Visceral Adipose Tissue Modulates Radiosensitivity in Oesophageal Adenocarcinoma

Oesophageal adenocarcinoma (OAC) is an exemplar model of obesity-associated cancer. Response to neoadjuvant chemoradiotherapy (NA CRT) is a clinical challenge. We examined if visceral adipose tissue and obesity status alter radiosensitivity in OAC. The radioresistant (OE33R) and radioresponsive (OE33P) OAC isogenic model was cultured with adipose tissue conditioned media from three patient cohorts: non-cancer patients, surgery only OAC patients and NA CRT OAC patients. Cell survival was characterised by clonogenic assay, metabolomic profiling by nuclear magnetic resonance spectroscopy and adipokine receptor gene expression by qPCR. A retrospective in vivo study compared tumour response to NA CRT in normal weight (n=53) versus overweight/obese patients (n=148). Adipose conditioned media (ACM) from all patient cohorts significantly increased radiosensitivity in radioresistant OE33R cells. ACM from the NA CRT OAC cohort increased radiosensitivity in OE33P cells. Metabolomic profiling demonstrated separation of the non-cancer and surgery only OAC cohorts and between the non-cancer and NA CRT OAC cohorts. Gene expression profiling of OE33P versus OE33R cells demonstrated differential expression of the adiponectin receptor-1 (AR1), adiponectin receptor-2 (AR2), leptin receptor (LepR) and neuropilin receptor-1 (NRP1) genes. In vivo overweight/obese OAC patients achieved an enhanced tumour response following NA CRT compared to normal weight patients. This study demonstrates that visceral adipose tissue modulates the cellular response to radiation in OA

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

The FANCM:p.Arg658* truncating variant is associated with risk of triple-negative breast cancer

Abstract: Breast cancer is a common disease partially caused by genetic risk factors. Germline pathogenic variants in DNA repair genes BRCA1, BRCA2, PALB2, ATM, and CHEK2 are associated with breast cancer risk. FANCM, which encodes for a DNA translocase, has been proposed as a breast cancer predisposition gene, with greater effects for the ER-negative and triple-negative breast cancer (TNBC) subtypes. We tested the three recurrent protein-truncating variants FANCM:p.Arg658*, p.Gln1701*, and p.Arg1931* for association with breast cancer risk in 67,112 cases, 53,766 controls, and 26,662 carriers of pathogenic variants of BRCA1 or BRCA2. These three variants were also studied functionally by measuring survival and chromosome fragility in FANCM−/− patient-derived immortalized fibroblasts treated with diepoxybutane or olaparib. We observed that FANCM:p.Arg658* was associated with increased risk of ER-negative disease and TNBC (OR = 2.44, P = 0.034 and OR = 3.79; P = 0.009, respectively). In a country-restricted analysis, we confirmed the associations detected for FANCM:p.Arg658* and found that also FANCM:p.Arg1931* was associated with ER-negative breast cancer risk (OR = 1.96; P = 0.006). The functional results indicated that all three variants were deleterious affecting cell survival and chromosome stability with FANCM:p.Arg658* causing more severe phenotypes. In conclusion, we confirmed that the two rare FANCM deleterious variants p.Arg658* and p.Arg1931* are risk factors for ER-negative and TNBC subtypes. Overall our data suggest that the effect of truncating variants on breast cancer risk may depend on their position in the gene. Cell sensitivity to olaparib exposure, identifies a possible therapeutic option to treat FANCM-associated tumors

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological systematic review of health technology assessments

Background: Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. Methods: We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. Results: The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. Conclusions: The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

A Pilot Study to Evaluate the Introduction of an Interprofessional Problem-based Learning Module

Patient care is complex and demands that health professionals work together effectively. Interprofessional education (IPE) encourages collaboration by educating students from different professions together. This study examined the effectiveness of IPE in terms of changing students’ perceptions of teamwork, professional identity, role, competency and autonomy, and the need for interdisciplinary co-operation. Two multidisciplinary cohorts of health science students (n=51 and n=48) elected to participate in a problem-based learning module (PBL). The module included problems addressing areas of professional identity and cases requiring a multidisciplinary team approach. Evaluation was undertaken using the Readiness for Interprofessional Learning Scale (RIPLS) and the Interdisciplinary Education Perception Scale (IPES). Regarding the RIPLS, both cohorts reported significant (

Low MiR-187 Expression Promotes Resistance to Chemoradiation Therapy In Vitro and Correlates with Treatment Failure in Patients with Esophageal Adenocarcinoma

Abstract Esophageal adenocarcinoma (EAC) has a poor prognosis and is increasing in incidence in many Western populations. Neoadjuvant chemoradiation therapy (CRT) followed by surgery is increasingly the standard of care for locally advanced EAC; however, resistance to treatment is a significant clinical problem. The identification of both novel biomarkers predicting response to treatment and novel therapeutic targets to enhance the efficacy of CRT is key to improving survival rates in EAC. In this study, we performed global microRNA (miRNA) profiling of pretreatment EAC biopsies and identified 67 miRNAs significantly altered in patients who are resistant to CRT. One of these miRNAs, miR-187, was significantly decreased in pretreatment EAC tumors from patients having a poor response to neoadjuvant CRT, highlighting downregulation of miR-187 as a potential mediator of treatment resistance in EAC. In vitro, miR-187 was demonstrated to play a functional role in modulating sensitivity to X-ray radiation and cisplatin in EAC and its dysregulation was demonstrated to be due to chromosomal alterations. In vitro, miR-187 altered expression of a diverse array of pathways, including the immune regulator complement component 3 (C3), serum levels of which we have previously demonstrated to predict patient response to CRT. In vivo, expression of C3 was significantly increased in tumors from patients having a poor response to CRT. This study highlights for the first time a role for miR-187 as a novel biomarker of response to CRT and a potential therapeutic target for enhancing the efficacy of CRT in EAC

Open Science COVID-19 Management Using Bayesian Inference

This is an open science, learning health systems project aiming to inform the management of COVID-19 using Bayesian inference through adaptive RCTs and decision support using Bayesian networks