SCLC DLL3 Biomarker Expression Predictor
POU2F1 acts as the master transcriptional activator of DLL3 in SCLC — meaning DLL3 expression levels, which determine whether patients respond to DLL3-targeted therapies like tarlatamab, are now mechanistically explainable and potentially predictable from upstream transcription factor activity.
SCLC DLL3 Biomarker Expression Predictor
POU2F1 acts as the master transcriptional activator of DLL3 in SCLC — meaning DLL3 expression levels, which determine whether patients respond to DLL3-targeted therapies like tarlatamab, are now mechanistically explainable and potentially predictable from upstream transcription factor activity.
Build an analysis tool and dataset explorer that lets researchers investigate the transcriptional regulators of DLL3 expression across SCLC molecular subtypes. Using publicly available SCLC genomic and transcriptomic datasets (CCLE, GEO, TCGA, and published SCLC cohorts), the tool would display correlations between POU2F1 expression, ASCL1 activity, and DLL3 levels across patient samples and cell lines. Users could query whether a given cell line or patient subtype is likely to be DLL3-high or DLL3-low based on upstream regulator activity.
The tool would also include a model of the POU2F1-ASCL1 co-binding regulatory circuit that drives high DLL3 expression, allowing researchers to understand which molecular perturbations (gain or loss of specific TFs) would predict changes in DLL3 levels. This could guide patient stratification for DLL3-targeted therapy trials.
Why this matters: tarlatamab and other DLL3-targeting therapies are clinically active in SCLC but not all patients respond, and the field lacks tools to predict DLL3 expression from upstream biology. If POU2F1/ASCL1 status can serve as a surrogate for DLL3 levels, this unlocks non-IHC approaches to treatment selection and could inform combination strategies that boost DLL3 expression in low-expressing tumors.
Who Is This For?
SCLC researchers, translational oncologists developing DLL3-targeted therapies, and bioinformaticians working on SCLC molecular subtype classification.
Skills & Tools Needed
- Bioinformatics (R/Python, Seurat, DESeq2 or similar)
- RNA-seq and transcription factor activity analysis
- Web application development for data visualization
- Knowledge of SCLC molecular subtypes and DLL3 biology
- Access to and analysis of public cancer genomics datasets (CCLE, GEO)
Feasibility
medium — Feasible for a bioinformatician with SCLC knowledge; public datasets exist, but building a polished interactive tool requires additional web development effort.
