Case Report: A Case Study on the Neurodevelopmental Profile of a Child With Pallister-Killian Syndrome and His Unaffected Twin.

Pallister-Killian syndrome is an uncommon genetic disorder that has broad developmental and multisystemic effects. While medical complications are widely reported throughout the literature, research on the neurodevelopmental profile has been limited. Case reports make up the majority of the few existing studies regarding the neurodevelopmental phenotype associated with this disorder. The current case report describes a 3-year-old male with Pallister-Killian syndrome (AF), reports the neurodevelopmental evaluation of his unaffected twin brother (MF), and outlines the results of an optical imaging study on both boys. AF presents with severe developmental delays, however, he ambulates with support and engages in conversation using his communication device. Most severely impaired was AF's speech and expressive language, with childhood apraxia of speech (CAS) as a possible explanation for these severe deficits. MF, the sibling, demonstrated neurotypical abilities and often advanced scores for his age. Both subjects completed a functional near-infrared spectroscopy (fNIRS) study, revealing decreased temporal and frontal lobe function in AF and typical functioning in MF. This case report expands on the existing literature on PKS by describing variances in fraternal twin presentation and novel reporting on fNIRS findings in both boys.

Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study.

BACKGROUND: Delays in the diagnosis of genetic syndromes are common, particularly in low and middle-income countries with limited access to genetic screening services. We, therefore, aimed to develop and evaluate a machine learning-based screening technology using facial photographs to evaluate a child's risk of presenting with a genetic syndrome for use at the point of care. METHODS: In this retrospective study, we developed a facial deep phenotyping technology based on deep neural networks and facial statistical shape models to screen children for genetic syndromes. We trained the machine learning models on facial photographs from children (aged <21 years) with a clinical or molecular diagnosis of a genetic syndrome and controls without a genetic syndrome matched for age, sex, and race or ethnicity. Images were obtained from three publicly available databases (the Atlas of Human Malformations in Diverse Populations of the National Human Genome Research Institute, Face2Gene, and the dataset available from Ferry and colleagues) and the archives of the Children's National Hospital (Washington, DC, USA), in addition to photographs taken on a standard smartphone at the Children's National Hospital. We designed a deep learning architecture structured into three neural networks, which performed image standardisation (Network A), facial morphology detection (Network B), and genetic syndrome risk estimation, accounting for phenotypic variations due to age, sex, and race or ethnicity (Network C). Data were divided randomly into 40 groups for cross validation, and the performance of the model was evaluated in terms of accuracy, sensitivity, and specificity in both the total population and stratified by race or ethnicity, age, and sex. FINDINGS: Our dataset included 2800 facial photographs of children (1318 [47%] female and 1482 [53%] male; 1576 [56%] White, 432 [15%] African, 430 [15%] Hispanic, and 362 [13%] Asian). 1400 children with 128 genetic conditions were included (the most prevalent being Williams-Beuren syndrome [19%], Cornelia de Lange syndrome [17%], Down syndrome [16%], 22q11.2 deletion [13%], and Noonan syndrome [12%] syndrome) in addition to 1400 photographs of matched controls. In the total population, our deep learning-based model had an accuracy of 88% (95% CI 87-89) for the detection of a genetic syndrome, with 90% sensitivity (95% CI 88-92) and 86% specificity (95% CI 84-88). Accuracy was greater in White (90%, 89-91) and Hispanic populations (91%, 88-94) than in African (84%, 81-87) and Asian populations (82%, 78-86). Accuracy was also similar in male (89%, 87-91) and female children (87%, 85-89), and similar in children younger than 2 years (86%, 84-88) and children aged 2 years or older (eg, 89% [87-91] for those aged 2 years to <5 years). INTERPRETATION: This genetic screening technology could support early risk stratification at the point of care in global populations, which has the potential accelerate diagnosis and reduce mortality and morbidity through preventive care. FUNDING: Children's National Hospital and Government of Abu Dhabi.

Missense variant contribution to USP9X-female syndrome.

USP9X is an X-chromosome gene that escapes X-inactivation. Loss or compromised function of USP9X leads to neurodevelopmental disorders in males and females. While males are impacted primarily by hemizygous partial loss-of-function missense variants, in females de novo heterozygous complete loss-of-function mutations predominate, and give rise to the clinically recognisable USP9X-female syndrome. Here we provide evidence of the contribution of USP9X missense and small in-frame deletion variants in USP9X-female syndrome also. We scrutinise the pathogenicity of eleven such variants, ten of which were novel. Combined application of variant prediction algorithms, protein structure modelling, and assessment under clinically relevant guidelines universally support their pathogenicity. The core phenotype of this cohort overlapped with previous descriptions of USP9X-female syndrome, but exposed heightened variability. Aggregate phenotypic information of 35 currently known females with predicted pathogenic variation in USP9X reaffirms the clinically recognisable USP9X-female syndrome, and highlights major differences when compared to USP9X-male associated neurodevelopmental disorders.

Preoperative evaluation and comprehensive risk assessment for children with Down syndrome.

Down syndrome is a common chromosome disorder affecting all body systems. This creates unique physiologic concerns that can affect safety during anesthesia and surgery. Little consensus exists, however, on the best way to evaluate children with Down syndrome in preparation for surgery. We review a number of salient topics affecting these children in the perioperative period, including cervical spine instability, cardiovascular abnormalities, pulmonary hypertension, upper airway obstruction, hematologic disturbances, prematurity, low birth weight, and the use of supplements and alternative therapies. Recommendations include obtaining a complete blood count to detect an increased risk for bleeding or stroke, and cardiology evaluation to identify patients with pulmonary hypertension, as well as undiagnosed or residual heart disease. Pediatric cardiac anesthesiologists and intensivists should be involved as needed. The potential for cervical spine instability should be considered, and the anesthesiologist may wish to have several options available both for the medications and equipment used. The child's family should always be asked if he or she is on any nutritional supplements, as some products marketed to families may have secondary effects such as inhibition of platelet function. Using this evaluation in presurgical planning will allow physicians to better consider the individual circumstances for their patients with Down syndrome. Our goal was to optimize patient safety by choosing the most appropriate setting and perioperative personnel, and to mitigate those risk factors amenable to intervention.

Important Considerations in the Initial Clinical Evaluation of the Dysmorphic Neonate.

BACKGROUND: The approach to clinical evaluation of the dysmorphic neonate can be challenging and multifaceted. It requires specialized knowledge of rare diagnoses and awareness of immediate versus long-term needs for the newborn and the family. PURPOSE: This review summarizes important considerations in the initial evaluation of genetic syndromes, which can present in the neonatal period with variable aspects of dysmorphism. METHODS: An overview of the literature in this area is provided. FINDINGS/RESULTS: Several overlapping areas of concern for working with this population are addressed, including communication with the family, fundamentals of the physical examination, common genetic disorders, syndromes, as well as palliative care and end of life decision making for the newborn in the context of family needs. IMPLICATIONS FOR PRACTICE: The initial approach for the neonatal practitioner needs to focus on various aspects of the newborn's care, including medical stabilization, determining whether immediate laboratory or imaging studies are needed, careful physical examination with particular attention to detail, appropriate and timely communication with the family, and knowledge of various specific aspects of rare diseases. IMPLICATIONS FOR RESEARCH: More research is needed to better understand how to best support the newborn born with dysmorphia or a rare disease. Particular attention needs to be focused on strategies to best support the family who is often in crisis during the neonatal period.

Dominant mutations in KAT6A cause intellectual disability with recognizable syndromic features.

Through a multi-center collaboration study, we here report six individuals from five unrelated families, with mutations in KAT6A/MOZ detected by whole-exome sequencing. All five different de novo heterozygous truncating mutations were located in the C-terminal transactivation domain of KAT6A: NM_001099412.1: c.3116_3117 delCT, p.(Ser1039∗); c.3830_3831insTT, p.(Arg1278Serfs∗17); c.3879 dupA, p.(Glu1294Argfs∗19); c.4108G>T p.(Glu1370∗) and c.4292 dupT, p.(Leu1431Phefs∗8). An additional subject with a 0.23 MB microdeletion including the entire KAT6A reading frame was identified with genome-wide array comparative genomic hybridization. Finally, by detailed clinical characterization we provide evidence that heterozygous mutations in KAT6A cause a distinct intellectual disability syndrome. The common phenotype includes hypotonia, intellectual disability, early feeding and oromotor difficulties, microcephaly and/or craniosynostosis, and cardiac defects in combination with subtle facial features such as bitemporal narrowing, broad nasal tip, thin upper lip, posteriorly rotated or low-set ears, and microretrognathia. The identification of human subjects complements previous work from mice and zebrafish where knockouts of Kat6a/kat6a lead to developmental defects.

Digital facial dysmorphology for genetic screening: Hierarchical constrained local model using ICA.

Down syndrome, the most common single cause of human birth defects, produces alterations in physical growth and mental retardation. If missed before birth, the early detection of Down syndrome is crucial for the management of patients and disease. However, the diagnostic accuracy for pediatricians prior to cytogenetic results is moderate and the access to specialists is limited in many social and low-economic areas. In this study, we propose a simple, non-invasive and automated framework for Down syndrome detection based on disease-specific facial patterns. Geometric and local texture features are extracted based on automatically detected anatomical landmarks to describe facial morphology and structure. To accurately locate the anatomical facial landmarks, a hierarchical constrained local model using independent component analysis (ICA) is proposed. We also introduce a data-driven ordering method for selecting dominant independent components in ICA. The hierarchical structure of the model increases the accuracy of landmark detection by fitting separate models to different groups. Then the most representative features are selected and we also demonstrate that they match clinical observations. Finally, a variety of classifiers are evaluated to discriminate between Down syndrome and healthy populations. The best performance achieved 0.967 accuracy and 0.956 F1 score using combined features and linear discriminant analysis. The method was also validated on a dataset with mixed genetic syndromes and high performance (0.970 accuracy and 0.930 F1 score) was also obtained. The promising results indicate that our method could assist in Down syndrome screening effectively in a simple, non-invasive way, and extensible to detection of other genetic syndromes.

Handheld optical coherence tomography during sedation in young children with optic pathway gliomas.

IMPORTANCE: Monitoring young children with optic pathway gliomas (OPGs) for visual deterioration can be difficult owing to age-related noncompliance. Optical coherence tomography (OCT) measures of retinal nerve fiber layer (RNFL) thickness have been proposed as a surrogate marker of vision but this technique is also limited by patient cooperation. OBJECTIVE: To determine whether measures of circumpapillary RNFL thickness, acquired with handheld OCT (HH-OCT) during sedation, can differentiate between young children with and without vision loss from OPGs. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional analysis of a prospective observational study was conducted at a tertiary-care children's hospital. Children with an OPG (sporadic or secondary to neurofibromatosis type 1) who were cooperative for visual acuity testing, but required sedation to complete magnetic resonance imaging, underwent HH-OCT imaging of the circumpapillary RNFL while sedated. MAIN OUTCOMES AND MEASURES: Area under the curve of the receiver operating characteristic, sensitivity, specificity, positive predictive value, and negative predictive value of the average and quadrant-specific RNFL thicknesses. RESULTS: Thirty-three children (64 eyes) met inclusion criteria (median age, 4.8 years; range, 1.8-12.6 years). In children with vision loss (abnormal visual acuity and/or visual field), RNFL thickness was decreased in all quadrants compared with the normal-vision group (P < .001 for all comparisons). Using abnormal criteria of less than 5% and less than 1%, the area under the curve was highest for the average RNFL thickness (0.96 and 0.97, respectively) compared with specific anatomic quadrants. The highest discrimination and predictive values were demonstrated for participants with 2 or more quadrants meeting less than 5% (sensitivity = 93.3; specificity = 97.9; positive predictive value = 93.3; and negative predictive value = 97.9) and less than 1% (sensitivity = 93.3; specificity = 100; positive predictive value = 100; and negative predictive value = 98.0) criteria. CONCLUSIONS AND RELEVANCE: Measures of RNFL thickness acquired with HH-OCT during sedation can differentiate between young children with and without vision loss from OPGs. For young children who do not cooperate with vision testing, HH-OCT measures may be a surrogate marker of vision. Longitudinal studies are needed to delineate the temporal relationship between RNFL decline and vision loss.

Ensemble learning for the detection of facial dysmorphology.

Down syndrome is the most common chromosomal condition that presents characteristic facial morphology and texture patterns. The early detection of Down syndrome through an automatic, non-invasive and simple way is desirable and critical to provide the best health management to newborns. In this study, we propose such a computer-aided diagnosis system for Down syndrome from photography based on facial analysis with ensemble learning. First, geometric and texture facial features are extracted based on automatically located facial landmarks, followed by feature fusion and selection. Then multiple classifiers (i.e. support vector machines, random forests and linear discriminant analysis) are adopted to identify patients with Down syndrome. An accurate and reliable decision is finally achieved by optimally combining the outputs of these individual classifiers via ensemble learning that captures both the shared and complementary information from different classifiers. The best performance was achieved by using the median ensemble rule with 0.967 accuracy, 0.977 precision and 0.933 recall.

Automated Down syndrome detection using facial photographs.

Down syndrome, the most common single cause of human birth defects, produces alterations in physical growth and mental retardation; its early detection is crucial. Children with Down syndrome generally have distinctive facial characteristics, which brings an opportunity for the computer-aided diagnosis of Down syndrome using photographs of patients. In this study, we propose a novel strategy based on machine learning techniques to detect Down syndrome automatically. A modified constrained local model is used to locate facial landmarks. Then geometric features and texture features based on local binary patterns are extracted around each landmark. Finally, Down syndrome is detected using a variety of classifiers. The best performance achieved 94.6% accuracy, 93.3% precision and 95.5% recall by using support vector machine with radial basis function kernel. The results indicate that our method could assist in Down syndrome screening effectively in a simple, non-invasive way.

Hierarchical constrained local model using ICA and its application to Down syndrome detection.

Conventional statistical shape models use Principal Component Analysis (PCA) to describe shape variations. However, such a PCA-based model assumes a Gaussian distribution of data. A model with Independent Component Analysis (ICA) does not require the Gaussian assumption and can additionally describe the local shape variation. In this paper, we propose a Hierarchical Constrained Local Model (HCLM) using ICA. The first or coarse level of HCLM locates the full landmark set, while the second level refines a relevant landmark subset. We then apply the HCLM to Down syndrome detection from photographs of young pediatric patients. Down syndrome is the most common chromosomal condition and its early detection is crucial. After locating facial anatomical landmarks using HCLM, geometric and local texture features are extracted and selected. A variety of classifiers are evaluated to identify Down syndrome from a healthy population. The best performance achieved 95.6% accuracy using support vector machine with radial basis function kernel. The results show that the ICA-based HCLM outperformed both PCA-based CLM and ICA-based CLM.

Haploinsufficiency of SOX5 at 12p12.1 is associated with developmental delays with prominent language delay, behavior problems, and mild dysmorphic features.

SOX5 encodes a transcription factor involved in the regulation of chondrogenesis and the development of the nervous system. Despite its important developmental roles, SOX5 disruption has yet to be associated with human disease. We report one individual with a reciprocal translocation breakpoint within SOX5, eight individuals with intragenic SOX5 deletions (four are apparently de novo and one inherited from an affected parent), and seven individuals with larger 12p12 deletions encompassing SOX5. Common features in these subjects include prominent speech delay, intellectual disability, behavior abnormalities, and dysmorphic features. The phenotypic impact of the deletions may depend on the location of the deletion and, consequently, which of the three major SOX5 protein isoforms are affected. One intragenic deletion, involving only untranslated exons, was present in a more mildly affected subject, was inherited from a healthy parent and grandparent, and is similar to a deletion found in a control cohort. Therefore, some intragenic SOX5 deletions may have minimal phenotypic effect. Based on the location of the deletions in the subjects compared to the controls, the de novo nature of most of these deletions, and the phenotypic similarities among cases, SOX5 appears to be a dosage-sensitive, developmentally important gene.

Further clinical and molecular delineation of the 15q24 microdeletion syndrome.

BACKGROUND: Chromosome 15q24 microdeletion syndrome is a rare genomic disorder characterised by intellectual disability, growth retardation, unusual facial morphology and other anomalies. To date, 20 patients have been reported; 18 have had detailed breakpoint analysis. AIM: To further delineate the features of the 15q24 microdeletion syndrome, the clinical and molecular characterisation of fifteen patients with deletions in the 15q24 region was performed, nearly doubling the number of reported patients. METHODS: Breakpoints were characterised using a custom, high-density array comparative hybridisation platform, and detailed phenotype information was collected for each patient. RESULTS: Nine distinct deletions with different breakpoints ranging in size from 266 kb to 3.75 Mb were identified. The majority of breakpoints lie within segmental duplication (SD) blocks. Low sequence identity and large intervals of unique sequence between SD blocks likely contribute to the rarity of 15q24 deletions, which occur 8-10 times less frequently than 1q21 or 15q13 microdeletions in our series. Two small, atypical deletions were identified within the region that help delineate the critical region for the core phenotype in the 15q24 microdeletion syndrome. CONCLUSION: The molecular characterisation of these patients suggests that the core cognitive features of the 15q24 microdeletion syndrome, including developmental delays and severe speech problems, are largely due to deletion of genes in a 1.1-Mb critical region. However, genes just distal to the critical region also play an important role in cognition and in the development of characteristic facial features associated with 15q24 deletions. Clearly, deletions in the 15q24 region are variable in size and extent. Knowledge of the breakpoints and size of deletion combined with the natural history and medical problems of our patients provide insights that will inform management guidelines. Based on common phenotypic features, all patients with 15q24 microdeletions should receive a thorough neurodevelopmental evaluation, physical, occupational and speech therapies, and regular audiologic and ophthalmologic screening.

Lovastatin as treatment for neurocognitive deficits in neurofibromatosis type 1: phase I study.

In a neurofibromatosis type 1 murine model, treatment with lovastatin reversed cognitive disabilities. We report on a phase I study examining the safety and tolerability of lovastatin in children with neurofibromatosis type 1. Twenty-four children with neurofibromatosis type 1 underwent a dose-escalation protocol for 3 months to identify the maximum tolerated dose and potential toxicity. Minimal side effects were evident, and no child experienced dose-limiting toxicity. Cognitive evaluations were completed before and after treatment, and the results suggested improvement in areas of verbal and nonverbal memory. Additional analyses, using reliable change indices, indicated improvements exceeding those of test-retest or practice effects in some participants. These observations may be analogous to the improvements observed in a neurofibromatosis type 1 murine model treated with lovastatin, although further study and replication are required. The safety and preliminary cognitive results support the need for a larger phase II trial in this population.

Developmental profile and trajectory of neuropsychological skills in a child with Kabuki syndrome: implications for assessment of syndromes associated with intellectual disability.

Kabuki syndrome (KS) is a rare genetic syndrome involving dysmorphic facial features,and reports of intellectual disability (ID). We examined the developmental trajectory of neuropsychological skills in a child with KS (seen at ages 4, 6, 7, 9, and 11). Examination of raw and age-corrected standard scores suggests that language-based skills developed appropriately, but visually based skills slowed and reached a plateau. Executive dysfunction and mood symptoms were also observed. While ID is described as a core feature of KS,some patients may not meet diagnostic criteria for ID, and may be better described as having specific deficits in nonverbal skills. Longitudinal neuropsychological assessment of children with KS and other syndromes associated with ID is warranted to understand the true prevalence of ID versus isolated cognitive impairments.

Molecular analysis expands the spectrum of phenotypes associated with GLI3 mutations.

A range of phenotypes including Greig cephalopolysyndactyly and Pallister-Hall syndromes (GCPS, PHS) are caused by pathogenic mutation of the GLI3 gene. To characterize the clinical variability of GLI3 mutations, we present a subset of a cohort of 174 probands referred for GLI3 analysis. Eighty-one probands with typical GCPS or PHS were previously reported, and we report the remaining 93 probands here. This includes 19 probands (12 mutations) who fulfilled clinical criteria for GCPS or PHS, 48 probands (16 mutations) with features of GCPS or PHS but who did not meet the clinical criteria (sub-GCPS and sub-PHS), 21 probands (6 mutations) with features of PHS or GCPS and oral-facial-digital syndrome, and 5 probands (1 mutation) with nonsyndromic polydactyly. These data support previously identified genotype-phenotype correlations and demonstrate a more variable degree of severity than previously recognized. The finding of GLI3 mutations in patients with features of oral-facial-digital syndrome supports the observation that GLI3 interacts with cilia. We conclude that the phenotypic spectrum of GLI3 mutations is broader than that encompassed by the clinical diagnostic criteria, but the genotype-phenotype correlation persists. Individuals with features of either GCPS or PHS should be screened for mutations in GLI3 even if they do not fulfill clinical criteria.

Holoprosencephaly due to numeric chromosome abnormalities.

Holoprosencephaly (HPE) is the most common malformation of the human forebrain. When a clinician identifies a patient with HPE, a routine chromosome analysis is often the first genetic test sent for laboratory analysis in order to assess for a structural or numerical chromosome anomaly. An abnormality of chromosome number is overall the most frequently identified etiology in a patient with HPE. These abnormalities include trisomy 13, trisomy 18, and triploidy, though several others have been reported. Such chromosome number abnormalities are almost universally fatal early in gestation or in infancy. Clinical features of specific chromosome number abnormalities may be recognized by phenotypic manifestations in addition to the HPE.