A faecal microbiota signature with high specificity for pancreatic cancer.

BACKGROUND: Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression. OBJECTIVE: To explore the faecal and salivary microbiota as potential diagnostic biomarkers. METHODS: We applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case-control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case-control study (n=76), in the validation phase. RESULTS: Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19-9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation. CONCLUSION: Taken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.

A multilayered post-GWAS assessment on genetic susceptibility to pancreatic cancer.

BACKGROUND: Pancreatic cancer (PC) is a complex disease in which both non-genetic and genetic factors interplay. To date, 40 GWAS hits have been associated with PC risk in individuals of European descent, explaining 4.1% of the phenotypic variance. METHODS: We complemented a new conventional PC GWAS (1D) with genome spatial autocorrelation analysis (2D) permitting to prioritize low frequency variants not detected by GWAS. These were further expanded via Hi-C map (3D) interactions to gain additional insight into the inherited basis of PC. In silico functional analysis of public genomic information allowed prioritization of potentially relevant candidate variants. RESULTS: We identified several new variants located in genes for which there is experimental evidence of their implication in the biology and function of pancreatic acinar cells. Among them is a novel independent variant in NR5A2 (rs3790840) with a meta-analysis p value = 5.91E-06 in 1D approach and a Local Moran's Index (LMI) = 7.76 in 2D approach. We also identified a multi-hit region in CASC8-a lncRNA associated with pancreatic carcinogenesis-with a lowest p value = 6.91E-05. Importantly, two new PC loci were identified both by 2D and 3D approaches: SIAH3 (LMI = 18.24), CTRB2/BCAR1 (LMI = 6.03), in addition to a chromatin interacting region in XBP1-a major regulator of the ER stress and unfolded protein responses in acinar cells-identified by 3D; all of them with a strong in silico functional support. CONCLUSIONS: This multi-step strategy, combined with an in-depth in silico functional analysis, offers a comprehensive approach to advance the study of PC genetic susceptibility and could be applied to other diseases.

Deciphering the complex interplay between pancreatic cancer, diabetes mellitus subtypes and obesity/BMI through causal inference and mediation analyses.

OBJECTIVES: To characterise the association between type 2 diabetes mellitus (T2DM) subtypes (new-onset T2DM (NODM) or long-standing T2DM (LSDM)) and pancreatic cancer (PC) risk, to explore the direction of causation through Mendelian randomisation (MR) analysis and to assess the mediation role of body mass index (BMI). DESIGN: Information about T2DM and related factors was collected from 2018 PC cases and 1540 controls from the PanGenEU (European Study into Digestive Illnesses and Genetics) study. A subset of PC cases and controls had glycated haemoglobin, C-peptide and genotype data. Multivariate logistic regression models were applied to derive ORs and 95% CIs. T2DM and PC-related single nucleotide polymorphism (SNP) were used as instrumental variables (IVs) in bidirectional MR analysis to test for two-way causal associations between PC, NODM and LSDM. Indirect and direct effects of the BMI-T2DM-PC association were further explored using mediation analysis. RESULTS: T2DM was associated with an increased PC risk when compared with non-T2DM (OR=2.50; 95% CI: 2.05 to 3.05), the risk being greater for NODM (OR=6.39; 95% CI: 4.18 to 9.78) and insulin users (OR=3.69; 95% CI: 2.80 to 4.86). The causal association between T2DM (57-SNP IV) and PC was not statistically significant (ORLSDM=1.08, 95% CI: 0.86 to 1.29, ORNODM=1.06, 95% CI: 0.95 to 1.17). In contrast, there was a causal association between PC (40-SNP IV) and NODM (OR=2.85; 95% CI: 2.04 to 3.98), although genetic pleiotropy was present (MR-Egger: p value=0.03). Potential mediating effects of BMI (125-SNPs as IV), particularly in terms of weight loss, were evidenced on the NODM-PC association (indirect effect for BMI in previous years=0.55). CONCLUSION: Findings of this study do not support a causal effect of LSDM on PC, but suggest that PC causes NODM. The interplay between obesity, PC and T2DM is complex.

Gallbladder disease, cholecystectomy, and pancreatic cancer risk in the International Pancreatic Cancer Case-Control Consortium (PanC4).

BACKGROUND: The association among gallbladder disease, cholecystectomy, and pancreatic cancer is unclear. Moreover, time interval between gallbladder disease or cholecystectomy and pancreatic cancer diagnosis is not considered in most previous studies. AIM: To quantify the association among gallbladder disease, cholecystectomy, and pancreatic cancer, considering time since first diagnosis of gallbladder disease or cholecystectomy. METHODS: We used data from nine case-control studies within the Pancreatic Cancer Case-Control Consortium, including 5760 cases of adenocarcinoma of the exocrine pancreas and 8437 controls. We estimated pooled odds ratios and the corresponding 95% confidence intervals by estimating study-specific odds ratios through multivariable unconditional logistic regression models, and then pooling the obtained estimates using fixed-effects models. RESULTS: Compared with patients with no history of gallbladder disease, the pooled odds ratio of pancreatic cancer was 1.69 (95% confidence interval, 1.51-1.88) for patients reporting a history of gallbladder disease. The odds ratio was 4.90 (95% confidence interval, 3.45-6.97) for gallbladder disease diagnosed <2 years before pancreatic cancer diagnosis and 1.11 (95% confidence interval, 0.96-1.29) when ≥2 years elapsed. The pooled odds ratio was 1.64 (95% confidence interval, 1.43-1.89) for patients who underwent cholecystectomy, as compared to those without cholecystectomy. The odds ratio was 7.00 (95% confidence interval, 4.13-11.86) for a surgery <2 years before pancreatic cancer diagnosis and 1.28 (95% confidence interval, 1.08-1.53) for a surgery ≥2 years before. CONCLUSIONS: There appears to be no long-term effect of gallbladder disease on pancreatic cancer risk, and at most a modest one for cholecystectomy. The strong short-term association can be explained by diagnostic bias and reverse causation.

Pancreatic Cancer Risk in Relation to Lifetime Smoking Patterns, Tobacco Type, and Dose-Response Relationships.

BACKGROUND: Despite smoking being a well-established risk factor for pancreatic cancer, there is a need to further characterize pancreatic cancer risk according to lifespan smoking patterns and other smoking features, such as tobacco type. Our aim was to deeply investigate them within a large European case-control study. METHODS: Tobacco smoking habits and other relevant information were obtained from 2,009 cases and 1,532 controls recruited in the PanGenEU study using standardized tools. Multivariate logistic regression analysis was performed to evaluate pancreatic cancer risk by smoking characteristics and interactions with other pancreatic cancer risk factors. Fractional polynomials and restricted cubic splines were used to test for nonlinearity of the dose-response relationships and to analyze their shape. RESULTS: Relative to never-smokers, current smokers [OR = 1.72; 95% confidence interval (95% CI), 1.39-2.12], those inhaling into the throat (OR = 1.48; 95% CI, 1.11-1.99) or chest (OR = 1.33; 95% CI, 1.12-1.58), and those using nonfiltered cigarettes (OR = 1.69; 95% CI, 1.10-2.61), were all at an increased pancreatic cancer risk. Pancreatic cancer risk was highest in current black tobacco smokers (OR = 2.09; 95% CI, 1.31-3.41), followed by blond tobacco smokers (OR = 1.43; 95% CI, 1.01-2.04). Childhood exposure to tobacco smoke relative to parental smoking was also associated with increased pancreatic cancer risk (OR = 1.24; 95% CI, 1.03-1.49). Dose-response relationships for smoking duration, intensity, cumulative dose, and smoking cessation were nonlinear and showed different shapes by tobacco type. Effect modification by family history of pancreatic cancer and diabetes was likely. CONCLUSIONS: This study reveals differences in pancreatic cancer risk by tobacco type and other habit characteristics, as well as nonlinear risk associations. IMPACT: This characterization of smoking-related pancreatic cancer risk profiles may help in defining pancreatic cancer high-risk populations.

Pancreatic cancer and autoimmune diseases: An association sustained by computational and epidemiological case-control approaches.

Deciphering the underlying genetic basis behind pancreatic cancer (PC) and its associated multimorbidities will enhance our knowledge toward PC control. The study investigated the common genetic background of PC and different morbidities through a computational approach and further evaluated the less explored association between PC and autoimmune diseases (AIDs) through an epidemiological analysis. Gene-disease associations (GDAs) of 26 morbidities of interest and PC were obtained using the DisGeNET public discovery platform. The association between AIDs and PC pointed by the computational analysis was confirmed through multivariable logistic regression models in the PanGen European case-control study population of 1,705 PC cases and 1,084 controls. Fifteen morbidities shared at least one gene with PC in the DisGeNET database. Based on common genes, several AIDs were genetically associated with PC pointing to a potential link between them. An epidemiologic analysis confirmed that having any of the nine AIDs studied was significantly associated with a reduced risk of PC (Odds Ratio (OR) = 0.74, 95% confidence interval (CI) 0.58-0.93) which decreased in subjects having ≥2 AIDs (OR = 0.39, 95%CI 0.21-0.73). In independent analyses, polymyalgia rheumatica, and rheumatoid arthritis were significantly associated with low PC risk (OR = 0.40, 95%CI 0.19-0.89, and OR = 0.73, 95%CI 0.53-1.00, respectively). Several inflammatory-related morbidities shared a common genetic component with PC based on public databases. These molecular links could shed light into the molecular mechanisms underlying PC development and simultaneously generate novel hypotheses. In our study, we report sound findings pointing to an association between AIDs and a reduced risk of PC.

Reduced risk of pancreatic cancer associated with asthma and nasal allergies.

OBJECTIVE: Studies indicate an inverse association between ductal adenocarcinoma of the pancreas (PDAC) and nasal allergies. However, controversial findings are reported for the association with asthma. Understanding PDAC risk factors will help us to implement appropriate strategies to prevent, treat and diagnose this cancer. This study assessed and characterised the association between PDAC and asthma and corroborated existing reports regarding the association between allergies and PDAC risk. DESIGN: Information about asthma and allergies was collated from 1297 PDAC cases and 1024 controls included in the PanGenEU case-control study. Associations between PDAC and atopic diseases were studied using multilevel logistic regression analysis. Meta-analyses of association studies on these diseases and PDAC risk were performed applying random-effects model. RESULTS: Asthma was associated with lower risk of PDAC (OR 0.64, 95% CI 0.47 to 0.88), particularly long-standing asthma (>=17 years, OR 0.39, 95% CI 0.24 to 0.65). Meta-analysis of 10 case-control studies sustained our results (metaOR 0.73, 95% CI 0.59 to 0.89). Nasal allergies and related symptoms were associated with lower risk of PDAC (OR 0.66, 95% CI 0.52 to 0.83 and OR 0.59, 95% CI 0.46 to 0.77, respectively). These results were supported by a meta-analysis of nasal allergy studies (metaOR 0.6, 95% CI 0.5 to 0.72). Skin allergies were not associated with PDAC risk. CONCLUSIONS: This study shows a consistent inverse association between PDAC and asthma and nasal allergies, supporting the notion that atopic diseases are associated with reduced cancer risk. These results point to the involvement of immune and/or inflammatory factors that may either foster or restrain pancreas carcinogenesis warranting further research to understand the molecular mechanisms driving this association.

Framework for the Integration of Genomics, Epigenomics and Transcriptomics in Complex Diseases.

OBJECTIVES: Different types of '-omics' data are becoming available in the post-genome era; still a single -omics assessment provides limited insights to understand the biological mechanism of complex diseases. Genomics, epigenomics and transcriptomics data provide insight into the molecular dysregulation of neoplastic diseases, among them urothelial bladder cancer (UBC). Here, we propose a detailed analytical framework necessary to achieve an adequate integration of the three sets of -omics data to ultimately identify previously hidden genetic mechanisms in UBC. METHODS: We built a multi-staged framework to study possible pair-wise combinations and integrated the data in three-way relationships. SNP genotypes, CpG methylation levels and gene expression levels were determined for a total of 70 individuals with UBC and with fresh tumour tissue available. RESULTS: We suggest two main hypothesis-based scenarios for gene regulation based on the -omics integration analysis, where DNA methylation affects gene expression and genetic variants co-regulate gene expression and DNA methylation. We identified several three-way trans-association 'hotspots' that are found at the molecular level and that deserve further studies. CONCLUSIONS: The proposed integrative framework allowed us to identify relationships at the whole-genome level providing some new biological insights and highlighting the importance of integrating -omics data.

Indigenous American ancestry is associated with arsenic methylation efficiency in an admixed population of northwest Mexico.

Many studies provide evidence relating lower human arsenic (As) methylation efficiency, represented by high percent urinary monomethylarsonic acid (MMA(V)), with several As-induced diseases, possibly due to the fact that MMA(V) serves as a proxy for MMA(III), the most toxic As metabolite. Some epidemiological studies suggested that indigenous Americans (AME) methylate As more efficiently; however, data supporting this have been equivocal. The aim of this study was to characterize the association between AME ancestry and As methylation efficiency using a panel of ancestry informative genetic markers to determine individual ancestry proportions in an admixed population (composed of two or more isolated ancestral populations) of 746 individuals environmentally exposed to As in northwest Mexico. Total urinary As (TAs) mean and range were 170.4 and 2.3-1053.5 µg/L, while percent AME (%AME) mean and range were 72.4 and 23-100. Adjusted (gender, age, AS3MT 7388/M287T haplotypes, body mass index [BMI], and TAs) multiple regression model showed that higher AME ancestry is significantly associated with lower percentage of urinary As excreted as MMA(V) (%uMMA) in this population (p < .01). Data also demonstrated a significant interaction between BMI and gender, indicating negative association between BMI and %uMMA, stronger in women than men (p < .01). Moreover, age and the AS3MT variants 7388 (intronic) and M287T (nonsynonymous) were also significantly associated with As methylation efficiency (p < .01). This study highlights the importance of BMI and indigenous American ancestry in some of the observed variability in As methylation efficiency, underscoring the need to be considered in epidemiology studies, particularly those carried out in admixed populations.

Association between body mass index and arsenic methylation efficiency in adult women from southwest U.S. and northwest Mexico.

Human arsenic methylation efficiency has been consistently associated with arsenic-induced disease risk. Interindividual variation in arsenic methylation profiles is commonly observed in exposed populations, and great effort has been put into the study of potential determinants of this variability. Among the factors that have been evaluated, body mass index (BMI) has not been consistently associated with arsenic methylation efficiency; however, an underrepresentation of the upper BMI distribution was commonly observed in these studies. This study investigated potential factors contributing to variations in the metabolism of arsenic, with specific interest in the effect of BMI where more than half of the population was overweight or obese. We studied 624 adult women exposed to arsenic in drinking water from three independent populations. Multivariate regression models showed that higher BMI, arsenic (+3 oxidation state) methyltransferase (AS3MT) genetic variant 7388, and higher total urinary arsenic were significantly associated with low percentage of urinary arsenic excreted as monomethylarsonic acid (%uMMA) or high ratio between urinary dimethylarsinic acid and uMMA (uDMA/uMMA), while AS3MT genetic variant M287T was associated with high %uMMA and low uDMA/uMMA. The association between BMI and arsenic methylation efficiency was also evident in each of the three populations when studied separately. This strong association observed between high BMI and low %uMMA and high uDMA/uMMA underscores the importance of BMI as a potential arsenic-associated disease risk factor, and should be carefully considered in future studies associating human arsenic metabolism and toxicity.

Genetic association between intronic variants in AS3MT and arsenic methylation efficiency is focused on a large linkage disequilibrium cluster in chromosome 10.

Differences in arsenic metabolism are known to play a role in individual variability in arsenic-induced disease susceptibility. Genetic variants in genes relevant to arsenic metabolism are considered to be partially responsible for the variation in arsenic metabolism. Specifically, variants in arsenic (3+ oxidation state) methyltransferase (AS3MT), the key gene in the metabolism of arsenic, have been associated with increased arsenic methylation efficiency. Of particular interest is the fact that different studies have reported that several of the AS3MT single nucleotide polymorphisms (SNPs) are in strong linkage-disequilibrium (LD), which also extends to a nearby gene, CYP17A1. In an effort to characterize the extent of the region in LD, we genotyped 46 SNPs in a 347,000 base region of chromosome 10 that included AS3MT in arsenic-exposed subjects from Mexico. Pairwise LD analysis showed strong LD for these polymorphisms, represented by a mean r(2) of 0.82, spanning a region that includes five genes. Genetic association analysis with arsenic metabolism confirmed the previously observed association between AS3MT variants, including this large cluster of linked polymorphisms, and arsenic methylation efficiency. The existence of a large genomic region sharing strong LD with polymorphisms associated with arsenic metabolism presents a predicament because the observed phenotype cannot be unequivocally assigned to a single SNP or even a single gene. The results reported here should be carefully considered for future genomic association studies involving AS3MT and arsenic metabolism.