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 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.
Dr. Lauren Stopfer, Scientist, Proteomics, BioNTech
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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