Geographically-weighted regression of knowledge and behaviour determinants to anti-malarial recommending and dispensing practice among medicine retailers in western Kenya: capacitating targeted interventions

Background Most patients with malaria seek treatment first in retail drug shops. Myriad studies have examined retailer behaviours and characteristics to understand the determinants to these behaviours. Geospatial methods are helpful in discovering if geographic location plays a role in the relationship between determinants and outcomes. This study aimed to discover if spatial autocorrelation exists in the relationship between determinants and retailer behaviours, and to provide specific geographic locations and target behaviours for tailoring future interventions. Methods Retailer behaviours and characteristics captured from a survey deployed to medicine retailers in the Webuye Demographic and Health Surveillance Site were analysed using geographic weighted regression to create prediction models for three separate outcomes: recommending the first-line anti-malarial therapy to adults, recommending the first-line anti-malarial therapy to children, and selling that therapy more than other anti-malarials. The estimated regression coefficients for each determinant, as well as the pseudo R2 values for each final model, were then mapped to assess spatial variability and local areas of best model fit. Results The relationships explored were found to be non-stationary, indicating that spatial heterogeneity exist in the data. The association between having a pharmacy-related health training and recommending the first-line anti-malarial treatment to adults was strongest around the peri-urban centre: comparing those with training in pharmacy to those without training (ORæ=æ5.75, pæ=æ0.021). The association between knowing the first-line anti-malarial and recommending it to children was strongest in the north of the study area compared to those who did not know the MOH-recommended anti-malarial (ORæ=æ2.34, pæ=æ0.070). This is also the area with the strongest association between attending a malaria workshop and selling the MOH-recommended anti-malarial more than other anti-malarials, compared to retailers who did not attend a workshop (ORæ=æ2.38, pæ=æ0.055). Conclusion Evidence suggests that spatial heterogeneity exists in these data, indicating that the relationship between determinants and behaviours varies across space. This is valuable information for intervention design, allowing efforts to focus on those factors that have the strongest relationship with their targeted behaviour within that geographic space, increasing programme efficiency and cost-effectiveness

Identification of a stable molecular signature in mammary tumor endothelial cells that persists in vitro

Long-term, in vitro propagation of tumor-specific endothelial cells (TEC) allows for functional studies and genome-wide expression profiling of clonally-derived, well-characterized subpopulations. Using a genetically engineered mouse model (GEMM) of mammary adenocarcinoma, we have optimized an isolation procedure and defined growth conditions for long-term propagation of mammary TEC. The isolated TEC maintain their endothelial specification and phenotype in culture. Furthermore, gene expression profiling of multiple TEC subpopulations revealed striking, persistent overexpression of several candidate genes including Irx2 and Zfp503 (transcription factors), Alcam and Cd133 (cell surface markers), Ccl4 and neurotensin (Nts) (angiocrine factors), and Gpr182 and Cnr2 (G protein-coupled receptors, GPCRs). Taken together, we have developed an effective method for isolating and culture-expanding mammary TEC, and uncovered several new TEC-selective genes whose overexpression persists even after long-term in vitro culture. These results suggest that the tumor microenvironment may induce changes in vascular endothelium in vivo that are stably transmittable in vitro

A Gene Optimization Strategy that Enhances Production of Fully Functional P-Glycoprotein in Pichia pastoris

Structural and biochemical studies of mammalian membrane proteins remain hampered by inefficient production of pure protein. We explored codon optimization based on highly expressed Pichia pastoris genes to enhance co-translational folding and production of P-glycoprotein (Pgp), an ATP-dependent drug efflux pump involved in multidrug resistance of cancers.Codon-optimized "Opti-Pgp" and wild-type Pgp, identical in primary protein sequence, were rigorously analyzed for differences in function or solution structure. Yeast expression levels and yield of purified protein from P. pastoris (∼130 mg per kg cells) were about three-fold higher for Opti-Pgp than for wild-type protein. Opti-Pgp conveyed full in vivo drug resistance against multiple anticancer and fungicidal drugs. ATP hydrolysis by purified Opti-Pgp was strongly stimulated ∼15-fold by verapamil and inhibited by cyclosporine A with binding constants of 4.2±2.2 µM and 1.1±0.26 µM, indistinguishable from wild-type Pgp. Maximum turnover number was 2.1±0.28 µmol/min/mg and was enhanced by 1.2-fold over wild-type Pgp, likely due to higher purity of Opti-Pgp preparations. Analysis of purified wild-type and Opti-Pgp by CD, DSC and limited proteolysis suggested similar secondary and ternary structure. Addition of lipid increased the thermal stability from T(m) ∼40 °C to 49 °C, and the total unfolding enthalpy. The increase in folded state may account for the increase in drug-stimulated ATPase activity seen in presence of lipids.The significantly higher yields of protein in the native folded state, higher purity and improved function establish the value of our gene optimization approach, and provide a basis to improve production of other membrane proteins

The melanoma-specific graded prognostic assessment does not adequately discriminate prognosis in a modern population with brain metastases from malignant melanoma

The melanoma-specific graded prognostic assessment (msGPA) assigns patients with brain metastases from malignant melanoma to 1 of 4 prognostic groups. It was largely derived using clinical data from patients treated in the era that preceded the development of newer therapies such as BRAF, MEK and immune checkpoint inhibitors. Therefore, its current relevance to patients diagnosed with brain metastases from malignant melanoma is unclear. This study is an external validation of the msGPA in two temporally distinct British populations.Performance of the msGPA was assessed in Cohort I (1997-2008, n=231) and Cohort II (2008-2013, n=162) using Kaplan-Meier methods and Harrell's c-index of concordance. Cox regression was used to explore additional factors that may have prognostic relevance.The msGPA does not perform well as a prognostic score outside of the derivation cohort, with suboptimal statistical calibration and discrimination, particularly in those patients with an intermediate prognosis. Extra-cerebral metastases, leptomeningeal disease, age and potential use of novel targeted agents after brain metastases are diagnosed, should be incorporated into future prognostic models.An improved prognostic score is required to underpin high-quality randomised controlled trials in an area with a wide disparity in clinical care

Secondary somatic mutations restoring RAD51C and RAD51D associated with acquired resistance to the PARP inhibitor rucaparib in high-grade ovarian carcinoma

High-grade epithelial ovarian carcinomas (OC) containing mutated BRCA1 or BRCA2 (BRCA1/2) homologous recombination (HR) genes are sensitive to platinum-based chemotherapy and poly(ADP-ribose) polymerase inhibitors (PARPi), while restoration of HR function due to secondary mutations in BRCA1/2 has been recognized as an important resistance mechanism. We sequenced core HR pathway genes in 12 pairs of pre-treatment and post-progression tumor biopsy samples collected from patients in ARIEL2 Part 1, a phase 2 study of the PARPi rucaparib as treatment for platinum-sensitive, relapsed OC. In six of 12 pre-treatment biopsies, a truncation mutation in BRCA1, RAD51C or RAD51D was identified. In five of six paired post-progression biopsies, one or more secondary mutations restored the open reading frame. Four distinct secondary mutations and spatial heterogeneity were observed for RAD51C. In vitro complementation assays and a patient-derived xenograft (PDX), as well as predictive molecular modeling, confirmed that resistance to rucaparib was associated with secondary mutations

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

<p>Abstract</p> <p>Background</p> <p>Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques.</p> <p>Methods</p> <p>In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques.</p> <p>Results</p> <p>Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve.</p> <p>Conclusion</p> <p>Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.</p

Endothelial-like properties of claudin-low breast cancer cells promote tumor vascular permeability and metastasis

The vasculature serves as the main conduit for breast tumor metastases and is a target of therapeutics in many tumor types. In this study, we aimed to determine if tumor-associated vascular properties could help to explain the differences observed in metastagenicity across the intrinsic subtypes of human breast tumors. Analysis of gene expression signatures from more than 3,000 human breast tumors found that genomic programs that measured vascular quantity, vascular proliferation, and a VEGF/Hypoxia-signature were the most highly expressed in claudin-low and basal-like tumors. The majority of the vascular gene signatures added metastasis-predictive information to immunohistochemistry-defined microvessel density scores and genomically defined-intrinsic subtype classification. Interestingly, pure claudin-low cell lines, and subsets of claudin-low-like cells within established basal-like cancer cell lines, exhibited endothelial/tube-like morphology when cultured on Matrigel. In vivo xenografts found that claudin-low tumors, but not luminal tumors, extensively perfused injected contrast agent through paracellular spaces and non-vascular tumor-lined channels. Taken together, the endothelial-like characteristics of the cancer cells, combined with both the amount and the physiologic state of the vasculature contribute to breast cancer metastatic progression. We hypothesize that the genetic signatures we have identified highlight patients that should respond most favorably to anti-vascular agents.Electronic supplementary materialThe online version of this article (doi:10.1007/s10585-013-9607-4) contains supplementary material, which is available to authorized users

Method for evaluating prediction models that apply the results of randomized trials to individual patients

<p>Abstract</p> <p>Introduction</p> <p>The clinical significance of a treatment effect demonstrated in a randomized trial is typically assessed by reference to differences in event rates at the group level. An alternative is to make individualized predictions for each patient based on a prediction model. This approach is growing in popularity, particularly for cancer. Despite its intuitive advantages, it remains plausible that some prediction models may do more harm than good. Here we present a novel method for determining whether predictions from a model should be used to apply the results of a randomized trial to individual patients, as opposed to using group level results.</p> <p>Methods</p> <p>We propose applying the prediction model to a data set from a randomized trial and examining the results of patients for whom the treatment arm recommended by a prediction model is congruent with allocation. These results are compared with the strategy of treating all patients through use of a net benefit function that incorporates both the number of patients treated and the outcome. We examined models developed using data sets regarding adjuvant chemotherapy for colorectal cancer and Dutasteride for benign prostatic hypertrophy.</p> <p>Results</p> <p>For adjuvant chemotherapy, we found that patients who would opt for chemotherapy even for small risk reductions, and, conversely, those who would require a very large risk reduction, would on average be harmed by using a prediction model; those with intermediate preferences would on average benefit by allowing such information to help their decision making. Use of prediction could, at worst, lead to the equivalent of an additional death or recurrence per 143 patients; at best it could lead to the equivalent of a reduction in the number of treatments of 25% without an increase in event rates. In the Dutasteride case, where the average benefit of treatment is more modest, there is a small benefit of prediction modelling, equivalent to a reduction of one event for every 100 patients given an individualized prediction.</p> <p>Conclusion</p> <p>The size of the benefit associated with appropriate clinical implementation of a good prediction model is sufficient to warrant development of further models. However, care is advised in the implementation of prediction modelling, especially for patients who would opt for treatment even if it was of relatively little benefit.</p

Expression of Six1 in luminal breast cancers predicts poor prognosis and promotes increases in tumor initiating cells by activation of extracellular signal-regulated kinase and transforming growth factor-beta signaling pathways

Abstract Introduction Mammary-specific overexpression of Six1 in mice induces tumors that resemble human breast cancer, some having undergone epithelial to mesenchymal transition (EMT) and exhibiting stem/progenitor cell features. Six1 overexpression in human breast cancer cells promotes EMT and metastatic dissemination. We hypothesized that Six1 plays a role in the tumor initiating cell (TIC) population specifically in certain subtypes of breast cancer, and that by understanding its mechanism of action, we could potentially develop new means to target TICs. Methods We examined gene expression datasets to determine the breast cancer subtypes with Six1 overexpression, and then examined its expression in the CD24low/CD44+ putative TIC population in human luminal breast cancers xenografted through mice and in luminal breast cancer cell lines. Six1 overexpression, or knockdown, was performed in different systems to examine how Six1 levels affect TIC characteristics, using gene expression and flow cytometric analysis, tumorsphere assays, and in vivo TIC assays in immunocompromised and immune-competent mice. We examined the molecular pathways by which Six1 influences TICs using genetic/inhibitor approaches in vitro and in vivo. Finally, we examined the expression of Six1 and phosphorylated extracellular signal-regulated kinase (p-ERK) in human breast cancers. Results High levels of Six1 are associated with adverse outcomes in luminal breast cancers, particularly the luminal B subtype. Six1 levels are enriched in the CD24low/CD44+ TIC population in human luminal breast cancers xenografted through mice, and in tumorsphere cultures in MCF7 and T47D luminal breast cancer cells. When overexpressed in MCF7 cells, Six1expands the TIC population through activation of transforming growth factor-beta (TGF-β) and mitogen activated protein kinase (MEK)/ERK signaling. Inhibition of ERK signaling in MCF7-Six1 cells with MEK1/2 inhibitors, U0126 and AZD6244, restores the TIC population of luminal breast cancer cells back to that observed in control cells. Administration of AZD6244 dramatically inhibits tumor formation efficiency and metastasis in cells that express high levels of Six1 ectopically or endogenously. Finally, we demonstrate that Six1 significantly correlates with phosphorylated ERK in human breast cancers. Conclusions Six1 plays an important role in the TIC population in luminal breast cancers and induces a TIC phenotype by enhancing both TGF-β and ERK signaling. MEK1/2 kinase inhibitors are potential candidates for targeting TICs in breast tumors

Pre-hospital management protocols and perceived difficulty in diagnosing acute heart failure

Aim To illustrate the pre-hospital management arsenals and protocols in different EMS units, and to estimate the perceived difficulty of diagnosing suspected acute heart failure (AHF) compared with other common pre-hospital conditions. Methods and results A multinational survey included 104 emergency medical service (EMS) regions from 18 countries. Diagnostic and therapeutic arsenals related to AHF management were reported for each type of EMS unit. The prevalence and contents of management protocols for common medical conditions treated pre-hospitally was collected. The perceived difficulty of diagnosing AHF and other medical conditions by emergency medical dispatchers and EMS personnel was interrogated. Ultrasound devices and point-of-care testing were available in advanced life support and helicopter EMS units in fewer than 25% of EMS regions. AHF protocols were present in 80.8% of regions. Protocols for ST-elevation myocardial infarction, chest pain, and dyspnoea were present in 95.2, 80.8, and 76.0% of EMS regions, respectively. Protocolized diagnostic actions for AHF management included 12-lead electrocardiogram (92.1% of regions), ultrasound examination (16.0%), and point-of-care testings for troponin and BNP (6.0 and 3.5%). Therapeutic actions included supplementary oxygen (93.2%), non-invasive ventilation (80.7%), intravenous furosemide, opiates, nitroglycerine (69.0, 68.6, and 57.0%), and intubation 71.5%. Diagnosing suspected AHF was considered easy to moderate by EMS personnel and moderate to difficult by emergency medical dispatchers (without significant differences between de novo and decompensated heart failure). In both settings, diagnosis of suspected AHF was considered easier than pulmonary embolism and more difficult than ST-elevation myocardial infarction, asthma, and stroke. Conclusions The prevalence of AHF protocols is rather high but the contents seem to vary. Difficulty of diagnosing suspected AHF seems to be moderate compared with other pre-hospital conditions