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Accurately identifying cell-populations is paramount to the quality of downstream analyses and overall interpretations of single-cell RNA-seq (scRNA-seq) datasets but remains a challenge. The quality of single-cell clustering depends on the proximity metric used to generate cell-to-cell distances. Accordingly, proximity metrics have been benchmarked for scRNA-seq clustering, typically with results averaged across datasets to identify a highest performing metric. However, the 'best-performing' metric varies between studies, with the performance differing significantly between datasets. This suggests that the unique structural properties of an scRNA-seq dataset, specific to the biological system under study, have a substantial impact on proximity metric performance. Previous benchmarking studies have omitted to factor the structural properties into their evaluations. To address this gap, we developed a framework for the in-depth evaluation of the performance of 17 proximity metrics with respect to core structural properties of scRNA-seq data, including sparsity, dimensionality, cell-population distribution and rarity. We find that clustering performance can be improved substantially by the selection of an appropriate proximity metric and neighbourhood size for the structural properties of a dataset, in addition to performing suitable pre-processing and dimensionality reduction. Furthermore, popular metrics such as Euclidean and Manhattan distance performed poorly in comparison to several lessor applied metrics, suggesting that the default metric for many scRNA-seq methods should be re-evaluated. Our findings highlight the critical nature of tailoring scRNA-seq analyses pipelines to the dataset under study and provide practical guidance for researchers looking to optimize cell-similarity search for the structural properties of their own data. [ABSTRACT FROM AUTHOR]

In search of redox mechanisms in breast cancer, we uncovered a striking role for glutathione peroxidase 2 (GPx2) in oncogenic signaling and patient survival. GPx2 loss stimulates malignant progression due to reactive oxygen species/hypoxia inducible factor-α (HIF1α)/ VEGFA (vascular endothelial growth factor A) signaling, causing poor perfusion and hypoxia, which were reversed by GPx2 reexpression or HIF1α inhibition. Ingenuity Pathway Analysis revealed a link between GPx2 loss, tumor angiogenesis, metabolic modulation, and HIF1α signaling. Single-cell RNA analysis and bioenergetic profiling revealed that GPx2 loss stimulated the Warburg effect in most tumor cell subpopulations, except for one cluster, which was capable of oxidative phosphorylation and glycolysis, as confirmed by coexpression of phosphorylated-AMPK and GLUT1. These findings underscore a unique role for redox signaling by GPx2 dysregulation in breast cancer, underlying tumor heterogeneity, leading to metabolic plasticity and malignant progression. [ABSTRACT FROM AUTHOR]

Hyperleptinaemia is a well-established therapeutic side effect of drugs inhibiting the androgen axis in prostate cancer (PCa), including main stay androgen deprivation therapy (ADT) and androgen targeted therapies (ATT). Given significant crossover between the adipokine hormone signalling of leptin and multiple cancer-promoting hallmark pathways, including growth, proliferation, migration, angiogenesis, metabolism and inflammation, targeting the leptin axis is therapeutically appealing, especially in advanced PCa where current therapies fail to be curative. In this study, we uncover leptin as a novel universal target in PCa and are the first to highlight increased intratumoural leptin and leptin receptor (LEPR) expression in PCa cells and patients' tumours exposed to androgen deprivation, as is observed in patients' tumours of metastatic and castrate resistant (CRPC) PCa. We also reveal the world-first preclinical evidence that demonstrates marked efficacy of targeted leptin-signalling blockade, using Allo-aca, a potent, specific, and safe LEPR peptide antagonist. Allo-aca-suppressed tumour growth and delayed progression to CRPC in mice bearing LNCaP xenografts, with reduced tumour v ascularity and altered pathways of apoptosis, transcription/translation, and energetics in tumours determined as potential mechanisms underpinning anti-tumour efficacy. We highlight LEPR blockade in combination with androgen axis inhibition represents a promising new therapeutic strategy vital in advanced PCa treatment. [ABSTRACT FROM AUTHOR]

Genome-wide regulatory networks enable cells to function, develop, and survive. Perturbation of these networks can lead to appearance of a disease phenotype. Inspired by Conrad Waddington's epigenetic landscape of cell development, we use a Hopfield network formalism to construct an attractor landscape model of disease progression based on protein- or gene-correlation networks of Parkinson's disease, glioma, and colorectal cancer. Attractors in this landscape correspond to normal and disease states of the cell. We introduce approaches to estimate the size and robustness of these attractors, and take a network-based approach to study their biological features such as the key genes and their functions associated with the attractors. Our results show that the attractor of cancer cells is wider than the attractor of normal cells, suggesting a heterogeneous nature of cancer. Perturbation analysis shows that robustness depends on characteristics of the input data (number of samples per time-point, and the fraction which converge to an attractor). We identify unique gene interactions at each stage, which reflect the temporal rewiring of the gene regulatory network (GRN) with disease progression. Our model of the attractor landscape, constructed from large-scale gene expression profiles of individual patients, captures snapshots of disease progression and identifies gene interactions specific to different stages, opening the way for development of stage-specific therapeutic strategies. [ABSTRACT FROM AUTHOR]