Germline BRCA2 K3326X and CHEK2 I157T mutations increase risk for sporadic pancreatic ductal adenocarcinoma.

Rare truncating BRCA2 K3326X (rs11571833) and pathogenic CHEK2 I157T (rs17879961) variants have previously been implicated in familial pancreatic ductal adenocarcinoma (PDAC), but not in sporadic cases. The effect of both mutations in important DNA repair genes on sporadic PDAC risk may shed light on the genetic architecture of this disease. Both mutations were genotyped in germline DNA from 2,935 sporadic PDAC cases and 5,626 control subjects within the PANcreatic Disease ReseArch (PANDoRA) consortium. Risk estimates were evaluated using multivariate unconditional logistic regression with adjustment for possible confounders such as sex, age and country of origin. Statistical analyses were two-sided with p values <0.05 considered significant. K3326X and I157T were associated with increased risk of developing sporadic PDAC (odds ratio (ORdom ) = 1.78, 95% confidence interval (CI) = 1.26-2.52, p = 1.19 × 10-3 and ORdom = 1.74, 95% CI = 1.15-2.63, p = 8.57 × 10-3 , respectively). Neither mutation was significantly associated with risk of developing early-onset PDAC. This retrospective study demonstrates novel risk estimates of K3326X and I157T in sporadic PDAC which suggest that upon validation and in combination with other established genetic and non-genetic risk factors, these mutations may be used to improve pancreatic cancer risk assessment in European populations. Identification of carriers of these risk alleles as high-risk groups may also facilitate screening or prevention strategies for such individuals, regardless of family history.

A systems approach identifies time-dependent associations of multimorbidities with pancreatic cancer risk.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is usually diagnosed in late adulthood; therefore, many patients suffer or have suffered from other diseases. Identifying disease patterns associated with PDAC risk may enable a better characterization of high-risk patients. METHODS: Multimorbidity patterns (MPs) were assessed from 17 self-reported conditions using hierarchical clustering, principal component, and factor analyses in 1705 PDAC cases and 1084 controls from a European population. Their association with PDAC was evaluated using adjusted logistic regression models. Time since diagnosis of morbidities to PDAC diagnosis/recruitment was stratified into recent (<3 years) and long term (≥3 years). The MPs and PDAC genetic networks were explored with DisGeNET bioinformatics-tool which focuses on gene-diseases associations available in curated databases. RESULTS: Three MPs were observed: gastric (heartburn, acid regurgitation, Helicobacter pylori infection, and ulcer), metabolic syndrome (obesity, type-2 diabetes, hypercholesterolemia, and hypertension), and atopic (nasal allergies, skin allergies, and asthma). Strong associations with PDAC were observed for ≥2 recently diagnosed gastric conditions [odds ratio (OR), 6.13; 95% confidence interval CI 3.01-12.5)] and for ≥3 recently diagnosed metabolic syndrome conditions (OR, 1.61; 95% CI 1.11-2.35). Atopic conditions were negatively associated with PDAC (high adherence score OR for tertile III, 0.45; 95% CI, 0.36-0.55). Combining type-2 diabetes with gastric MP resulted in higher PDAC risk for recent (OR, 7.89; 95% CI 3.9-16.1) and long-term diagnosed conditions (OR, 1.86; 95% CI 1.29-2.67). A common genetic basis between MPs and PDAC was observed in the bioinformatics analysis. CONCLUSIONS: Specific multimorbidities aggregate and associate with PDAC in a time-dependent manner. A better characterization of a high-risk population for PDAC may help in the early diagnosis of this cancer. The common genetic basis between MP and PDAC points to a mechanistic link between these conditions.

The Global Genome Biodiversity Network (GGBN) Data Standard specification.

Genomic samples of non-model organisms are becoming increasingly important in a broad range of studies from developmental biology, biodiversity analyses, to conservation. Genomic sample definition, description, quality, voucher information and metadata all need to be digitized and disseminated across scientific communities. This information needs to be concise and consistent in today's ever-increasing bioinformatic era, for complementary data aggregators to easily map databases to one another. In order to facilitate exchange of information on genomic samples and their derived data, the Global Genome Biodiversity Network (GGBN) Data Standard is intended to provide a platform based on a documented agreement to promote the efficient sharing and usage of genomic sample material and associated specimen information in a consistent way. The new data standard presented here build upon existing standards commonly used within the community extending them with the capability to exchange data on tissue, environmental and DNA sample as well as sequences. The GGBN Data Standard will reveal and democratize the hidden contents of biodiversity biobanks, for the convenience of everyone in the wider biobanking community. Technical tools exist for data providers to easily map their databases to the standard.Database URL: http://terms.tdwg.org/wiki/GGBN_Data_Standard.