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Purpose@#Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors have greatly improved survival in EGFR-mutant (EGFRm) non–small cell lung cancer (NSCLC); however, their effects on the tumor microenvironment (TME) are unknown. We assessed the changes induced by neoadjuvant erlotinib therapy (NE) in the TME of operable EGFRm NSCLC. @*Materials and Methods@#This was a single-arm phase II trial for neoadjuvant/adjuvant erlotinib therapy in patients with stage II/IIIA EGFRm NSCLC (EGFR exon 19 deletion or L858R mutations). Patients received up to 2 cycles of NE (150 mg/day) for 4 weeks, followed by surgery and adjuvant erlotinib or vinorelbine plus cisplatin therapy depending on observed NE response. TME changes were assessed based on gene expression analysis and mutation profiling. @*Results@#A total of 26 patients were enrolled; the median age was 61, 69% were female, 88% were stage IIIA, and 62% had L858R mutation. Among 25 patients who received NE, the objective response rate was 72% (95% confidence interval [CI], 52.4 to 85.7). The median disease-free and overall survival (OS) were 17.9 (95% CI, 10.5 to 25.4) and 84.7 months (95% CI, 49.7 to 119.8), respectively. Gene set enrichment analysis in resected tissues revealed upregulation of interleukin, complement, cytokine, transforming growth factor β, and hedgehog pathways. Patients with upregulated pathogen defense, interleukins, and T-cell function pathways at baseline exhibited partial response to NE and longer OS. Patients with upregulated cell cycle pathways at baseline exhibited stable/progressive disease after NE and shorter OS. @*Conclusion@#NE modulated the TME in EGFRm NSCLC. Upregulation of immune-related pathways was associated with better outcomes.
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The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.
RESUMEN
The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.