Rendong Yang
Northwestern University, IL·Genetics
Cells cut and rejoin gene messages in different ways, a process called alternative splicing. This lets one gene make several protein versions.
- Fields
Genetics
Computer Science
Biochemistry
- Topics
exitrons
exitron splicing
alternative splicing
- Grants
- 1 known grant, through Jun 2027
What they work on
- Build an integrated computational framework to detect and validate exitrons, coding-exon regions that can be spliced out like introns, using multi-omics data from several sequencing platforms, and identify splicing factors that favor exitron splicing in cancers.
- Develop statistical approaches to find genes and pathways enriched in exitron splicing changes, and use semi-supervised machine learning on transcriptomic features to predict exitron-linked cancer driver genes.
- Create a tool to identify exitron-derived neoantigens, validate them with mass spectrometry immunopeptidome data, and test their link to outcomes in patients receiving immune checkpoint inhibitor therapy.
More about the research
In cancer, splicing often goes wrong, but some unusual splicing types are poorly studied because tools to detect them are lacking. This project focuses on exitrons. An exitron is a stretch inside a protein-coding exon that can be cut out as if it were an intron. Removing it changes the resulting protein, possibly altering its working parts or chemical modification sites. Exitron splicing is disrupted in cancer and often hits cancer-related genes. Tumor-specific exitron splicing may also create unusual protein fragments, called neoantigens, that the immune system could recognize. Such neoantigens could be targets for cancer vaccines or T-cell therapies.
Why it matters
Exitron splicing may produce abnormal proteins that help cancers grow, and may also create markers the immune system can target. This work could help predict which cancer patients will respond to immune checkpoint inhibitor therapy. It could also point to new neoantigen targets for cancer vaccines or T-cell therapies, and give researchers a dedicated computational platform for exitron analysis.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Theresa Guo
University of California-San Diego, CA·Medicine & Clinical Research·Early-career
Predict and rank splice-variant-derived peptides, short protein fragments made only by tumors with abnormal RNA splicing, from head and neck squamous cell carcinoma using an established computational pipeline.
Liang Chen
University of Southern California, CA·Genetics
Relate NMD's fine-tuning of gene expression to the genes cancer cells depend on, using NMD residual signatures built from DepMap cell line data and tested for predictive power in TCGA tumor samples.
Nazanin Majd
University of Texas MD Anderson Cancer Center, TX·Medicine & Clinical Research
Run a phase II clinical trial of the checkpoint inhibitors nivolumab and ipilimumab in patients with Shh medulloblastoma, and test whether clinical outcomes correlate with U1 (r.3A>G) snRNA mutation status.
The school
Northwestern University: 71 labs on PI Pages, $1.3B a year on research, 536 research doctorates a year.
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