The Promise of Multi-Omics and Clinical Data Integration to Identify and Target Personalized Healthcare Approaches in Autism Spectrum Disorders

Abstract

Complex diseases are caused by a combination of genetic and environmental factors, creating a difficult challenge for diagnosis and defining subtypes. This review article describes how distinct disease subtypes can be identified through integration and analysis of clinical and multi-omics data. A broad shift toward molecular subtyping of disease using genetic and omics data has yielded successful results in cancer and other complex diseases. To determine molecular subtypes, patients are first classified by applying clustering methods to different types of omics data, then these results are integrated with clinical data to characterize distinct disease subtypes. An example of this molecular-data-first approach is in research on Autism Spectrum Disorder (ASD), a spectrum of social communication disorders marked by tremendous etiological and phenotypic heterogeneity. In the case of ASD, omics data such as exome sequences and gene and protein expression data are combined with clinical data such as psychometric testing and imaging to enable subtype identification. Novel ASD subtypes have been proposed, such as CHD8, using this molecular subtyping approach. Broader use of molecular subtyping in complex disease research is impeded by data heterogeneity, diversity of standards, and ineffective analysis tools. The future of molecular subtyping for ASD and other complex diseases calls for an integrated resource to identify disease mechanisms, classify new patients, and inform effective treatment options. This in turn will empower and accelerate precision medicine and personalized healthcare.

Emerging Clues and Altered Metabolic Findings in Autism: Breakthroughs and Prospects from Omics Studies

“…The only two FDA-approved medications against ASD, namely Risperidone and Aripiprazole, are serotonergic antipsychotics, at least in part. These medications are intended to alleviate the ASD core symptoms of irritability, disrupted sociability, and aggressive/self-injurious malbehaviors [3][4][5].…”

An Appeal to the Global Health Community for a Tripartite Innovation: An “Essential Diagnostics List,” “Health in All Policies,” and “See-Through 21st Century Science and Ethics”

“…Diagnostics spanning a wide range of biotechnologies, including genomics, proteomics, metabolomics, and nanotechnology, are emerging as companion tests to innovative medicines (Conde and Artzi, 2015;Higdon et al, 2015). The 19 th WHO Model List of Essential Medicines brings to the fore innovative medicines for hepatitis C, breast cancer, leukemia, and multi-drug resistant tuberculosis (World Health Organization, 2015).…”

“…From a discovery science standpoint, not all innovative medicines may attain the status of an ''essential medicine.'' But they are more likely to emerge out of the laboratory and ''into the street'' if we move away from the traditional one-size-fits-all model of drug development by recognizing the multi-omics basis of personto-person heterogeneity in drug safety and efficacy by diagnostic tests (Higdon et al 2015;Ö zdemir and Lerer, 2005). When an innovative medicine attached to a companion diagnostic is granted an essential medicine status, there is a need for parallel policy and regulatory mechanisms to govern and evaluate the diagnostic counterpart of such innovative essential medicines.…”

Integration of multi-omics data for integrative gene regulatory network inference

“…Biological systems involve a sequence of complex interactions in multiple biological processes such as genetic, epigenetic, and transcriptional regulation (Kristensen, 2014; Higdon, 2015; Rebollar, 2016; Cisek et al, 2015). Specifically, gene expression is a product of sequential interactions of single nucleotide polymorphism (SNP), copy number variation (CNV), histone modifications, transcription factor, DNA methylation, other genes in relevant pathways, and many other factors (Aure et al, 2013; Wagner et al, 2014; Kang et al, 2015; Kim et al, 2015; Kang et al, 2016).…”