Stem-like cells with luminal progenitor phenotype survive castration in human prostate cancer

Castration is the standard therapy for advanced prostate cancer (PC). Although this treatment is initially effective, tumors invariably relapse as incurable, castration-resistant PC (CRPC). Adaptation of androgen-dependent PC cells to an androgen-depleted environment or selection of pre-existing, CRPC cells have been proposed as mechanisms of CRPC development. Stem cell (SC)-like PC cells have been implicated not only as tumor initiating/maintaining in PC but also as tumor-reinitiating cells in CRPC. Recently, castration-resistant cells expressing the NK3 homeobox 1 (Nkx3-1) (CARNs), the other luminal markers cytokeratin 18 (CK18) and androgen receptor (AR), and possessing SC properties, have been found in castrated mouse prostate and proposed as the cell-of-origin of CRPC. However, the human counterpart of CARNs has not been identified yet. Here, we demonstrate that in the human PC xenograft BM18, pre-existing SC-like and neuroendocrine (NE) PC cells are selected by castration and survive as totally quiescent. SC-like BM18 cells, displaying the SC markers aldehyde dehydrogenase 1A1 or NANOG, coexpress the luminal markers NKX3-1, CK18, and a low level of AR (AR(low)) but not basal or NE markers. These CR luminal SC-like cells, but not NE cells, reinitiate BM18 tumor growth after androgen replacement. The AR(low) seems to mediate directly both castration survival and tumor reinitiation. This study identifies for the first time in human PC SC-/CARN-like cells that may represent the cell-of-origin of tumor reinitiation as CRPC. This finding will be fundamental for refining the hierarchy among human PC cancer cells and may have important clinical implications.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.