Generic placeholder image

Reviews on Recent Clinical Trials


ISSN (Print): 1574-8871
ISSN (Online): 1876-1038

Review Article

New Strategies for Clinical Trials in Autism Spectrum Disorder

Author(s): Rini Pauly, Catherine A. Ziats, Ludovico Abenavoli, Charles E. Schwartz and Luigi Boccuto*

Volume 16, Issue 2, 2021

Published on: 20 November, 2020

Page: [131 - 137] Pages: 7

DOI: 10.2174/1574887115666201120093634

Price: $65


Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that poses several challenges in terms of clinical diagnosis and investigation of molecular etiology. The lack of knowledge on the pathogenic mechanisms underlying ASD has hampered the clinical trials that so far have tried to target ASD behavioral symptoms. In order to improve our understanding of the molecular abnormalities associated with ASD, a deeper and more extensive genetic profiling of targeted individuals with ASD was needed.

Methods: The recent availability of new and more powerful sequencing technologies (third-generation sequencing) has allowed to develop novel strategies for the characterization of comprehensive genetic profiles of individuals with ASD. In particular, this review will describe integrated approaches based on the combination of various omics technologies that will lead to a better stratification of targeted cohorts for the design of clinical trials in ASD.

Results: In order to analyze the big data collected by assays such as the whole genome, epigenome, transcriptome, and proteome, it is critical to develop an efficient computational infrastructure. Machine learning models are instrumental to identify non-linear relationships between the omics technologies and, therefore, establish a functional informative network among the different data sources.

Conclusion: The potential advantage provided by these new integrated omics-based strategies is better characterization of the genetic background of ASD cohorts, to identify novel molecular targets for drug development, and ultimately offer a more personalized approach in the design of clinical trials for ASD.

Keywords: Autism Spectrum Disorder (ASD), Clinical trials, Whole-Genome Sequencing (WGS), RNA sequencing, Genome-wide methylation/Epi-signatures, Artificial Intelligence (AI), multi-omic analysis.

Graphical Abstract
Pauly R, Schwartz CE. The Future of Clinical Diagnosis: Moving Functional Genomics Approaches to the Bedside. Adv Mol Pathol 2019; 2(1): 13-9.
Weiner DJ, Wigdor EM, Ripke S, et al. iPSYCH-Broad Autism Group; Psychiatric Genomics Consortium Autism Group. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat Genet 2017; 49(7): 978-85.
[] [PMID: 28504703]
Pizzo L, Jensen M, Polyak A, et al. Rare variants in the genetic background modulate cognitive and developmental phenotypes in individuals carrying disease-associated variants. Genet Med 2019; 21(4): 816-25.
[] [PMID: 30190612]
An JY, Lin K, Zhu L, et al. Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science 2018; 362(6420): eaat6576.
[] [PMID: 30545852]
Garg P, Sharp AJ. Screening for rare epigenetic variations in autism and schizophrenia. Hum Mutat 2019; 40(7): 952-61.
[] [PMID: 30900359]
Andrews SV, Sheppard B, Windham GC, et al. Case-control meta-analysis of blood DNA methylation and autism spectrum disorder. Mol Autism 2018; 9: 40.
[] [PMID: 29988321]
Modabbernia A, Velthorst E, Reichenberg A. Environmental risk factors for autism: an evidence-based review of systematic reviews and meta-analyses. Mol Autism 2017; 8: 13.
[] [PMID: 28331572]
Grove J, Ripke S, Als TD, et al. Autism Spectrum Disorder Working Group of the Psychiatric Genomics Consortium; BUPGEN; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium; 23andMe Research Team. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet 2019; 51(3): 431-44.
[] [PMID: 30804558]
Green ED, Guyer MS. National Human Genome Research Institute. Charting a course for genomic medicine from base pairs to bedside. Nature 2011; 470(7333): 204-13.
[] [PMID: 21307933]
Muzafar Beigh M. Next-Generation Sequencing: The Translational Medicine Approach from “Bench to Bedside to Population” (Vol. 3). Medicines (Basel) 2016; 3(2): 14.
Richards S, Aziz N, Bale S, et al. ACMG Laboratory Quality Assurance Committee. 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(5): 405-24.
[] [PMID: 25741868]
Stavropoulos DJ, Merico D, Jobling R, et al. Whole Genome Sequencing Expands Diagnostic Utility and Improves Clinical Management in Pediatric Medicine. NPJ Genom Med 2016; 1: 15012.
[] [PMID: 28567303]
Ganna A, Satterstrom FK, Zekavat SM, et al. GoT2D/T2D-GENES Consortium; SIGMA Consortium Helmsley IBD Exome Sequencing Project; FinMetSeq Consortium; iPSYCH-Broad Consortium. Quantifying the impact of rare and ultrarare coding variation across the phenotypic spectrum. Am J Hum Genet 2018; 102(6): 1204-11.
[] [PMID: 29861106]
He Z, Xu B, Buxbaum J, Ionita-Laza I. A genome-wide scan statistic framework for whole-genome sequence data analysis. Nat Commun 2019; 10(1): 3018.
[] [PMID: 31289270]
Werling DM, Brand H, An JY, et al. An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder. Nat Genet 2018; 50(5): 727-36.
[] [PMID: 29700473]
Schaaf CP, Betancur C, Yuen RKC, et al. A framework for an evidence-based gene list relevant to autism spectrum disorder. Nat Rev Genet 2020; 21(6): 367-76.
[] [PMID: 32317787]
Caspar SM, Dubacher N, Kopps AM, Meienberg J, Henggeler C, Matyas G. Clinical sequencing: From raw data to diagnosis with lifetime value. Clin Genet 2018; 93(3): 508-19.
[] [PMID: 29206278]
Mostovoy Y, Levy-Sakin M, Lam J, et al. A hybrid approach for de novo human genome sequence assembly and phasing. Nat Methods 2016; 13(7): 587-90.
[] [PMID: 27159086]
Barseghyan H, Tang W, Wang RT, et al. Next-generation mapping: a novel approach for detection of pathogenic structural variants with a potential utility in clinical diagnosis. Genome Med 2017; 9(1): 90.
[] [PMID: 29070057]
Egger G, Liang G, Aparicio A, Jones PA. Epigenetics in human disease and prospects for epigenetic therapy. Nature 2004; 429(6990): 457-63.
[] [PMID: 15164071]
Gibney ER, Nolan CM. Epigenetics and gene expression. Heredity 2010; 105(1): 4-13.
[] [PMID: 20461105]
Lim DH, Maher ER. DNA methylation: a form of epigenetic control of gene expression. Obstet Gynaecol 2010; 12: 37-42.
Li D, Zhang B, Xing X, Wang T. Combining MeDIP-seq and MRE-seq to investigate genome-wide CpG methylation. Methods 2015; 72: 29-40.
[] [PMID: 25448294]
Li Y, Tollefsbol TO. DNA methylation detection: bisulfite genomic sequencing analysis. Methods Mol Biol 2011; 791: 11-21.
[] [PMID: 21913068]
Schenkel LC, Schwartz C, Skinner C, et al. Clinical Validation of Fragile X Syndrome Screening by DNA Methylation Array. J Mol Diagn 2016; 18(6): 834-41.
[] [PMID: 27585064]
Aref-Eshghi E, Rodenhiser DI, Schenkel LC, et al. Care4Rare Canada Consortium. Genomic DNA Methylation Signatures Enable Concurrent Diagnosis and Clinical Genetic Variant Classification in Neurodevelopmental Syndromes. Am J Hum Genet 2018; 102(1): 156-74.
[] [PMID: 29304373]
Aref-Eshghi E, Bend EG, Hood RL, et al. BAFopathies’ DNA methylation epi-signatures demonstrate diagnostic utility and functional continuum of Coffin-Siris and Nicolaides-Baraitser syndromes. Nat Commun 2018; 9(1): 4885.
[] [PMID: 30459321]
Aref-Eshghi E, Bend EG, Colaiacovo S, et al. Diagnostic Utility of Genome-wide DNA Methylation Testing in Genetically Unsolved Individuals with Suspected Hereditary Conditions. Am J Hum Genet 2019; 104(4): 685-700.
[] [PMID: 30929737]
Sztainberg Y, Zoghbi HY. Lessons learned from studying syndromic autism spectrum disorders. Nat Neurosci 2016; 19(11): 1408-17.
[] [PMID: 27786181]
Li D, Tian L, Hakonarson H. Increasing diagnostic yield by RNA-Sequencing in rare disease-bypass hurdles of interpreting intronic or splice-altering variants. Ann Transl Med 2018; 6(7): 126.
[] [PMID: 29955586]
Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet 2016; 17(5): 257-71.
[] [PMID: 26996076]
Kremer LS, Bader DM, Mertes C, et al. Genetic diagnosis of Mendelian disorders via RNA sequencing. Nat Commun 2017; 8: 15824.
[] [PMID: 28604674]
Gonorazky HD, Naumenko S, Ramani AK, et al. Expanding the Boundaries of RNA Sequencing as a Diagnostic Tool for Rare Mendelian Disease. Am J Hum Genet 2019; 104(3): 466-83.
[] [PMID: 30827497]
Rentas S, Rathi KS, Kaur M, et al. Diagnosing Cornelia de Lange syndrome and related neurodevelopmental disorders using RNA sequencing. Genet Med 2020; 22(5): 927-36.
[] [PMID: 31911672]
Chong J, Soufan O, Li C, et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 2018; 46(W1): W486-94.
[] [PMID: 29762782]
Boccuto L, Chen C-F, Pittman AR, et al. Decreased tryptophan metabolism in patients with autism spectrum disorders. Mol Autism 2013; 4(1): 16.
[] [PMID: 23731516]
Li C, Brazill JM, Liu S, et al. Spermine synthase deficiency causes lysosomal dysfunction and oxidative stress in models of Snyder-Robinson syndrome. Nat Commun 2017; 8(1): 1257.
[] [PMID: 29097652]
Smith AM, Natowicz MR, Braas D, et al. A Metabolomics Approach to Screening for Autism Risk in the Children’s Autism Metabolome Project. Autism Res 2020; 13(8): 1270-85.
[] [PMID: 32558271]
Findlay GM, Daza RM, Martin B, et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature 2018; 562(7726): 217-22.
[] [PMID: 30209399]
Starita LM, Islam MM, Banerjee T, et al. A Multiplex Homology-Directed DNA Repair Assay Reveals the Impact of More Than 1,000 BRCA1 Missense Substitution Variants on Protein Function. Am J Hum Genet 2018; 103(4): 498-508.
[] [PMID: 30219179]
Starita LM, Ahituv N, Dunham MJ, et al. Variant Interpretation: Functional Assays to the Rescue. Am J Hum Genet 2017; 101(3): 315-25.
[] [PMID: 28886340]
Fowler DM, Araya CL, Fleishman SJ, et al. High-resolution mapping of protein sequence-function relationships. Nat Methods 2010; 7(9): 741-6.
[] [PMID: 20711194]
Ernst A, Gfeller D, Kan Z, et al. Coevolution of PDZ domain-ligand interactions analyzed by high-throughput phage display and deep sequencing. Mol Biosyst 2010; 6(10): 1782-90.
[] [PMID: 20714644]
Hietpas RT, Jensen JD, Bolon DNA. Experimental illumination of a fitness landscape. Proc Natl Acad Sci USA 2011; 108(19): 7896-901.
[] [PMID: 21464309]
Weile J, Roth FP. Multiplexed assays of variant effects contribute to a growing genotype-phenotype atlas. Hum Genet 2018; 137(9): 665-78.
[] [PMID: 30073413]
Movva R, Greenside P, Marinov GK, Nair S, Shrikumar A, Kundaje A. Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays. PLoS One 2019; 14(6): e0218073.
[] [PMID: 31206543]
Hoskinson DC, Dubuc AM, Mason-Suares H. The current state of clinical interpretation of sequence variants. Curr Opin Genet Dev 2017; 42: 33-9.
[] [PMID: 28157586]
Zhou J, Theesfeld CL, Yao K, Chen KM, Wong AK, Troyanskaya OG. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet 2018; 50(8): 1171-9.
[] [PMID: 30013180]
Argelaguet R, Velten B, Arnol D, et al. Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol 2018; 14(6): e8124.
[] [PMID: 29925568]
Singh A, Shannon CP, Gautier B, et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 2019; 35(17): 3055-62.
[] [PMID: 30657866]
Ipe J, Swart M, Burgess KS, Skaar TC. High-Throughput Assays to Assess the Functional Impact of Genetic Variants: A Road Towards Genomic-Driven Medicine. Clin Transl Sci 2017; 10(2): 67-77.
[] [PMID: 28213901]
Perrino C, Barabási AL, Condorelli G, et al. Epigenomic and transcriptomic approaches in the post-genomic era: path to novel targets for diagnosis and therapy of the ischaemic heart? Position Paper of the European Society of Cardiology Working Group on Cellular Biology of the Heart. Cardiovasc Res 2017; 113(7): 725-36.
[] [PMID: 28460026]
Carter MT, Scherer SW. Autism spectrum disorder in the genetics clinic: a review. Clin Genet 2013; 83(5): 399-407.
[] [PMID: 23425232]
Qin M, Huang T, Kader M, et al. R-baclofen reverses a social behavior deficit and elevated protein synthesis in a mouse model of fragile X syndrome. Int J Neuropsychopharmacol 2015; 18(9): pyv034.
[] [PMID: 25820841]
Veenstra-VanderWeele J, Cook EH, King BH, et al. Arbaclofen in Children and Adolescents with Autism Spectrum Disorder: A Randomized, Controlled, Phase 2 Trial. Neuropsychopharmacology 2017; 42(7): 1390-8.
[] [PMID: 27748740]
Zhu Y, Tazearslan C, Suh Y. Challenges and progress in interpretation of non-coding genetic variants associated with human disease. Exp Biol Med (Maywood) 2017; 242(13): 1325-34.
[] [PMID: 28581336]
Myers SM, Challman TD, Bernier R, et al. Insufficient Evidence for “Autism-Specific” Genes. Am J Hum Genet 2020; 106(5): 587-95.
[] [PMID: 32359473]
Woods NT, Baskin R, Golubeva V, et al. Functional assays provide a robust tool for the clinical annotation of genetic variants of uncertain significance. NPJ Genom Med 2016; 1: 16001.
[] [PMID: 28781887]

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy