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Endometrial cancer (EC) is the most prevalent gynaecological cancer in high‐income countries and its incidence is continuing to rise sharply. Simple and objective tools to reliably detect women with EC are urgently needed. We recently developed and validated the DNA methylation (DNAme)‐based women's cancer risk identification—quantitative polymerase chain reaction test for endometrial cancer (WID‐qEC) test that could address this need. Here, we demonstrate that the stability of the WID‐qEC test remains consistent regardless of: (i) the cervicovaginal collection device and sample media used (Cervex brush and PreservCyt or FLOQSwab and eNAT), (ii) the collector of the specimen (gynaecologist‐ or patient‐based), and (iii) the precise sampling site (cervical, cervicovaginal and vaginal). Furthermore, we demonstrate sample stability in eNAT medium for 7 days at room temperature, greatly facilitating the implementation of the test into diagnostic laboratory workflows. When applying FLOQSwabs (Copan) in combination with the eNAT (Copan) sample collection media, the sensitivity and specificity of the WID‐qEC test to detect uterine (i.e., endometrial and cervical) cancers in gynaecologist‐taken samples was 92.9% (95% confidence interval [CI] = 75.0%–98.8%) and 98.6% (95% CI = 91.7%–99.9%), respectively, whilst the sensitivity and specificity in patient collected self‐samples was 75.0% (95% CI = 47.4%–91.7%) and 100.0% (95% CI = 93.9%–100.0%), respectively. Taken together these data confirm the robustness and clinical potential of the WID‐qEC test. [ABSTRACT FROM AUTHOR]

The impact of a pathogen on host disease can only be studied in samples covering the entire spectrum of pathogenesis. Persistent oncogenic human papilloma virus (HPV) infection is the most common cause for cervical cancer. Here, we investigate HPV‐induced host epigenome‐wide changes prior to development of cytological abnormalities. Using cervical sample methylation array data from disease‐free women with or without an oncogenic HPV infection, we develop the WID (Women's cancer risk identification)‐HPV, a signature reflective of changes in the healthy host epigenome related to high‐risk HPV strains (AUC = 0.78, 95% CI: 0.72‐0.85, in nondiseased women). Looking at HPV‐associated changes across disease development, HPV‐infected women with minor cytological alterations (cervical intraepithelial neoplasia grade 1/2, CIN1/2), but surprisingly not those with precancerous changes or invasive cervical cancer (CIN3+), show an increased WID‐HPV index, indicating the WID‐HPV may reflect a successful viral clearance response absent in progression to cancer. Further investigation revealed the WID‐HPV is positively associated with apoptosis (ρ = 0.48; P <.001) and negatively associated with epigenetic replicative age (ρ = −0.43; P <.001). Taken together, our data suggest the WID‐HPV captures a clearance response associated with apoptosis of HPV‐infected cells. This response may be dampened or lost with increased underlying replicative age of infected cells, resulting in progression to cancer. [ABSTRACT FROM AUTHOR]

The incidence of endometrial cancer is rising. Measures to identify women at risk and to detect endometrial cancer earlier are required to reduce the morbidity triggered by the aggressive treatment required for advanced endometrial cancer. We developed the WID‐EC (Women's cancer risk IDentification‐Endometrial Cancer) test, which is based on DNA methylation at 500 CpG sites, in a discovery set of cervical liquid‐based cytology samples from 1086 women with and without an endometrial cancer (217 cancer cases and 869 healthy controls) with a worse prognosis (grade 3 or ≥stage IB). We validated the WID‐EC test in an independent external validation set of 64 endometrial cancer cases and 225 controls. We further validated the test in 150 healthy women (prospective set) who provided a cervical sample as part of the routine Swedish cervical screening programme, 54 of whom developed endometrial cancer within 3 years of sample collection. The WID‐EC test identified women with endometrial cancer with a receiver operator characteristic area under the curve (AUC) of 0.92 (95% CI: 0.88‐0.97) in the external set and of 0.82 (95% CI: 0.74‐0.89) in the prospective validation set. Using an optimal cutoff, cancer cases were detected with a sensitivity of 86% and a specificity of 90% in the external validation set, and a sensitivity and specificity of 52% and 98% respectively in the prospective validation set. The WID‐EC test can identify women with or at risk of endometrial cancer. [ABSTRACT FROM AUTHOR]

The contributions of Joseph V. Brady to behavioral pharmacology span more than 50 years and range from early studies using the Estes-Skinner ("conditioned emotional response") procedure to examine drug effects and various physiological processes in experimental animals to the implementation of mobile methadone treatment services and to small group behavioral analyses in simulated space environments. This expansive range of activities is based on Brady's insight and innovative use of behavioral procedures, his spirited and unabashed enthusiasm for the discipline and its philosophical underpinnings, together with a collegiality and commitment to the experimental analysis of behavior that is both legendary and inspirational. These contributions are summarized and highlighted in this tribute that focuses primarily on Brady's contributions to behavioral pharmacology but which also acknowledges his conceptual and technical contributions spanning multiple disciplines. (Contains 4 figures.)

Peter B. Dews played a significant role in shaping the distinctive characteristics and defining the underlying principles of the discipline of behavioral pharmacology. His early and sophisticated use of schedules of reinforcement in the 1950s, incorporated from research in the experimental analysis of behavior and integrated into the discipline of pharmacology, provided tremendous insight, inspiration, and impetus to the newly emerging field of behavioral pharmacology. The experimental findings generated by Dews' research, blending the sophisticated use of behavior and pharmacological principles together with the elegant manner of their presentation and far-reaching implications, provided the force and momentum to establish and direct behavioral pharmacology for several decades. This article attempts to capture some of Dews' research that integrated and inspired the blending of sophisticated behavioral work with that of pharmacology. (Contains 2 figures.)

Problem-solving treatment of depression is a brief intervention specifically designed for primary care (PST-PC). To date, predictors of an optimal response to PST-PC have not been studied. In primary care, knowing such factors is essential for proper triage decisions. Patient, therapist, and process variables were evaluated for their ability to predict remission of minor depression or dysthymia in patients treated with PST-PC. The most salient predictors of remission were the ability to understand the PST-PC rationale and to apply the PST-PC procedure in early treatment sessions, having a cognitive-behavioral therapist, and, for dysthymia, having a lower depression severity level at baseline. These results provide preliminary evidence of some factors associated with an optimal response to PST-PC and also present challenges for the ability to broadly disseminate the intervention. Modifications to the existing PST-PC training program and directions for future research are discussed.

Abstract Background Aberrant expression of small noncoding RNAs called microRNAs (miRNAs) is a common feature of several human diseases. The objective of the study was to identify miRNA modulation in patients with complex regional pain syndrome (CRPS) a chronic pain condition resulting from dysfunction in the central and/or peripheral nervous systems. Due to a multitude of inciting pathologies, symptoms and treatment conditions, the CRPS patient population is very heterogeneous. Our goal was to identify differentially expressed miRNAs in blood and explore their utility in patient stratification. Methods We profiled miRNAs in whole blood from 41 patients with CRPS and 20 controls using TaqMan low density array cards. Since neurogenic inflammation is known to play a significant role in CRPS we measured inflammatory markers including chemokines, cytokines, and their soluble receptors in blood from the same individuals. Correlation analyses were performed for miRNAs, inflammatory markers and other parameters including disease symptoms, medication, and comorbid conditions. Results Three different groups emerged from miRNA profiling. One group was comprised of 60% of CRPS patients and contained no control subjects. miRNA profiles from the remaining patients were interspersed among control samples in the other two groups. We identified differential expression of 18 miRNAs in CRPS patients. Analysis of inflammatory markers showed that vascular endothelial growth factor (VEGF), interleukin1 receptor antagonist (IL1Ra) and monocyte chemotactic protein-1 (MCP1) were significantly elevated in CRPS patients. VEGF and IL1Ra showed significant correlation with the patients reported pain levels. Analysis of the patients who were clustered according to their miRNA profile revealed correlations that were not significant in the total patient population. Correlation analysis of miRNAs detected in blood with additional parameters identified miRNAs associated with comorbidities such as headache, thyroid disorder and use of narcotics and antiepileptic drugs. Conclusions miRNA profiles can be useful in patient stratification and have utility as potential biomarkers for pain. Differentially expressed miRNAs can provide molecular insights into gene regulation and could lead to new therapeutic intervention strategies for CRPS.

Background: The phase III MRC COIN trial showed no statistically significant benefit from adding the EGFR-target cetuximab to oxaliplatin-based chemotherapy in first-line treatment of advanced colorectal cancer. This study exploits additional information on HER2-HER3 dimerization to achieve patient stratification and reveal previously hidden subgroups of patients who had differing disease progression and treatment response.Methods: HER2-HER3 dimerization was quantified by fluorescence lifetime imaging microscopy in primary tumor samples from 550 COIN trial patients receiving oxaliplatin and fluoropyrimidine chemotherapy with or without cetuximab. Bayesian latent class analysis and covariate reduction was performed to analyze the effects of HER2-HER3 dimer, RAS mutation, and cetuximab on progression-free survival and overall survival (OS). All statistical tests were two-sided.Results: Latent class analysis on a cohort of 398 patients revealed two patient subclasses with differing prognoses (median OS = 1624 days [95% confidence interval [CI] = 1466 to 1816 days] vs 461 days [95% CI = 431 to 504 days]): Class 1 (15.6%) showed a benefit from cetuximab in OS (hazard ratio = 0.43, 95% CI = 0.25 to 0.76, P = .004). Class 2 showed an association of increased HER2-HER3 with better OS (hazard ratio = 0.64, 95% CI = 0.44 to 0.94, P = .02). A class prediction signature was formed and tested on an independent validation cohort (n = 152) validating the prognostic utility of the dimer assay. Similar subclasses were also discovered in full trial dataset (n = 1630) based on 10 baseline clinicopathological and genetic covariates.Conclusions: Our work suggests that the combined use of HER dimer imaging and conventional mutation analyses will be able to identify a small subclass of patients (>10%) who will have better prognosis following chemotherapy. A larger prospective cohort will be required to confirm its utility in predicting the outcome of anti-EGFR treatment. [ABSTRACT FROM AUTHOR]

Large-scale electronic health records (EHRs) present an opportunity to quickly identify suitable individuals in order to directly invite them to participate in an observational study. EHRs can contain data from millions of individuals, raising the question of how to optimally select a cohort of size n from a larger pool of size N. In this article, we propose a simple selective recruitment protocol that selects a cohort in which covariates of interest tend to have a uniform distribution. We show that selectively recruited cohorts potentially offer greater statistical power and more accurate parameter estimates than randomly selected cohorts. Our protocol can be applied to studies with multiple categorical and continuous covariates. We apply our protocol to a numerically simulated prospective observational study using an EHR database of stable acute coronary disease patients from 82 089 individuals in the U.K. Selective recruitment designs require a smaller sample size, leading to more efficient and cost-effective studies. [ABSTRACT FROM AUTHOR]

With the success rate of drugs for CNS indications at an all-time low, new approaches are needed to turn the tide of failed clinical trials. This paper reviews the history of CNS drug Discovery over the last 60 years and proposes a new paradigm based on the lessons learned. The initial wave of successful therapeutics discovered using careful clinical observations was followed by an emphasis on a phenotypic target-agnostic approach, often leading to successful drugs with a rich pharmacology. The subsequent introduction of molecular biology and the focus on a target-driven strategy has largely dominated drug discovery efforts over the last 30 years, but has not increased the probability of success, because these highly selective molecules are unlikely to address the complex pathological phenotypes of most CNS disorders. In many cases, reliance on preclinical animal models has lacked robust translational power. We argue that Quantitative Systems Pharmacology (QSP), a mechanism-based computer model of biological processes informed by preclinical knowledge and enhanced by neuroimaging and clinical data could be a new powerful knowledge generator engine and paradigm for rational polypharmacy. Progress in the academic discipline of computational neurosciences, allows one to model the effect of pathology and therapeutic interventions on neuronal circuit firing activity that can relate to clinical phenotypes, driven by complex properties of specific brain region activation states. The model is validated by optimizing the correlation between relevant emergent properties of these neuronal circuits and historical clinical and imaging datasets. A rationally designed polypharmacy target profile will be discovered using reverse engineering and sensitivity analysis. Small molecules will be identified using a combination of Artificial Intelligence methods and computational modeling, tested subsequently in heterologous cellular systems with human targets. Animal models will be used to establish target engagement and for ADME-Tox, with the QSP approach complemented by in vivo preclinical models that can be further refined to increase predictive validity. The QSP platform can also mitigate the variability in clinical trials with the concept of virtual patients. Because the QSP platform integrates knowledge from a wide variety of sources in an actionable simulation, it offers the possibility of substantially improving the success rate of CNS R&D programs while, at the same time, reducing both cost and the number of animals. [ABSTRACT FROM AUTHOR]