Cholesterol accumulation is documented in various malignancies including breast cancer. Consequently, depleting cholesterol in cancer cells can serve as a viable treatment strategy. We identified the potency of 2-hydroxypropyl-β-cyclodextrin (HPβCD), a cholesterol-depletor in vitro against two breast cancer cell lines: MCF-7 (Oestrogen-receptor positive, ER+) and MDA-MB-231 (Triple negative breast cancer (TNBC)). The results were then compared against two non-cancerous cell lines using cytotoxic-, apoptosis-, and cholesterol-based assays. Treatment with HPβCD showed preferential and significant cytotoxic potential in cancer cells, inducing apoptosis in both cancer cell lines (p < 0.001). This was mediated due to significant depletion of cholesterol (p < 0.001). We further tested HPβCD in a MF-1 mice (n = 14) xenograft model and obtained 73.9%, 94% and 100% reduction in tumour size for late-, intermediate-, and early-stage TNBC, respectively. We also detected molecular-level perturbations in the expression patterns of several genes linked to breast cancer and cholesterol signalling pathways using RT²-PCR arrays and have identified SFRP1 as a direct binding partner to HPβCD through SPR drug interaction analysis. This work unravels mechanistic insights into HPβCD-induced cholesterol depletion, which leads to intrinsic apoptosis induction. Results from this study potentiate employing cholesterol depletion as a promising unconventional anticancer therapeutic strategy, which warrants future clinical investigations.

The aim of this study is to enhance the extraction of informative features from complex data through the application of topological data analysis (TDA) using novel topological overlapping measures. Topological data analysis has emerged as a promising methodology for extracting meaningful insights from complex datasets. Existing approaches in TDA often involve extrapolating data points using distance correlation measures, which subsequently constrain downstream predictive tasks. Our objective is to improve the construction of the Vietoris–Rips simplicial complex by introducing topological overlapping measures. These measures take into account the interplay of direct connection strengths and shared neighbours, leading to the identification of persistent topological features. We propose the utilisation of topological overlapping measures to optimise the construction of the Vietoris–Rips simplicial complex, offering a more refined representation of complex data structures. The application of topological overlapping measures results in the identification of plentiful persistent topological features. This enhancement contributes to an improvement of up to 20% in cancer phenotype prediction across different cancer types. Our study demonstrates the effectiveness of utilising topological overlapping measures in optimising the construction of the Vietoris–Rips simplicial complex. The identified persistent topological features significantly enhance the predictive accuracy of cancer phenotypes. This novel approach has the potential to advance the field of topological data analysis and improve our understanding of complex data structures, particularly in the context of cancer research and predictive modelling. Further exploration and application of these measures may yield valuable insights in various domains dealing with intricate datasets.

Patient-derived organoids are valuable for modeling disease pathogenesis, therapeutic screening, and advancing personalized medicine. Here, we present a protocol for generating and characterizing intestinal organoids from South African patients, using both healthy and cancerous tissues. We describe steps for generating organoid cultures, quantitative reverse-transcription polymerase chain reaction (RT-qPCR), immunofluorescence microscopy, and histological staining. Notably, we provide a detailed method for cholesterol profiling in organoids and tissues, offering a powerful tool for researchers exploring lipid biology in organoid systems.