12 Nov Panel of Salivary RNA Biomarkers Could Identify Autism
MedicalResearch.com Interview with:
Steven D. Hicks, M.D.,Ph.D
Department of Pediatrics
Penn State College of Medicine
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: Since autism has both genetic and environmental underpinnings, my colleagues and I suspected that transcriptional elements (e.g. regulatory RNA molecules) might be different in the saliva of children with autism compared to peers without autism. We used a non-biased approach to analyze saliva from 372 children, and allowed machine learning techniques to inform which RNA elements best predicted autism status. To our surprise, microbial RNA levels and human RNA levels were equally powerful in predicting which children had autism. This may be because some children with autism eat restricted diets, resist tooth brushing, or put foreign objects in their mouths. The end result was a panel of 32 RNAs (20 human and 12 bacterial) that identified autism with 87% accuracy. Interestingly, when we tested the panel in a completely separate set of 84 children (including children from a different geographic region) the accuracy remained 88%.
MedicalResearch.com: What should readers take away from your report?
Response: For years basic scientists have explored the genetic and epigenetic mechanisms underlying autism spectrum disorder. This study attempts to build upon that knowledge base, and apply it in a way that could have a significant impact on the clinical care we provide for children with autism. Though additional validation of these results is needed, our findings suggest that a biologic test for autism may be part of physicians’ toolkits in the near future. Such a test would need to be administered by a supervising physician and interpreted in the context of established behavioral assessments. Based on the make-up of our study cohort (~50% autism, ~25% non-autism developmental delay, ~25% children with typical development), such a test would need to be employed as a diagnostic adjunct (not a broad screening tool).
For example, general pediatricians might apply it in patients with a positive score on the modified checklist for autism in toddlers (MCHAT), to improve the specificity of referrals to developmental specialists. Alternatively, developmental specialists might employ the this type of technology to provide an additional level of diagnostic evidence (e.g. in children with borderline behavioral assessments, or in cases where parents were skeptical of initial diagnoses).
MedicalResearch.com: What recommendations do you have for future research as a result of this work?
Response: We have already begun follow-up work to validate these findings in a larger and more geographically diverse cohort of children. Our ongoing project seeks to track saliva RNA profiles for 12 months after children receive an autism diagnosis, to determine if salivary RNA profiles change with successful early intervention. This multi-site study is being funded by the National Institutes of Mental Health. Children in the current study were 19 months to 6 years, 11 months of age. It will be interesting to see if saliva RNA profiles can prospectively predict autism risk in infants under the age of 12 months.
We are also partnering with the Autism Treatment Network to investigate how saliva RNA profiles respond to minocycline therapy in children with autism enrolled in their clinical trial.
MedicalResearch.com: Is there anything else you would like to add?
Response: The “control group” for this study included children with non-autism developmental delay, which is critical for the development of any realistic autism test. In developing the diagnostic algorithm, we controlled for medical and demographic features which might confound a test that relies on saliva RNA (e.g. asthma, body mass index, time since last meal). Results in the separate validation group did not show evidence that these factors biased test performance. However, we plan to continue validation of the findings through our ongoing multi-site study.
This work was funded through a grant from the Kirson-Kolodner-Fedder Charitable Fund at the Baltimore Community Foundation to the Penn State College of Medicine and the State University of New York Upstate Medical University. Additional funding was provided by a grant from the National Institutes of Mental Health to Quadrant Biosciences. Quadrant Biosciences is a biotechnology company who seeks to bring this technology to medical practice, and for whom I serve as a paid consultant.
Front. Genet., 09 November 2018 | https://doi.org/10.3389/fgene.2018.00534
Steven D. Hicks, Alexander T. Rajan, Kayla E. Wagner Sarah Barns2, Randall L. Carpenter and Frank A. Middleton
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