SCLCCellMiner

SCLCCellMiner integrates genomic, epigenomic, transcriptomic, and pharmacogenomic data from 118 small cell lung cancer (SCLC) cell lines to enable molecular characterization, epigenetic analysis, and drug-sensitivity profiling for translational research.

Key Features:

• Extensive omics data integration: Aggregates exome sequencing, microarray, RNA-seq, copy-number variation, methylomics/high-resolution methylomes, and microRNA profiling for 118 SCLC cell lines.
• Drug sensitivity profiling: Contains detailed drug sensitivity testing results and drug-response profiles across the SCLC cell-line panel.
• Reproducibility across datasets: Enables cross-dataset comparisons demonstrating reproducibility with CCLE, GDSC, CTRP, NCI, and UTSW datasets.
• SCLC classification framework (NAPY): Validates and annotates SCLC classification based on NEUROD1, ASCL1, POU2F3, and YAP1 and links these factors to transcription networks.
• Pathway and network annotation: Connects master transcription factors to key pathways including NOTCH, HIPPO, and MYC gene networks.
• Surface marker identification: Identifies subset-specific surface markers relevant to antibody-targeted therapies.
• SCLC-Y subset characterization: Describes SCLC-Y cell lines as expressing the NOTCH pathway and antigen-presenting machinery (APM), showing responsiveness to mTOR and AKT inhibitors, and exhibiting a native immune predisposition suggesting sensitivity to immune checkpoint inhibitors.
• Epigenetic insights: Reports global hypomethylation and histone gene methylation patterns consistent with cellular plasticity in SCLC cell lines.
• Therapeutic implication framework: Provides a molecularly informed framework to prioritize therapies such as NOTCH activators, YAP1 inhibitors, and immune checkpoint inhibitors for defined SCLC subsets.

Scientific Applications:

• Biological characterization: Supports characterization of SCLC molecular subtypes and transcriptional networks.
• Biomarker validation: Enables validation of potential genomic, epigenomic, and expression biomarkers across cell lines.
• Therapeutic hypothesis generation: Facilitates generation of hypotheses linking molecular features to sensitivity to mTOR, AKT, YAP1, NOTCH-related interventions, and immune checkpoint inhibitors.
• Preclinical drug prioritization: Informs prioritization of candidate drugs and targeted therapies using integrated drug-sensitivity profiles.
• Personalized therapy concept development: Supports development of molecular classification–based strategies for personalized treatment selection.

Methodology:

Integrates and analyzes exome sequencing, microarray, RNA‑seq, copy-number, methylome (high-resolution methylomes), microRNA profiling, and drug-sensitivity data from 118 SCLC cell lines and compares reproducibility across CCLE, GDSC, CTRP, NCI, and UTSW datasets.