Oncolytic Virus Response Signature Database
RP1 oncolytic virus induces two molecularly distinct CD8+ T cell populations — progenitor-like precursors and terminal effectors — and a precursor-associated gene signature correlates with clinical response to RP1 plus PD-1 blockade in real melanoma patients, making this a candidate predictive…
Oncolytic Virus Response Signature Database
RP1 oncolytic virus induces two molecularly distinct CD8+ T cell populations — progenitor-like precursors and terminal effectors — and a precursor-associated gene signature correlates with clinical response to RP1 plus PD-1 blockade in real melanoma patients, making this a candidate predictive biomarker for oncolytic virus therapy.
Build a curated database of T cell gene signatures associated with response to oncolytic virus (OV) therapies, organized by virus type, cancer type, and combination partner (e.g., with checkpoint inhibitors). Each entry would include the signature genes, the study that identified them, the patient population, the response endpoint used, and links to the underlying data. The database would also cross-reference these signatures with known exhaustion, progenitor, and effector T cell programs from broader immunotherapy literature.
The platform would include an analysis module where researchers can upload their own transcriptomic data and query it against known OV response signatures — similar to how GSEA queries gene sets. A clinical trial tracker would link each signature to ongoing trials where it could serve as a stratification biomarker.
Oncolytic virus therapies are entering a critical phase: RP1 (vusolimogene oderparepvec) in combination with cemiplimab recently showed strong results in melanoma, and the field is now asking who responds and why. A centralized, cross-study resource for OV response signatures would accelerate biomarker validation, inform patient selection, and help researchers understand the shared vs. distinct mechanisms across different OV platforms.
Who Is This For?
Immuno-oncology researchers studying oncolytic viruses, biomarker scientists designing response prediction tools, and clinical investigators running OV therapy trials.
Skills & Tools Needed
- Bioinformatics (gene signature analysis, GSEA, scRNA-seq)
- Database design and web development
- Immunology knowledge (T cell states, exhaustion programs)
- Literature curation and data extraction
- ClinicalTrials.gov API integration for trial tracking
Feasibility
medium — The database and analysis module are technically achievable; the main challenge is assembling initial curated content and building the gene expression query layer, which requires dedicated bioinformatics expertise.
Inspired by: Intratumoural oncolytic HSV-1 reshapes the local and systemic immune landscape through CD8+ T cell reprogramming
