Landscape of surfaceome and endocytome in human glioma is divergent and depends on cellular spatial organization

Abstract

Therapeutic strategies directed at the tumor surfaceome (TS), including checkpoint inhibitor blocking antibodies, antibody drug conjugates (ADCs), and chimeric antigen receptor T (CAR-T) cells, provide a new armament to fight cancer. However, a remaining bottleneck is the lack of strategies to comprehensively interrogate patient tumors for potential TS targets. Here, we have developed a platform (tumor surfaceome mapping [TS-MAP]) integrated with a newly curated TS classifier (SURFME) that allows profiling of primary 3D cultures and intact patient glioma tumors with preserved tissue architecture. Moreover, TS-MAP specifically identifies proteins capable of endocytosis as tractable targets for ADCs and other modalities requiring toxic payload internalization. In high-grade gliomas that remain among the most aggressive forms of cancer, we show that cellular spatial organization (2D vs. 3D) fundamentally transforms the surfaceome and endocytome (e.g., integrins, proteoglycans, semaphorins, and cancer stem cell markers) with general implications for target screening approaches, as exemplified by an ADC targeting EGFR. The TS-MAP platform was further applied to profile the surfaceome and endocytome landscape in a cohort of freshly resected gliomas. We found a highly diverse TS repertoire between patient tumors, not directly associated with grade and histology, which highlights the need for individualized approaches. Our data provide additional layers of understanding fundamental to the future development of immunotherapy strategies, as well as procedures for proteomics-based target identification and selection. The TS-MAP platform should be widely applicable in efforts aiming at a better understanding of how to harness the TS for personalized immunotherapy.

sBioSITe enables sensitive identification of the cell surface proteome through direct enrichment of biotinylated peptides

“…The diameter of each bubble is representative of the intensity of biotinylated peptides from that protein. C Pie chart showing the proportions of various proteins that were present in the resources referred to: SURFY (the in silico human surfaceome [ 3 ]), the Cancer Surfaceome Atlas [ 18 ], SURFME [ 19 ] and UniProt for annotations of secreted proteins [ 20 ]. D Relative proportions of the Gene Ontology annotations for molecular function of the identified biotinylated proteins …”

“…We next assessed the robustness of the sBioSITe method for enrichment of proteins that are known to be expressed on the cell surface. We compared the list of biotinylated proteins to three resources: one, SURFY, a machine learning-based in silico surface proteome database [ 3 ], two, the Cancer Surfaceome Atlas, a database of genes encoding surface proteins that is based on experimental evidence, computational prediction and database annotation [ 18 ] and three, SURFME, a manually curated catalog of surface proteins [ 19 ]. Additionally, as many secreted proteins are part of the extracellular matrix which can also be labeled or can act as ligands that bind surface proteins, we made additional annotations for biotinylated proteins with “secreted” as their subcellular location in the UniProt resource [ 20 ].…”

Tumor suppressor role of the complement inhibitor CSMD1 and its role in TNF-induced neuroinflammation in gliomas

“…Patient samples were fixed with 4% PFA, cryopreserved in 0.5 M sucrose and embedded in OCT for cryosectioning as described [ 31 ]. Samples were then incubated with 5% goat serum followed by primary antibody incubation overnight at 4°C.…”

Mechanisms of a Mycobacterium tuberculosis Active Peptide

“…The LC–MS/MS analysis was performed using a Tribrid mass spectrometer fusion equipped with a Nanospray Flex ion source and coupled with an EASY-nLC 1000 ultrahigh pressure liquid chromatography (UHPLC) pump (Thermo Fischer Scientific, Waltham, MA, USA) [ 19 ]. The proteins, 1 µg, were injected into the LC–MS/MS system.…”