SpliceMutr

SpliceMutr identifies splicing-derived neoantigens from tumor and normal transcriptomic data to predict MHC-binding affinity and estimate splicing antigenicity scores.

Key Features:

• Tumor-specific antigenic splice variant identification: SpliceMutr identifies tumor-specific antigenic splice variants from matched tumor and normal data.
• MHC-binding affinity prediction: The pipeline predicts MHC-binding affinity for identified splicing-derived neoantigens.
• Splicing antigenicity scoring: SpliceMutr estimates splicing antigenicity scores per gene and per sample.
• Pan-cancer analysis capability: The tool enables generation of splicing-derived neoantigens and neoantigenicity scores across all cancer types.

Scientific Applications:

• Cancer immunotherapy biomarker discovery: SpliceMutr correlates splicing antigenicity with established biomarkers of antitumor immunity across The Cancer Genome Atlas cancer types.
• Splicing machinery mutation analysis: The tool identifies increased splicing antigenicity in carriers of mutations within splicing machinery genes.
• Immune checkpoint inhibitor response prediction: SpliceMutr analyzes changes in splicing antigen abundance from baseline to post-treatment in melanoma patients treated with mono- or combined immune checkpoint inhibition, finding reduced splicing antigens in progressors and increased splicing antigenicity in responders.
• Tumor versus normal splicing antigenicity comparison: The pipeline demonstrates higher splicing antigenicity in tumor samples compared with normal tissue.

Methodology:

SpliceMutr analyzes transcriptomic data from tumor and normal samples to identify splicing-derived neoantigens, predicts MHC-binding affinity, and estimates splicing antigenicity scores per gene.