The innately heterodimeric K _2P filter permits two C-type gating mechanisms, pinching and dilation, to operate in one channel.

Adipose tissue is part of the prostate cancer (PCa) microenvironment not only in the periprostatic area, but also in the most frequent metastatic sites, such as bone marrow and pelvic lymph nodes. The involvement of periprostatic adipose tissue (PPAT) in the aggressiveness of PCa is strongly suggested by numerous studies. Many molecules play a role in the reciprocal interaction between adipocytes and PCa cells, including adipokines, hormones, lipids, and also lipophilic pollutants stored in adipocytes. The crosstalk has consequences not only on cancer cell growth and metastatic potential, but also on adipocytes. Although most of the molecules released by PPAT are likely to promote tumor growth and the migration of cancer cells, others, such as the adipokine adiponectin and the n-6 or n-3 polyunsaturated fatty acids (PUFAs), have been shown to have anti-tumor properties. The effects of PPAT on PCa cells might therefore depend on the balance between the pro- and anti-tumor components of PPAT. In addition, genetic and environmental factors involved in the risk and/or aggressiveness of PCa, including obesity and diet, are able to modulate the interactions between PPAT and cancer cells and their consequences on the growth and the metastatic potential of PCa.

SK3 channels are potassium channels found to promote tumor aggressiveness. We have previously demonstrated that SK3 is regulated by synthetic ether lipids, but the role of endogenous ether lipids is unknown. Here, we have studied the role of endogenous alkyl- and alkenyl-ether lipids on SK3 channels and on the biology of cancer cells. Experiments revealed that the suppression of alkylglycerone phosphate synthase or plasmanylethanolamine desaturase 1, which are key enzymes for alkyl- and alkenyl-ether-lipid synthesis, respectively, decreased SK3 expression by increasing micro RNA (miR)-499 and miR-208 expression, leading to a decrease in SK3-dependent calcium entry, cell migration, and matrix metalloproteinase 9-dependent cell adhesion and invasion. We identified several ether lipids that promoted SK3 expression and found a differential role of alkyl- and alkenyl-ether lipids on SK3 activity. The expressions of alkylglycerone phosphate synthase, SK3, and miR were associated in clinical samples emphasizing the clinical consistency of our observations. To our knowledge, this is the first report showing that ether lipids differentially control tumor aggressiveness by regulating an ion channel. This insight provides new possibilities for therapeutic interventions, offering clinicians an opportunity to manipulate ion channel dysfunction by adjusting the composition of ether lipids.

Calcium (Ca2+) is an essential second messenger that controls numerous cellular functions. Characteristics of intracellular Ca2+ oscillations define Ca2+ signatures representatives of the phenotype of a cell. Oncogenic functions such as migration, proliferation or resistance to chemotherapy have been associated with aberrant Ca2+ fluxes. However, the identification of Ca2+ signatures representatives of the oncogenic properties of cancer cells remains to be addressed. To characterize and investigate the heterogeneity of oncogenic Ca2+ signatures, we proposed an unbiased scalable method that combines single cell calcium imaging with graph-based unsupervised and artificial neural networks. From an initial dataset of 27,439 agonist-induced Ca2+ responses elicited in a panel of 16 prostate and colorectal cancer cell lines, we discriminate 26 clusters of Ca2+ responses using unbiased unsupervised clustering. From these clusters, we generate Ca2+ signatures for each cancer model allowing to functionally compare different cancer models. In parallel, we propose supervised neural network models predicting characteristics of a single cancer cell based on its profile of Ca2+ responses. We applied those methods to characterized a remodeling of Ca2+ signatures associated with acquired docetaxel resistance (12,911 cells) or in the course of the interaction of cancer cells with fibroblasts (34,676 cells). At single cell level, our supervised neural network succeeded to identify docetaxel-resistant cancer cells and to distinguish cancer cells from fibroblasts on the sole measure of agonist-induced Ca2+ response. Our method demonstrates the potential of Ca2+ profiling for discriminating cancer cells and predict their phenotypic characteristics at single cell level, and provides a framework for researchers to investigate the remodeling of the Ca2+ signature during cancer development.