ISOTOPE
ISOTOPE identifies tumor-specific splicing-derived epitopes from RNA sequencing data to detect neoepitopes presented by MHC-I complexes for cancer immunotherapy research.
Key Features:
- Comprehensive identification: Systematically identifies splicing-derived neoepitopes from RNA-seq to provide genome-scale analysis of splicing alterations.
- Integration with mass spectrometry: Integrates RNA-seq-based predictions with mass spectrometry analysis of MHC-I-associated proteins to validate candidate neoepitopes.
- Focus on splicing events: Targets splicing alterations including exonizations, neoskipping, and A5_A3 events as sources of novel epitopes, noting they can produce more candidates than somatic mutations.
- Event-specific analysis: Categorizes event types and uses read counts mapped to genomic junctions to assess event expression.
- Junction read-count input: Accepts input files containing read counts mapped to all possible junctions generated by tools such as Junckey.
- MHC-I binding assessment: Predicts or assesses MHC-I binding affinity of candidate splicing-derived epitopes.
Scientific Applications:
- Immunotherapy response prediction: Identifies splicing-derived neoepitopes and assesses their MHC-I binding affinity to aid prediction of responses to immune therapies, while noting no difference in epitope counts between responders and non-responders in reported analyses.
- Molecular characterization of tumors: Provides genome-scale analysis of splicing alterations to investigate their impact on tumor immunogenicity and therapy effectiveness.
- Facilitating epitope discovery: Supports prediction of candidate epitopes from splicing events to inform development of personalized cancer vaccines and other targeted therapies.
Methodology:
Processes junction read-count inputs (from tools such as Junckey) to identify significantly expressed splicing events (exonizations, neoskipping, A5_A3), predicts splicing-derived epitopes and assesses their MHC-I binding, and integrates mass spectrometry of MHC-I-associated proteins for validation.
Topics
Details
- License:
- GPL-3.0
- Tool Type:
- command-line tool
- Programming Languages:
- Python
- Added:
- 1/18/2021
- Last Updated:
- 2/11/2021
Operations
Publications
Trincado JL, Reixachs-Sole M, Pérez-Granado J, Fugmann T, Sanz F, Yokota J, Eyras E. ISOTOPE: ISOform-guided prediction of epiTOPEs in cancer. Unknown Journal. 2020. doi:10.1101/2020.06.18.159244.