Proceedings of the National Academy of SciencesBioinformaticsPeer-reviewed journal article
IRIS: Big data-informed discovery of cancer immunotherapy targets arising from pre-mRNA alternative splicing
Yang PaniD, John W. Phillips, Beatrice D. ZhangiD, Miyako NoguchiiD, Eric KutscheraiD, Jami McLaughlin, Pavlo A. NesterenkoiD, Zhiyuan MaoiD, Nathanael Joshua Bangayan, Robert WangiD, Wendy TraniD, Harry Taegyun YangiD, Yuanyuan WangiD, Yang XuiD, Matthew B. ObusaniD, Donghui Cheng, Alex LeeiD, Kathryn E. Kadash-EdmondsoniD, Ameya S. ChamphekariD, Cristina Puig-SausiD, Antoni RibasiD, Robert Michael PrinsiD, Christopher S. SeetiD, Gay M. CrooksiD, Owen N. WitteiD, Yi XingiD
- Citations
- 51
- FWCI
- 5.95
- Field percentile
- top 2%
Abstract
Aberrant alternative splicing (AS) is widespread in cancer, leading to an extensive but largely unexploited repertoire of potential immunotherapy targets. Here we describe IRIS, a computational platform leveraging large-scale cancer and normal transcriptomics data to discover AS-derived tumor antigens for T-cell receptor (TCR) and chimeric antigen receptor T-cell (CAR-T) therapies. Applying IRIS to RNA-Seq data from 22 glioblastomas resected from patients, we identified candidate epitopes and validated their recognition by patient T cells, demonstrating IRISs utility for expanding targeted cancer immunotherapy.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
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