Tumor-Specific Splicing Neo Antigen Detection (TS-SNAD) is a method designed to discover novel splicing neoantigens, which are a dominant source of tumor-specific peptides. This approach integrates long-read sequencing and short-read sequencing technologies to achieve its goals:
- Long-read sequencing enables the discovery of previously unannotated transcripts, while
- Short-read sequencing quantifies the expression of these novel transcripts with high throughput.
This hybrid sequencing approach helps identify tumor-specific transcripts and their unique splice forms, leading to the generation of new antigens. Additionally, Whole Genome Sequencing (WGS) or Optitype is employed to predict an individual's MHC-I molecules, focusing on shared MHC-I molecules to ensure broader applicability across a larger patient population.
Furthermore, netMHCpan is used to predict the binding affinity of these new antigens to MHC-I molecules and assess their immunogenicity.
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01_optitype.sh
- OptiType is a novel HLA genotyping algorithm based on integer linear programming.
- It can produce accurate 4-digit HLA genotyping predictions from NGS data by simultaneously selecting all major and minor HLA Class I alleles.
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02_pvactools.sh
- pVACtools is a suite of cancer immunotherapy tools designed to identify and prioritize neoantigens derived from tumor-specific novel transcripts.
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03_generate_neoantigen.sh & 05_neoantigen_filter.R
- These tools enable the identification of tumor-specific exon-exon splicing junctions and the selection of novel neoantigens from all antigen candidates.
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04_neoantigen_abundance.sh
- This step refines the selection of neoantigens by considering the abundance of the originating transcript and the universality of HLA-I molecules.
This approach provides a comprehensive pipeline to discover and prioritize neoantigens, enhancing the potential for more effective cancer immunotherapies.