REGISTER HERE   Peptides bound to class I major histocompatibility complexes (MHC) play a critical role in immune cell recognition and can trigger an immune response in many disease contexts including cancer, infectious disease, and autoimmunity. The collection of peptide MHCs (pMHCs) present on the cell surface, commonly referred to as the “immunopeptidome,” serves as a rich source of disease-specific targets. Methods to identify the immunopeptidome rely heavily on mass spectrometry to identify thousands of unique pMHC complexes in a relatively unbiased manner. However, analysing MHC MS datasets pose unique challenges over traditional proteomics datasets due to their non-tryptic nature.   Here we leverage deep learning via the Thermo Scientific INFERYS Rescoring workflow in Thermo Scientific Proteome Discoverer software to enhance the depth of pMHC identifications from MS analysis, increasing the number of tumor-associated antigens identified in in vitro samples and human tumor specimens. We further apply this analysis framework in conjunction with quantitative immunopeptidomics methods to identify tumor antigens that selectively increase in presentation in response to drug treatment and develop therapeutic strategies to exploit these treatment-modulated antigens as targets for immunotherapy.   Learn: • How to use immunopeptidomics to study antigen presentation of MHC molecules • Understand how artificial intelligence helps dig deeper into immunopeptidomics mass spectrometry data to identify tumor antigens • Understand how implementing quantitative mass spectrometry-based approaches can identify treatment-modulated tumor antigens as targets for immunotherapy   REGISTER HERE   Speaker: Dr. Lauren Stopfer Lauren earned a B.S. from the University of Wisconsin-Madison and Ph.D. from the Massachusetts Institute of Technology studying phosphoproteomics, immunopeptidomics, and systems biology. She is currently a scientist on the Proteomics team at BioNTech US, where she focuses on applying proteomics and immunopeptidomics methodologies to oncology and infectious disease applications.
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  In the development of biotherapeutics, a thorough understanding of a molecule’s product quality attributes (PQAs), and their effect on various structure-function relationships and long-term stability, is essential for ensuring the safety and efficacy of the product. At present, numerous routine chromatographic and electrophoretic assays are used to characterize and monitor individual PQAs. However, execution of multiple routine methods for batch release, stability time-points, and process/formulation development support becomes time and resource intensive, and often provides an indirect measure of biologically relevant PQAs. Introduced in 2015, the multi-attribute method (MAM), based on LC-MS peptide mapping and automation principles, provides simultaneous and site-specific detection, identification, quantitation, and quality control (monitoring) of PQAs.   A dedicated Pfizer team has been regularly employing MAM on an in-house MAM platform to support biotherapeutic process and product development. In parallel, this team has continually explored and implemented improvements in the Pfizer MAM platform, including sample preparation and data processing automation, to move toward the next generation of MAM. Recently, a pre-commercial demo model of the new Thermo Scientific Orbitrap Exploris MX mass detector was evaluated in-house by the Pfizer MAM team. Here, the results of the evaluation and an assessment of the Orbitrap Exploris MX mass detector’s suitability as a next generation MAM instrument are presented.   Learn: Pfizer MAM platform milestones for characterization and routine monitoring Automation of sample handling and data processing and reporting Evaluation and optimization of the Orbitrap Exploris MX mass detector for MAM REGISTER HERE
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