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Analysis of circulating tumour DNA could stratify cancer risk in symptomatic patients. We aimed to evaluate the performance of a methylation-based multicancer early detection (MCED) diagnostic test in symptomatic patients referred from primary care. We did a multicentre, prospective, observational study at National Health Service (NHS) hospital sites in England and Wales. Participants aged 18 or older referred with non-specific symptoms or symptoms potentially due to gynaecological, lung, or upper or lower gastrointestinal cancers were included and gave a blood sample when they attended for urgent investigation. Participants were excluded if they had a history of or had received treatment for an invasive or haematological malignancy diagnosed within the preceding 3 years, were taking cytotoxic or demethylating agents that might interfere with the test, or had participated in another study of a GRAIL MCED test. Patients were followed until diagnostic resolution or up to 9 months. Cell-free DNA was isolated and the MCED test performed blinded to the clinical outcome. MCED predictions were compared with the diagnosis obtained by standard care to establish the primary outcomes of overall positive and negative predictive value, sensitivity, and specificity. Outcomes were assessed in participants with a valid MCED test result and diagnostic resolution. SYMPLIFY is registered with ISRCTN (ISRCTN10226380) and has completed follow-up at all sites. 6238 participants were recruited between July 7 and Nov 30, 2021, across 44 hospital sites. 387 were excluded due to staff being unable to draw blood, sample errors, participant withdrawal, or identification of ineligibility after enrolment. Of 5851 clinically evaluable participants, 376 had no MCED test result and 14 had no information as to final diagnosis, resulting in 5461 included in the final cohort for analysis with an evaluable MCED test result and diagnostic outcome (368 [6·7%] with a cancer diagnosis and 5093 [93·3%] without a cancer diagnosis). The median age of participants was 61·9 years (IQR 53·4–73·0), 3609 (66·1%) were female and 1852 (33·9%) were male. The MCED test detected a cancer signal in 323 cases, in whom 244 cancer was diagnosed, yielding a positive predictive value of 75·5% (95% CI 70·5–80·1), negative predictive value of 97·6% (97·1–98·0), sensitivity of 66·3% (61·2–71·1), and specificity of 98·4% (98·1–98·8). Sensitivity increased with increasing age and cancer stage, from 24·2% (95% CI 16·0–34·1) in stage I to 95·3% (88·5–98·7) in stage IV. For cases in which a cancer signal was detected among patients with cancer, the MCED test's prediction of the site of origin was accurate in 84·8% (95% CI 79·5–89·0) of cases. Sensitivity 80·4% (95% CI 66·1–90·6) and negative predictive value 99·1% (98·2–99·6) were highest for patients with symptoms mandating investigation for upper gastrointestinal cancer. This first large-scale prospective evaluation of an MCED diagnostic test in a symptomatic population demonstrates the feasibility of using an MCED test to assist clinicians with decisions regarding urgency and route of referral from primary care. Our data provide the basis for a prospective, interventional study in patients presenting to primary care with non-specific signs and symptoms. GRAIL Bio UK.

Background: Pancreatoduodenectomy with venous resection (PDVR) may be performed to achieve tumour clearance in patients with a pancreatic ductal adenocarcinoma (PDAC) with venous involvement. This study aimed to evaluate the impact of PDVR on PDAC outcomes. Methods: In total, 435 PDAC patients with either R0 status (n = 322) or R1 status within the superior mesenteric vein groove (n = 113) were extracted from the Recurrence After Whipple’s (RAW) study dataset. PDVR patients were matched in a 1:2 ratio with standard PD patients. Comparisons were then made between the two groups (surgical radicality and survival). Results: A total of 81 PDVRs were matched with 162 PDs. Neoadjuvant chemotherapy (5.7% vs. 13.6%, p = 0.032) and R1 resection rates (17.9% vs. 42%, p < 0.001) were higher in the PDVR group. Risk factors for R1 resection included venous resection (p < 0.001 for sleeve and p = 0.034 for segmental resection), pT3 (p = 0.007), and pN1 stage (p = 0.045). PDVR patients had lower median overall survival (OS, 21 vs. 30 months (m), p = 0.023) and disease-free survival (DFS, 17 m vs. 24 m, p = 0.043). Among PDVR patients, R status did not impact on OS (R0: 23 m, R1: 21 m, p = 0.928) or DFS (R0: 18 m, R1: 17 m, p = 0.558). Irrespective of R status, systemic recurrence was higher in the PDVR group (p = 0.034). Conclusions: Independent of R status, the PDVR group had lower overall survival and higher systemic recurrence rates.

Pancreatic cancer remains one of the highest-mortality cancers worldwide, with most patients diagnosed at an advanced stage, making curative surgical resection impossible for many. As research efforts focus on early detection, we reviewed the current literature on the use of genetic risk markers and patient risk factors to estimate a person’s lifetime risk via polygenic risk scores (PRSs) and mixed models that combine PRSs with clinical risk factors. Our analysis demonstrates that combining genetic data with patient risk factors significantly improves risk prediction. Through a systematic review and meta-analysis, we show that predictive models perform better in populations with Caucasian ancestry compared to non-Caucasian ancestry. Additionally, increasing the number of genomic markers improves the predictive power of these models. Finally, we consider the potential of rare variants to further enhance the accuracy of these risk prediction models for pancreatic cancer.

BackgroundHuman growth factors are potential therapeutic agents for various inflammatory disorders affecting the gastrointestinal tract. However, they are unstable when administered orally and systemic administration requires high doses increasing the risk of unwanted side effects. Live microorganism-based delivery systems can overcome these problems although they suffer from the inability to control heterologous protein production and there are concerns regarding biosafety and environmental contamination.MethodsTo overcome these limitations we have developed a new live bacteria drug-delivery system using the human commensal gut bacterium Bacteroides ovatus engineered to secrete human growth factors in response to dietary xylan. The anaerobic nature of B ovatus provides an inherent biosafety feature. B ovatus strains expressing human keratinocyte growth factor-2, which plays a central role in intestinal epithelial homeostasis and repair (BO-KGF), were generated by homologous recombination and evaluated using the dextran sodium sulfate (DSS)-induced model of intestinal epithelial injury and colitis.ResultsIn response to xylan BO-KGF produced biologically active KGF both in vitro and in vivo. In DSS treated mice administration of xylan and BO-KGF had a significant therapeutic effect in reducing weight loss, improving stool consistency, reducing rectal bleeding, accelerating healing of damaged epithelium, reducing inflammation and neutrophil infiltration, reducing expression of pro-inflammatory cytokines, and accelerating production of goblet cells. BO-KGF and xylan treatment also had a marked prophylactic effect limiting the development of inflammation and disruption of the epithelial barrier.ConclusionThis novel, diet-regulated, live bacterial drug delivery system may be applicable to treating various bowel disorders.

IntroductionDetecting cancer earlier improves treatment options and long-term survival. A multicancer early detection test that reliably picks up early-stage cancer would potentially save lives and reduce the cost of treating cancer. One promising candidate is the Enlighten test, which applies machine learning to plasma amino acid concentrations to detect cancer. In a cohort of 77 patients recently diagnosed with breast, colorectal, pancreatic or prostate cancer, 60 (78%) were detected by the test (sensitivity), with no false positives in 20 healthy controls. The MODERNISED study will further develop the Enlighten test to detect 10 different cancers by adding bladder, lung, melanoma, oesophageal, ovarian and renal cancer to the test.Methods and analysisMODERNISED (ISRCTN17299125) is a multicentre prospective, non-interventional, case–control study. We aim to recruit 1000 adult participants with a recent cancer diagnosis, 250 adult participants with symptoms of cancer where a cancer diagnosis was ruled out by the National Health Service (NHS) standard of care and 100 healthy adult volunteers. Cancer tissue of origin (ToO) will include bladder, breast, colorectal, lung, melanoma, oesophageal, ovarian, pancreatic, prostate and renal. Participants in the two non-cancer cohorts who are later diagnosed with cancer will be moved to the cancer cases cohort. The primary aim is to train and validate a machine learning algorithm to detect cancer, which will be evaluated by AUROC. Secondary aims include training and validating an algorithm to predict ToO and stage of cancer, exploring differences in performance by demographics and estimating how sensitivity varies across specificity cut-offs of 95%, 99% and 99.9%. These results will provide a statistically powered estimate of how well the Enlighten test can discriminate between individuals with and without cancer, which can then be validated for clinical use in further research.Ethics and disseminationThis study is sponsored by University Hospital Southampton NHS Foundation Trust and has been approved by the Health Research Authority and Health and Care Research West Midlands (24/WM/0234). Results will be presented at scientific meetings and published in international peer-reviewed journals. Lay summaries of study progress and findings will be published on the Southampton Clinical Trial Unit’s website.Trial registration numberISRCTN17299125.

Background The relationship between physical activity and incident pancreatic cancer is poorly defined, and the evidence to date is inconsistent, largely due to small sample sizes and insufficient incident outcomes. Using the UK Biobank cohort dataset, the association between physical activity levels at recruitment and incident pancreatic ductal adenocarcinoma (PDAC) at follow-up was analysed. Method Physical activity, the key exposure, was quantified using Metabolic Equivalent Task (MET) values and categorised into walking, moderate, and vigorous activity. These categories were each analysed in quartiles. Summed activity was analysed both in quartiles and using International Physical Activity Questionnaire (IPAQ) activity levels (low, moderate, high). Univariate hazard ratios (HRs) and multivariable-adjusted HRs (aHRs) with 95% confidence intervals (CIs) were calculated using Cox regression analyses. Results A total of 542 incident PDAC cases and 2,139 controls (1:4 matching for age and sex) with a median (IQR) follow-up of 6.8 (1.7) years were analysed. No significant association was found in walking, moderate, and vigorous activities. In summed activity, only the 3rd quartile showed a statistically significant inverse association with PDAC risk (aHR 0.67, 95% CI 0.52-0.86, p<0.01). For IPAQ activity, the moderate and high activity groups showed borderline statistically significant associations with incident PDAC (aHR 0.80, 95% CI 0.63-1.00, p=0.05, and aHR 0.80, 95% CI 0.64-1.01, p=0.06, respectively). Conclusion The large UK Biobank cohort study did not show a strong association between physical activity levels and the development of incident PDAC.

Imbalanced class distribution in the medical dataset is a challenging task that hinders classifying disease correctly. It emerges when the number of healthy class instances being much larger than the disease class instances. To solve this problem, we proposed undersampling the healthy class instances to improve disease class classification. This model is named Hellinger Distance Undersampling (HDUS). It employs the Hellinger Distance to measure the resemblance between majority class instance and its neighbouring minority class instances to separate classes effectively and boost the discrimination power for each class. An extensive experiment has been conducted on four imbalanced medical datasets using three classifiers to compare HDUS with a baseline model and three state-of-the-art undersampling models. The outcomes display that HDUS can perform better than other models in terms of sensitivity, F1 measure, and balanced accuracy.

Pancreatic cancer (PC) is the fifth leading cause of cancer-related death in the UK. The incidence of PC is increasing, with little or no improvement in overall survival and the best chance for long-term survival in patients with PC relies on early detection and surgical resection. In this study, we propose the use of a diagnostic algorithm that combines tests of pancreatic exocrine function (faecal elastase-1 (FE-1) test and the C-mixed triglyceride ( C-MTG) breath test) to identify patients with PC that urgently needs imaging studies. This prospective pilot (proof of concept) study will be carried out on 25 patients with resectable PC, 10 patients with chronic pancreatitis and 25 healthy volunteers. This study will construct a predictive algorithm for PC, using two tests of pancreatic exocrine function, FE-1 test and the C-MTG breath test. Continuous flow isotope ratio mass spectrometry in the C-MTG breath test will be used to analyse enriched CO in exhaled breath samples. The additional predictive benefit of other potential biomarkers of PC will also be considered. Potential biomarkers of PC showing abilities to discriminate between patients with PC from healthy subjects or patients with chronic pancreatitis will be selected by metabolomic analysis. The study was approved by the North of Scotland Research and Ethics Committee on 1 October 2020 (reference: 20/NS/0105, favourable opinion granted). The results will be disseminated in presentations at academic national/international conferences and publication in peer-review journals.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is insidious, with only 15–20% of those diagnosed suitable for surgical resection as it is either too advanced and has invaded local structures or has already spread to distant sites. The associated tumor microenvironment provides a protective shield which limits the efficacy of chemotherapeutic agents, but also impairs the delivery of nutrients required for the PDAC cells. To compensate for this, metabolic adaptions occur to provide alternative sources of fuel. The aim of this study is to explore metabolomic differences between participants with resectable PDAC compared to healthy volunteers (HV). The objectives were to use nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to determine if resectable PDAC induces sufficient metabolic adaptations and variations which could be used to discriminate between the two groups. Methods: Plasma samples were collected from fasted individuals with resectable PDAC (n = 23, median age 68 [IQR 56–75], 69.6% male) and HV (n = 24, median age 63 [IQR 58–71], 54.2% male). Samples were analyzed using NMR and the Biocrates MxP Quant 500 kit at University Hospital Southampton. Results: NMR spectroscopy identified six independent metabolites that significantly discriminated between the PDAC and HV groups, including elevated plasma concentrations of 3-hydroxybutyrate and citrate, with decreased amounts of glutamine and histidine. MS analysis identified 84 metabolites with a significant difference between the PDAC and HV cohorts. The metabolites with a fold change (FC) > 1.5 in the PDAC population were conjugated bile acids (taurocholic acid, glycocholic acid, and glycochenodexoycholic acid). Discussion: In conclusion, using metabolomics, biochemical differences between resectable PDAC and HV were detected. These differences indicate metabolic plasticity and utilization of alternative fuel sources.

Background: The role of dysglycaemia as a risk marker for Pancreatic Ductal Adenocarcinoma (PDAC) is uncertain. We investigated the relationship between glycated haemoglobin (HbA1c) and incident PDAC using a retrospective cohort study within the UK Biobank. Methods: A study involving 499,804 participants from the UK Biobank study was undertaken. Participants were stratified by diabetes mellitus (DM) status, and then by HbA1c values < 42 mmol/mol, 42–47 mmol/mol, or ≥48 mmol/mol. Cox proportional hazard models were used to describe the association between HbA1c category (with time-varying interactions) and incident PDAC. Results: PDAC occurred in 1157 participants during 11.6 (10.9–12.3) years follow up [(median (interquartile range)]. In subjects without known DM at baseline, 12 months after recruitment, the adjusted hazard ratios (aHR, 95% CI) for incident PDAC for HbA1c 42–47 mmol/mol compared to HbA1c < 42 mmol/mol (reference group) was 2.10 (1.31–3.37, p = 0.002); and was 8.55 (4.58–15.99, p < 0.001) for HbA1c ≥ 48 mmol/mol. The association between baseline HbA1c and incident PDAC attenuated with increasing duration of time of follow-up to PDAC diagnosis. Conclusions: Dysglycaemia detected by elevated HbA1c is associated with an increased risk of PDAC. The strength of the association between elevated HbA1c and incident PDAC is inversely proportional to the time from detecting dysglycaemia but remains significant for at least 60 months following HbA1c testing.