TY - JOUR
T1 - Validation of a Salivary RNA Test for Childhood Autism Spectrum Disorder
AU - Hicks, Steven D.
AU - Rajan, Alexander T.
AU - Wagner, Kayla E.
AU - Barns, Sarah
AU - Carpenter, Randall L.
AU - Middleton, Frank A.
N1 - Funding Information:
The authors thank Jessica Bieler, MPH (Penn State), Richard Uhlig and Cynthia Dowd Greene (Quadrant Biosciences Inc.) for assistance with study design. Thanks to Jeanette Ramer, MD and Cheryl Tierney, MD (Penn State), and Carroll Grant, Ph.D., Cynthia Brightman, MD, and Diane Montgomery, MD (SUNY Upstate) for assistance with participant identification. We acknowledge Eric Chin MD, Alexandra Confair, Andy Tarasiuk, Molly Carney, Falisha Gillman, MD, Julie Vallati, Nicole Verdiglione, Maria Chroneos, Rachel Pauley (Penn State), Angela Savage and Parisa Afshari, MD, Ph.D., (SUNY Upstate) and Jean Gehricke, Ph.D., and Sharina Alejo (UC Irvine) for assistance with participant recruitment and sample collection. We thank Dongliang Wang, Ph.D., (SUNY Upstate) and Jeremy Williams (Quadrant Biosciences) for guidance with data processing and statistical analysis. Funding. This work was supported by the National Institutes of Mental Health (R41 MH111347), the Kirson-Kolodner-Fedder Charitable Fund at the Baltimore Community Foundation, and Quadrant Biosciences Inc.
Publisher Copyright:
© Copyright © 2018 Hicks, Rajan, Wagner, Barns, Carpenter and Middleton.
PY - 2018/11/9
Y1 - 2018/11/9
N2 - Background: The diagnosis of autism spectrum disorder (ASD) relies on behavioral assessment. Efforts to define biomarkers of ASD have not resulted in an objective, reliable test. Studies of RNA levels in ASD have demonstrated potential utility, but have been limited by a focus on single RNA types, small sample sizes, and lack of developmental delay controls. We hypothesized that a saliva-based poly-“omic” RNA panel could objectively distinguish children with ASD from their neurotypical peers and children with non-ASD developmental delay. Methods: This multi-center cross-sectional study included 456 children, ages 19–83 months. Children were either neurotypical (n = 134) or had a diagnosis of ASD (n = 238), or non-ASD developmental delay (n = 84). Comprehensive human and microbial RNA abundance was measured in the saliva of all participants using unbiased next generation sequencing. Prior to analysis, the sample was randomly divided into a training set (82% of subjects) and an independent validation test set (18% of subjects). The training set was used to develop an RNA-based algorithm that distinguished ASD and non-ASD children. The validation set was not used in model development (feature selection or training) but served only to validate empirical accuracy. Results: In the training set (n = 372; mean age 51 months; 75% male; 51% ASD), a set of 32 RNA features (controlled for demographic and medical characteristics), identified ASD status with a cross-validated area under the curve (AUC) of 0.87 (95% CI: 0.86–0.88). In the completely separate validation test set (n = 84; mean age 50 months; 85% male; 60% ASD), the algorithm maintained an AUC of 0.88 (82% sensitivity and 88% specificity). Notably, the RNA features were implicated in physiologic processes related to ASD (axon guidance, neurotrophic signaling). Conclusion: Salivary poly-omic RNA measurement represents a novel, non-invasive approach that can accurately identify children with ASD. This technology could improve the specificity of referrals for ASD evaluation or provide objective support for ASD diagnoses.
AB - Background: The diagnosis of autism spectrum disorder (ASD) relies on behavioral assessment. Efforts to define biomarkers of ASD have not resulted in an objective, reliable test. Studies of RNA levels in ASD have demonstrated potential utility, but have been limited by a focus on single RNA types, small sample sizes, and lack of developmental delay controls. We hypothesized that a saliva-based poly-“omic” RNA panel could objectively distinguish children with ASD from their neurotypical peers and children with non-ASD developmental delay. Methods: This multi-center cross-sectional study included 456 children, ages 19–83 months. Children were either neurotypical (n = 134) or had a diagnosis of ASD (n = 238), or non-ASD developmental delay (n = 84). Comprehensive human and microbial RNA abundance was measured in the saliva of all participants using unbiased next generation sequencing. Prior to analysis, the sample was randomly divided into a training set (82% of subjects) and an independent validation test set (18% of subjects). The training set was used to develop an RNA-based algorithm that distinguished ASD and non-ASD children. The validation set was not used in model development (feature selection or training) but served only to validate empirical accuracy. Results: In the training set (n = 372; mean age 51 months; 75% male; 51% ASD), a set of 32 RNA features (controlled for demographic and medical characteristics), identified ASD status with a cross-validated area under the curve (AUC) of 0.87 (95% CI: 0.86–0.88). In the completely separate validation test set (n = 84; mean age 50 months; 85% male; 60% ASD), the algorithm maintained an AUC of 0.88 (82% sensitivity and 88% specificity). Notably, the RNA features were implicated in physiologic processes related to ASD (axon guidance, neurotrophic signaling). Conclusion: Salivary poly-omic RNA measurement represents a novel, non-invasive approach that can accurately identify children with ASD. This technology could improve the specificity of referrals for ASD evaluation or provide objective support for ASD diagnoses.
UR - http://www.scopus.com/inward/record.url?scp=85062770770&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062770770&partnerID=8YFLogxK
U2 - 10.3389/fgene.2018.00534
DO - 10.3389/fgene.2018.00534
M3 - Article
AN - SCOPUS:85062770770
VL - 9
JO - Frontiers in Genetics
JF - Frontiers in Genetics
SN - 1664-8021
M1 - 534
ER -