The road from mutation to next generation phenotyping: contribution of deep learning technology (Face2Gene) to diagnosis neurofibromatosis type 1

Year 2022, Volume: 8 Issue: 2, 145 - 154, 04.03.2022

Muhsin Elmas Başak Göğüş

https://doi.org/10.18621/eurj.894631

Cited By: 1

Abstract

Objectives: Genetics is one of the fastest growing medical fields in the last 10 years. While new analysis methods such as Next Generation Sequencing have been developed, the use of artificial intelligence like Face2Gene in this field has also been developed. The aim of this study is to evaluate the clinical, genetic and dysmorphic findings of Neurofibromatosis type 1 (NF1) patients, a disease of the RASopathy group. At the same time, another aim of this study is to evaluate and compare with other RASopathies diseases the success of Face2Gene application which is one of the NGP technologies, in this group of diseases.

Methods: This study is a retrospective archive scan. 14 patients from 3 different patient groups were selected for the study. Face2Gene analysis was performed for these groups. Detailed clinical, genetic and dysmorphic findings of NF1 patients were also examined.

Results: As a result of the genetic analysis of NF1 patients, one patient had novel mutation. The most detected mutation type is nonsense mutation (42,8%). The most common finding in magnetic resonance imaging was hamartoma (29%). Face2Gene suggested that NF1 in top-3 for 10 of 14 NF1 patients. Additionally, at the comparison of NF1 patients and non-NF1 RASopathies patients resulted as AUC was 0.749 and p value was 0.134.

Conclusion: Considering the developments in technology in the last 10 years, it is thought that artificial intelligence applications such as Face2Gene will be used a lot in the routines of medical doctors in the next 10 years.

Keywords

Neurofibromatosis 1, Cafe-au-Lait Spots, Deep Learning, Artificial Intelligence

References

• 1. Basel D. 25 - Dysmorphology. In: Kliegman RM, Lye PS, Bordini BJ, Toth H, Basel D, editors. Nelson Pediatr. Symptom-Based Diagnosis. Elsevier; 2018. p. 393-410.e1.
• 2. Starbuck J. On the antiquity of trisomy 21: moving towards a quantitative diagnosis of Down syndrome in historic material culture. J Contemp Anthropol 2011;2:18-44.
• 3. Ruggieri M, Praticò AD, Caltabiano R, Polizzi A. Early history of the different forms of neurofibromatosis from ancient Egypt to the British Empire and beyond: first descriptions, medical curiosities, misconceptions, landmarks, and the persons behind the syndromes. Am J Med Genet A 2018;176:515-50.
• 4. Hart TC, Hart PS. Genetic studies of craniofacial anomalies: clinical implications and applications. Orthod Craniofac Res 2009;12:212-20.
• 5. Fischer C, Schweigert S, Spreckelsen C, Vogel F. Programs, databases, and expert systems for human geneticists--a survey. Hum Genet 1996;97:129-37.
• 6. Gurovich Y, Hanani Y, Bar O, Fleischer N, Gelbman D, Basel-salmon L, et al. DeepGestalt - identifying rare genetic syndromes using deep learning. arXiv 2017:1801-07637v1
• 7. Gurovich Y, Hanani Y, Bar O, Nadav G, Fleischer N, Gelbman D, et al. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med 2019;25:60-4.
• 8. Mishima H, Suzuki H, Doi M, Miyazaki M, Watanabe S. Evaluation of Face2Gene using facial images of patients with congenital dysmorphic syndromes recruited in Japan. J Hum Genet 2019;64:789-94.
• 9. Tidyman WE, Rauen KA. Pathogenetics of the RASopathies. Hum Mol Genet 2016;25:R123-32.
• 10. Evans DG, Howard E, Giblin C, Clancy T, Spencer H, Huson SM, et al. Birth incidence and prevalence of tumor-prone syndromes: estimates from a UK family genetic register service. Am J Med Genet 2010;152A:327-32.
• 11. Valero MC, Pascual-Castroviejo I, Velasco E, Moreno F, Hernández-Chico C. Identification of de novo deletions at the NF1 gene: no preferential paternal origin and phenotypic analysis of patients. Hum Genet 1997;99:720-6.
• 12. Boyd KP, Korf BR, Theos A. Neurofibromatosis type 1. J Am Acad Dermatol 2009;61:1-14.
• 13. Williams VC, Lucas J, Babcock MA, Gutmann DH, Bruce B, Maria BL. Neurofibromatosis type 1 revisited. Pediatrics 2009;123:124-33.
• 14. Valero MC, Martín Y, Hernández-Imaz E, Hernández AM, Meleán G, Valero AM, et al. A highly sensitive genetic protocol to detect NF1 mutations. J Mol Diagnostics 2011;13:113-22.
• 15. Pantel JT, Zhao M, Mensah MA, Hajjir N, Hsieh T-C, Hanani Y, et al. Advances in computer-assisted syndrome recognition by the example of inborn errors of metabolism. J Inherit Metab Dis 2018;41:533-9.
• 16. Allanson JE, Biesecker LG, Carey JC, Hennekam R. Elements of morphology: introduction. Am J Med Genet A 2009;149A:2-5.
• 17. Retterer K, Juusola J, Cho MT, Vitazka P, Millan F, Gibellini F, et al. Clinical application of whole-exome sequencing across clinical indications. Genet Med 2016;18:696-704.
• 18. Robert L. Nussbaum MDFF, McInnes RR, Willard HF. Thompson & Thompson Genetics in Medicine. Elsevier Health Sciences. 2015.
• 19. Zhang J, Tong H, Fu X, Zhang Y, Liu J, Cheng R, et al. Molecular characterization of NF1 and neurofibromatosis type 1 genotype-phenotype correlations in a Chinese population. Sci Rep 2015;5:1-5.
• 20. Thomson SAM, Fishbein L, Wallace MR. NFI mutations and molecular testing. J Child Neurol 2002;17:555-61.
• 21. Pros E, Gómez C, Martín T, Fábregas P, Serra E, Lázaro C. Nature and mRNA effect of 282 different NF1 point mutations: focus on splicing alterations. Hum Mutat 2008;29:E173-93.
• 22. Calì F, Chiavetta V, Ruggeri G, Piccione M, Selicorni A, Palazzo D, et al. Mutation spectrum of NF1 gene in Italian patients with neurofibromatosis type 1 using Ion Torrent PGMTM platform. Eur J Med Genet 2017;60:93-9.
• 23. Jeong SY, Park SJ, Kim HJ. The spectrum of NF1 mutations in Korean patients with neurofibromatosis type 1. J Korean Med Sci 2006;21:107-12.
• 24. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015;17:405-24.
• 25. Koczkowska M, Chen Y, Callens T, Gomes A, Sharp A, Johnson S, et al. Genotype-phenotype correlation in NF1: evidence for a more severe phenotype associated with missense mutations affecting NF1 codons 844–848. Am J Hum Genet 2018;102:69-87.
• 26. Evans DG, Bowers N, Burkitt-Wright E, Miles E, Garg S, Scott-Kitching V, et al. Comprehensive RNA analysis of the NF1 gene in classically affected NF1 affected individuals meeting NIH criteria has high sensitivity and mutation negative testing is reassuring in isolated cases with pigmentary features only. EBioMedicine 2016;7:212-20.
• 27. Rosenbaum T, Engelbrecht V, Krölls W, Van Dorsten FA, Hoehn-Berlage M, Lenard HG. MRI abnormalities in neurofibromatosis type 1 (NF1): a study of men and mice. Brain Dev 1999;21:268-73.
• 28. Sabbagh A, Pasmant E, Laurendeau I, Parfait B, Barbarot S, Guillot B, et al. Unravelling the genetic basis of variable clinical expression in neurofibromatosis 1. Hum Mol Genet 2009;18:2768-78.
• 29. Sánchez Marco SB, López Pisón J, Calvo Escribano C, González Viejo I, Miramar Gallart MD, Samper Villagrasa P. Neurological manifestations of neurofibromatosis type 1: our experience. Neurologia (Engl Ed) 2019;S0213-4853(19)30077-5.
• 30. Keleşoğlu KS, Keskin S, Sivri M, Erdoğan H, Nayman A, Koplay M. [Neurofibromatosis type 1: Cranial MRI findings]. Genel Tip Derg 2014;24:150-4. [Article in Turkish]