The introduction of the Cancer Research UK Stratified Medicine Programme 2 (CRUK SMP2) in North East England; lessons learned and experience gained

Introduction: The CRUK SMP2 programme was set-up to evaluate the feasibility of performing large scale molecular analysis within the NHS on the (often small) diagnostic biopsies obtained in NSCLC. The results are used to allocate patients to an appropriate molecular therapy within the “umbrella” MATRIX trial. Newcastle opened SMP2 on 01/10/2014. Here we report our first year’s experience. Methods: NSCLC patients with PS 0–2 were consented onto the CRUK SMP2. Matched residual diagnostic tissue and blood were sent to All Wales Genetics Laboratory, Cardiff. Samples with >70ng DNA were assessed for 28 oncogenes using Next Genuine Sequencing on the Illumina SMP2 panel. Results: 116 patients were consented from 6/10/14–1/10/15 referred from 12 oncologists. The data on patient/sample flow is shown in Fig 1. Median survival was 161 days from consent. The 1st sample was sent to Cardiff on 28/1/15 as the Illumina panel was undergoing fi- nal validation. 50 samples have been sent; 11 had insufficient DNA; these samples had lower cell number (but with no impact of necrosis/tumour proportion); The most commonly altered gene was K-Ras (13 of 22 adenocarcinomas). Only 2 patients with results from >25 of the 28 genes had no tier 1 or 2 ie potentially treatable molecular abnormalities. The median time from consent to result was 109 days (range 45–250) with delays occurring throughout the pathway. Conclusion: Patients and oncologists are keen to be involved in molecular profiling; but patients need to be consented early to allow results to guide therapy. Prioritisation of samples is key. Not all samples are suitable for analysis due to small cell number or low tumour proportion. Molecular analysis may require extra resource in pathology, if it is to become standard of care. The first 4 patients to start treatment on MATRIX were enrolled from 27/8/15 in Newcastle. Disclosure: All authors have declared no conflicts of interest

Leptin enhances the secretion of interleukin (IL)-18, but not IL-1β, from human monocytes via activation of caspase-1

Circulating levels of leptin are elevated in type-2 diabetes mellitus (T2DM) and leptin plays a role in immune responses. Elevated circulating IL-18 levels are associated with clinical complications of T2DM. IL-18 regulates cytokine secretion and the function of a number of immune cells including T-cells, neutrophils and macrophages and as such has a key role in immunity and inflammation. Pro-inflammatory monocytes exhibiting elevated cytokine secretion are closely associated with inflammation in T2DM, however, little is known about the role of leptin in modifying monocyte IL-18 secretion. We therefore aimed to investigate the effect of leptin on IL-18 secretion by monocytes. We report herein that leptin increases IL-18 secretion in THP-1 and primary human monocytes but has no effect on IL-18 mRNA. Leptin and LPS signalling in monocytes occurs by overlapping but distinct pathways. Thus, in contrast to a strong stimulation by LPS, leptin has no effect on IL-1β mRNA levels or IL-1β secretion. In addition, LPS stimulates the secretion of IL-6 but leptin did not whereas both treatments up regulate IL-8 secretion from the same cells. Although leptin (and LPS) has a synergistic effect with exogenous ATP on IL-18 secretion in both THP-1 and primary monocytes, experiments involving ATP assays and pharmacological inhibition of ATP signalling failed to provide any evidence that endogenous ATP secreted by leptin-stimulated monocytes was responsible for enhancement of monocyte IL-18 secretion by leptin. Analysis of the action of caspase-1 revealed that leptin up regulates caspase-1 activity and the effect of leptin on IL-18 release is prevented by caspase-1 inhibitor (Ac-YVAD-cmk). These data suggest that leptin activates IL-18 processing rather than IL-18 transcription. In conclusion, leptin enhances IL-18 secretion via modulation of the caspase-1 inflammasome function and acts synergistically with ATP in this regard. This process may contribute to aberrant immune responses in T2DM and other conditions of hyperleptinemia

Treatment of periodontitis reduces systemic inflammation in type 2 diabetes

Aims: To assess the impact of periodontal treatment on systemic inflammation in type 2 diabetes. Materials and methods: Adults with type 2 diabetes (n = 83) and without diabetes (controls, n = 75) were recruited, and participants with periodontitis received periodontal treatment and 12 months' follow-up. Biomarkers for periodontal inflammation (gingival crevicular fluid interleukin-6, tumour necrosis factor-α, interleukin-1β, interferon-γ, matrix metalloproteinase-8, matrix metalloproteinase-9, adiponectin) and serum markers of inflammation and diabetes control (glycated haemoglobin, high sensitivity C-reactive protein, interleukin-6, tumour necrosis factor-α, interleukin-1β, interferon-γ, leptin, adiponectin) were measured. Structural equation modelling was used to evaluate periodontal treatment effects on oral and systemic inflammation. Results: Periodontal treatment resulted in significant improvements in clinical status and reductions in gingival crevicular fluid biomarkers from baseline to month 12. Structural equation modelling identified that, at baseline, individuals with diabetes and periodontitis had significantly higher systemic inflammation than non-diabetic controls with periodontitis (Δ = 0.20, p = .002), with no significant differences between groups for oral inflammation. There was a greater reduction in systemic inflammation following periodontal treatment in individuals with diabetes and periodontitis compared to those with periodontitis but not diabetes (Δ = -0.25, p = .01). Conclusions: Diabetes and periodontitis together appear to increase systemic inflammation, with evidence of reductions following periodontal treatment

Discovery, validation and diagnostic ability of multiple protein-based biomarkers in saliva and GCF to distinguish between health and periodontal diseases.

Aim: To discover and validate differential protein biomarker expression in saliva and gingival crevicular fluid (GCF) to discriminate objectively between periodontal health and plaque-induced periodontal disease states. Materials and methods: 190 participants were recruited from two centres (Birmingham and Newcastle upon Tyne, UK) comprising healthy, gingivitis, periodontitis and edentulous donors. Samples from the Birmingham cohort were analysed by quantitative mass spectrometry proteomics for biomarker discovery. Shortlisted candidate proteins were then verified by enzyme-linked immunosorbent assay in both cohorts. Leave-one-out cross validation logistic regression analysis was used to identify the best performing biomarker panels. Results: 95 proteins were identified in both GCF and saliva samples and 15 candidate proteins were selected based upon differences discovered between the donor groups. The best performing panels to distinguish between: health or gingivitis and periodontitis contained matrix metalloproteinase-9 (MMP9), S100A8, alpha-1-acid glycoprotein (A1AGP), pyruvate kinase and age (area under the curve (AUC) 0.970); health and gingivitis contained MMP9, S100A8, A1AGP, pyruvate kinase but not age (AUC 0.768); and mild-moderate and advanced periodontitis contained MMP9, S100A8, A1AGP, pyruvate kinase and age (AUC 0.789). Conclusion(s): Biomarker panels containing four proteins with and without age as a further parameter can distinguish between periodontal health and disease states