Current models of stem cell biology assume that normal and neoplastic stem cells reside at the apices of hierarchies and differentiate into nonstem progeny in a unidirectional manner. Here we identify a subpopulation of basal-like human mammary epithelial cells that departs from that assumption, spontaneously dedifferentiating into stem-like cells. Moreover, oncogenic transformation enhances the spontaneous conversion, so that nonstem cancer cells give rise to cancer stem cell (CSC)-like cells in vitro and in vivo. We further show that the differentiation state of normal cells-of-origin is a strong determinant of posttransformation behavior. These findings demonstrate that normal and CSC-like cells can arise de novo from more differentiated cell types and that hierarchical models of mammary stem cell biology should encompass bidirectional interconversions between stem and nonstem compartments. The observed plasticity may allow derivation of patient-specific adult stem cells without genetic manipulation and holds important implications for therapeutic strategies to eradicate cancer.breast cancer | dedifferentiation

Background Hereditary transthyretin (ATTRv) amyloidosis is a rare, inherited, progressive disease caused by mutations in the transthyretin (TTR) gene. We aimed to assess the efficacy and safety of long-term treatment with patisiran, an RNA interference therapeutic that inhibits TTR production, in patients with ATTRv amyloidosis with polyneuropathy. MethodsThis multi-country, multi-centre, open-label extension (OLE) trial enrolled patients at 43 sites in 19 countries as of 24 September 2018. Patients were eligible if they had completed the phase 3 APOLLO (randomised, double-blind, placebo-controlled [2:1], 18-month study) or phase 2 OLE (single-arm, 24-month study) parent studies and tolerated the study drug. Eligible patients from APOLLO (APOLLO-patisiran [received patisiran during APOLLO] and APOLLO-placebo [received placebo during APOLLO] groups) and the phase 2 OLE (phase 2 OLE patisiran group) studies enrolled in this Global OLE trial and receive patisiran 0•3 mg/kg by intravenous infusion every 3 weeks for up to 5 years. Efficacy assessments include measures of polyneuropathy (modified Neuropathy Impairment Score +7 [mNIS+7]), quality of life, autonomic symptoms, nutritional status, disability, ambulation status, motor function, and cardiac stress. Patients included in the current efficacy analyses are those who had completed 12-month efficacy assessments as of the data cut-off. Safety analyses included all patients who received ≥1 dose of patisiran up to the data cut-off. The Global OLE is ongoing with no new enrolment, and current findings are based on the 12-month interim analysis. The study is registered with ClinicalTrials.gov, NCT02510261.

Background Predicting the clinical course of prostate cancer is challenging due to the wide biological spectrum of the disease. The objective of our study was to identify prostate cancer prognostic markers in patients ‘sera using a multi-omics discovery platform. Methods Pre-surgical serum samples collected from a longitudinal, racially diverse, prostate cancer patient cohort (N = 382) were examined. Linear Regression and Bayesian computational approaches integrated with multi-omics, were used to select markers to predict biochemical recurrence (BCR). BCR-free survival was modeled using unadjusted Kaplan–Meier estimation curves and multivariable Cox proportional hazards analysis, adjusted for key pathologic variables. Receiver operating characteristic (ROC) curve statistics were used to examine the predictive value of markers in discriminating BCR events from non-events. The findings were further validated by creating a training set (N = 267) and testing set (N = 115) from the cohort. Results Among 382 patients, 72 (19%) experienced a BCR event in a median follow-up time of 6.9 years. Two proteins—Tenascin C (TNC) and Apolipoprotein A1V (Apo-AIV), one metabolite—1-Methyladenosine (1-MA) and one phospholipid molecular species phosphatidic acid (PA) 18:0-22:0 showed a cumulative predictive performance of AUC = 0.78 [OR (95% CI) = 6.56 (2.98–14.40), P < 0.05], in differentiating patients with and without BCR event. In the validation set all four metabolites consistently reproduced an equivalent performance with high negative predictive value (NPV; > 80%) for BCR. The combination of pTstage and Gleason score with the analytes, further increased the sensitivity [AUC = 0.89, 95% (CI) = 4.45–32.05, P < 0.05], with an increased NPV (0.96) and OR (12.4) for BCR. The panel of markers combined with the pathological parameters demonstrated a more accurate prediction of BCR than the pathological parameters alone in prostate cancer. Conclusions In this study, a panel of serum analytes were identified that complemented pathologic patient features in predicting prostate cancer progression. This panel offers a new opportunity to complement current prognostic markers and to monitor the potential impact of primary treatment versus surveillance on patient oncological outcome.

Pembrolizumab salvage add-on therapy in patients with radioiodine-refractory (RAIR), progressive differentiated thyroid cancer (DTC) progressing on lenvatinib: Results of a multicenter phase II International Thyroid Oncology Group Trial

Structural learning of Bayesian networks (BNs) from observational data has gained increasing applied use and attention from various scientific and industrial areas. The mathematical theory of BNs and their optimization is well developed. Although there are several open-source BN learners in the public domain, none of them are able to handle both small and large feature space data and recover network structures with acceptable accuracy. bAIcis Ò is a novel BN learning and simulation software from BERG. It was developed with the goal of learning BNs from ''Big Data'' in health care, often exceeding hundreds of thousands features when research is conducted in genomics or multi-omics. This article provides a comprehensive performance evaluation of bAIcis and its comparison with the open-source BN learners. The study investigated synthetic datasets of discrete, continuous, and mixed data in small and large feature space, respectively. The results demonstrated that bAIcis outperformed the publicly available algorithms in structure recovery precision in almost all of the evaluated settings, achieving the true positive rates of 0.9 and precision of 0.8. In addition, bAIcis supports all data types, including continuous, discrete, and mixed variables. It is effectively parallelized on a distributed system and can work with datasets of thousands of features that are infeasible for any of the publicly available tools with a desired level of recovery accuracy.