Basal cell carcinoma (BCC) is the most common skin cancer worldwide, with South Africa having the highest incidence rate only after Australia. The most effective treatment modality for BCC is tumor excision via Mohs surgery (pioneered by Dr. Frederic Mohs of the University of Wisconsin in 1930), a microscopically controlled surgery that removes a tumor piecemeal in layers until each layer is free of any neoplastic tissue. The major drawback of Mohs excision is that the surgeon might miss any neoplastic tissue as the tumor margin is not always well defined, and the tumor often could extend beyond the superficial layers of skin. Moreover, it's a time-consuming, expensive procedure that takes generally 3-4 h, at times even more, if several rounds of excisions are warranted. In South Africa, at the time of writing, therapy using the surgery cost around R45,000. The status quo thus necessitates identifying BCC cells both in the superficial layers and beyond the layers of the skin in individual patients. Our aim was to identify BCC-specific cell surface proteins and design, engineer, and test a range of SNAPtag–based antibody fusion proteins that would specifically bind to and detect such BCC cell surface receptors. The SNAP-tag antibody technology is based on the genetic fusion of a disease-specific ligand to a protein tag derived from O6-alkylguanine-DNA alkyltransferase, which would allow for covalent auto-labeling of the corresponding antibody based fusion proteins with benzylguanine-modified (BG) substrates (e.g., fluorophores) under physiological conditions with high efficiency at 1:1 stoichiometry. This would allow to develop a unique immunological screening modality which should allow to visually label BCC lesions for a more precise surgical excision. The best-performing SNAP-tag–based diagnostic antibodies resulting from these studies would be further evaluated in the future in suitable mouse models, thus aiming to reduce the time needed for surgical removal of BCC lesions and complete removal of the tumor from both superficial and deep layers of the skin by a single-excision procedure. We used an integrated computational tool to re-analyze publicly available cDNA microarray data in combination with theoretical search to identify BCC-associated antigens. Accordingly, six different antigens were selected and single-chain variable fragments (scFv) targeting these antigens were cloned in fusion with SNAP-tag encoding gene into a custom expression vector for production in a secretory mammalian system. scFv-SNAP-tag protein was isolated from the cell culture supernatant by immobilized metal affinity chromatography and eluted protein samples were analyzed by gel electrophoresis and immunoblotting. The absolute amount of the full-length protein was quantified by densitometry. Purified scFv-SNAP-tag proteins were validated for specific binding to corresponding antigen-positive cells by flow cytometry and confocal microscopy. Of the six different scFv-SNAP-tag fusion proteins cloned, four were successfully expressed in HEK293T cells. The specific binding to EpCAM, EMA, CSPG4, and CD138 antigenexpressing cell lines was observed on incubation with scFvUBS54-SNAP-tag, scFvID405- SNAP-tag, 9.2.27scFv-SNAP-tag, and scFvh-STL002-SNAP-tag, respectively. In addition, we showed the selective cell killing effect of scFvUBS54-SNAP after conjugating it with the cytotoxic drug BG-modified auristatin-F (BG-AF). In conclusion, we identified various cell surface antigens along with one possibly novel antigen for BCC detection and therapy. Further, we successfully designed and synthesized SNAP tag based antibody fusion proteins and showed their functional activity by selective binding to the corresponding antigens on the surface of tumor cells. Based on these findings, we presume that these antibodies can effectively bind to BCC and can confirm EpCAM as one of the target antigens, which has already been reported to be a standard immunophenotypic marker for differential BCC diagnosis.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/36763 |
| Date | 29 August 2022 |
| Creators | Madheswaran, Suresh |
| Contributors | Barth, Stefan |
| Publisher | Faculty of Health Sciences, Department of Clinical Laboratory Sciences |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Doctoral Thesis, Doctoral, PhD |
| Format | application/pdf |
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