DNA templated Click Chemistry via 5-vinyl-2'-deoxyuridine and an acridine-tetrazine conjugate induces DNA damage and apoptosis in cancer cells.

AIMS: Click Chemistry is providing valuable tools to biomedical research, but its direct use in therapies remains nearly unexplored. For cancer treatment, nucleoside analogues (NA) such as 5-vinyl-2'-deoxyuridine (VdU) can be metabolically incorporated into cancer cell DNA and subsequently "clicked" to form a toxic product. The inverse electron-demand Diels-Alder (IEDDA) reaction between VdU and an acridine-tetrazine conjugate (PINK) has previously been used to label cell nuclei of cultured cells. Here, we report tandem usage of VdU and PINK to induce cytotoxicity. MAIN METHODS: Cell lines were subsequently treated with VdU and PINK, and cell viability was measured via well confluency and 3D tumor spheroid assays. DNA damage and apoptosis were evaluated using Western Blotting and cell cycle analysis by flow cytometry. Double stranded DNA break (DSB) formation was measured using the comet assay. Apoptosis was assessed by fluorescent detection of externalized phosphatidylserine residues. KEY FINDINGS: We report that the combination of VdU and PINK synergistically induces cytotoxicity in cultured human cells. The combination of VdU and PINK strongly reduced cell viability in 2D and 3D cultured cancer cells. Mechanistically, the compounds induced DNA damage through DSB formation, which leads to S-phase accumulation and apoptosis. SIGNIFICANCE: The combination of VdU and PINK represents a novel and promising DNA-templated "click" approach for cancer treatment via selective induction of DNA damage.

Survival Analysis of High-Dimensional Data With Graph Convolutional Networks and Geometric Graphs.

This article proposes a survival model based on graph convolutional networks (GCNs) with geometric graphs directly constructed from high-dimensional features. First, we clarify that the graphs used in GCNs play an important role in processing the relational information of samples, and the graphs that align well with the underlying data structure could be beneficial for survival analysis. Second, we show that sparse geometric graphs derived from high-dimensional data are more favorable compared with dense graphs when used in GCNs for survival analysis. Third, from this insight, we propose a model for survival analysis based on GCNs. By using multiple sparse geometric graphs and a proposed sequential forward floating selection algorithm, the new model is able to simultaneously perform survival analysis and unveil the local neighborhoods of samples. The experimental results on real-world datasets show that the proposed survival analysis approach based on GCNs outperforms a variety of existing methods and indicate that geometric graphs can aid survival analysis of high-dimensional data.