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Kong X, Wang Z, Sun J, Qi X, Qiu Q, Ding X. Facial recognition for disease diagnosis using a deep learning convolutional neural network: a systematic review and meta-analysis. Postgrad Med J 2024:qgae061. [PMID: 39102373 DOI: 10.1093/postmj/qgae061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/20/2024] [Accepted: 04/24/2024] [Indexed: 08/07/2024]
Abstract
BACKGROUND With the rapid advancement of deep learning network technology, the application of facial recognition technology in the medical field has received increasing attention. OBJECTIVE This study aims to systematically review the literature of the past decade on facial recognition technology based on deep learning networks in the diagnosis of rare dysmorphic diseases and facial paralysis, among other conditions, to determine the effectiveness and applicability of this technology in disease identification. METHODS This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for literature search and retrieved relevant literature from multiple databases, including PubMed, on 31 December 2023. The search keywords included deep learning convolutional neural networks, facial recognition, and disease recognition. A total of 208 articles on facial recognition technology based on deep learning networks in disease diagnosis over the past 10 years were screened, and 22 articles were selected for analysis. The meta-analysis was conducted using Stata 14.0 software. RESULTS The study collected 22 articles with a total sample size of 57 539 cases, of which 43 301 were samples with various diseases. The meta-analysis results indicated that the accuracy of deep learning in facial recognition for disease diagnosis was 91.0% [95% CI (87.0%, 95.0%)]. CONCLUSION The study results suggested that facial recognition technology based on deep learning networks has high accuracy in disease diagnosis, providing a reference for further development and application of this technology.
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Affiliation(s)
- Xinru Kong
- Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan City, Shandong Province 250355, China
- Department of Vertigo Center, Air Force Specialized Medical Center, Beijing 100142, China
| | - Ziyue Wang
- Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan City, Shandong Province 250355, China
| | - Jie Sun
- Rizhao Central Hospital, Rizhao, Shandong 276800, China
| | - Xianghua Qi
- Department of Neurology II, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan City, Shandong Province 25000, China
| | - Qianhui Qiu
- Department of Otolaryngology and Head and Neck Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Cardiovsacular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Science, Guangzhou 510000, China
| | - Xiao Ding
- Department of Neurology II, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan City, Shandong Province 25000, China
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Huang Y, Sun H, Chen Q, Shen J, Han J, Shan S, Wang S. Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome. BMC Pediatr 2024; 24:361. [PMID: 38783283 PMCID: PMC11118109 DOI: 10.1186/s12887-024-04827-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Noonan syndrome (NS) is a rare genetic disease, and patients who suffer from it exhibit a facial morphology that is characterized by a high forehead, hypertelorism, ptosis, inner epicanthal folds, down-slanting palpebral fissures, a highly arched palate, a round nasal tip, and posteriorly rotated ears. Facial analysis technology has recently been applied to identify many genetic syndromes (GSs). However, few studies have investigated the identification of NS based on the facial features of the subjects. OBJECTIVES This study develops advanced models to enhance the accuracy of diagnosis of NS. METHODS A total of 1,892 people were enrolled in this study, including 233 patients with NS, 863 patients with other GSs, and 796 healthy children. We took one to 10 frontal photos of each subject to build a dataset, and then applied the multi-task convolutional neural network (MTCNN) for data pre-processing to generate standardized outputs with five crucial facial landmarks. The ImageNet dataset was used to pre-train the network so that it could capture generalizable features and minimize data wastage. We subsequently constructed seven models for facial identification based on the VGG16, VGG19, VGG16-BN, VGG19-BN, ResNet50, MobileNet-V2, and squeeze-and-excitation network (SENet) architectures. The identification performance of seven models was evaluated and compared with that of six physicians. RESULTS All models exhibited a high accuracy, precision, and specificity in recognizing NS patients. The VGG19-BN model delivered the best overall performance, with an accuracy of 93.76%, precision of 91.40%, specificity of 98.73%, and F1 score of 78.34%. The VGG16-BN model achieved the highest AUC value of 0.9787, while all models based on VGG architectures were superior to the others on the whole. The highest scores of six physicians in terms of accuracy, precision, specificity, and the F1 score were 74.00%, 75.00%, 88.33%, and 61.76%, respectively. The performance of each model of facial recognition was superior to that of the best physician on all metrics. CONCLUSION Models of computer-assisted facial recognition can improve the rate of diagnosis of NS. The models based on VGG19-BN and VGG16-BN can play an important role in diagnosing NS in clinical practice.
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Affiliation(s)
- Yulu Huang
- Department of Pediatric Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No. 106, Zhongshan 2nd Road, Yuexiu District, Guangzhou, China
| | - Haomiao Sun
- Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, No. 6 South Science Academy Road, Haidian District, Beijing, China
- University of Chinese Academy of Sciences, No. 80 Zhongguancun Road East, Haidian District, Beijing, China
| | - Qinchang Chen
- Department of Pediatric Cardiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106, Zhongshan 2nd Road, Yuexiu District, Guangzhou, China
| | - Junjun Shen
- Department of Pediatric Cardiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106, Zhongshan 2nd Road, Yuexiu District, Guangzhou, China
| | - Jin Han
- Prenatal diagnosis center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, No. 9 Jinsui Road, Tianhe District, Guangzhou, China
| | - Shiguang Shan
- Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, No. 6 South Science Academy Road, Haidian District, Beijing, China.
- University of Chinese Academy of Sciences, No. 80 Zhongguancun Road East, Haidian District, Beijing, China.
| | - Shushui Wang
- Department of Pediatric Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No. 106, Zhongshan 2nd Road, Yuexiu District, Guangzhou, China.
- Department of Pediatric Cardiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106, Zhongshan 2nd Road, Yuexiu District, Guangzhou, China.
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Ciancia S, Madeo SF, Calabrese O, Iughetti L. The Approach to a Child with Dysmorphic Features: What the Pediatrician Should Know. CHILDREN (BASEL, SWITZERLAND) 2024; 11:578. [PMID: 38790573 PMCID: PMC11120268 DOI: 10.3390/children11050578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/01/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024]
Abstract
The advancement of genetic knowledge and the discovery of an increasing number of genetic disorders has made the role of the geneticist progressively more complex and fundamental. However, most genetic disorders present during childhood; thus, their early recognition is a challenge for the pediatrician, who will be also involved in the follow-up of these children, often establishing a close relationship with them and their families and becoming a referral figure. In this review, we aim to provide the pediatrician with a general knowledge of the approach to treating a child with a genetic syndrome associated with dysmorphic features. We will discuss the red flags, the most common manifestations, the analytic collection of the family and personal medical history, and the signs that should alert the pediatrician during the physical examination. We will offer an overview of the physical malformations most commonly associated with genetic defects and the way to describe dysmorphic facial features. We will provide hints about some tools that can support the pediatrician in clinical practice and that also represent a useful educational resource, either online or through apps downloaded on a smartphone. Eventually, we will offer an overview of genetic testing, the ethical considerations, the consequences of incidental findings, and the main indications and limitations of the principal technologies.
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Affiliation(s)
- Silvia Ciancia
- Pediatric Unit, Department of Medical and Surgical Sciences for Mothers, Children and Adults, University of Modena and Reggio Emilia, Largo del Pozzo 71, 41124 Modena, Italy
| | - Simona Filomena Madeo
- Pediatric Unit, Department of Medical and Surgical Sciences for Mothers, Children and Adults, University of Modena and Reggio Emilia, Largo del Pozzo 71, 41124 Modena, Italy
| | - Olga Calabrese
- Medical Genetics Unit, Department of Medical and Surgical Sciences for Mothers, Children and Adults, University of Modena and Reggio Emilia, 41124 Modena, Italy
| | - Lorenzo Iughetti
- Pediatric Unit, Department of Medical and Surgical Sciences for Mothers, Children and Adults, University of Modena and Reggio Emilia, Largo del Pozzo 71, 41124 Modena, Italy
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Ascaso Á, Arnedo M, Puisac B, Latorre-Pellicer A, Del Rincón J, Bueno-Lozano G, Pié J, Ramos FJ. Cornelia de Lange Spectrum. An Pediatr (Barc) 2024; 100:352-362. [PMID: 38735830 DOI: 10.1016/j.anpede.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/11/2024] [Indexed: 05/14/2024] Open
Abstract
Cornelia de Lange syndrome (CdLS) is a rare congenital developmental disorder with multisystemic involvement. The clinical presentation is highly variable, but the classic phenotype, characterized by distinctive craniofacial features, pre- and postnatal growth retardation, extremity reduction defects, hirsutism and intellectual disability can be distinguished from the nonclassic phenotype, which is generally milder and more difficult to diagnose. In addition, the clinical features overlap with those of other neurodevelopmental disorders, so the use of consensus clinical criteria and artificial intelligence tools may be helpful in confirming the diagnosis. Pathogenic variants in NIPBL, which encodes a protein related to the cohesin complex, have been identified in more than 60% of patients, and pathogenic variants in other genes related to this complex in another 15%: SMC1A, SMC3, RAD21, and HDAC8. Technical advances in large-scale sequencing have allowed the description of additional genes (BRD4, ANKRD11, MAU2), but the lack of molecular diagnosis in 15% of individuals and the substantial clinical heterogeneity of the syndrome suggest that other genes and mechanisms may be involved. Although there is no curative treatment, there are symptomatic/palliative treatments that paediatricians should be aware of. The main medical complication in classic SCdL is gastro-esophageal reflux (GER), which should be treated early.
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Affiliation(s)
- Ángela Ascaso
- Consulta de Pediatría, Centro de Salud Delicias Sur, Zaragoza, Spain
| | - María Arnedo
- Laboratorio de Genética Clínica y Genómica Funcional, Facultad de Medicina, Universidad de Zaragoza, Zaragoza, Spain
| | - Beatriz Puisac
- Laboratorio de Genética Clínica y Genómica Funcional, Facultad de Medicina, Universidad de Zaragoza, Zaragoza, Spain
| | - Ana Latorre-Pellicer
- Laboratorio de Genética Clínica y Genómica Funcional, Facultad de Medicina, Universidad de Zaragoza, Zaragoza, Spain
| | - Julia Del Rincón
- Unidad de Genética Clínica, Servicio de Pediatría, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain
| | - Gloria Bueno-Lozano
- Unidad de Genética Clínica, Servicio de Pediatría, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain
| | - Juan Pié
- Laboratorio de Genética Clínica y Genómica Funcional, Facultad de Medicina, Universidad de Zaragoza, Zaragoza, Spain
| | - Feliciano J Ramos
- Unidad de Genética Clínica, Servicio de Pediatría, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain.
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Hennocq Q, Garcelon N, Bongibault T, Bouygues T, Marlin S, Amiel J, Boutaud L, Douillet M, Lyonnet S, Pingault V, Picard A, Rio M, Attie-Bitach T, Khonsari RH, Roux N. Artificial intelligence-based diagnosis in fetal pathology using external ear shapes. Prenat Diagn 2024. [PMID: 38635411 DOI: 10.1002/pd.6577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 03/28/2024] [Accepted: 04/07/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVE Here we trained an automatic phenotype assessment tool to recognize syndromic ears in two syndromes in fetuses-=CHARGE and Mandibulo-Facial Dysostosis Guion Almeida type (MFDGA)-versus controls. METHOD We trained an automatic model on all profile pictures of children diagnosed with genetically confirmed MFDGA and CHARGE syndromes, and a cohort of control patients, collected from 1981 to 2023 in Necker Hospital (Paris) with a visible external ear. The model consisted in extracting landmarks from photographs of external ears, in applying geometric morphometry methods, and in a classification step using machine learning. The approach was then tested on photographs of two groups of fetuses: controls and fetuses with CHARGE and MFDGA syndromes. RESULTS The training set contained a total of 1489 ear photographs from 526 children. The validation set contained a total of 51 ear photographs from 51 fetuses. The overall accuracy was 72.6% (58.3%-84.1%, p < 0.001), and 76.4%, 74.9%, and 86.2% respectively for CHARGE, control and MFDGA fetuses. The area under the curves were 86.8%, 87.5%, and 90.3% respectively for CHARGE, controls, and MFDGA fetuses. CONCLUSION We report the first automatic fetal ear phenotyping model, with satisfactory classification performances. Further validations are required before using this approach as a diagnostic tool.
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Affiliation(s)
- Quentin Hennocq
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Laboratoire 'Forme et Croissance Du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | | | - Thomas Bongibault
- Imagine Institute, INSERM UMR1163, Paris, France
- Laboratoire 'Forme et Croissance Du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Thomas Bouygues
- Imagine Institute, INSERM UMR1163, Paris, France
- Laboratoire 'Forme et Croissance Du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Sandrine Marlin
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Jeanne Amiel
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Lucile Boutaud
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | | | - Stanislas Lyonnet
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Vèronique Pingault
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Arnaud Picard
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Marlèe Rio
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Tania Attie-Bitach
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Roman H Khonsari
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Laboratoire 'Forme et Croissance Du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Nathalie Roux
- Imagine Institute, INSERM UMR1163, Paris, France
- Faculté de Médecine, Université de Paris Cité, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
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Carrer A, Romaniello MG, Calderara ML, Mariani M, Biondi A, Selicorni A. Application of the Face2Gene tool in an Italian dysmorphological pediatric clinic: Retrospective validation and future perspectives. Am J Med Genet A 2024; 194:e63459. [PMID: 37927205 DOI: 10.1002/ajmg.a.63459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 11/07/2023]
Abstract
Neurodevelopmental disorders exhibit recurrent facial features that can suggest the genetic diagnosis at a glance, but recognizing subtle dysmorphisms is a specialized skill that requires very long training. Face2Gene (FDNA Inc) is an innovative computer-aided phenotyping tool that analyses patient's portraits and suggests 30 candidate syndromes with similar morphology in a prioritized list. We hypothesized that the software could support even expert physicians in the diagnostic workup of genetic conditions. In this study, we assessed the performance of Face2Gene in an Italian dysmorphological pediatrics clinic. We uploaded two-dimensional face pictures of 145 children affected by genetic conditions with typical phenotypic traits. All diagnoses were previously confirmed by cytogenetic or molecular tests. Overall, the software's differential included the correct syndrome in most cases (98%). We evaluated the efficiency of the algorithm even considering the rareness of the genetic conditions. All "common" diagnoses were correctly identified, most of them with high diagnostic accuracy (93% in top-3 matches). Finally, the performance for the most common pediatric syndromes was calculated. Face2Gene performed well even for ultra-rare genetic conditions (75% within top-3 matches and 83% within top-10 matches). Expert geneticists maybe do not need computer support to recognize common syndromes, but our results prove that the tool can be useful not only for general pediatricians but also in dysmorphological clinics for ultra-rare genetic conditions.
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Affiliation(s)
- Alessia Carrer
- Department of Health Sciences, University of Milan, Milan, Italy
- Mariani Foundation Center for Fragile Child, Pediatric Unit ASST Lariana, Como, Italy
| | - Maria Giovanna Romaniello
- Mariani Foundation Center for Fragile Child, Pediatric Unit ASST Lariana, Como, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Maria Letizia Calderara
- Mariani Foundation Center for Fragile Child, Pediatric Unit ASST Lariana, Como, Italy
- Department of Medicine and Surgery, University of Insubria, Varese, Italy
| | - Milena Mariani
- Mariani Foundation Center for Fragile Child, Pediatric Unit ASST Lariana, Como, Italy
| | - Andrea Biondi
- Department of Medicine and Surgery, University of Insubria, Varese, Italy
- Paediatrics, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | - Angelo Selicorni
- Mariani Foundation Center for Fragile Child, Pediatric Unit ASST Lariana, Como, Italy
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Hennocq Q, Paternoster G, Collet C, Amiel J, Bongibault T, Bouygues T, Cormier-Daire V, Douillet M, Dunaway DJ, Jeelani NO, van de Lande LS, Lyonnet S, Ong J, Picard A, Rickart AJ, Rio M, Schievano S, Arnaud E, Garcelon N, Khonsari RH. AI-based diagnosis and phenotype - Genotype correlations in syndromic craniosynostoses. J Craniomaxillofac Surg 2024:S1010-5182(24)00055-6. [PMID: 39187417 DOI: 10.1016/j.jcms.2024.02.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 02/02/2024] [Indexed: 08/28/2024] Open
Abstract
Apert (AS), Crouzon (CS), Muenke (MS), Pfeiffer (PS), and Saethre Chotzen (SCS) are among the most frequently diagnosed syndromic craniosynostoses. The aims of this study were (1) to train an innovative model using artificial intelligence (AI)-based methods on two-dimensional facial frontal, lateral, and external ear photographs to assist diagnosis for syndromic craniosynostoses vs controls, and (2) to screen for genotype/phenotype correlations in AS, CS, and PS. We included retrospectively and prospectively, from 1979 to 2023, all frontal and lateral pictures of patients genetically diagnosed with AS, CS, MS, PS and SCS syndromes. After a deep learning-based preprocessing, we extracted geometric and textural features and used XGboost (eXtreme Gradient Boosting) to classify patients. The model was tested on an independent international validation set of genetically confirmed patients and non-syndromic controls. Between 1979 and 2023, we included 2228 frontal and lateral facial photographs corresponding to 541 patients. In all, 70.2% [0.593-0.797] (p < 0.001) of patients in the validation set were correctly diagnosed. Genotypes linked to a splice donor site of FGFR2 in Crouzon-Pfeiffer syndrome (CPS) caused a milder phenotype in CPS. Here we report a new method for the automatic detection of syndromic craniosynostoses using AI.
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Affiliation(s)
- Quentin Hennocq
- Imagine Institute, INSERM UMR1163, 75015, Paris, France; Département de chirurgie maxillo-faciale et chirurgie plastique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, 75015, Paris, France; Laboratoire 'Forme et Croissance du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Faculté de Médecine, Université Paris Cité, Paris, France.
| | - Giovanna Paternoster
- Imagine Institute, INSERM UMR1163, 75015, Paris, France; Département de neurochirurgie, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
| | - Corinne Collet
- Département de génétique moléculaire, Hôpital Robert Debré, Université de Paris Cité, Paris, France
| | - Jeanne Amiel
- Imagine Institute, INSERM UMR1163, 75015, Paris, France; Service de médecine génomique des maladies rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
| | - Thomas Bongibault
- Imagine Institute, INSERM UMR1163, 75015, Paris, France; Laboratoire 'Forme et Croissance du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Faculté de Médecine, Université Paris Cité, Paris, France
| | - Thomas Bouygues
- Imagine Institute, INSERM UMR1163, 75015, Paris, France; Laboratoire 'Forme et Croissance du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Faculté de Médecine, Université Paris Cité, Paris, France
| | - Valérie Cormier-Daire
- Imagine Institute, INSERM UMR1163, 75015, Paris, France; Service de médecine génomique des maladies rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
| | | | - David J Dunaway
- UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Nu Owase Jeelani
- UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Lara S van de Lande
- UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK; Department of Oral and Maxillofacial Surgery, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Stanislas Lyonnet
- Imagine Institute, INSERM UMR1163, 75015, Paris, France; Service de médecine génomique des maladies rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
| | - Juling Ong
- UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Arnaud Picard
- Département de chirurgie maxillo-faciale et chirurgie plastique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
| | - Alexander J Rickart
- UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Marlène Rio
- Imagine Institute, INSERM UMR1163, 75015, Paris, France; Service de médecine génomique des maladies rares, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
| | - Silvia Schievano
- UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Eric Arnaud
- Département de neurochirurgie, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, 75015, Paris, France; Clinique Marcel Sembat (Ramsay), Boulogne, France
| | | | - Roman H Khonsari
- Imagine Institute, INSERM UMR1163, 75015, Paris, France; Département de chirurgie maxillo-faciale et chirurgie plastique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, 75015, Paris, France; Laboratoire 'Forme et Croissance du Crâne', Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Faculté de Médecine, Université Paris Cité, Paris, France; Département de neurochirurgie, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
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8
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Hennocq Q, Willems M, Amiel J, Arpin S, Attie-Bitach T, Bongibault T, Bouygues T, Cormier-Daire V, Corre P, Dieterich K, Douillet M, Feydy J, Galliani E, Giuliano F, Lyonnet S, Picard A, Porntaveetus T, Rio M, Rouxel F, Shotelersuk V, Toutain A, Yauy K, Geneviève D, Khonsari RH, Garcelon N. Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome. Sci Rep 2024; 14:2330. [PMID: 38282012 PMCID: PMC10822856 DOI: 10.1038/s41598-024-52691-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 01/22/2024] [Indexed: 01/30/2024] Open
Abstract
The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9-99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729-0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.
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Affiliation(s)
- Quentin Hennocq
- Imagine Institute, INSERM UMR1163, 75015, Paris, France.
- Service de chirurgie maxillo-faciale et chirurgie plastique, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France.
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France.
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France.
- Laboratoire 'Forme et Croissance du Crâne', Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France.
- Hôpital Necker-Enfants Malades, 149 rue de Sèvres, 75015, Paris, France.
| | - Marjolaine Willems
- Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier University, Montpellier, France
| | - Jeanne Amiel
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Stéphanie Arpin
- Service de Génétique, CHU Tours, UMR 1253, iBrain, Université de Tours, Inserm, Tours, France
| | - Tania Attie-Bitach
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Thomas Bongibault
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Laboratoire 'Forme et Croissance du Crâne', Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
| | - Thomas Bouygues
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Laboratoire 'Forme et Croissance du Crâne', Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
| | - Valérie Cormier-Daire
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Pierre Corre
- Nantes Université, CHU Nantes, Service de chirurgie maxillo-faciale et stomatologie, 44000, Nantes, France
- Nantes Université, Oniris, UnivAngers, CHU Nantes, INSERM, Regenerative Medicine and Skeleton, RMeS, UMR 1229, 44000, Nantes, France
| | - Klaus Dieterich
- Univ. Grenoble Alpes, Inserm, U1209, IAB, CHU Grenoble Alpes, 38000, Grenoble, France
| | | | | | - Eva Galliani
- Service de chirurgie maxillo-faciale et chirurgie plastique, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
| | | | - Stanislas Lyonnet
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Arnaud Picard
- Service de chirurgie maxillo-faciale et chirurgie plastique, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
| | - Thantrira Porntaveetus
- Center of Excellence in Genomics and Precision Dentistry, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
| | - Marlène Rio
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
- Service de médecine génomique des maladies rares, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Flavien Rouxel
- Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier University, Montpellier, France
| | - Vorasuk Shotelersuk
- Center of Excellence for Medical Genomics, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Annick Toutain
- Service de Génétique, CHU Tours, UMR 1253, iBrain, Université de Tours, Inserm, Tours, France
| | - Kevin Yauy
- Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier University, Montpellier, France
| | - David Geneviève
- Département de Génétique Médicale, Maladies Rares et Médecine Personnalisée, Génétique clinique, CHU Montpellier, Centre de référence anomalies du développement SOOR, INSERM U1183, Montpellier University, Montpellier, France
| | - Roman H Khonsari
- Imagine Institute, INSERM UMR1163, 75015, Paris, France
- Service de chirurgie maxillo-faciale et chirurgie plastique, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Paris, France
- Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Paris, France
- Faculté de Médecine, Université de Paris Cité, 75015, Paris, France
- Laboratoire 'Forme et Croissance du Crâne', Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
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Abarca-Barriga HH, Punil Luciano R, Vásquez Sotomayor F. Cornelia de Lange Syndrome Caused by an Intragenic Heterozygous Deletion in RAD21 Detected through Very-High-Resolution Chromosomal Microarray Analysis. Genes (Basel) 2023; 14:2212. [PMID: 38137034 PMCID: PMC10742884 DOI: 10.3390/genes14122212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 12/24/2023] Open
Abstract
Cornelia de Lange syndrome is a genetic and clinically heterogeneous entity, caused by at least five genes. It is characterized by short stature, gestalt facies, microcephaly, neurodevelopmental disorders, and other anomalies. In this report, we present a 13-year-old female patient with microcephaly, cleft palate, polydactyly, short stature, triangular facies, frontal bossing, a bulbous nose, an overfolded helix, limited pronosupination, and an anomalous uterus. No neurodevelopmental disorders were reported. A chromosomal microarray analysis of 6.5 million markers was performed in the proband and her parents. The results showed a de novo heterozygous microdeletion of exons 9-14 within RAD21, which confirmed the diagnosis of Cornelia de Lange syndrome type 4. Our patient did not show any neurologic phenotype (until the time of diagnosis), although neurodevelopmental disorders are frequently present in patients with Cornelia de Lange syndrome type 4, and despite carrying a deletion that was larger than previously reported. Therefore, unknown genetic modifiers or intrinsic mechanisms of RAD21 variants may exist and should be studied.
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Affiliation(s)
- Hugo H. Abarca-Barriga
- Instituto de Investigaciones de Ciencias Biomédicas, Universidad Ricardo Palma, Lima 15039, Peru;
- Servicio de Genética & Errores Innatos del Metabolismo, Instituto Nacional de Salud del Niño Breña, Lima 15083, Peru;
| | - Renzo Punil Luciano
- Servicio de Genética & Errores Innatos del Metabolismo, Instituto Nacional de Salud del Niño Breña, Lima 15083, Peru;
| | - Flor Vásquez Sotomayor
- Instituto de Investigaciones de Ciencias Biomédicas, Universidad Ricardo Palma, Lima 15039, Peru;
- Servicio de Genética & Errores Innatos del Metabolismo, Instituto Nacional de Salud del Niño Breña, Lima 15083, Peru;
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10
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Hennocq Q, Bongibault T, Marlin S, Amiel J, Attie-Bitach T, Baujat G, Boutaud L, Carpentier G, Corre P, Denoyelle F, Djate Delbrah F, Douillet M, Galliani E, Kamolvisit W, Lyonnet S, Milea D, Pingault V, Porntaveetus T, Touzet-Roumazeille S, Willems M, Picard A, Rio M, Garcelon N, Khonsari RH. AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes. Front Pediatr 2023; 11:1171277. [PMID: 37664547 PMCID: PMC10469912 DOI: 10.3389/fped.2023.1171277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 07/26/2023] [Indexed: 09/05/2023] Open
Abstract
Introduction Mandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes. Methods The training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set. Results We trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838-0.999] (p < 0.001) and an AUC (Area Under the Curve) of 0.975 within controls (binary classification). Balanced accuracies were 0.811 [0.648-0.920] (p = 0.002) in a first multiclass design (MFDM vs. controls and differential diagnoses) and 0.813 [0.544-0.960] (p = 0.003) in a second multiclass design (MFDM vs. differential diagnoses). Conclusion This is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers.
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Affiliation(s)
- Quentin Hennocq
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
- Laboratoire ‘Forme et Croissance du Crâne’, Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
| | - Thomas Bongibault
- Imagine Institute, INSERM UMR1163, Paris, France
- Laboratoire ‘Forme et Croissance du Crâne’, Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
| | - Sandrine Marlin
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Jeanne Amiel
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Tania Attie-Bitach
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Geneviève Baujat
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Lucile Boutaud
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Georges Carpentier
- CHU Lille, Inserm, Service de Chirurgie Maxillo-Faciale et Stomatologie, U1008-Controlled Drug Delivery Systems and Biomaterial, Université de Lille, Lille, France
| | - Pierre Corre
- Department of Oral and Maxillofacial Surgery, INSERM U1229—Regenerative Medicine and Skeleton RMeS, Nantes, France
- Department of Oral and Maxillofacial Surgery, Nantes University, CHU Nantes, Nantes, France
| | - Françoise Denoyelle
- Department of Paediatric Otolaryngology, AP-HP, Hôpital Necker-Enfants Malades, Paris, France
| | | | | | - Eva Galliani
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Wuttichart Kamolvisit
- Center of Excellence for Medical Genomics, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Genomics and Precision Dentistry, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
| | - Stanislas Lyonnet
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Dan Milea
- Duke-NUS Medical School Singapore, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Véronique Pingault
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Thantrira Porntaveetus
- Center of Excellence for Medical Genomics, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Genomics and Precision Dentistry, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
| | - Sandrine Touzet-Roumazeille
- CHU Lille, Inserm, Service de Chirurgie Maxillo-Faciale et Stomatologie, U1008-Controlled Drug Delivery Systems and Biomaterial, Université de Lille, Lille, France
| | - Marjolaine Willems
- Département de Génétique Clinique, CHRU de Montpellier, Hôpital Arnaud de Villeneuve, Institute for Neurosciences of Montpellier, INSERM, Univ Montpellier, Montpellier, France
| | - Arnaud Picard
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Marlène Rio
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Médecine Génomique des Maladies Rares, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | | | - Roman H. Khonsari
- Imagine Institute, INSERM UMR1163, Paris, France
- Service de Chirurgie Maxillo-Faciale et Chirurgie Plastique, Hôpital Necker—Enfants Malades, Assistance Publique—Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
- Laboratoire ‘Forme et Croissance du Crâne’, Faculté de Médecine, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
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Ruiter AM, Wang Z, Yin Z, Naber WC, Simons J, Blom JT, van Gemert JC, Verschuuren JJGM, Tannemaat MR. Assessing facial weakness in myasthenia gravis with facial recognition software and deep learning. Ann Clin Transl Neurol 2023; 10:1314-1325. [PMID: 37292032 PMCID: PMC10424649 DOI: 10.1002/acn3.51823] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/10/2023] Open
Abstract
OBJECTIVE Myasthenia gravis (MG) is an autoimmune disease leading to fatigable muscle weakness. Extra-ocular and bulbar muscles are most commonly affected. We aimed to investigate whether facial weakness can be quantified automatically and used for diagnosis and disease monitoring. METHODS In this cross-sectional study, we analyzed video recordings of 70 MG patients and 69 healthy controls (HC) with two different methods. Facial weakness was first quantified with facial expression recognition software. Subsequently, a deep learning (DL) computer model was trained for the classification of diagnosis and disease severity using multiple cross-validations on videos of 50 patients and 50 controls. Results were validated using unseen videos of 20 MG patients and 19 HC. RESULTS Expression of anger (p = 0.026), fear (p = 0.003), and happiness (p < 0.001) was significantly decreased in MG compared to HC. Specific patterns of decreased facial movement were detectable in each emotion. Results of the DL model for diagnosis were as follows: area under the curve (AUC) of the receiver operator curve 0.75 (95% CI 0.65-0.85), sensitivity 0.76, specificity 0.76, and accuracy 76%. For disease severity: AUC 0.75 (95% CI 0.60-0.90), sensitivity 0.93, specificity 0.63, and accuracy 80%. Results of validation, diagnosis: AUC 0.82 (95% CI: 0.67-0.97), sensitivity 1.0, specificity 0.74, and accuracy 87%. For disease severity: AUC 0.88 (95% CI: 0.67-1.0), sensitivity 1.0, specificity 0.86, and accuracy 94%. INTERPRETATION Patterns of facial weakness can be detected with facial recognition software. Second, this study delivers a 'proof of concept' for a DL model that can distinguish MG from HC and classifies disease severity.
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Affiliation(s)
- Annabel M. Ruiter
- Department of NeurologyLeiden University Medical CenterLeidenthe Netherlands
| | - Ziqi Wang
- Vision LabDelft University of TechnologyDelftthe Netherlands
| | - Zhao Yin
- Vision LabDelft University of TechnologyDelftthe Netherlands
| | - Willemijn C. Naber
- Department of NeurologyLeiden University Medical CenterLeidenthe Netherlands
| | - Jerrel Simons
- Department of NeurologyLeiden University Medical CenterLeidenthe Netherlands
| | - Jurre T. Blom
- Medical Illustrator at www.jurreblom.nlApeldoornthe Netherlands
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12
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Tang J, Han J, Xue J, Zhen L, Yang X, Pan M, Hu L, Li R, Jiang Y, Zhang Y, Jing X, Li F, Chen G, Zhang K, Zhu F, Liao C, Lu L. A Deep-Learning-Based Method Can Detect Both Common and Rare Genetic Disorders in Fetal Ultrasound. Biomedicines 2023; 11:1756. [PMID: 37371851 DOI: 10.3390/biomedicines11061756] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 05/25/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
A global survey indicates that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses can only be performed after babies are born. Abnormal facial characteristics have been identified in various genetic diseases; however, current facial identification technologies cannot be applied to prenatal diagnosis. We developed Pgds-ResNet, a fully automated prenatal screening algorithm based on deep neural networks, to detect high-risk fetuses affected by a variety of genetic diseases. In screening for Trisomy 21, Trisomy 18, Trisomy 13, and rare genetic diseases, Pgds-ResNet achieved sensitivities of 0.83, 0.92, 0.75, and 0.96, and specificities of 0.94, 0.93, 0.95, and 0.92, respectively. As shown in heatmaps, the abnormalities detected by Pgds-ResNet are consistent with clinical reports. In a comparative experiment, the performance of Pgds-ResNet is comparable to that of experienced sonographers. This fetal genetic screening technology offers an opportunity for early risk assessment and presents a non-invasive, affordable, and complementary method to identify high-risk fetuses affected by genetic diseases. Additionally, it has the capability to screen for certain rare genetic conditions, thereby enhancing the clinic's detection rate.
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Affiliation(s)
- Jiajie Tang
- School of Information Management, Wuhan University, Wuhan 430072, China
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
- Obstetrics and Gynecology Medical Center, Dongguan Kanghua Hospital, Dongguan 523080, China
- Center for Healthcare Big Data Research, The Big Data Institute, Wuhan University, Wuhan 430072, China
| | - Jin Han
- School of Information Management, Wuhan University, Wuhan 430072, China
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
- Obstetrics and Gynecology Medical Center, Dongguan Kanghua Hospital, Dongguan 523080, China
| | - Jiaxin Xue
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Li Zhen
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Xin Yang
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Min Pan
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Lianting Hu
- Medical Big Data Center, Guangdong Provincial People's Hospital, Guangzhou 510317, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangzhou 510317, China
| | - Ru Li
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Yuxuan Jiang
- School of Information Management, Wuhan University, Wuhan 430072, China
| | - Yongling Zhang
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Xiangyi Jing
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Fucheng Li
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Guilian Chen
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Kanghui Zhang
- School of Information Management, Wuhan University, Wuhan 430072, China
| | - Fanfan Zhu
- School of Information Management, Wuhan University, Wuhan 430072, China
| | - Can Liao
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
| | - Long Lu
- School of Information Management, Wuhan University, Wuhan 430072, China
- Prenatal Diagnosis Center/Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China
- Center for Healthcare Big Data Research, The Big Data Institute, Wuhan University, Wuhan 430072, China
- School of Public Health, Wuhan University, Wuhan 430072, China
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13
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Ciancia S, Goedegebuure WJ, Grootjen LN, Hokken-Koelega ACS, Kerkhof GF, van der Kaay DCM. Computer-aided facial analysis as a tool to identify patients with Silver-Russell syndrome and Prader-Willi syndrome. Eur J Pediatr 2023:10.1007/s00431-023-04937-x. [PMID: 36947243 PMCID: PMC10257592 DOI: 10.1007/s00431-023-04937-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 03/03/2023] [Accepted: 03/15/2023] [Indexed: 03/23/2023]
Abstract
Genetic syndromes often show facial features that provide clues for the diagnosis. However, memorizing these features is a challenging task for clinicians. In the last years, the app Face2Gene proved to be a helpful support for the diagnosis of genetic diseases by analyzing features detected in one or more facial images of affected individuals. Our aim was to evaluate the performance of the app in patients with Silver-Russell syndrome (SRS) and Prader-Willi syndrome (PWS). We enrolled 23 pediatric patients with clinically or genetically diagnosed SRS and 29 pediatric patients with genetically confirmed PWS. One frontal photo of each patient was acquired. Top 1, top 5, and top 10 sensitivities were analyzed. Correlation with the specific genetic diagnosis was investigated. When available, photos of the same patient at different ages were compared. In the SRS group, Face2Gene showed top 1, top 5, and top 10 sensitivities of 39%, 65%, and 91%, respectively. In 41% of patients with genetically confirmed SRS, SRS was the first syndrome suggested, while in clinically diagnosed patients, SRS was suggested as top 1 in 33% of cases (p = 0.74). Face2Gene performed better in younger patients with SRS: in all patients in whom a photo taken at a younger age than the age of enrollment was available, SRS was suggested as top 1, albeit with variable degree of probability. In the PWS group, the top 1, top 5, and top 10 sensitivities were 76%, 97%, and 100%, respectively. PWS was suggested as top 1 in 83% of patients genetically diagnosed with paternal deletion of chromosome 15q11-13 and in 60% of patients presenting with maternal uniparental disomy of chromosome 15 (p = 0.17). The performance was uniform throughout the investigated age range (1-15 years). CONCLUSION In addition to a thorough medical history and detailed clinical examination, the Face2Gene app can be a useful tool to support clinicians in identifying children with a potential diagnosis of SRS or PWS. WHAT IS KNOWN • Several genetic syndromes present typical facial features that may provide clues for the diagnosis. • Memorizing all syndromic facial characteristics is a challenging task for clinicians. WHAT IS NEW • Face2Gene may represent a useful support for pediatricians for the diagnosis of genetic syndromes. • Face2Gene app can be a useful tool to integrate in the diagnostic path of patients with SRS and PWS.
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Affiliation(s)
- Silvia Ciancia
- Department of Pediatrics, Subdivision of Endocrinology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, Netherlands
- Post-Graduate School of Pediatrics, Department of Medical and Surgical Sciences for Mothers, Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Wesley J Goedegebuure
- Department of Pediatrics, Subdivision of Endocrinology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, Netherlands
| | - Lionne N Grootjen
- Department of Pediatrics, Subdivision of Endocrinology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, Netherlands
| | - Anita C S Hokken-Koelega
- Department of Pediatrics, Subdivision of Endocrinology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, Netherlands
| | - Gerthe F Kerkhof
- Department of Pediatrics, Subdivision of Endocrinology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, Netherlands
| | - Daniëlle C M van der Kaay
- Department of Pediatrics, Subdivision of Endocrinology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, Netherlands.
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14
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de Morales HGV, Wang HLV, Garber K, Cheng X, Corces VG, Li H. Expansion of the genotypic and phenotypic spectrum of CTCF-related disorder guides clinical management: 43 new subjects and a comprehensive literature review. Am J Med Genet A 2023; 191:718-729. [PMID: 36454652 PMCID: PMC9928606 DOI: 10.1002/ajmg.a.63065] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/22/2022] [Accepted: 11/18/2022] [Indexed: 12/05/2022]
Abstract
Monoallelic variants of CTCF cause an autosomal dominant neurodevelopmental disorder with a wide range of features, including impacts on the brain, growth, and craniofacial development. A growing number of subjects with CTCF-related disorder (CRD) have been identified due to the increased application of exome sequencing, and further delineation of the clinical spectrum of CRD is needed. Here, we examined the clinical features, including facial profiles, and genotypic spectrum of 107 subjects with identified CTCF variants, including 43 new and 64 previously described subjects. Among the 43 new subjects, 23 novel variants were reported. The cardinal clinical features in subjects with CRD included intellectual disability/developmental delay (91%) with speech delay (65%), motor delay (53%), feeding difficulties/failure to thrive (66%), ocular abnormalities (56%), musculoskeletal anomalies (53%), and behavioral problems (52%). Other congenital anomalies were also reported, but none of them were common. Our findings expanded the genotypic and phenotypic spectrum of CRD that will guide genetic counseling, management, and surveillance care for patients with CRD. Additionally, a newly built facial gestalt on the Face2Gene tool will facilitate prompt recognition of CRD by physicians and shorten a patient's diagnostic odyssey.
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Affiliation(s)
| | - Hsiao-Lin V. Wang
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA. 30322
| | - Kathryn Garber
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA. 30322
| | - Xiaodong Cheng
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX. 77030
| | - Victor G. Corces
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA. 30322
| | - Hong Li
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA. 30322
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA. 30322
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15
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Conti B, Rinaldi B, Rimoldi M, Villa R, Iascone M, Gangi S, Porro M, Ajmone PF, Colli AM, Mosca F, Bedeschi MF. Chung-Jansen syndrome can mimic Cornelia de Lange syndrome: Another player among chromatinopathies? Am J Med Genet A 2023; 191:1586-1592. [PMID: 36843271 DOI: 10.1002/ajmg.a.63164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 12/13/2022] [Accepted: 02/03/2023] [Indexed: 02/28/2023]
Abstract
Cornelia de Lange syndrome (CdLS) is a rare multisystem congenital neurodevelopmental disorder (NDD) characterized by distinctive facial anomalies, short stature, developmental delay, hirsutism, gastrointestinal abnormalities and upper limb reduction defects. CdLS syndrome is associated with causative variants in genes encoding for the cohesin complex, a cellular machinery involved in chromatid pairing, DNA repair and gene-expression regulation. In this report, we describe a familial case of a syndromic presentation in a 4-year-old patient (P1) and in his mother (P2). Trio-based Whole Exome Sequencing (WES) performed on P1 was first negative. Since his phenotypic evolution during the follow-up was reminiscent of the CdLS spectrum, a reanalysis of WES data, focused on CdLS-related genes, was requested. Although no alterations in those genes was detected, we identified the likely pathogenetic variant c.40G > A (p.Glu14Lys) in the PHIP gene, in the meanwhile associated with Chung-Jansen syndrome. Reverse phenotyping carried out in both patients confirmed the molecular diagnosis. CHUJANS belongs to NDDs, featuring developmental delay, mild-to-moderate intellectual disability, behavioral problems, obesity and facial dysmorphisms. Moreover, as here described, CHUJANS shows a significant overlap with the CdLS spectrum, with specific regard to facial gestalt. On the basis of our findings, we suggest to include PHIP among genes routinely analyzed in patients belonging to the CdLS spectrum.
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Affiliation(s)
- Beatrice Conti
- Medical Genetics Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Berardo Rinaldi
- Medical Genetics Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Martina Rimoldi
- Medical Genetics Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Roberta Villa
- Medical Genetics Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Maria Iascone
- Laboratorio di Genetica Medica, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Silvana Gangi
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Matteo Porro
- Pediatric Physical Medicine & Rehabilitation Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Paola Francesca Ajmone
- Child and Adolescent Neuropsychiatric Service (UONPIA), Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Anna Maria Colli
- Cardiology Department, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Mosca
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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16
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Hennocq Q, Bongibault T, Bizière M, Delassus O, Douillet M, Cormier-Daire V, Amiel J, Lyonnet S, Marlin S, Rio M, Picard A, Khonsari RH, Garcelon N. An automatic facial landmarking for children with rare diseases. Am J Med Genet A 2023; 191:1210-1221. [PMID: 36714960 DOI: 10.1002/ajmg.a.63126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/31/2023]
Abstract
Two to three thousand syndromes modify facial features: their screening requires the eye of an expert in dysmorphology. A widely used tool in shape characterization is geometric morphometrics based on landmarks, which are precise and reproducible anatomical points. Landmark positioning is user dependent and time consuming. Many automatic landmarking tools are currently available but do not work for children, because they have mainly been trained using photographic databases of healthy adults. Here, we developed a method for building an automatic landmarking pipeline for frontal and lateral facial photographs as well as photographs of external ears. We evaluated the algorithm on patients diagnosed with Treacher Collins (TC) syndrome as it is the most frequent mandibulofacial dysostosis in humans and is clinically recognizable although highly variable in severity. We extracted photographs from the photographic database of the maxillofacial surgery and plastic surgery department of Hôpital Necker-Enfants Malades in Paris, France with the diagnosis of TC syndrome. The control group was built from children admitted for craniofacial trauma or skin lesions. After testing two methods of object detection by bounding boxes, a Haar Cascade-based tool and a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based tool, we evaluated three different automatic annotation algorithms: the patch-based active appearance model (AAM), the holistic AAM, and the constrained local model (CLM). The final error corresponding to the distance between the points placed by automatic annotation and those placed by manual annotation was reported. We included, respectively, 1664, 2044, and 1375 manually annotated frontal, profile, and ear photographs. Object recognition was optimized with the Faster R-CNN-based detector. The best annotation model was the patch-based AAM (p < 0.001 for frontal faces, p = 0.082 for profile faces and p < 0.001 for ears). This automatic annotation model resulted in the same classification performance as manually annotated data. Pretraining on public photographs did not improve the performance of the model. We defined a pipeline to create automatic annotation models adapted to faces with congenital anomalies, an essential prerequisite for research in dysmorphology.
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Affiliation(s)
- Quentin Hennocq
- Imagine Institute, INSERM UMR 1163, Paris, France.,Département de chirurgie maxillo-faciale et chirurgie plastique pédiatrique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
| | | | | | | | | | - Valérie Cormier-Daire
- Fédération de médecine génomique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Jeanne Amiel
- Fédération de médecine génomique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Stanislas Lyonnet
- Fédération de médecine génomique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Sandrine Marlin
- Fédération de médecine génomique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Marlène Rio
- Fédération de médecine génomique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Arnaud Picard
- Département de chirurgie maxillo-faciale et chirurgie plastique pédiatrique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
| | - Roman Hossein Khonsari
- Département de chirurgie maxillo-faciale et chirurgie plastique pédiatrique, Hôpital Necker - Enfants Malades, Assistance Publique - Hôpitaux de Paris, Centre de Référence des Malformations Rares de la Face et de la Cavité Buccale MAFACE, Filière Maladies Rares TeteCou, Faculté de Médecine, Université de Paris Cité, Paris, France
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17
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Thurzo A, Strunga M, Havlínová R, Reháková K, Urban R, Surovková J, Kurilová V. Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics? SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207752. [PMID: 36298103 PMCID: PMC9607180 DOI: 10.3390/s22207752] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/05/2022] [Accepted: 10/11/2022] [Indexed: 05/28/2023]
Abstract
The current paradigm shift in orthodontic treatment planning is based on facially driven diagnostics. This requires an affordable, convenient, and non-invasive solution for face scanning. Therefore, utilization of smartphones' TrueDepth sensors is very tempting. TrueDepth refers to front-facing cameras with a dot projector in Apple devices that provide real-time depth data in addition to visual information. There are several applications that tout themselves as accurate solutions for 3D scanning of the face in dentistry. Their clinical accuracy has been uncertain. This study focuses on evaluating the accuracy of the Bellus3D Dental Pro app, which uses Apple's TrueDepth sensor. The app reconstructs a virtual, high-resolution version of the face, which is available for download as a 3D object. In this paper, sixty TrueDepth scans of the face were compared to sixty corresponding facial surfaces segmented from CBCT. Difference maps were created for each pair and evaluated in specific facial regions. The results confirmed statistically significant differences in some facial regions with amplitudes greater than 3 mm, suggesting that current technology has limited applicability for clinical use. The clinical utilization of facial scanning for orthodontic evaluation, which does not require accuracy in the lip region below 3 mm, can be considered.
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Affiliation(s)
- Andrej Thurzo
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Martin Strunga
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Romana Havlínová
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Katarína Reháková
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Renata Urban
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Jana Surovková
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Veronika Kurilová
- Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovičova 3, 81219 Bratislava, Slovakia
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18
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Gil-Salvador M, Latorre-Pellicer A, Lucia-Campos C, Arnedo M, Darnaude MT, Díaz de Bustamante A, Villares R, Palma Milla C, Puisac B, Musio A, Ramos FJ, Pié J. Case report: A novel case of parental mosaicism in SMC1A gene causes inherited Cornelia de Lange syndrome. Front Genet 2022; 13:993064. [PMID: 36246631 PMCID: PMC9554350 DOI: 10.3389/fgene.2022.993064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/05/2022] [Indexed: 11/18/2022] Open
Abstract
Ultimate advances in genetic technologies have permitted the detection of transmitted cases of congenital diseases due to parental gonadosomatic mosaicism. Regarding Cornelia de Lange syndrome (CdLS), up to date, only a few cases are known to follow this inheritance pattern. However, the high prevalence of somatic mosaicism recently reported in this syndrome (∼13%), together with the disparity observed in tissue distribution of the causal variant, suggests that its prevalence in this disorder could be underestimated. Here, we report a new case of parental gonadosomatic mosaicism in SMC1A gene that causes inherited CdLS, in which the mother of the patient carries the causative variant in very low allele frequencies in buccal swab and blood. While the affected child presents with typical CdLS phenotype, his mother does not show any clinical manifestations. As regards SMC1A, the difficulty of clinical identification of carrier females has been already recognized, as well as the gender differences observed in CdLS expressivity when the causal variant is found in this gene. Currently, the use of DNA deep-sequencing techniques is highly recommended when it comes to molecular diagnosis of patients, as well as in co-segregation studies. These enable us to uncover gonadosomatic mosaic events in asymptomatic or oligosymptomatic parents that had been overlooked so far, which might have great implications regarding genetic counseling for recurrence risk.
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Affiliation(s)
- Marta Gil-Salvador
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology and Physiology, School of Medicine, CIBERER and IIS-Aragon, University of Zaragoza, Zaragoza, Spain
| | - Ana Latorre-Pellicer
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology and Physiology, School of Medicine, CIBERER and IIS-Aragon, University of Zaragoza, Zaragoza, Spain
| | - Cristina Lucia-Campos
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology and Physiology, School of Medicine, CIBERER and IIS-Aragon, University of Zaragoza, Zaragoza, Spain
| | - María Arnedo
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology and Physiology, School of Medicine, CIBERER and IIS-Aragon, University of Zaragoza, Zaragoza, Spain
| | | | | | - Rebeca Villares
- Neuropediatrics, University Hospital of Móstoles, Madrid, Spain
| | | | - Beatriz Puisac
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology and Physiology, School of Medicine, CIBERER and IIS-Aragon, University of Zaragoza, Zaragoza, Spain
| | - Antonio Musio
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Pisa, Italy
| | - Feliciano J. Ramos
- Unit of Clinical Genetics, Service of Paediatrics, Department of Paediatrics, University Hospital “Lozano Blesa”, School of Medicine, CIBERER and IIS-Aragon, University of Zaragoza, Zaragoza, Spain
| | - Juan Pié
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology and Physiology, School of Medicine, CIBERER and IIS-Aragon, University of Zaragoza, Zaragoza, Spain
- *Correspondence: Juan Pié,
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19
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A Novel Intragenic Duplication in the HDAC8 Gene Underlying a Case of Cornelia de Lange Syndrome. Genes (Basel) 2022; 13:genes13081413. [PMID: 36011323 PMCID: PMC9408140 DOI: 10.3390/genes13081413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/03/2022] [Accepted: 08/06/2022] [Indexed: 11/16/2022] Open
Abstract
Cornelia de Lange syndrome (CdLS) is a multisystemic genetic disorder characterized by distinctive facial features, growth retardation, and intellectual disability, as well as various systemic conditions. It is caused by genetic variants in genes related to the cohesin complex. Single-nucleotide variations are the best-known genetic cause of CdLS; however, copy number variants (CNVs) clearly underlie a substantial proportion of cases of the syndrome. The NIPBL gene was thought to be the locus within which clinically relevant CNVs contributed to CdLS. However, in the last few years, pathogenic CNVs have been identified in other genes such as HDAC8, RAD21, and SMC1A. Here, we studied an affected girl presenting with a classic CdLS phenotype heterozygous for a de novo ~32 kbp intragenic duplication affecting exon 10 of HDAC8. Molecular analyses revealed an alteration in the physiological splicing that included a 96 bp insertion between exons 9 and 10 of the main transcript of HDAC8. The aberrant transcript was predicted to generate a truncated protein whose accessibility to the active center was restricted, showing reduced ease of substrate entry into the mutated enzyme. Lastly, we conclude that the duplication is responsible for the patient’s phenotype, highlighting the contribution of CNVs as a molecular cause underlying CdLS.
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20
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Park S, Kim J, Song TY, Jang DH. Case Report: The success of face analysis technology in extremely rare genetic diseases in Korea: Tatton–Brown–Rahman syndrome and Say–Barber –Biesecker–Young–Simpson variant of ohdo syndrome. Front Genet 2022; 13:903199. [PMID: 35991575 PMCID: PMC9382078 DOI: 10.3389/fgene.2022.903199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/04/2022] [Indexed: 11/25/2022] Open
Abstract
Tatton–Brown–Rahman syndrome (TBRS) and Say–Barber–Biesecker– Young–Simpson variant of Ohdo syndrome (SBBYSS) are extremely rare genetic disorders with less than 100 reported cases. Patients with these disorders exhibit a characteristic facial dysmorphism: TBRS is characterized by a round face, a straight and thick eyebrow, and prominent maxillary incisors, whereas SBBYSS is characterized by mask-like facies, blepharophimosis, and ptosis. The usefulness of Face2Gene as a tool for the identification of dysmorphology syndromes is discussed, because, in these patients, it suggested TBRS and SBBYSS within the top five candidate disorders. Face2Gene is useful for the diagnosis of extremely rare diseases in Korean patients, suggesting the possibility of expanding its clinical applications.
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21
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Matthews H, Vanneste M, Katsura K, Aponte D, Patton M, Hammond P, Baynam G, Spritz R, Klein OD, Hallgrimsson B, Peeters H, Claes P. Refining nosology by modelling variation among facial phenotypes: the RASopathies. J Med Genet 2022; 60:jmedgenet-2021-108366. [PMID: 35858754 PMCID: PMC9852361 DOI: 10.1136/jmedgenet-2021-108366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 05/18/2022] [Indexed: 01/24/2023]
Abstract
BACKGROUND In clinical genetics, establishing an accurate nosology requires analysis of variations in both aetiology and the resulting phenotypes. At the phenotypic level, recognising typical facial gestalts has long supported clinical and molecular diagnosis; however, the objective analysis of facial phenotypic variation remains underdeveloped. In this work, we propose exploratory strategies for assessing facial phenotypic variation within and among clinical and molecular disease entities and deploy these techniques on cross-sectional samples of four RASopathies: Costello syndrome (CS), Noonan syndrome (NS), cardiofaciocutaneous syndrome (CFC) and neurofibromatosis type 1 (NF1). METHODS From three-dimensional dense surface scans, we model the typical phenotypes of the four RASopathies as average 'facial signatures' and assess individual variation in terms of direction (what parts of the face are affected and in what ways) and severity of the facial effects. We also derive a metric of phenotypic agreement between the syndromes and a metric of differences in severity along similar phenotypes. RESULTS CFC shows a relatively consistent facial phenotype in terms of both direction and severity that is similar to CS and NS, consistent with the known difficulty in discriminating CFC from NS based on the face. CS shows a consistent directional phenotype that varies in severity. Although NF1 is highly variable, on average, it shows a similar phenotype to CS. CONCLUSIONS We established an approach that can be used in the future to quantify variations in facial phenotypes between and within clinical and molecular diagnoses to objectively define and support clinical nosologies.
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Affiliation(s)
- Harold Matthews
- Department of Human Genetics, KU Leuven, Leuven, Flemish Brabant, Belgium
- Medical Imaging Research Center, UZ Leuven, Leuven, Flemish Brabant, Belgium
- Facial Sciences Research Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Michiel Vanneste
- Department of Human Genetics, KU Leuven, Leuven, Flemish Brabant, Belgium
- Medical Imaging Research Center, UZ Leuven, Leuven, Flemish Brabant, Belgium
| | - Kaitlin Katsura
- Program in Craniofacial Biology, Departments of Orofacial Sciences and Pediatrics, and Institute for Human Genetics, University of California San Francisco, San Francisco, California, USA
| | - David Aponte
- Department of Cell Biology and Anatomy, University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Michael Patton
- Medical Genetics Unit, St George's University of London, London, UK
| | - Peter Hammond
- Department of Human Genetics, KU Leuven, Leuven, Flemish Brabant, Belgium
| | - Gareth Baynam
- Western Australian Register of Developmental Anomalies, King Edward Memorial Hospital, Perth, Western Australia, Australia
- Telethon Kids Institute and Division of Paediatrics, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Western Australia, Australia
- School of Earth and Planetary Sciences, Faculty of Science and Engineering, Curtin University, Perth, Western Australia, Australia
- Faculty of Medicine, Notre Dame University, Fremantle, Western Australia, Australia
| | - Richard Spritz
- Department of Paediatrics, University of Colorado Denver School of Medicine, Aurora, Colorado, USA
| | - Ophir D Klein
- Program in Craniofacial Biology, Departments of Orofacial Sciences and Pediatrics, and Institute for Human Genetics, University of California San Francisco, San Francisco, California, USA
| | - Benedikt Hallgrimsson
- Department of Cell Biology and Anatomy, University of Calgary Cumming School of Medicine, Calgary, Alberta, Canada
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Flemish Brabant, Belgium
| | - Peter Claes
- Department of Human Genetics, KU Leuven, Leuven, Flemish Brabant, Belgium
- Medical Imaging Research Center, UZ Leuven, Leuven, Flemish Brabant, Belgium
- Facial Sciences Research Group, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Electrical Engineering ESAT/PSI, KU Leuven, Leuven, Flemish Brabant, Belgium
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22
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Review on Facial-Recognition-Based Applications in Disease Diagnosis. Bioengineering (Basel) 2022; 9:bioengineering9070273. [PMID: 35877324 PMCID: PMC9311612 DOI: 10.3390/bioengineering9070273] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/13/2022] [Accepted: 06/20/2022] [Indexed: 01/19/2023] Open
Abstract
Diseases not only manifest as internal structural and functional abnormalities, but also have facial characteristics and appearance deformities. Specific facial phenotypes are potential diagnostic markers, especially for endocrine and metabolic syndromes, genetic disorders, facial neuromuscular diseases, etc. The technology of facial recognition (FR) has been developed for more than a half century, but research in automated identification applied in clinical medicine has exploded only in the last decade. Artificial-intelligence-based FR has been found to have superior performance in diagnosis of diseases. This interdisciplinary field is promising for the optimization of the screening and diagnosis process and assisting in clinical evaluation and decision-making. However, only a few instances have been translated to practical use, and there is need of an overview for integration and future perspectives. This review mainly focuses on the leading edge of technology and applications in varieties of disease, and discusses implications for further exploration.
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23
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Pallotta MM, Di Nardo M, Sarogni P, Krantz ID, Musio A. Disease-associated c-MYC downregulation in human disorders of transcriptional regulation. Hum Mol Genet 2022; 31:1599-1609. [PMID: 34849865 PMCID: PMC9122636 DOI: 10.1093/hmg/ddab348] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/24/2021] [Accepted: 11/24/2021] [Indexed: 11/12/2022] Open
Abstract
Cornelia de Lange syndrome (CdLS) is a rare multiorgan developmental disorder caused by pathogenic variants in cohesin genes. It is a genetically and clinically heterogeneous dominant (both autosomal and X-linked) rare disease. Increasing experimental evidence indicates that CdLS is caused by a combination of factors, such as gene expression dysregulation, accumulation of cellular damage and cellular aging, which collectively contribute to the CdLS phenotype. The CdLS phenotype overlaps with a number of related diagnoses such as KBG syndrome and Rubinstein-Taybi syndrome both caused by variants in chromatin-associated factors other than cohesin. The molecular basis underlying these overlapping phenotypes is not clearly defined. Here, we found that cells from individuals with CdLS and CdLS-related diagnoses are characterized by global transcription disturbance and share common dysregulated pathways. Intriguingly, c-MYC (subsequently referred to as MYC) is downregulated in all cell lines and represents a convergent hub lying at the center of dysregulated pathways. Subsequent treatment with estradiol restores MYC expression by modulating cohesin occupancy at its promoter region. In addition, MYC activation leads to modification in expression in hundreds of genes, which in turn reduce the oxidative stress level and genome instability. Together, these results show that MYC plays a pivotal role in the etiopathogenesis of CdLS and CdLS-related diagnoses and represents a potential therapeutic target for these conditions.
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Affiliation(s)
- Maria M Pallotta
- Institute for Genetic and Biomedical Research (IRGB), National Research Council (CNR), 56124 Pisa, Italy
| | - Maddalena Di Nardo
- Institute for Genetic and Biomedical Research (IRGB), National Research Council (CNR), 56124 Pisa, Italy
| | - Patrizia Sarogni
- Institute for Genetic and Biomedical Research (IRGB), National Research Council (CNR), 56124 Pisa, Italy
| | - Ian D Krantz
- Roberts Individualized Medical Genetics Center, Division of Human Genetics, The Department of Pediatrics, The Children's Hospital of Philadelphia, and the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Antonio Musio
- Institute for Genetic and Biomedical Research (IRGB), National Research Council (CNR), 56124 Pisa, Italy
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24
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Austin-Tse CA, Jobanputra V, Perry DL, Bick D, Taft RJ, Venner E, Gibbs RA, Young T, Barnett S, Belmont JW, Boczek N, Chowdhury S, Ellsworth KA, Guha S, Kulkarni S, Marcou C, Meng L, Murdock DR, Rehman AU, Spiteri E, Thomas-Wilson A, Kearney HM, Rehm HL. Best practices for the interpretation and reporting of clinical whole genome sequencing. NPJ Genom Med 2022; 7:27. [PMID: 35395838 PMCID: PMC8993917 DOI: 10.1038/s41525-022-00295-z] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 02/17/2022] [Indexed: 01/19/2023] Open
Abstract
Whole genome sequencing (WGS) shows promise as a first-tier diagnostic test for patients with rare genetic disorders. However, standards addressing the definition and deployment practice of a best-in-class test are lacking. To address these gaps, the Medical Genome Initiative, a consortium of leading health care and research organizations in the US and Canada, was formed to expand access to high quality clinical WGS by convening experts and publishing best practices. Here, we present best practice recommendations for the interpretation and reporting of clinical diagnostic WGS, including discussion of challenges and emerging approaches that will be critical to harness the full potential of this comprehensive test.
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Affiliation(s)
- Christina A Austin-Tse
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Vaidehi Jobanputra
- Molecular Diagnostics Laboratory, New York Genome Center, New York, NY, USA
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | | | - David Bick
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | | | - Eric Venner
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Ted Young
- Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada
| | - Sarah Barnett
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | | | - Nicole Boczek
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
- Center for Individualized Medicine, College of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Shimul Chowdhury
- Rady Children's Institute for Genomic Medicine, San Diego, CA, USA
| | | | - Saurav Guha
- Molecular Diagnostics Laboratory, New York Genome Center, New York, NY, USA
| | - Shashikant Kulkarni
- Baylor Genetics and Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Cherisse Marcou
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Linyan Meng
- Baylor Genetics and Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - David R Murdock
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Atteeq U Rehman
- Molecular Diagnostics Laboratory, New York Genome Center, New York, NY, USA
| | - Elizabeth Spiteri
- Department of Pathology, Stanford Medicine, Stanford University, Stanford, CA, USA
| | | | - Hutton M Kearney
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Heidi L Rehm
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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25
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Subclinical myocardial dysfunction is revealed by speckle tracking echocardiography in patients with Cornelia de Lange syndrome. Int J Cardiovasc Imaging 2022; 38:2291-2302. [PMID: 36434327 PMCID: PMC9700592 DOI: 10.1007/s10554-022-02612-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 03/30/2022] [Indexed: 12/14/2022]
Abstract
This study assesses a possible cardiac dysfunction in individuals with Cornelia de Lange syndrome (CdLS) without diagnosed congenital heart disease (CHD) and its association with other factors. Twenty patients and 20 controls were included in the study divided into three age-dependent groups (A: < 10 yrs, B: 10-20 yrs, C: > 20 yrs), and were evaluated using conventional echocardiography, tissue doppler imaging (TDI), two-dimensional speckle tracking and genetic and biochemical analyses. The left ventricular global longitudinal strain (GLS) was altered (< 15.9%) in 55% of patients, being pathological in the older group (A: 19.7 ± 6.6; B: -17.2 ± 4.7; C: -13.6 ± 2.9). The speckle tracking technique revealed a downward trend in the values of strain, strain rate and velocity, especially in the oldest group. Likewise, the ejection fraction (LVEF) and shortening fraction (LVFS) values, although preserved, also showed a decreased with age (p < 0.05). The analytical markers of cardiovascular risk and cardiac function showed no alterations. The molecular analyses revealed 16 individuals carrying pathogenic variants in NIPBL, two with variants in SMC1A, one with a variant in RAD21 and one with a HDAC8 variant. This is the first systematic approach that demonstrates that individuals with CdLS may present early cardiomyopathy, which can be detected by speckle tracking technique even before the appearance of clinical symptoms and the alteration of other echocardiographic or analytical parameters. For all these reasons, cardiological followup is suggested even in the absence of CHD, especially from adolescence onwards.
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26
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Su Z, Liang B, Shi F, Gelfond J, Šegalo S, Wang J, Jia P, Hao X. Deep learning-based facial image analysis in medical research: a systematic review protocol. BMJ Open 2021; 11:e047549. [PMID: 34764164 PMCID: PMC8587597 DOI: 10.1136/bmjopen-2020-047549] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 08/18/2021] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION Deep learning techniques are gaining momentum in medical research. Evidence shows that deep learning has advantages over humans in image identification and classification, such as facial image analysis in detecting people's medical conditions. While positive findings are available, little is known about the state-of-the-art of deep learning-based facial image analysis in the medical context. For the consideration of patients' welfare and the development of the practice, a timely understanding of the challenges and opportunities faced by research on deep-learning-based facial image analysis is needed. To address this gap, we aim to conduct a systematic review to identify the characteristics and effects of deep learning-based facial image analysis in medical research. Insights gained from this systematic review will provide a much-needed understanding of the characteristics, challenges, as well as opportunities in deep learning-based facial image analysis applied in the contexts of disease detection, diagnosis and prognosis. METHODS Databases including PubMed, PsycINFO, CINAHL, IEEEXplore and Scopus will be searched for relevant studies published in English in September, 2021. Titles, abstracts and full-text articles will be screened to identify eligible articles. A manual search of the reference lists of the included articles will also be conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was adopted to guide the systematic review process. Two reviewers will independently examine the citations and select studies for inclusion. Discrepancies will be resolved by group discussions till a consensus is reached. Data will be extracted based on the research objective and selection criteria adopted in this study. ETHICS AND DISSEMINATION As the study is a protocol for a systematic review, ethical approval is not required. The study findings will be disseminated via peer-reviewed publications and conference presentations. PROSPERO REGISTRATION NUMBER CRD42020196473.
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Affiliation(s)
- Zhaohui Su
- Center on Smart and Connected Health Technologies, Mays Cancer Center, School of Nursing, UT Health San Antonio, San Antonio, Texas, USA
| | - Bin Liang
- Department of Radiation Oncology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - J Gelfond
- Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, Texas, UK
| | - Sabina Šegalo
- Department of Microbiology, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Jing Wang
- College of Nursing, Florida State University, Tallahassee, Florida, USA
| | - Peng Jia
- Department of Land Surveying and Geo-Informatics, University of Twente, Enschede, Netherlands
- International Initiative on Spatial Lifecourse Epidemiology (ISLE), Enschede, UK
| | - Xiaoning Hao
- Division of Health Security Research, National Health Commission of the People's Republic of China, Beijing, Beijing, China
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27
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Latorre-Pellicer A, Ascaso Á, Lucia-Campos C, Gil-Salvador M, Arnedo M, Antoñanzas R, Ayerza-Casas A, Marcos-Alcalde I, Gómez-Puertas P, Ramos FJ, Pié J, Puisac B. Things are not always what they seem: From Cornelia de Lange to KBG phenotype in a girl with genetic variants in NIPBL and ANKRD11. Mol Genet Genomic Med 2021; 9:e1826. [PMID: 34617417 PMCID: PMC8606202 DOI: 10.1002/mgg3.1826] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/09/2021] [Accepted: 09/16/2021] [Indexed: 01/30/2023] Open
Affiliation(s)
- Ana Latorre-Pellicer
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, Zaragoza, Spain
| | - Ángela Ascaso
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, Zaragoza, Spain
| | - Cristina Lucia-Campos
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, Zaragoza, Spain
| | - Marta Gil-Salvador
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, Zaragoza, Spain
| | - María Arnedo
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, Zaragoza, Spain
| | - Rebeca Antoñanzas
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, Zaragoza, Spain
| | - Ariadna Ayerza-Casas
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, Zaragoza, Spain.,Unit of Paediatric Cardiology, Service of Paediatrics, Hospital Universitario Miguel Servet, Zaragoza, Spain
| | - Iñigo Marcos-Alcalde
- Molecular Modeling Group, Centro de Biología Molecular Severo Ochoa, CBMSO (CSIC-UAM), Madrid, Spain.,Biosciences Research Institute, School of Experimental Sciences, Universidad Francisco de Vitoria, Madrid, Spain
| | - Paulino Gómez-Puertas
- Biosciences Research Institute, School of Experimental Sciences, Universidad Francisco de Vitoria, Madrid, Spain
| | - Feliciano J Ramos
- Unit of Clinical Genetics, Service of Paediatrics, Hospital Clínico Universitario Lozano Blesa, Department of Paediatrics, School of Medicine, Universidad de Zaragoza, Zaragoza, Spain
| | - Juan Pié
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, Zaragoza, Spain
| | - Beatriz Puisac
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, Zaragoza, Spain
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28
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Parenti I, Mallozzi MB, Hüning I, Gervasini C, Kuechler A, Agolini E, Albrecht B, Baquero-Montoya C, Bohring A, Bramswig NC, Busche A, Dalski A, Guo Y, Hanker B, Hellenbroich Y, Horn D, Innes AM, Leoni C, Li YR, Lynch SA, Mariani M, Medne L, Mikat B, Milani D, Onesimo R, Ortiz-Gonzalez X, Prott EC, Reutter H, Rossier E, Selicorni A, Wieacker P, Wilkens A, Wieczorek D, Zackai EH, Zampino G, Zirn B, Hakonarson H, Deardorff MA, Gillessen-Kaesbach G, Kaiser FJ. ANKRD11 variants: KBG syndrome and beyond. Clin Genet 2021; 100:187-200. [PMID: 33955014 DOI: 10.1111/cge.13977] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/27/2021] [Accepted: 04/30/2021] [Indexed: 12/18/2022]
Abstract
Mutations affecting the transcriptional regulator Ankyrin Repeat Domain 11 (ANKRD11) are mainly associated with the multisystem developmental disorder known as KBG syndrome, but have also been identified in individuals with Cornelia de Lange syndrome (CdLS) and other developmental disorders caused by variants affecting different chromatin regulators. The extensive functional overlap of these proteins results in shared phenotypical features, which complicate the assessment of the clinical diagnosis. Additionally, re-evaluation of individuals at a later age occasionally reveals that the initial phenotype has evolved toward clinical features more reminiscent of a developmental disorder different from the one that was initially diagnosed. For this reason, variants in ANKRD11 can be ascribed to a broader class of disorders that fall within the category of the so-called chromatinopathies. In this work, we report on the clinical characterization of 23 individuals with variants in ANKRD11. The subjects present primarily with developmental delay, intellectual disability and dysmorphic features, and all but two received an initial clinical diagnosis of either KBG syndrome or CdLS. The number and the severity of the clinical signs are overlapping but variable and result in a broad spectrum of phenotypes, which could be partially accounted for by the presence of additional molecular diagnoses and distinct pathogenic mechanisms.
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Affiliation(s)
- Ilaria Parenti
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
| | - Mark B Mallozzi
- Department of Internal Medicine, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, USA
| | - Irina Hüning
- Institut für Humangenetik, Universität zu Lübeck, Lübeck, Germany
| | - Cristina Gervasini
- Genetica Medica, Dipartimento di Scienze della Salute, Università degli Studi di Milano, Milan, Italy
| | - Alma Kuechler
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
| | - Emanuele Agolini
- Laboratory of Medical Genetics, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy
| | - Beate Albrecht
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
| | - Carolina Baquero-Montoya
- Department of Pediatrics, Hospital Pablo Tobón Uribe, Medellín, Colombia
- Genetics Unit, Sura Ayudas Diagnosticas, Medellín, Colombia
| | - Axel Bohring
- Institut für Humangenetik, Westfälische Wilhelms-Universität, Münster, Germany
| | - Nuria C Bramswig
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
| | - Andreas Busche
- Institut für Humangenetik, Westfälische Wilhelms-Universität, Münster, Germany
| | - Andreas Dalski
- Institut für Humangenetik, Universität zu Lübeck, Lübeck, Germany
| | - Yiran Guo
- Center for Applied Genomics and Center for Data Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Britta Hanker
- Institut für Humangenetik, Universität zu Lübeck, Lübeck, Germany
| | | | - Denise Horn
- Institute of Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - A Micheil Innes
- Department of Medical Genetics and Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Chiara Leoni
- Center for Rare Diseases and Birth Defects, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Yun R Li
- Center for Applied Genomics and Center for Data Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Medical Scientist Training Program, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Sally Ann Lynch
- Department of Clinical Genetics, Children's Health Ireland (CHI) at Crumlin, Dublin, Ireland
| | - Milena Mariani
- Centro Fondazione Mariani per il Bambino Fragile ASST-Lariana Sant'Anna Hospital, Department of Pediatrics, San Fermo della Battaglia (Como), Italy
| | - Livija Medne
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Barbara Mikat
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
| | - Donatella Milani
- Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico Milano, Milan, Italy
| | - Roberta Onesimo
- Center for Rare Diseases and Birth Defects, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Xilma Ortiz-Gonzalez
- Department of Pediatrics, Division of Neurology, Epilepsy Neurogenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Eva Christina Prott
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
- Institut für Praenatale Medizin & Humangenetik, Wuppertal, Germany
| | - Heiko Reutter
- Institute of Human Genetics, University Hospital of Bonn, Bonn, Germany
- Department of Neonatology and Pediatric Intensive Care, University Hospital of Bonn, Bonn, Germany
| | - Eva Rossier
- Institut für Medizinische Genetik und Angewandte Genomik, Universität Tübingen, Tübingen, Germany
- Genetikum Stuttgart, Genetic Counselling and Diagnostics, Stuttgart, Germany
| | - Angelo Selicorni
- Centro Fondazione Mariani per il Bambino Fragile ASST-Lariana Sant'Anna Hospital, Department of Pediatrics, San Fermo della Battaglia (Como), Italy
| | - Peter Wieacker
- Institut für Humangenetik, Westfälische Wilhelms-Universität, Münster, Germany
| | - Alisha Wilkens
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Dagmar Wieczorek
- Institute of Human Genetics, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Elaine H Zackai
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Giuseppe Zampino
- Center for Rare Diseases and Birth Defects, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Birgit Zirn
- Genetikum Stuttgart, Genetic Counselling and Diagnostics, Stuttgart, Germany
| | - Hakon Hakonarson
- Center for Applied Genomics and Center for Data Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Matthew A Deardorff
- Department of Pathology and Laboratory Medicine and Pediatrics, Children's Hospital Los Angeles, Los Angeles, California, USA
- Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | | | - Frank J Kaiser
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
- Essener Zentrum für Seltene Erkrankungen (EZSE), Universitätsmedizin Essen, Essen, Germany
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29
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Latorre-Pellicer A, Gil-Salvador M, Parenti I, Lucia-Campos C, Trujillano L, Marcos-Alcalde I, Arnedo M, Ascaso Á, Ayerza-Casas A, Antoñanzas-Pérez R, Gervasini C, Piccione M, Mariani M, Weber A, Kanber D, Kuechler A, Munteanu M, Khuller K, Bueno-Lozano G, Puisac B, Gómez-Puertas P, Selicorni A, Kaiser FJ, Ramos FJ, Pié J. Clinical relevance of postzygotic mosaicism in Cornelia de Lange syndrome and purifying selection of NIPBL variants in blood. Sci Rep 2021; 11:15459. [PMID: 34326454 PMCID: PMC8322329 DOI: 10.1038/s41598-021-94958-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 07/19/2021] [Indexed: 11/09/2022] Open
Abstract
Postzygotic mosaicism (PZM) in NIPBL is a strong source of causality for Cornelia de Lange syndrome (CdLS) that can have major clinical implications. Here, we further delineate the role of somatic mosaicism in CdLS by describing a series of 11 unreported patients with mosaic disease-causing variants in NIPBL and performing a retrospective cohort study from a Spanish CdLS diagnostic center. By reviewing the literature and combining our findings with previously published data, we demonstrate a negative selection against somatic deleterious NIPBL variants in blood. Furthermore, the analysis of all reported cases indicates an unusual high prevalence of mosaicism in CdLS, occurring in 13.1% of patients with a positive molecular diagnosis. It is worth noting that most of the affected individuals with mosaicism have a clinical phenotype at least as severe as those with constitutive pathogenic variants. However, the type of genetic change does not vary between germline and somatic events and, even in the presence of mosaicism, missense substitutions are located preferentially within the HEAT repeat domain of NIPBL. In conclusion, the high prevalence of mosaicism in CdLS as well as the disparity in tissue distribution provide a novel orientation for the clinical management and genetic counselling of families.
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Affiliation(s)
- Ana Latorre-Pellicer
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, CIBERER-GCV02 and IIS-Aragon, 50009, Zaragoza, Spain
| | - Marta Gil-Salvador
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, CIBERER-GCV02 and IIS-Aragon, 50009, Zaragoza, Spain
| | - Ilaria Parenti
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
| | - Cristina Lucia-Campos
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, CIBERER-GCV02 and IIS-Aragon, 50009, Zaragoza, Spain
| | - Laura Trujillano
- Unit of Clinical Genetics, Service of Paediatrics, Hospital Clínico Universitario Lozano Blesa, Department of Paediatrics, School of Medicine, Universidad de Zaragoza, CIBERER-GCV02 and IIS-Aragon, 50009, Zaragoza, Spain
| | - Iñigo Marcos-Alcalde
- Molecular Modelling Group, Centro de Biología Molecular Severo Ochoa, CBMSO (CSIC-UAM), 28049, Madrid, Spain
- Biosciences Research Institute, School of Experimental Sciences, Universidad Francisco de Vitoria, 28223, Pozuelo de Alarcón, Madrid, Spain
| | - María Arnedo
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, CIBERER-GCV02 and IIS-Aragon, 50009, Zaragoza, Spain
| | - Ángela Ascaso
- Unit of Clinical Genetics, Service of Paediatrics, Hospital Clínico Universitario Lozano Blesa, Department of Paediatrics, School of Medicine, Universidad de Zaragoza, CIBERER-GCV02 and IIS-Aragon, 50009, Zaragoza, Spain
| | - Ariadna Ayerza-Casas
- Unit of Paediatric Cardiology, Service of Paediatrics, Hospital Universitario Miguel Servet, 50009, Zaragoza, Spain
| | - Rebeca Antoñanzas-Pérez
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, CIBERER-GCV02 and IIS-Aragon, 50009, Zaragoza, Spain
| | - Cristina Gervasini
- Genetica Medica, Dipartimento di Scienze della Salute, Università degli Studi di Milano, Milano, Italy
| | - Maria Piccione
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, Palermo, Italy
| | - Milena Mariani
- Centro Fondazione Mariani per il Bambino Fragile, Department of Pediatrics, ASST-Lariana Sant'Anna Hospital, San Fermo della Battaglia (Como), Italy
| | - Axel Weber
- Institute of Human Genetics, Justus-Liebig-University, Giessen, Germany
| | - Deniz Kanber
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
| | - Alma Kuechler
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
| | - Martin Munteanu
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
| | - Katharina Khuller
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
| | - Gloria Bueno-Lozano
- Unit of Clinical Genetics, Service of Paediatrics, Hospital Clínico Universitario Lozano Blesa, Department of Paediatrics, School of Medicine, Universidad de Zaragoza, CIBERER-GCV02 and IIS-Aragon, 50009, Zaragoza, Spain
| | - Beatriz Puisac
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, CIBERER-GCV02 and IIS-Aragon, 50009, Zaragoza, Spain
| | - Paulino Gómez-Puertas
- Molecular Modelling Group, Centro de Biología Molecular Severo Ochoa, CBMSO (CSIC-UAM), 28049, Madrid, Spain
| | - Angelo Selicorni
- Centro Fondazione Mariani per il Bambino Fragile, Department of Pediatrics, ASST-Lariana Sant'Anna Hospital, San Fermo della Battaglia (Como), Italy
| | - Frank J Kaiser
- Institut für Humangenetik, Universitätsklinikum Essen, Universität Duisburg-Essen, Essen, Germany
- Essener Zentrum für Seltene Erkrankungen (EZSE), Universitätsmedizin Essen, Universitätsklinikum Essen, Essen, Germany
| | - Feliciano J Ramos
- Unit of Clinical Genetics, Service of Paediatrics, Hospital Clínico Universitario Lozano Blesa, Department of Paediatrics, School of Medicine, Universidad de Zaragoza, CIBERER-GCV02 and IIS-Aragon, 50009, Zaragoza, Spain.
| | - Juan Pié
- Unit of Clinical Genetics and Functional Genomics, Department of Pharmacology-Physiology, School of Medicine, Universidad de Zaragoza, CIBERER-GCV02 and IIS-Aragon, 50009, Zaragoza, Spain.
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Selicorni A, Mariani M, Lettieri A, Massa V. Cornelia de Lange Syndrome: From a Disease to a Broader Spectrum. Genes (Basel) 2021; 12:1075. [PMID: 34356091 PMCID: PMC8307173 DOI: 10.3390/genes12071075] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 07/06/2021] [Accepted: 07/12/2021] [Indexed: 12/23/2022] Open
Abstract
Cornelia de Lange syndrome (CdLS) is a genetic disease that exemplifies the evolution of knowledge in the field of rare genetic disorders. Originally described as a unique pattern of major and minor anomalies, over time this syndrome has been shown to be characterized by a significant variability of clinical expression. By increasing the number of patients described, knowledge of the natural history of the condition has been enriched with the demonstration of the relative frequency of various potential comorbidities. Since 2006, the discovery of CdLS's molecular basis has shown an equally vast genetic heterogeneity linked to the presence of variants in genes encoding for the cohesin complex pathway. The most recent clinical-genetic data led to the classification of the "original syndrome" into a "clinical spectrum" that foresees the presence of classic patients, of non-classic forms, and of conditions that show a modest phenotypic overlapping with the original disease. Finally, the knowledge of the molecular basis of the disease has allowed the development of basic research projects that could lay the foundations for the development of possible innovative pharmacological treatments.
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Affiliation(s)
- Angelo Selicorni
- Mariani Foundation Center for Fragile Child, Pediatric Unit ASST Lariana, 22100 Como, Italy;
| | - Milena Mariani
- Mariani Foundation Center for Fragile Child, Pediatric Unit ASST Lariana, 22100 Como, Italy;
| | - Antonella Lettieri
- Department of Health Sciences, Università degli Studi di Milano, 20142 Milano, Italy; (A.L.); (V.M.)
- CRC Aldo Ravelli for Neurotechnology and Experimental Brain Therapeutics, Department of Health Sciences, Università degli Studi di Milano, 20142 Milano, Italy
| | - Valentina Massa
- Department of Health Sciences, Università degli Studi di Milano, 20142 Milano, Italy; (A.L.); (V.M.)
- CRC Aldo Ravelli for Neurotechnology and Experimental Brain Therapeutics, Department of Health Sciences, Università degli Studi di Milano, 20142 Milano, Italy
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31
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Mak BC, Sanchez Russo R, Gambello MJ, Fleischer N, Black ED, Leslie E, Murphy MM, Mulle JG. Craniofacial features of 3q29 deletion syndrome: Application of next-generation phenotyping technology. Am J Med Genet A 2021; 185:2094-2101. [PMID: 33938623 PMCID: PMC8250870 DOI: 10.1002/ajmg.a.62227] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 03/23/2021] [Accepted: 03/27/2021] [Indexed: 12/21/2022]
Abstract
3q29 deletion syndrome (3q29del) is a recurrent deletion syndrome associated with neuropsychiatric disorders and congenital anomalies. Dysmorphic facial features have been described but not systematically characterized. This study aims to detail the 3q29del craniofacial phenotype and use a machine learning approach to categorize individuals with 3q29del through analysis of 2D photos. Detailed dysmorphology exam and 2D facial photos were ascertained from 31 individuals with 3q29del. Photos were used to train the next-generation phenotyping algorithm DeepGestalt (Face2Gene by FDNA, Inc, Boston, MA) to distinguish 3q29del cases from controls and all other recognized syndromes. Area under the curve of receiver operating characteristic curves (AUC-ROC) was used to determine the capacity of Face2Gene to identify 3q29del cases against controls. In this cohort, the most common observed craniofacial features were prominent forehead (48.4%), prominent nose tip (35.5%), and thin upper lip vermillion (25.8%). The FDNA technology showed an ability to distinguish cases from controls with an AUC-ROC value of 0.873 (p = 0.006) and led to the inclusion of 3q29del as one of the supported syndromes. This study found a recognizable facial pattern in 3q29del, as observed by trained clinical geneticists and next-generation phenotyping technology. These results expand the potential application of automated technology such as FDNA in identifying rare genetic syndromes, even when facial dysmorphology is subtle.
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Affiliation(s)
- Bryan C Mak
- Department of Human Genetics, Emory University School, of Medicine, Atlanta, Georgia, USA
| | - Rossana Sanchez Russo
- Department of Human Genetics, Emory University School, of Medicine, Atlanta, Georgia, USA
| | - Michael J Gambello
- Department of Human Genetics, Emory University School, of Medicine, Atlanta, Georgia, USA.,Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA
| | | | - Emily D Black
- Department of Human Genetics, Emory University School, of Medicine, Atlanta, Georgia, USA
| | - Elizabeth Leslie
- Department of Human Genetics, Emory University School, of Medicine, Atlanta, Georgia, USA
| | - Melissa M Murphy
- Department of Human Genetics, Emory University School, of Medicine, Atlanta, Georgia, USA
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- Department of Human Genetics, Emory University School, of Medicine, Atlanta, Georgia, USA
| | - Jennifer Gladys Mulle
- Department of Human Genetics, Emory University School, of Medicine, Atlanta, Georgia, USA.,Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
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Targeted Gene Sequencing, Bone Health, and Body Composition in Cornelia de Lange Syndrome. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11020710] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The aim of this study was to evaluate bone health and body composition by dual-energy X-ray absorptiometry (DXA) in individuals with Cornelia de Lange Syndrome (CdLS). Overall, nine individuals with CdLS (five females, all Caucasian, aged 5–38 years) were assessed. Total body less head (TBLH) and lumbar spine (LS) scans were performed, and bone serum biomarkers were determined. Molecular analyses were carried out and clinical scores and skeletal features were assessed. Based on deep sequencing of a custom target gene panel, it was discovered that eight of the nine CdLS patients had potentially causative genetic variants in NIPBL. Fat and lean mass indices (FMI and LMI) were 3.4–11.1 and 8.4–17.0 kg/m2, respectively. For TBLH areal bone mineral density (aBMD), after adjusting for height for age Z-score of children and adolescents, two individuals (an adolescent and an adult) had low BMD (aBMD Z-scores less than –2.0 SD). Calcium, phosphorus, 25-OH-vitamin D, parathyroid hormone, and alkaline phosphatase levels were 2.08–2.49 nmol/L, 2.10–3.75 nmol/L, 39.94–78.37 nmol/L, 23.4–80.3 pg/mL, and 43–203 IU/L, respectively. Individuals with CdLS might have normal adiposity and low levels of lean mass measured with DXA. Bone health in this population seems to be less of a concern during childhood and adolescence. However, they might be at risk for impaired bone health due to low aBMD in adulthood.
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Pantel JT, Hajjir N, Danyel M, Elsner J, Abad-Perez AT, Hansen P, Mundlos S, Spielmann M, Horn D, Ott CE, Mensah MA. Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study. J Med Internet Res 2020; 22:e19263. [PMID: 33090109 PMCID: PMC7644377 DOI: 10.2196/19263] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/26/2020] [Accepted: 07/26/2020] [Indexed: 12/11/2022] Open
Abstract
Background Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. Objective The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. Methods Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. Results We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). Conclusions DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.
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Affiliation(s)
- Jean Tori Pantel
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Nurulhuda Hajjir
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Klinik für Pädiatrie mit Schwerpunkt Gastroenterologie, Nephrologie und Stoffwechselmedizin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Magdalena Danyel
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Berlin Center for Rare Diseases, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Jonas Elsner
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Angela Teresa Abad-Perez
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Peter Hansen
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States
| | - Stefan Mundlos
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,RG Development & Disease, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Malte Spielmann
- RG Development & Disease, Max Planck Institute for Molecular Genetics, Berlin, Germany.,Institute of Human Genetics, University of Lübeck, Lübeck, Germany
| | - Denise Horn
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Claus-Eric Ott
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Martin Atta Mensah
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
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Gonzalez Garcia A, Malone J, Li H. A novel mosaic variant on SMC1A reported in buccal mucosa cells, albeit not in blood, of a patient with Cornelia de Lange-like presentation. Cold Spring Harb Mol Case Stud 2020; 6:mcs.a005322. [PMID: 32532882 PMCID: PMC7304356 DOI: 10.1101/mcs.a005322] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 04/14/2020] [Indexed: 12/31/2022] Open
Abstract
Mosaicism in Cornelia de Lange syndrome (CdLS) has been reported in clinically diagnosed CdLS patients with negative molecular testing using blood as the specimen, particularly in the NIPBL gene. Here we report a novel mosaic variant in SMC1A identified in the buccal swab DNA of a patient with a mild CdLS phenotype. Our patient presented with global developmental delay, dysmorphic features, microcephaly, and short stature, with no limb defect. Face2Gene, a digital tool that analyzes facial morphology, demonstrated a 97% match between our patient and the CdLS gestalt. An initial next-generation sequencing (NGS)-based CdLS panel test, including NIPBL, HDAC8, RAD21, SMC1A, and SMC3, completed using DNA isolated from leukocytes, was negative, and subsequent trio exome sequencing was nondiagnostic. The exome identified biallelic variants of uncertain significance in a candidate gene, NSMCE2. In the pursuit of a molecular diagnosis, a second NGS-based CdLS panel test was ordered on a buccal swab specimen and a novel variant, c.793_795delGAG (p.Glu265del) in SMC1A, was detected at 60% mosaicism. Retrospective analysis of the former panel and exome data revealed the SMC1A variant at 4% and 2%, respectively, both far below standard reporting thresholds. Given that mosaicism has been frequently reported in CdLS, we suggest selecting a different tissue for testing in clinically suspected CdLS cases, even after negative molecular results via blood specimen.
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Affiliation(s)
- Aixa Gonzalez Garcia
- Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322, USA
| | - Julia Malone
- Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322, USA
| | - Hong Li
- Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322, USA.,Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia 30322, USA
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Tripon F, Bogliș A, Micheu C, Streață I, Bănescu C. Pitt-Hopkins Syndrome: Clinical and Molecular Findings of a 5-Year-Old Patient. Genes (Basel) 2020; 11:genes11060596. [PMID: 32481733 PMCID: PMC7349262 DOI: 10.3390/genes11060596] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 05/20/2020] [Accepted: 05/27/2020] [Indexed: 11/23/2022] Open
Abstract
Pitt Hopkins syndrome (PTHS) is a very rare condition and until now, approximately 500 patients were reported worldwide, of which not all are genetically confirmed. Usually, individuals with variants affecting exons 1 to 5 in the TCF4 gene associate mild intellectual disability (ID), between exons 5 to 8, moderate to severe ID and sometimes have some of the characteristics of PTHS, and variants starting from exon 9 to exon 20 associate a typical PTHS phenotype. In this report, we describe the clinical and molecular findings of a Caucasian boy diagnosed with PTHS. PTHS phenotype is described including craniofacial dysmorphism with brachycephaly, biparietal narrowing, wide nasal bridge, thin and linear lateral eyebrows, palpebral edema, full cheeks, short philtrum, wide mouth with prominent and everted lips, prominent Cupid’s bow, downturned corners of the mouth, microdontia and also the clinical management of the patient. The previously and the current diagnosis scores are described in this report and also the challenges and their benefits for an accurate and early diagnosis.
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Affiliation(s)
- Florin Tripon
- Laboratory of Medical Genetics, Emergency Clinical County Hospital Târgu Mureș, 540136 Târgu Mureș, Romania; (F.T.); (C.B.)
- Department of Genetics, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania
- Laboratory of Molecular Biology/Genetics, Center for Advanced Medical and Pharmaceutical Research, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania
| | - Alina Bogliș
- Laboratory of Medical Genetics, Emergency Clinical County Hospital Târgu Mureș, 540136 Târgu Mureș, Romania; (F.T.); (C.B.)
- Department of Genetics, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania
- Laboratory of Molecular Biology/Genetics, Center for Advanced Medical and Pharmaceutical Research, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania
- Correspondence: ; Tel.: +40-265-21-55-51
| | - Cristian Micheu
- Child Neurology Psychiatry Clinic, Clinical County Hospital Mureș, 540072 Târgu Mureş, Romania;
| | - Ioana Streață
- Regional Center for Medical Genetics Dolj—Clinical County Emergency Hospital Craiova, University of Medicine and Pharmacy Craiova, 200642 Craiova, Romania;
| | - Claudia Bănescu
- Laboratory of Medical Genetics, Emergency Clinical County Hospital Târgu Mureș, 540136 Târgu Mureș, Romania; (F.T.); (C.B.)
- Department of Genetics, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania
- Laboratory of Molecular Biology/Genetics, Center for Advanced Medical and Pharmaceutical Research, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș, 540142 Târgu Mureș, Romania
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