Project
HCC1395 Neoantigen Prioritization Workflow
Evidence-based computational prioritization of candidate neoantigens under experimental feasibility constraints.
Overview
The HCC1395 Neoantigen Prioritization Workflow is a reproducible computational project for ranking candidate mutation-derived neoantigens in the HCC1395/HCC1395BL tumor-normal model. It integrates tumor-normal variant evidence, HLA-informed MHC-I binding prediction, RNA support, primer feasibility assessment, evidence tiering, robustness analysis, and selected DNA-level validation summaries.
Motivation
Computational neoantigen prediction can produce a large candidate space, while experimental validation capacity is limited. Ranking candidates only by predicted binding affinity can miss practical constraints such as expression support, DNA evidence, mutant-versus-wildtype contrast, primer feasibility, and technical validation readiness.
Workflow
- 01
Tumor-normal variant processing
- 02
Candidate mutation filtering
- 03
HLA typing and MHC-I binding prediction
- 04
EVN-based evidence scoring
- 05
Primer feasibility assessment
- 06
Evidence tiering
- 07
Robustness analysis
- 08
DNA-level validation summary
Key Outputs
- Ranked candidate tables and final shortlist summaries
- Evidence tier distributions for prioritized candidates
- Primer feasibility ranking comparisons
- EVN baseline and calibrated scoring comparisons
- DNA-level validation summary for selected candidates
Technical Stack
Scientific Scope and Limitations
This workflow prioritizes candidate neoantigens computationally and includes DNA-level validation for selected mutation sites. It does not directly validate peptide presentation, T-cell recognition, or immunogenicity. Results should be interpreted as a prioritization workflow for translational bioinformatics research, not as a clinically validated vaccine design system.