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.