All Cases
Drug R&D
Tumor Neoantigen Prediction & Ranking
Project Question
A research team needed to prioritize tumor neoantigens for immunotherapy target selection. The challenge was ranking hundreds of candidate peptides by predicted immunogenicity and HLA binding affinity.
Analysis Approach
Combined in-silico binding prediction, immunogenicity scoring, and population HLA frequency weighting to generate a ranked candidate list with supporting evidence.
Input Data
- Tumor mutation data (VCF)
- Patient HLA typing
- Reference peptide libraries
Deliverables
- Ranked neoantigen table
- HLA binding affinity heatmap
- Immunogenicity score distribution
- Candidate shortlist with rationale
Representative HLA binding affinity heatmap generated from mock data for layout demonstration.
This output represents the analytical approach and visualization style. Actual project results depend on input data quality, sample size, and validation strategy.