SynapDx Autism Gene Expression Analysis Study (STORY) (STORY)
This study will prospectively enroll approximately 880 children, at least 18 months and less than 60 months of age, who have been referred to a pediatric developmental evaluation center. Enrolled children will have blood drawn for RNA gene expression analysis and optionally for metabolite, lipid and DNA analysis and undergo a clinical evaluation to determine the presence or absence of a diagnosis of ASD.
The primary objective of this study is:
- To develop an algorithm to classify blood RNA gene expression patterns to maximize agreement between the classification and a clinical assessment of presence or absence of Autism Spectrum Disorders (ASD).
The secondary objectives of this study are:
- To develop an algorithm to classify plasma metabolite and/or lipid profiles in such a way as to maximize agreement between the classification and a clinical assessment of presence or absence of ASD.
- To prospectively assess the clinical sensitivity and specificity of the plasma metabolite and/or lipid profile classification algorithm in a separate population consisting of children referred to a developmental evaluation clinic for a possible developmental disorder (DD).
- To evaluate clinical sensitivity and specificity of various combinations of gene expression signature, metabolite and/or lipid signatures, and presence of ASD-associated genetic variation detected by chromosomal microarray analysis (CMA) or sequencing protein-coding regions of the genome.
|Study Design:||Observational Model: Cohort
Time Perspective: Prospective
|Official Title:||SynapDx Autism Spectrum Disorder Gene Expression Analysis Study|
- RNA gene expression in peripheral blood [ Time Frame: Within 30 days of collection ] [ Designated as safety issue: No ]
- Metabolites, lipids and DNA variation in peripheral blood [ Time Frame: Within 30 days of collection ] [ Designated as safety issue: No ]
Biospecimen Retention: Samples With DNA
|Study Start Date:||March 2013|
|Study Completion Date:||December 2014|
|Primary Completion Date:||July 2014 (Final data collection date for primary outcome measure)|
In the Development Phase, analyses will be performed until the classification algorithm is finalized.
The Validation Phase will assess the performance of the finalized classification algorithm in 300 subjects.
The primary aim of this study is to define a gene expression signature indicative of ASD and to establish its clinical sensitivity and specificity. Clinician diagnosis of ASD will be made using DSM-5 as the reference standard instrument. The widely used diagnostic instrument Autism Diagnostic Observation Schedule (ADOS-2) is a typical component of the clinical assessment for a child diagnosed with ASD and this evaluation will be performed on all study participants. Secondary aims of this study are (1) to define metabolite and lipid signatures indicative of ASD and establish their clinical sensitivity and specificity and (2) to determine clinical sensitivity and specificity of various combinations of gene expression signature, metabolite and/or lipid signatures, and presence of ASD-associated genetic variation detected by CMA and sequencing protein-coding regions of the genome
Analyses: Details of the analysis will be specified in a Statistical Analysis Plan (SAP), which will include procedures for handling outliers, missing data, and differences across sites. The SAP will be reviewed and approved by a committee of Principal Investigators (PIs) before unblinding of the validation set.
Primary analyses: The primary outcomes of the study will be the estimates of the clinical sensitivity and specificity of the SDX-002 test to classify subjects according to DSM-5 ASD diagnosis, with associated 95% confidence intervals. Sensitivity and specificity will be assessed on the Validation Phase population based on agreement with the clinical diagnosis of presence or absence of Autism Spectrum Disorder by DSM-5 (published May 2013).
The gene expression signature will be trained on the 500 subject Development Phase set, using 5-fold cross validation over the results of several machine learning algorithms, including partial least squares, support vector machines, and boosted decision trees. The training procedure will generate estimated ROC curves for each method, as well as confidence intervals for the area under the curve (AUC). The final choice of a machine-learning algorithm will be based on AUC, as well as on the estimated performance at the chosen operating point on the ROC curve. The operating point will be chosen to provide high sensitivity at an acceptable specificity.
Secondary analyses: In the majority of patients enrolled to date, consent was obtained for collection of an optional bio-repository sample. The intended analysis of the bio-repository samples has now been established (see also, secondary objectives listed above). The metabolomic and lipomic signatures will be similarly assessed on the subset of the 500 subjects in the Development Phase set who consent to a bio-repository sample. In addition to the gene expression signature, metabolite and/or lipid signatures and DNA analysis will be combined with the gene expression signature in various configurations and the impact of these additional measures on clinical sensitivity and specificity will be evaluated. If these additional metabolomic/lipomic signatures and/or DNA analyses improve test performance, these elements may be included in the SDX-002 assay.
Sensitivity and specificity the final SDX-002 assay will also be assessed among subpopulations using demographic information and the results of developmental testing. There are likely to be few subjects in many of these subpopulations and caution will be used in interpreting the results. Planned subpopulation analyses include gender, age, ethnicity, ASD DSM-IV-TR diagnostic subcategory, DSM-5 ASD severity level (social communication and restricted interests/repetitive behaviors) and ADOS-2 scores.
Please refer to this study by its ClinicalTrials.gov identifier: NCT01810341
Show 20 Study Locations
|Study Director:||Barbara Rathmell, MD||SynapDx Corp|