Coexistent ARID1A–PIK3CA mutations promote ovarian clear-cell tumorigenesis through pro-tumorigenic inflammatory cytokine signalling

Ovarian clear-cell carcinoma (OCCC) is an aggressive form of ovarian cancer with high ARID1A mutation rates. Here we present a mutant mouse model of OCCC. We find that ARID1A inactivation is not sufficient for tumor formation, but requires concurrent activation of the phosphoinositide 3-kinase catalytic subunit, PIK3CA. Remarkably, the mice develop highly penetrant tumors with OCCC-like histopathology, culminating in hemorrhagic ascites and a median survival period of 7.5 weeks. Therapeutic treatment with the pan-PI3K inhibitor, BKM120, prolongs mouse survival by inhibiting tumor cell growth. Cross-species gene expression comparisons support a role for IL-6 inflammatory cytokine signaling in OCCC pathogenesis. We further show that ARID1A and PIK3CA mutations cooperate to promote tumor growth through sustained IL-6 overproduction. Our findings establish an epistatic relationship between SWI/SNF chromatin remodeling and PI3K pathway mutations in OCCC and demonstrate that these pathways converge on pro-tumorigenic cytokine signaling. We propose that ARID1A protects against inflammation-driven tumorigenesis

Developing positional awareness in sustainability science: four archetypes for early career scientists working in an SDG world

Although the Sustainable Development Goals (SDGs) provide a framework to guide and inform research at the interface between science and policy, engaging in sustainability science is not a value-free process and implies making a number of choices. This is especially pertinent to early career researchers (ECRs) who are faced with the need to engage with the content and frame of the SDGs, while navigating critical engagement in knowledge production. Here, we propose a framework to help early career sustainability scholars navigate these tensions. We describe four archetypes at play in sustainability research and argue that these positions allow ECRs to reflexively navigate their roles and purposes in sustainability research.publishedVersio

Implementing machine learning methods with complex survey data: Lessons learned on the impacts of accounting sampling weights in gradient boosting.

Despite the prominent use of complex survey data and the growing popularity of machine learning methods in epidemiologic research, few machine learning software implementations offer options for handling complex samples. A major challenge impeding the broader incorporation of machine learning into epidemiologic research is incomplete guidance for analyzing complex survey data, including the importance of sampling weights for valid prediction in target populations. Using data from 15, 820 participants in the 1988-1994 National Health and Nutrition Examination Survey cohort, we determined whether ignoring weights in gradient boosting models of all-cause mortality affected prediction, as measured by the F1 score and corresponding 95% confidence intervals. In simulations, we additionally assessed the impact of sample size, weight variability, predictor strength, and model dimensionality. In the National Health and Nutrition Examination Survey data, unweighted model performance was inflated compared to the weighted model (F1 score 81.9% [95% confidence interval: 81.2%, 82.7%] vs 77.4% [95% confidence interval: 76.1%, 78.6%]). However, the error was mitigated if the F1 score was subsequently recalculated with observed outcomes from the weighted dataset (F1: 77.0%; 95% confidence interval: 75.7%, 78.4%). In simulations, this finding held in the largest sample size (N = 10,000) under all analytic conditions assessed. For sample sizes <5,000, sampling weights had little impact in simulations that more closely resembled a simple random sample (low weight variability) or in models with strong predictors, but findings were inconsistent under other analytic scenarios. Failing to account for sampling weights in gradient boosting models may limit generalizability for data from complex surveys, dependent on sample size and other analytic properties. In the absence of software for configuring weighted algorithms, post-hoc re-calculations of unweighted model performance using weighted observed outcomes may more accurately reflect model prediction in target populations than ignoring weights entirely

Association of Ultraviolet Radiation Exposure with Dermatomyositis in a National Myositis Patient Registry.

OBJECTIVE: Dermatomyositis (DM) has been associated with geospatial differences in ultraviolet (UV) radiation, but the role of individual determinants of UV exposure prior to diagnosis is unknown. METHODS: We analyzed questionnaire data from 1350 adults in a U.S. national myositis registry (638 with DM, 422 polymyositis [PM], and 290 inclusion body myositis [IBM] diagnosed at ages 18-65 years), examining the likelihood of DM compared with PM and IBM diagnosis, in relation to self-reported sunburn history and job- and hobby-related sun exposures in the year prior to diagnosis. We estimated odds ratios (OR) and 95% confidence intervals (CI) using logistic regression adjusted for age, skin tone, and sex, to determine the association of individual UV exposures with DM diagnosis. We also evaluated the proportion of DM by maximum daily ambient UV exposure, based on UV-B erythemal irradiances for participant residence the year prior to diagnosis. RESULTS: DM was associated with sunburn in the year before diagnosis (two or more sunburns, OR=1.77; 95%CI 1.28, 2.43 vs. PM/IBM; one sunburn OR=1.44; 95%CI 1.06, 1.95) and with having elevated job- or hobby-related sun exposure (high OR=1.64; 95%CI 1.08, 2.49 or moderate exposure OR=1.35; 95%CI 1.02, 1.78 vs. low or no exposure). Ambient UV intensity was associated with DM in females (beta=3.97, P=0.046), but not overall. CONCLUSION: Our findings suggest that high or moderate personal exposure to intense sunlight is associated with developing DM compared with other types of myositis. Prospective research on UV exposure as a modifiable risk factor for DM is warranted. This article is protected by copyright. All rights reserved

Environmental management of asthma in clinical practice: Results from the 2012 National Ambulatory Medical Care Survey

Background: The National Asthma Education and Prevention Program guidelines emphasize environmental control as an integral part of asthma management; however, limited national-level data exist on how clinicians implement environmental control recommendations. Objective: We analyzed data on clinicians’ self-reported use of recommended environmental control practices in a nationally representative sample (n = 1645) of primary care physicians, asthma specialists, and advanced practice providers from the National Asthma Survey of Physicians, a supplemental questionnaire to the 2012 National Ambulatory Medical Care Survey. Methods: We examined clinician and practice characteristics as well as clinicians’ decisions and strategies regarding environmental trigger assessment and environmental control across provider groups. Regression modeling was used to identify clinician and practice characteristics associated with implementation of guideline recommendations. Results: A higher percentage of specialists assessed asthma triggers at home, school, and/or work than primary care or advanced practice providers (almost always: 53.6% vs 29.4% and 23.7%, respectively, P 93%) recommended avoidance of secondhand tobacco smoke, whereas recommendations regarding cooking appliances (eg, proper ventilation) were infrequent. Although assessment and recommendation practices differed between clinician groups, modeling results showed that clinicians who reported almost always assessing asthma control were 5- to 6-fold more likely to assess environmental asthma triggers. Use of asthma action plans was also strongly associated with implementation of environmental control recommendations. Conclusions: Environmental assessment and recommendations to patients varied among asthma care providers. High adherence to other key guideline components, such as assessing asthma control, was associated with environmental assessment and recommendation practices on environmental control