bioRxivCancer BiologyPreprint
Extending structural surfaceomics to identify aberrant conformations of tumor surface proteins as potential immunotherapy targets
Audrey KishishitaiD, Sabine Cismoski, Tianna GrantiD, Rucha Deo, Sanjana PrudhviiD, Catherine SueiD, Abhilash BarpandaiD, Clinton YuiD, Sanjyot Vinayak ShenoyiD, Sarah Berman, Audrey G. Reeves, Haolong LiiD, Tianyi LiuiD, Akul Naik, Deeptarup BiswasiD, Fenglong JiaoiD, Yi HeiD, MATTHEW J. HANCOCKiD, Radhika DalaliD, Arthur O. ZalevskyiD, Michael R. HoopmanniD, Chun YeiD, Rosa VineriD, Felix Yi-Chung FengiD, Kamal MandaliD, , Arun P. WiitaiD
Abstract
The complement of tumor cell surface proteins, or "surfaceome", is a rich source of potential immunotherapy targets. To move beyond expression-based target discovery, we previously described "structural surfaceomics," combining crosslinking mass spectrometry (XL-MS) with surface protein biotinylation to identify conformation-selective targets. In our prior work, we applied this method to a single model of acute myeloid leukemia (AML), identifying active integrin beta-2 as a promising target. Here, we expand structural surfaceomics to identify additional immunotherapy targets and surface protein biology across additional models of AML, multiple myeloma, and prostate cancer, as well as donor peripheral blood mononuclear cells. Utilizing these models and different chemical crosslinkers, we compile an extensive database of 5,209 crosslinks. We characterize both shared and unique crosslink-based features, identifying 1,612 disease model-specific crosslinks, including 212 potentially defining tumor-specific conformations based on distance constraint violations relative to AlphaFold predictions. We further implement a suite of emerging modeling tools to predict tumor-specific protein structures. We probe crosslinking patterns suggesting multiple myeloma-specific CD48 and AML-specific integrin 1/{beta}4 heterodimer conformations. This work establishes a resource for cancer structural biology by implementation of structural surfaceomics. Our findings also point toward more realistic protein design models, potentially enabling systematic detection of targetable cancer-specific epitopes for next-generation immunotherapies.
- organism: human
- assay: proteomics
- research focus: cell type annotation
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
- Citations
- 0
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