Citations

Purpose The relevance of inherited pathogenic mutations in cancer predisposition genes in pancreatic cancer is not well understood. We aimed to assess the characteristics of patients with pancreatic cancer referred for hereditary cancer genetic testing and to estimate the risk of pancreatic cancer associated with mutations in panel-based cancer predisposition genes in this high-risk population. Methods Patients with pancreatic cancer (N = 1,652) were identified from a 140,000-patient cohort undergoing multigene panel testing of predisposition genes between March 2012 and June 2016. Gene-level mutation frequencies relative to Exome Aggregation Consortium and Genome Aggregation Database reference controls were assessed. Results The frequency of germline cancer predisposition gene mutations among patients with pancreatic cancer was 20.73%. Mutations in ATM, BRCA2, CDKN2A, MSH2, MSH6, PALB2, and TP53 were associated with high pancreatic cancer risk (odds ratio, > 5), and mutations in BRCA1 were associated with moderate risk (odds ratio, > 2). In a logistic regression model adjusted for age at diagnosis and family history of cancer, ATM and BRCA2 mutations were associated with personal history of breast or pancreatic cancer, whereas PALB2 mutations were associated with family history of breast or pancreatic cancer. Conclusion These findings provide insight into the spectrum of mutations expected in patients with pancreatic cancer referred for cancer predisposition testing. Mutations in eight genes confer high or moderate risk of pancreatic cancer and may prove useful for risk assessment for pancreatic and other cancers. Family and personal histories of breast cancer are strong predictors of germline mutations.

The highly polymorphic human leukocyte antigen (HLA) class I molecules present peptide antigens to CD8+ T cells, inducing immunity against infections and cancers. Quality control mediated by peptide loading complex (PLC) components is expected to ensure the cell surface expression of stable peptide-HLA class I complexes. This is exemplified by HLA-B*08:01 in primary human lymphocytes, with both expression level and half-life at the high end of the measured HLA-B expression and stability hierarchies. Conversely, low expression on lymphocytes is measured for three HLA-B allotypes that bind peptides with proline at position 2, which are disfavored by the transporter associated with antigen processing. Surprisingly, these lymphocyte-specific expression and stability differences become reversed or altered in monocytes, which display larger intracellular pools of HLA class I than lymphocytes. Together, the findings indicate that allele and cell-dependent variations in antigen acquisition pathways influence HLA-B surface expression levels, half-lives and receptivity to exogenous antigens.

ObjectiveTo examine the association between macrophage migration inhibitory factor (MIF) promoter polymorphisms and granulomatosis with polyangiitis (GPA) in human subjects, and to assess the role of MIF in a murine model of granulomatous vasculitis.MethodsThe human study involved 1,077 patients with GPA and healthy controls whose serum was genotyped by capillary electrophoresis for the MIF −794 CATT5−8 promoter microsatellite (rs5844572). MIF promoter, CATT‐length–dependent gene expression in response to β‐glucan was assessed by gene reporter assays. In mouse studies, granulomatous disease was induced by injection of Candida albicans β‐glucan into wild‐type (WT) or Mif‐knockout (Mif‐KO) C57BL/6 mice and C57BL/6 mice transgenically overexpressing Mif in lung epithelium (Mif lung–Tg2.1). Mice were treated with a neutralizing anti‐MIF antibody and analyzed for the density of pulmonary granulomas, expression of inflammatory chemokines, and frequency of mortality.ResultsThe percentage of human subjects carrying >5 CATT repeats in each MIF allele (high genotypic MIF expressers) was 60.2% among patients with GPA and 53.9% among healthy controls (adjusted P = 0.049). In response to granulomatous stimulation, human MIF gene expression increased proportionally with CATT length. Mif lung–Tg2.1 mice exhibited more pulmonary granulomas than WT mice, which in turn showed more granulomas than Mif‐KO mice. A significantly higher percentage of Mif lung–Tg2.1 mice, compared to Mif‐KO or WT mice, died when injected with Candida albicans β‐glucan, and treatment of these mice with an anti‐MIF monoclonal antibody protected against a lethal outcome. Levels of MIF‐dependent neutrophil/macrophage chemokines were elevated in the bronchoalveolar lavage fluid or plasma of Mif lung–Tg2.1 mice.ConclusionPatients with GPA have an increased frequency of high MIF expression CATT alleles. Higher Mif expression increases the incidence of mortality and pulmonary granulomas in Mif lung–Tg2.1 mice, while anti‐MIF treatment protects these mice against death. Blockade of MIF in high genotypic MIF expressers may therefore offer a selective pharmacologic therapy for GPA.

AbstractMotivationThe use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP–SNP interactions.ResultsWe tested two candidate approaches: the ‘Five-Full’ and ‘AA9int’ method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP–SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP–SNP interactions in large-scale studies.Availability and implementationThe ‘AA9int’ and ‘parAA9int’ functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/.Supplementary informationSupplementary data are available at Bioinformatics online.

BackgroundHeart rate and systolic blood pressure (SBP) are prognostic markers in heart failure (HF) with reduced ejection fraction (HFrEF). Their combination in rate pressure product (RPP) as well as their role in heart failure with preserved ejection fraction (HFpEF) remains unclear.HypothesisRPP and its components are associated with HFpEF outcomes.MethodsWe performed an analysis of Acute Study of Clinical Effectiveness of Nesiritide in Subjects With Decompensated Heart Failure (ASCEND‐HF; http://www.clinicaltrials.gov NCT00475852), which studied 7141 patients with acute HF. HFpEF was defined as left ventricular ejection fraction ≥40%. Outcomes were assessed by baseline heart rate, SBP, and RPP, as well as the change of these variables using adjusted Cox models.ResultsAfter multivariable adjustment, in‐hospital change but not baseline heart rate, SBP, and RPP were associated with 30‐day mortality/HF hospitalization (hazard ratio [HR]: 1.17 per 5‐bpm heart rate, HR: 1.20 per 10‐mm Hg SBP, and HR: 1.02 per 100 bpm × mm Hg RPP; all P < 0.05). Baseline SBP was associated with 180‐day mortality (HR: 0.88 per 10‐mm Hg, P = 0.028). Though change in RPP was associated with 30‐day mortality/HF hospitalization, the RPP baseline variable did not provide additional associative information with regard to outcomes when compared with assessment of baseline heart rate and SBP variables alone.ConclusionsAn increase in heart rate and SBP from baseline to discharge was associated with increased 30‐day mortality/HF hospitalization in HFpEF patients with acute exacerbation. These findings suggest value in monitoring the trend of vital signs during HFpEF hospitalization.

This study examines the importance of four psychosocial factors—personality, cognitive appraisal of quality of life, social support, and current reserve-building—in predicting treatment burden in chronically ill patients. Chronically ill patients ( n = 446) completed web-based measures. Structural equation modeling was used to investigate psychosocial factors predicting treatment burden. Reserve-building activities indirectly reduced treatment burden by: (1) reducing health worries appraisals, (2) reducing financial difficulties, (3) increasing calm and peaceful appraisals, and (4) increasing perceived social support. These findings point to key behaviors that chronically ill people can use to attenuate their treatment burden.