SCLC

SCLC integrates pharmacogenomic, DNA methylation, histone modification, and ChIP-seq data from the National Cancer Institute (NCI), the Sanger Institute's Genomics of Drug Sensitivity in Cancer (GDSC), and the Broad Institute's Cancer Cell Line Encyclopedia (CCLE) and CTRP to support biomarker discovery and translational research in small cell lung cancer.

Key Features:

• Data integration: Aggregates pharmacogenomic datasets from NCI, GDSC, CCLE, and CTRP for cross-database analyses.
• DNA methylation analysis: Incorporates comprehensive DNA methylation data and examines both promoter and gene body methylation patterns.
• Gene expression prediction: Evaluates promoter and gene body methylation to improve prediction of gene expression levels.
• Gene-specific insights: Highlights methylation–expression relationships for therapeutically relevant genes such as MGMT, SLFN11, and DLL3.
• Super-enhancer integration: Integrates H3K27ac histone modification data and ChIP-seq datasets for transcription factors NEUROD1, ASCL1, and POU2F3 to identify SE-regulated genes.
• Methylation–expression relationships in SE genes: Identifies inverse relationships between genic methylation and expression in SE-covered genes such as NEUROD1 and MYC.

Scientific Applications:

• Biomarker and target discovery: Enables identification of novel therapeutic targets and biomarkers in small cell lung cancer.
• Therapeutic response prediction: Uses promoter and gene body methylation patterns to inform prediction of gene expression relevant to therapy.
• Super-enhancer biology: Facilitates identification of super-enhancer regulated genes implicated in SCLC pathogenesis via H3K27ac and TF ChIP-seq integration.
• SCLC subtype classification: Supports analyses relevant to neuroendocrine classification of SCLC through methylation and expression patterns of key genes.

Methodology:

Integration of pharmacogenomic datasets from NCI, GDSC, CCLE and CTRP; analysis of promoter and gene body DNA methylation and correlation with gene expression; integration of H3K27ac histone modification and ChIP-seq datasets for NEUROD1, ASCL1, and POU2F3 to identify super-enhancer regulated genes.