Special cases in Cornelia de Lange syndrome: The Spanish experience

Pié, Juan; Puisac, Beatriz; Hernández-Marcos, María; Teresa-Rodrigo, María Esperanza; Gil-Rodríguez, María Concepción; Baquero-Montoya, Carolina; Ramos-Cáceres, Maria; Bernal, María Luisa; Ayerza-Casas, Ariadna; Bueno, Inés; Gómez‐Puertas, Paulino; Ramos, Feliciano J.

doi:10.1002/ajmg.c.31501

Cited by 21 publications

(12 citation statements)

References 28 publications

(43 reference statements)

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“…Somatic mosaicism in NIPBL is frequent in CdLS patients and might further contribute to the different expression of symptoms among patients [3][4][5]. Furthermore, even in the presence of variants affecting the same causative gene, a wide clinical spectrum, from classical to mildly affected patients, is known [6]. The cohesin complex is not only involved in sister chromatids cohesion, but it also exhibits novel biological functions, such as the regulation of gene transcription [7][8][9], thus extending the range of pathomechanisms relevant for cohesinopathies and potentially explaining the variability in the clinical manifestations of CdLS [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes

Latorre‐Pellicer

Ascaso

Trujillano

et al. 2020

IJMS

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Characteristic or classic phenotype of Cornelia de Lange syndrome (CdLS) is associated with a recognisable facial pattern. However, the heterogeneity in causal genes and the presence of overlapping syndromes have made it increasingly difficult to diagnose only by clinical features. DeepGestalt technology, and its app Face2Gene, is having a growing impact on the diagnosis and management of genetic diseases by analysing the features of affected individuals. Here, we performed a phenotypic study on a cohort of 49 individuals harbouring causative variants in known CdLS genes in order to evaluate Face2Gene utility and sensitivity in the clinical diagnosis of CdLS. Based on the profile images of patients, a diagnosis of CdLS was within the top five predicted syndromes for 97.9% of our cases and even listed as first prediction for 83.7%. The age of patients did not seem to affect the prediction accuracy, whereas our results indicate a correlation between the clinical score and affected genes. Furthermore, each gene presents a different pattern recognition that may be used to develop new neural networks with the goal of separating different genetic subtypes in CdLS. Overall, we conclude that computer-assisted image analysis based on deep learning could support the clinical diagnosis of CdLS.

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Section: Introductionmentioning

confidence: 99%

Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes

Latorre‐Pellicer

Ascaso

Trujillano

et al. 2020

IJMS

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“…In CdLS patients negative for germline mutations, mosaic alterations can be detected. Due to the high sensitivity of the method, NGS seems to be the most appropriate technique to identify such variants (17, 19). To date, several patients with mosaic variants in CdLS-related genes have been reported (17, 20, 21).…”

Section: Discussionmentioning

confidence: 99%

“…Based on the previous study, approximately 20% of NIPBL variants are located within intron sequences (19). In this study, three splice site variants have been identified in the NIPBL gene in four patients, three with classic CdLS and one with a mild phenotype (mosaic splice site variant).…”

Section: Discussionmentioning

confidence: 99%

Genetic Mosaicism in a Group of Patients With Cornelia de Lange Syndrome

2019

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Background: Cornelia de Lange Syndrome (CdLS) is a heterogeneous disorder. Diverse expression of clinical symptoms can be caused by a variety of pathogenic variants located within the sequence of different genes correlated with the cohesin complex. Methods: Sixty-nine patients with confirmed clinical diagnosis of CdLS were enrolled in the study. Blood and buccal swab samples were collected for molecular studies. Mutational analysis was performed using the Next Generation (deep) Sequencing (NGS) covering 24 genes. In addition, the MLPA technique was applied to detect large rearrangements of NIPBL . Results: MLPA and NGS analysis were performed in 66 (95,7%) and 67 (97,1%) patients, respectively. Large rearrangements of NIPBL were not identified in the studied group. Germline pathogenic variants were detected in 18 (26,1%) patients. Fourteen variants (20,3%) were identified in NIPBL , two (2,9%) in SMC1A , and two (2,9%) in HDAC8 . In total, 13 (18,8%) buccal swabs were suitable for deep sequencing. Mosaic variants were found in four (30,8%; 4/13) patients negative for germline alterations. Three mosaic substitutions were detected in NIPBL while one in KMT2A gene. Conclusions: Comprehensive and sensitive molecular techniques allow detecting novel pathogenic variants responsible for the molecular basis of CdLS. In addition, molecular testing of different tissues should be applied since such an approach allows detect mosaic variants specific for a subgroup of CdLS patients. Finally, to test possible pathogenicity of intronic variants, RNA analysis should be conducted.

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“…Up to 70% of clinically diagnosed CdLS patients harbor heterozygous pathogenic variants in NIPBL gene (OMIM 608667) ( 5 , 6 ) which encodes a regulatory protein loading the cohesin complex onto sister chromatids. More than 300 pathogenic variants, mostly point mutations, have been found evenly spread across the whole NIPBL gene in CdLS patients ( 4 , 7 ). Frameshift variants, the most prevalent NIPBL defects, commonly underlie a severe phenotype, while pathogenic missense variants are often associated to mild disease ( 8 ).…”

Section: Introductionmentioning

confidence: 99%

Recurrence and Familial Inheritance of Intronic NIPBL Pathogenic Variant Associated With Mild CdLS

et al. 2018

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Splicing pathogenic variants account for a notable fraction of NIPBL alterations underlying Cornelia de Lange syndrome but are likely underrepresented, due to overlooking of non-canonical intronic variants by traditional and contemporary sequencing methods. We describe five subjects, belonging to three families, displaying a mild Cornelia de Lange syndrome phenotype who carry the NIPBL pathogenic variant c.5329–15A>G, affecting the IVS27 branch site, yet reported in a single case. By RNA analysis we evidenced two alternative transcripts: the exon 28 in frame skipped transcript, described in the published case and an out-of-frame transcript retaining 14 nucleotides of IVS27 3′end. Even if both aberrant transcripts are at negligible levels, their presence justifies the CdLS phenotype shared by our patients consisting of borderline-mild cognitive impairment and slight but typical facial dysmorphisms. Transmission of the pathogenic variant from pauci-symptomatic mother to her siblings emphasizes the need of molecular diagnosis extended to deep intronic regions in patients with subtle but recognizable CdLS phenotype.

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Special cases in Cornelia de Lange syndrome: The Spanish experience

Cited by 21 publications

References 28 publications

Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes

Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes

Genetic Mosaicism in a Group of Patients With Cornelia de Lange Syndrome

Recurrence and Familial Inheritance of Intronic NIPBL Pathogenic Variant Associated With Mild CdLS

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