Tumor Neoantigen Prediction & Immunotherapy Assessment
Tumor Neoantigen Prediction
Integrating whole-exome and transcriptome sequencing data through automated pipelines for somatic mutation detection, HLA typing, MHC affinity prediction, and immunogenicity assessment. Outputting high-confidence candidate neoantigen rankings to support early R&D decisions for tumor vaccines and cell therapies.
Applicable Scenarios
- Personalized tumor vaccine design
- TCR-T / CAR-T target screening
- Immune checkpoint inhibitor combination strategies
- Neoadjuvant therapy response prediction
Data Inputs
- WES raw data (tumor + matched normal)
- RNA-seq expression data
- HLA typing results (or inferred from WES)
- Clinical pathology information (optional)
Deliverables
- Candidate neoantigen ranking (with MHC affinity, mutation position, expression level)
- Immunogenicity score and confidence grading
- HLA-restricted subset analysis
- Standardized PDF/HTML report
Analysis Pipeline
Raw Data QC
Fastq QC, adapter trimming, quality filtering
Alignment
BWA / STAR alignment to reference genome
Variant Calling
Mutect2 / Strelka2 somatic mutation detection
HLA Typing
OptiType / HLA-HD high-resolution typing
MHC Prediction
NetMHCpan / MHCflurry affinity prediction
Immunogenicity
Immunogenicity scoring, self-antigen filtering, clonality assessment
Ranking & Report
Multi-dimensional weighted ranking, standardized report output
Related Case
Tumor Neoantigen Prediction & Ranking
Built an automated WES/RNA-seq analysis pipeline for tumor neoantigen discovery, integrating HLA typing, MHC prediction, immunogenicity assessment, and candidate peptide ranking, connected to mRNA design, IVT, LNP encapsulation, and quality validation.
Deliverables
- Automated Analysis Pipeline
- Candidate Neoantigen Ranking Table
- mRNA Sequence Design Plan
- LNP Preparation & QC Records
- Standardized Delivery Report