Presented By: Daniel P. Wickland, PhD
Speaker Biography: Daniel P. Wickland, Ph.D., develops computational methods to analyze high-dimensional genomic data. Dr. Wickland applies those approaches to identify biomarkers associated with disease and treatment response. As a bioinformatician, Dr. Wickland collaborates closely with laboratory scientists and clinician-investigators to provide analytic support for translational research studies in multiple complex diseases. This includes glioma, breast cancer and Alzheimer's disease. His work aids the design of novel personalized therapeutics to empower precision medicine.
Webinar: Comprehensive Profiling of Cancer Neoantigens From Aberrant RNA Splicing
Webinar Abstract: Cancer neoantigens arise from protein-altering somatic mutations in tumor and rank among the most promising next-generation immuno-oncology agents when used in combination with immune checkpoint inhibitors. We previously developed a computational framework, REAL-neo, for identification, quality control, and prioritization of both class-I and class-II human leukocyte antigen (HLA)-presented neoantigens resulting from somatic single nucleotide mutations (SNMs), small insertions and deletions (INDELs), and gene fusions. In this study, we developed a new module, SPLICE-neo, to identify neoantigens from aberrant RNA transcripts from two distinct sources: 1) DNA mutations within splice sites and 2) de novo RNA aberrant splicings. Using SPLICE-neo, we identified abundant splicing...
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