48 research outputs found
1,4-dihydroxy quininib activates ferroptosis pathways in metastatic uveal melanoma and reveals a novel prognostic biomarker signature
Uveal melanoma (UM) is an ocular cancer, with propensity for lethal liver metastases. When metastatic UM (MUM) occurs, as few as 8% of patients survive beyond two years. Efficacious treatments for MUM are urgently needed. 1,4-dihydroxy quininib, a cysteinyl leukotriene receptor 1 (CysLT1) antagonist, alters UM cancer hallmarks in vitro, ex vivo and in vivo. Here, we investigated the 1,4-dihydroxy quininib mechanism of action and its translational potential in MUM. Proteomic profiling of OMM2.5 cells identified proteins differentially expressed after 1,4-dihydroxy quininib treatment. Glutathione peroxidase 4 (GPX4), glutamate-cysteine ligase modifier subunit (GCLM), heme oxygenase 1 (HO-1) and 4 hydroxynonenal (4-HNE) expression were assessed by immunoblots. Biliverdin, glutathione and lipid hydroperoxide were measured biochemically. Association between the expression of a specific ferroptosis signature and UM patient survival was performed using public databases. Our data revealed that 1,4-dihydroxy quininib modulates the expression of ferroptosis markers in OMM2.5 cells. Biochemical assays validated that GPX4, biliverdin, GCLM, glutathione and lipid hydroperoxide were significantly altered. HO-1 and 4-HNE levels were significantly increased in MUM tumor explants from orthotopic patient-derived xenografts (OPDX). Expression of genes inhibiting ferroptosis is significantly increased in UM patients with chromosome 3 monosomy. We identified IFerr, a novel ferroptosis signature correlating with UM patient survival. Altogether, we demontrated that in MUM cells and tissues, 1,4-dihydroxy quininib modulates key markers that induce ferroptosis, a relatively new type of cell death driven by iron-dependent peroxidation of phospholipids. Furthermore, we showed that high expression of specific genes inhibiting ferroptosis is associated with a worse UM prognosis, thus, the IFerr signature is a potential prognosticator for which patients develop MUM. All in all, ferroptosis has potential as a clinical biomarker and therapeutic target for MUM
Evaluation of a Theory-Informed Implementation Intervention for the Management of Acute Low Back Pain in General Medical Practice: The IMPLEMENT Cluster Randomised Trial
Introduction: This cluster randomised trial evaluated an intervention to decrease x-ray referrals and increase giving advice to stay active for people with acute low back pain (LBP) in general practice.
Methods: General practices were randomised to either access to a guideline for acute LBP (control) or facilitated interactive workshops (intervention). We measured behavioural predictors (e.g. knowledge, attitudes and intentions) and fear avoidance beliefs. We were unable to recruit sufficient patients to measure our original primary outcomes so we introduced other outcomes measured at the general practitioner (GP) level: behavioural simulation (clinical decision about vignettes) and rates of x-ray and CT-scan (medical administrative data). All those not involved in the delivery of the intervention were blinded to allocation.
Results: 47 practices (53 GPs) were randomised to the control and 45 practices (59 GPs) to the intervention. The number of GPs available for analysis at 12 months varied by outcome due to missing confounder information; a minimum of 38 GPs were available from the intervention group, and a minimum of 40 GPs from the control group. For the behavioural constructs, although effect estimates were small, the intervention group GPs had greater intention of practising consistent with the guideline for the clinical behaviour of x-ray referral. For behavioural simulation, intervention group GPs were more likely to adhere to guideline recommendations about x-ray (OR 1.76, 95%CI 1.01, 3.05) and more likely to give advice to stay active (OR 4.49, 95%CI 1.90 to 10.60). Imaging referral was not statistically significantly different between groups and the potential importance of effects was unclear; rate ratio 0.87 (95%CI 0.68, 1.10) for x-ray or CT-scan.
Conclusions: The intervention led to small changes in GP intention to practice in a manner that is consistent with an evidence-based guideline, but it did not result in statistically significant changes in actual behaviour.
Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN01260600009853
Functionality and feedback: a realist synthesis of the collation, interpretation and utilisation of patient-reported outcome measures data to improve patient care
Background: The feedback of patient-reported outcome measures (PROMs) data is intended to support the care of individual patients and to act as a quality improvement (QI) strategy. Objectives: To (1) identify the ideas and assumptions underlying how individual and aggregated PROMs data are intended to improve patient care, and (2) review the evidence to examine the circumstances in which and processes through which PROMs feedback improves patient care. Design: Two separate but related realist syntheses: (1) feedback of aggregate PROMs and performance data to improve patient care, and (2) feedback of individual PROMs data to improve patient care. Interventions: Aggregate – feedback and public reporting of PROMs, patient experience data and performance data to hospital providers and primary care organisations. Individual – feedback of PROMs in oncology, palliative care and the care of people with mental health problems in primary and secondary care settings. Main outcome measures: Aggregate – providers’ responses, attitudes and experiences of using PROMs and performance data to improve patient care. Individual – providers’ and patients’ experiences of using PROMs data to raise issues with clinicians, change clinicians’ communication practices, change patient management and improve patient well-being. Data sources: Searches of electronic databases and forwards and backwards citation tracking. Review methods: Realist synthesis to identify, test and refine programme theories about when, how and why PROMs feedback leads to improvements in patient care. Results: Providers were more likely to take steps to improve patient care in response to the feedback and public reporting of aggregate PROMs and performance data if they perceived that these data were credible, were aimed at improving patient care, and were timely and provided a clear indication of the source of the problem. However, implementing substantial and sustainable improvement to patient care required system-wide approaches. In the care of individual patients, PROMs function more as a tool to support patients in raising issues with clinicians than they do in substantially changing clinicians’ communication practices with patients. Patients valued both standardised and individualised PROMs as a tool to raise issues, but thought is required as to which patients may benefit and which may not. In settings such as palliative care and psychotherapy, clinicians viewed individualised PROMs as useful to build rapport and support the therapeutic process. PROMs feedback did not substantially shift clinicians’ communication practices or focus discussion on psychosocial issues; this required a shift in clinicians’ perceptions of their remit.
Strengths and limitations: There was a paucity of research examining the feedback of aggregate PROMs data to providers, and we drew on evidence from interventions with similar programme theories (other forms of performance data) to test our theories. Conclusions: PROMs data act as ‘tin openers’ rather than ‘dials’. Providers need more support and guidance on how to collect their own internal data, how to rule out alternative explanations for their outlier status and how to explore the possible causes of their outlier status. There is also tension between PROMs as a QI strategy versus their use in the care of individual patients; PROMs that clinicians find useful in assessing patients, such as individualised measures, are not useful as indicators of service quality.
Future work: Future research should (1) explore how differently performing providers have responded to aggregate PROMs feedback, and how organisations have collected PROMs data both for individual patient care and to improve service quality; and (2) explore whether or not and how incorporating PROMs into patients’ electronic records allows multiple different clinicians to receive PROMs feedback, discuss it with patients and act on the data to improve patient care