR = 2.61, 95% CI = 1.40-4.86 and ANC-related HR = 0.57, 95% CI = 0.41-0.81). Priority should be given to developing models to predict ICI-related toxicity and their validation in various settings, and such models should assess the impact of patient sex on the risk of toxicity.