Interactions Between Genetic Variants and Environmental Factors Affect Risk of Esophageal Adenocarcinoma and Barrett’s Esophagus

Background & Aims: Genome-wide association studies (GWAS) have identified more than 20 susceptibility loci for esophageal adenocarcinoma (EA) and Barrett’s esophagus (BE). However, variants in these loci account for a small fraction of cases of EA and BE. Genetic factors might interact with environmental factors to affect risk of EA and BE. We aimed to identify single nucleotide polymorphisms (SNPs) that may modify the associations of body mass index (BMI), smoking, and gastroesophageal reflux disease (GERD), with risks of EA and BE. Methods: We collected data on single BMI measurements, smoking status, and symptoms of GERD from 2284 patients with EA, 3104 patients with BE, and 2182 healthy individuals (controls) participating in the Barrett’s and Esophageal Adenocarcinoma Consortium GWAS, the UK Barrett’s Esophagus Gene Study, and the UK Stomach and Oesophageal Cancer Study. We analyzed 993,501 SNPs in DNA samples of all study subjects. We used standard case–control logistic regression to test for gene-environment interactions. Results: For EA, rs13429103 at chromosome 2p25.1, near the RNF144A-LOC339788 gene, showed a borderline significant interaction with smoking status (P = 2.18×10-7). Ever smoking was associated with an almost 12-fold increase in risk of EA among individuals with rs13429103-AA genotype (odds ratio=11.82; 95% CI, 4.03–34.67). Three SNPs (rs12465911, rs2341926, rs13396805) at chromosome 2q23.3, near the RND3-RBM43 gene, interacted with GERD symptoms (P = 1.70×10-7, P = 1.83×10-7, and P = 3.58×10-7, respectively) to affect risk of EA. For BE, rs491603 at chromosome 1p34.3, near the EIF2C3 gene, and rs11631094 at chromosome 15q14, at the SLC12A6 gene, interacted with BMI (P = 4.44×10-7) and pack-years of smoking history (P = 2.82×10-7), respectively. Conclusion: The associations of BMI, smoking, and GERD symptoms with risks of EA and BE appear to vary with SNPs at chromosomes 1, 2, and 15. Validation of these suggestive interactions is warranted

Automated scoring of tumor-infiltrating lymphocytes informs risk of death from thin melanoma: A nested case-case study (Letter)

To the Editor: Thin primary cutaneous melanomas (thickness ≤1.0 mm) have an excellent prognosis, with 10-year melanoma-specific survival rates of over 94%.1 However, they constitute the majority of new melanoma diagnoses, and due to their frequency account for approximately one-quarter of total melanoma deaths.2 While accurate identification of high-risk patients could facilitate heightened surveillance and potentially systemic therapy, standard prognostic factors such as ulceration, mitotic rate, and sentinel node biopsy are rarely positive in thin melanoma, restricting their utility in stratification.3Full Tex

Structural Insights into Notch Receptor-Ligand Interactions

Pioneering cell aggregation experiments from the Artavanis-Tsakonas group in the late 1980's localized the core ligand recognition sequence in the Drosophila Notch receptor to epidermal growth factor-like (EGF) domains 11 and 12. Since then, advances in protein expression, structure determination methods and functional assays have enabled us to define the molecular basis of the core receptor/ligand interaction and given new insights into the architecture of the Notch complex at the cell surface. We now know that Notch EGF11 and 12 interact with the Delta/Serrate/LAG-2 (DSL) and C2 domains of ligand and that membrane-binding, together with additional protein-protein interactions outside the core recognition domains, are likely to fine-tune generation of the Notch signal. Furthermore, structure determination of O-glycosylated variants of Notch alone or in complex with receptor fragments, has shown that these sugars contribute directly to the binding interface, as well as to stabilizing intra-molecular domain structure, providing some mechanistic insights into the observed modulatory effects of O-glycosylation on Notch activity.Future challenges lie in determining the complete extracellular architecture of ligand and receptor in order to understand (i) how Notch/ligand complexes may form at the cell surface in response to physiological cues, (ii) the role of lipid binding in stabilizing the Notch/ligand complex, (iii) the impact of O-glycosylation on binding and signalling and (iv) to dissect the different pathologies that arise as a consequence of mutations that affect proteins involved in the Notch pathway

Determining Risk of Barrett’s Esophagus and Esophageal Adenocarcinoma Based on Epidemiologic Factors and Genetic Variants

Background & Aims: We developed comprehensive models to determine risk of Barrett’s esophagus (BE) or esophageal adenocarcinoma (EAC) based on genetic and non-genetic factors. Methods: We used pooled data from 3288 patients with BE, 2511 patients with EAC, and 2177 individuals without either (controls) from participants in the international Barrett’s and EAC consortium as well as the United Kingdom’s BE gene study and stomach and esophageal cancer study. We collected data on 23 genetic variants associated with risk for BE or EAC, and constructed a polygenic risk score (PRS) for cases and controls by summing the risk allele counts for the variants weighted by their natural log-transformed effect estimates (odds ratios) extracted from genome-wide association studies. We also collected data on demographic and lifestyle factors (age, sex, smoking, body mass index, use of nonsteroidal anti-inflammatory drugs) and symptoms of gastroesophageal reflux disease (GERD). Risk models with various combinations of non-genetic factors and the PRS were compared for their accuracy in identifying patients with BE or EAC using the area under the receiver operating characteristic curve (AUC) analysis. Results: Individuals in the highest quartile of risk, based on genetic factors (PRS), had a 2-fold higher risk of BE (odds ratio, 2.22; 95% confidence interval, 1.89–2.60) or EAC (odds ratio, 2.46; 95% confidence interval, 2.07–2.92) than individual in the lowest quartile of risk based on PRS. Risk models developed based on only demographic or lifestyle factors or GERD symptoms identified patients with BE or EAC with AUC values ranging from 0.637 to 0.667. Combining data on demographic or lifestyle factors with data on GERD symptoms identified patients with BE with an AUC of 0.793 and patients with EAC with an AUC of 0.745. Including PRSs with these data only minimally increased the AUC values for BE (to 0.799) and EAC (to 0.754). Including the PRSs in the model developed based on non-genetic factors resulted in a net reclassification improvement for BE of 3.0% and for EAC of 5.6%. Conclusions: We used data from 3 large databases of patients from studies of BE or EAC to develop a risk prediction model based on genetic, clinical, and demographic/lifestyle factors. We identified a PRS that increases discrimination and net reclassification of individuals with vs without BE and EAC. However, the absolute magnitude of improvement is not sufficient to justify its clinical use