Characterization of a Clinically and Biologically Defined Subgroup of Patients with Autism Spectrum Disorder and Identification of a Tailored Combination Treatment.
Laura Pérez-CanoLuigi BoccutoFrancesco SirciJose Manuel HidalgoSamuel ValentiniMattia BosioXavier Liogier D'ArdhuyCindy D SkinnerLauren CascioSujata SrikanthKelly JonesCaroline B BuchananSteven A SkinnerBaltazar Gomez-MancillaJean-Marc HyvelinEmre GuneyLynn DurhamPublished in: Biomedicines (2024)
Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. The diagnosis of ASD is currently based on behavior criteria, which overlooks the diversity of genetic, neurophysiological, and clinical manifestations. Failure to acknowledge such heterogeneity has hindered the development of efficient drug treatments for ASD and other NDDs. DEPI ® (Databased Endophenotyping Patient Identification) is a systems biology, multi-omics, and machine learning-driven platform enabling the identification of subgroups of patients with NDDs and the development of patient-tailored treatments. In this study, we provide evidence for the validation of a first clinically and biologically defined subgroup of patients with ASD identified by DEPI, ASD Phenotype 1 (ASD-Phen1). Among 313 screened patients with idiopathic ASD, the prevalence of ASD-Phen1 was observed to be ~24% in 84 patients who qualified to be enrolled in the study. Metabolic and transcriptomic alterations differentiating patients with ASD-Phen1 were consistent with an over-activation of NF-κB and NRF2 transcription factors, as predicted by DEPI. Finally, the suitability of STP1 combination treatment to revert such observed molecular alterations in patients with ASD-Phen1 was determined. Overall, our results support the development of precision medicine-based treatments for patients diagnosed with ASD.
Keyphrases
- autism spectrum disorder
- attention deficit hyperactivity disorder
- intellectual disability
- machine learning
- transcription factor
- healthcare
- end stage renal disease
- chronic kidney disease
- emergency department
- oxidative stress
- clinical trial
- risk factors
- immune response
- magnetic resonance imaging
- single cell
- peritoneal dialysis
- genome wide
- magnetic resonance
- big data
- inflammatory response
- ejection fraction
- high throughput
- deep learning
- working memory
- prognostic factors
- copy number
- toll like receptor
- phase iii