Esophageal atresia and tracheoesophageal fistula (EA/TEF) are major congenital malformations affecting 1:3500 live births. Current research efforts are focused on understanding the etiology of these defects. We describe well-known animal models, human syndromes, and associations involving EA/TEF, indicating its etiologically heterogeneous nature. Recent advances in genotyping technology and in knowledge of human genetic variation will improve clinical counseling on etiologic factors. This review provides a clinical summary of environmental and genetic factors involved in EA/TEF.

Tuberous sclerosis complex (TSC) is an autosomal dominant disorder caused by mutations in TSC1 and TSC2. Conventional DNA diagnostic screens identify a TSC1 or TSC2 mutation in 75 - 90% of individuals categorised with definite TSC. The remaining individuals either have a mutation that is undetectable using conventional methods, or possibly a mutation in another as yet unidentified gene. Here we apply a targeted Next Generation Sequencing (NGS) approach to screen the complete TSC1 and TSC2 genomic loci in 7 individuals fulfilling the clinical diagnostic criteria for definite TSC in whom no TSC1 or TSC2 mutations were identified using conventional screening methods. We identified and confirmed pathogenic mutations in 3 individuals. In the remaining individuals we identified variants of uncertain clinical significance. The identified variants included mosaic changes, changes located deep in intronic sequences and changes affecting promoter regions that would not have been identified using exon-only based analyses. Targeted NGS of the TSC1 and TSC2 loci is a suitable method to increase the yield of mutations identified in the TSC patient population.

Purpose. Although studies on uveal melanoma (UM) revealed prognostic significance of chromosomal aberrations, they resulted in classification errors in survival prediction. A robust prognostic classifier with strong predictive value and further insight in genes responsible for poor prognosis were obtained by performing a gene-expression profile in tumors of UM patients for which extensive clinical, histopathologic, cytogenetic, and follow-up data were available. Furthermore, the UM microarray expression data were compared with cytogenetic data. methods. Gene-expression profiles of 46 UMs were obtained with microchip assays. Data were analyzed with cluster-analysis and predictive analysis of microarrays (PAM) software and validated with real-time PCR. The prognostic significance of UMs with specific molecular signatures was determined. Furthermore, LAP analysis resulted in the identification of differentially expressed chromosomal regions. results. The primary UMs were classified in two distinct molecular classes with a strong prognostic value (P < 0.001; hazard ratio 7.7). Classifier gene sets for microarray class and disease-free survival were validated with real-time PCR, and the predictive value of the UM class marker set was validated with gene-expression profiles of tumors provided by other institutions, showing a sensitivity of 0.93 and specificity of 1.00 for class II tumors. A locally adaptive statistical procedure identified two regions on the short arm of chromosome 3 with decreased gene-expression in tumors with shorter disease-free survival. conclusions. Microarray classification outperforms known prognostic indicators for UM, such as clinical, histopathologic, and cytogenetic parameters. In addition, the identified regions with lower expressed genes on 3p could harbor genes that are responsible for the poor prognosis of patients with UM.

Uveal melanoma (UM) is fatal in ~50% of patients as a result of disseminated disease. This study aims to externally validate the Liverpool Uveal Melanoma Prognosticator Online V3 (LUMPO3) to determine its reliability in predicting survival after treatment for choroidal melanoma when utilizing external data from other ocular oncology centers. Anonymized data of 1836 UM patients from seven international ocular oncology centers were analyzed with LUMPO3 to predict the 10-year survival for each patient in each external dataset. The analysts were masked to the patient outcomes. Model predictions were sent to an independent statistician to evaluate LUMPO3’s performance using discrimination and calibration methods. LUMPO3’s ability to discriminate between UM patients who died of metastatic UM and those who were still alive was fair-to-good, with C-statistics ranging from 0.64 to 0.85 at year 1. The pooled estimate for all external centers was 0.72 (95% confidence interval: 0.68 to 0.75). Agreement between observed and predicted survival probabilities was generally good given differences in case mix and survival rates between different centers. Despite the differences between the international cohorts of patients with primary UM, LUMPO3 is a valuable tool for predicting all-cause mortality in this disease when using data from external centers.