Array-based molecular karyotyping in 115 VATER/VACTERL and VATER/VACTERL-like patients identifies disease-causing copy number variations

BackgroundThe acronym VATER/VACTERL refers to the rare nonrandom association of the following component features (CF): vertebral defects (V), anorectal malformations (A), cardiac defects (C), tracheoesophageal fistula with or without esophageal atresia, renal malformations (R), and limb defects (L). Patients presenting with at least three CFs are diagnosed as having VATER/VACTERL association while patients presenting with only two CFs are diagnosed as having VATER/VACTERL-like phenotypes. Recently, rare causative copy number variations (CNVs) have been identified in patients with VATER/VACTERL association and VATER/VACTERL-like phenotypes. MethodsTo detect further causative CNVs we performed array based molecular karyotyping in 75 VATER/VACTERL and 40 VATER/VACTERL-like patients. ResultsFollowing the application of stringent filter criteria, we identified 13 microdeletions and seven microduplications in 20 unrelated patients all of which were absent in 1,307 healthy inhouse controls (n < 0.0008). Among these, microdeletion at 17q12 was confirmed to be de novo. Three microdeletions at 5q23.1, 16q23.3, 22q11.21, and one microduplication at 10q11.21 were all absent in the available parent. Microdeletion of chromosomal region 22q11.21 was previously found in VATER/VACTERL patients rendering it to be causative in our patient. The remaining 15 CNVs were inherited from a healthy parent. ConclusionIn two of 115 patients' causative CNVs were found (2%). The remaining identified rare CNVs represent candidates for further evaluation. Rare inherited CNVs may constitute modifiers of, or contributors to, multifactorial VATER/VACTERL or VATER/VACTERL-like phenotypes. Birth Defects Research 109:1063-1069, 2017. (c) 2017 Wiley Periodicals, Inc

Array-based molecular karyotyping in 115 VATER/VACTERL and VATER/VACTERL-like patients identifies disease-causing copy number variations

BackgroundThe acronym VATER/VACTERL refers to the rare nonrandom association of the following component features (CF): vertebral defects (V), anorectal malformations (A), cardiac defects (C), tracheoesophageal fistula with or without esophageal atresia, renal malformations (R), and limb defects (L). Patients presenting with at least three CFs are diagnosed as having VATER/VACTERL association while patients presenting with only two CFs are diagnosed as having VATER/VACTERL-like phenotypes. Recently, rare causative copy number variations (CNVs) have been identified in patients with VATER/VACTERL association and VATER/VACTERL-like phenotypes. MethodsTo detect further causative CNVs we performed array based molecular karyotyping in 75 VATER/VACTERL and 40 VATER/VACTERL-like patients. ResultsFollowing the application of stringent filter criteria, we identified 13 microdeletions and seven microduplications in 20 unrelated patients all of which were absent in 1,307 healthy inhouse controls (n < 0.0008). Among these, microdeletion at 17q12 was confirmed to be de novo. Three microdeletions at 5q23.1, 16q23.3, 22q11.21, and one microduplication at 10q11.21 were all absent in the available parent. Microdeletion of chromosomal region 22q11.21 was previously found in VATER/VACTERL patients rendering it to be causative in our patient. The remaining 15 CNVs were inherited from a healthy parent. ConclusionIn two of 115 patients' causative CNVs were found (2%). The remaining identified rare CNVs represent candidates for further evaluation. Rare inherited CNVs may constitute modifiers of, or contributors to, multifactorial VATER/VACTERL or VATER/VACTERL-like phenotypes. Birth Defects Research 109:1063-1069, 2017. (c) 2017 Wiley Periodicals, Inc

Human exome and mouse embryonic expression data implicate ZFHX3, TRPS1, and CHD7 in human esophageal atresia

Introduction: Esophageal atresia with or without tracheoesophageal fistula (EA/TEF) occurs approximately 1 in 3.500 live births representing the most common malformation of the upper digestive tract. Only half a century ago, EA/TEF was fatal among affected newborns suggesting that the steady birth prevalence might in parts be due to mutational de novo events in genes involved in foregut development. Methods: To identify mutational de novo events in EA/TEF patients, we surveyed the exome of 30 case-parent trios. Identified and confirmed de novo variants were prioritized using in silico prediction tools. To investigate the embryonic role of genes harboring prioritized de novo variants we performed targeted analysis of mouse transcriptome data of esophageal tissue obtained at the embryonic day (E) E8.5, E12.5, and postnatal. Results: In total we prioritized 14 novel de novo variants in 14 different genes (APOL2, EEF1D, CHD7, FANCB, GGT6, KIAA0556, NFX1, NPR2, PIGC, SLC5A2, TANC2, TRPS1, UBA3, and ZFHX3) and eight rare de novo variants in eight additional genes (CELSR1, CLP1, GPR133, HPS3, MTA3, PLEC, STAB1, and PPIP5K2). Through personal communication during the project, we identified an additional EA/TEF case-parent trio with a rare de novo variant in ZFHX3. In silico prediction analysis of the identified variants and comparative analysis of mouse transcriptome data of esophageal tissue obtained at E8.5, E12.5, and postnatal prioritized CHD7, TRPS1, and ZFHX3 as EA/TEF candidate genes. Re-sequencing of ZFHX3 in additional 192 EA/TEF patients did not identify further putative EA/TEF-associated variants. Conclusion: Our study suggests that rare mutational de novo events in genes involved in foregut development contribute to the development of EA/TEF

Human exome and mouse embryonic expression data implicateZFHX3,TRPS1, andCHD7in human esophageal atresia

Introduction Esophageal atresia with or without tracheoesophageal fistula (EA/TEF) occurs approximately 1 in 3.500 live births representing the most common malformation of the upper digestive tract. Only half a century ago, EA/TEF was fatal among affected newborns suggesting that the steady birth prevalence might in parts be due to mutationalde novoevents in genes involved in foregut development. Methods To identify mutationalde novoevents in EA/TEF patients, we surveyed the exome of 30 case-parent trios. Identified and confirmedde novovariants were prioritized usingin silicoprediction tools. To investigate the embryonic role of genes harboring prioritizedde novovariants we performed targeted analysis of mouse transcriptome data of esophageal tissue obtained at the embryonic day (E) E8.5, E12.5, and postnatal. Results In total we prioritized 14 novelde novovariants in 14 different genes (APOL2,EEF1D,CHD7,FANCB,GGT6,KIAA0556,NFX1,NPR2,PIGC,SLC5A2,TANC2,TRPS1,UBA3, andZFHX3) and eight rarede novovariants in eight additional genes (CELSR1,CLP1,GPR133,HPS3,MTA3,PLEC,STAB1, andPPIP5K2). Through personal communication during the project, we identified an additional EA/TEF case-parent trio with a rarede novovariant inZFHX3.In silicoprediction analysis of the identified variants and comparative analysis of mouse transcriptome data of esophageal tissue obtained at E8.5, E12.5, and postnatal prioritizedCHD7,TRPS1, andZFHX3as EA/TEF candidate genes. Re-sequencing ofZFHX3in additional 192 EA/TEF patients did not identify further putative EA/TEF-associated variants. Conclusion Our study suggests that rare mutationalde novoevents in genes involved in foregut development contribute to the development of EA/TEF

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd