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.
ABSTRACT
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.
ABSTRACT
The motivation for research on 3D printing of protective face shields was the urgent societal demand for healthcare in the fight against the spread of COVID19 pandemic. Research is based on a literature review that shows that objects produced by additive technologies do not always have consistent quality suitable for the given purpose of use. Besides, they have different effects on the environment and leave different footprints. The overall goal of the research was to find out the most suitable thermoplastic material for printing shield frames in terms of mechanical properties, geometric accuracy, weight, printing time, filament price, and environmental sustainability. Fused deposition modeling (FDM) technology was used for 3D printing, and three different filaments were investigated: polylactic acid (PLA), polyethylene terephthalate (PETG), and polyhydroxyalkanoate (PHA). The weighted sum method for multi-objective optimization was used. Finally, PHA material was chosen, mainly due to its environmental sustainability, as it has the most negligible impact on the environment.
