Please join us on Tuesday, November 14th, 2017 at University of Illinois at Chicago. Dinner starts at 6:30 PM with the seminar immediately following at 7:00 PM. Please be sure to register so our sponsors know how much food to order! We hope you can join us! To register click here
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Title: Tumor Neoantigens Derived From RNA Sequence Analysis
Speaker: Dr. Shaojun Tang from Georgetown University
Abstract: Successful treatment of cancers with Immune Checkpoint Inhibitors (ICIs) has been associated with the mutational load of tumors. The biological rationale for this association between mutational load and ICI response is that neoantigens are generated by mutations in protein coding sequences that provide a steady flow of neoantigens to prime the immune system for the production of antigen-specific tumor-infiltrating lymphocytes (TILs). It is thought that mutant protein fragments will lead to altered MHC/peptide recognition and immune cell activation; ICI treatment enhances TIL functionality. Neoantigens are also relevant for an alternative, cell-based immunotherapeutic approach, i.e. Adoptive Cell Transfer (ACT). This concept of neoantigens derived from DNA mutations has led to an intense line of investigation to uncover relevant neoantigens. However, there has been mixed success with the current neoantigen discovery approach based on DNA mutation analysis of tumor samples by exome sequencing of genomic DNA. The current concept of neoantigens derived from mutant DNA ignores an alternative mechanism that can also generate neoantigens in cancers: Posttranscriptional editing of primary RNA. Here we propose to use full-length Single Molecule Real Time (SMRT) RNAseq to uncover pathologically edited mRNAs in cancers and complement the discovery of pathologic mRNA. We will discuss the respective algorithms and propose the combination with identification of candidate neoantigen peptides by mass spectrometry.
About Dr. Tang: Dr. Shaojun Tang is an Assistant Professor, Innovation Center for Biomedical Informatics (ICBI), Department of Oncology, at Georgetown University. His research primarily focuses on the development of predictive biomarkers for the analysis of checkpoint inhibitor cancer immunotherapy and targeted therapy clinical data, including breast cancer and melanoma data sets. He is also interested in precision medicine, translational bioinformatics through multi-‘omics’ molecular profiling using various machine learning approaches. Further areas of substantive interest include clinical trial studies, biomarker discovery and health informatics data mining. He holds a Ph.D. in Bioinformatics and a M.Sc. in Computer Engineering from University of Florida. Subsequently, he conducted three years of research at Harvard Medical School, before he joined Georgetown University in January 2016 as an Assistant Professor.