Blood-Based Gene Expression Signatures of Autistic Infants and Toddlers

Abstract

Objective

Autism spectrum disorders (ASDs) are highly heritable neurodevelopmental disorders that onset clinically during the first years of life. ASD-risk biomarkers expressed early in life could significantly impact diagnosis and treatment, but no transcriptome-wide biomarker classifiers derived from fresh blood samples from children with autism have yet emerged.

Method

Using a community-based, prospective, longitudinal method, we identified 60 infants and toddlers at-risk for ASDs (autistic disorder and pervasive developmental disorder), 34 at-risk for language delay (LD), 17 at-risk for global developmental delay (DD), and 68 typically developing (TD) comparison children. Diagnoses were confirmed via longitudinal follow-up. Each child's mRNA expression profile in peripheral blood mononuclear cells (PBMCs) was determined by microarray.

Results

Potential ASD biomarkers were discovered in one half of the sample and used to build a classifier with high diagnostic accuracy in the remaining half of the sample.

Conclusions

The mRNA expression abnormalities reliably observed in PBMCs, which are safely and easily assayed in babies, offer the first potential peripheral blood-based early biomarker panel of risk for autism in infants and toddlers. Future work should verify these biomarkers and evaluate if they may also serve as indirect indices of deviant molecular neural mechanisms in autism.

INTRODUCTION

Autism spectrum disorders (ASDs), such as autistic disorder (AD) and pervasive developmental disorder not otherwise specified (PDD), are among the most prevalent neurodevelopmental disorders, collectively affecting almost 1% of the U.S. population.¹ These are also among the most severe and debilitating disorders of early brain development, typically emerging between infancy and early childhood, and commonly imposing lifelong disability on affected children as well as considerable strain on their caretakers and society at large. One way to combat ASDs would be to discover biological signatures—or “biomarkers”—of early risk for ASDs which could be exploited for early detection and treatment and, thereby, improve long-term clinical outcomes.²

Most prior transcriptome-wide studies of gene-expression in ASDs have utilized postmortem brain tissue^{3, 4} or transformed lymphoblastoid cell lines.^5–8 Both approaches are quite useful and revealing, often observing dysregulation of viable candidate pathways in autism, but both have limitations, such as the inability to fully account for agonal factors or the effects of viral transformation on the observed gene-expression differences in autism. In contrast, Kuwano et al.⁹ sought autism biomarkers in peripheral leukocytes of young adults with autism, observing dysregulation of 16 genes related to cell morphology, cellular assembly and organization, and nerve system development and function. While informative regarding persistent gene-expression abnormalities in peripheral blood in individuals with ASDs (and perhaps reflecting the central nervous system pathology accompanying the disorder), this study cannot shed light on the peripheral-blood transcriptomic events accompanying the emergence of autism in its earliest developmental stages of infancy or toddlerhood.

Here we report the cross-sectional results of whole-transcriptome expression profiling in freshly drawn peripheral blood mononuclear cells (PBMCs) from Wave I of data-collection in a longitudinal study of very young children (12–46 months) with or at-risk for a variety of neurodevelopmental disorders including ASDs, language delay (LD), or global developmental delay (DD), as well as children consistently judged to be typically developing (TD) and children initially judged as potentially at-risk but, upon further evaluation, found to be developing typically (type-I errors; TIEs). Our analyses were designed to reveal in peripheral blood a biomarker signature for ASDs that might also reveal important biological differences between TD children, children with delays not on the autism spectrum, and children at-risk for or having an ASD.

METHOD

Subjects

Protections

The Institutional Review Boards of the University of California, San Diego and SUNY Upstate Medical University approved all procedures. After referral, all minor subjects assented to the study procedures, and one or both parents or legal guardians of each subject provided written informed consent for their child to participate.

Ascertainment

Participants were obtained via a general population-based screening method called the One-Year Well-Baby Check-Up Approach, which engages community pediatricians to screen for autism using the Communication and Symbolic Behavior Scales Developmental Profile (CSBS DP) Infant-Toddler questionnaire^{10, 11} during regular checkups.¹² Over 135 pediatricians in San Diego County agreed to screen all consecutive infants and toddlers seen for routine visits. All subjects failing the CSBS DP at their pediatrician's office were provisionally identified as at-risk for one of the disorders of interest and then referred to our Center for further evaluation. Subjects who passed the CSBS were identified as TD, and pediatricians randomly referred such subjects to our Center for further evaluation as unaffected comparison subjects. Using this approach, toddlers as young as 12-months were recruited and tracked every six months until at least their third birthday, thus allowing for the prospective study of autism beginning at 12 months.

Diagnoses

ASDs were initially diagnosed based on the clinical judgment of a Ph.D.-level psychologist with over 10 years of experience in autism using the three initial modules of the Autism Diagnostic Observation Schedule (ADOS)¹³ and the revised algorithms described by Gotham et al.¹⁴ While several ASD subjects were only one year old at the time of first blood sampling, all but one have been tracked and diagnosed with an ASD using the toddler module of the ADOS¹⁵ at age two or beyond, when the diagnosis of autism can be made reliably. Final diagnoses for participants with an ASD older than 30 months (including some of the youngest subjects who had previously been diagnosed with or at-risk for an ASD based on the ADOS) were confirmed with the Autism Diagnostic Interview–Revised (ADI-R).¹⁵ Subjects who failed the CSBS DP Infant-Toddler questionnaire but who passed the ADOS were diagnosed with LD if one or both of the language subtest scores of the Mullen Scales of Early Learning were more than one standard deviation lower than expected for that age (T-score <40). Since a significant percentage of individuals with ASDs exhibit language deficits not restricted to the pragmatic component of language,^16–19 this approach to diagnosing LD may not be highly specific; however, this criterion paired with ADOS evaluations not consistent with an ASD is sufficient to establish that these subjects have some language delay and are not on the autistic spectrum, making them suitable as an affected, non-autistic comparison sample for establishing the specificity of our ASD biomarkers. Subjects who failed the CSBS DP Infant-Toddler questionnaire but who passed the ADOS were diagnosed with global DD if scores were more than one standard deviation lower than expected on three or more subtests of the Mullen Scales and the overall developmental quotient was more than one standard deviation lower than expected (<85). Subjects who failed the CSBS DP Infant-Toddler questionnaire but who passed the ADOS and who did not deviate from norms on any of the Mullen Scales or the developmental quotient were judged to be developing typically and identified as TIEs.

Laboratory Methods

From each subject, 4ml of venous blood was collected into EDTA-coated collection tubes and immediately transferred to an RNase-free laboratory where all subsequent procedures took place, including RNA isolation and storage at −80°C within six hours. Methods of mRNA extraction, stabilization, isolation, storage, quantitation, reverse transcription, and microarray hybridization, as well as methods of microarray scanning and data import, normalization, and transformation, were consistent with our prior work,²⁰ (see Supplemental Method, available online).

Quality Control and Final Sample Constitution

The quality of mRNA samples was quantified by the RNA Integrity Number (RIN) and, according to convention,²¹ values of 6.0 or greater were deemed acceptable; values observed in our sample ranged from 9.0–10.0. A total of 339 samples selected for analysis in Wave I had acceptable levels of mRNA quantity, purity, and quality. Once microarray data were generated, the first three principal components of global gene expression patterns in each individual were visualized. Subjects whose values on each of these three principal components were beyond four standard deviations from the grand mean (n=5) were removed from further analyses, as this type of deviation indicates that typical patterns of global gene co-expression and correlation have been disrupted (by either a biological process or, more likely in our experience, a laboratory technical problem). Eighteen additional subjects were removed due to low signal intensity as observed by box-plot and evidenced by an average signal more than two standard deviations below the mean, which likely indicates generally low hybridization efficiency and unreliability of the data from those arrays. Of the remaining 316 quality-assured samples, 179 were first-timepoint samples from a child proband in one of the six diagnostic groups described in Table 1. The remaining 137 samples were from parents or siblings of probands, or from probands evaluated at a second timepoint, and so were not formally analyzed for the present report. There was no significant overall sex difference between the six diagnostic groups (χ²_[5]=10.61, p=0.060), but they differed significantly in age (F[5,173]=13.77, p<0.0001), reflecting that the LD and TIE groups were significantly younger than the AD, DD, PDD, and TD groups (Scheffe test, all p<0.05). This age difference should be put into context however, as the full age range for our entire sample is only 34 months, which is still relatively narrow for a study of early autism; nevertheless, all subsequent statistical analyses accounted for age as a covariate. As expected, AD and PDD groups differed significantly from both the TD and the TIE control groups on all clinical scale scores, including the Mullen Early Learning Composite Score, Vineland Adaptive Behavior Scales Standard Score, Autism Diagnostic Observation Schedule–Toddler (ADOS-T) Social Affect Score, ADOS-T Restricted/Repetitive Score, and ADOS-T Total Score. The AD and PDD groups also differed from each other on the ADOS-T Social Affect and Total Scores, with the AD group showing overall greater symptom severity. The majority of subjects in the full sample and within each diagnostic group was of Caucasian ancestry, with no other ancestral group (Asian, Pacific Islander, African-American, American Indian, or Mixed) individually comprising more than 10% of any diagnostic group, and there were no differences in overall ancestral composition between any of the diagnostic groups (Pearson χ²₍₂₅₎=21.516; p=0.664). To increase sample size and inferential power, and maintain consistency with the conceptualization of AD and PDD as disorders on the same spectrum, we pooled these samples together into one ASD group for all subsequent analyses; likewise, we pooled both typically developing groups (TD and TIE) into one unaffected control (CNT) group for all subsequent analyses, acknowledging that other interesting combinations and comparisons could be tested.

Table 1.

Demographic and Clinical Characteristics of the Six Diagnostic Groups

	Diagnostic Group
	AD	PDD	DD	LD	TD	TIE
Sample size, n	37	23	17	34	27	41
Sex, male:female	29:8	20:3	13:4	26:8	14:13	26:15
Age, range in months	13–46	12–41	13–43	12–32	12–45	13–38
Age mean months (s.d.)	26.9 ( 9.2)	28.7 ( 8.1)	25.0 ( 8.5)	17.4 ( 4.6)a	22.0 ( 9.0)	16.8 ( 5.8)a
Mullen Early Learning Composite Score, mean (s.d.)	69.2 (19.8)b	77.4 (14.8)b	63.6 (12.6)	94.3 (12.8)	116.6 (13.3)	105.4 (14.0)
Vineland Adaptive Behavior Scales Standard Score, mean (s.d.)	79.9 (12.2)c	82.6 ( 9.8)c	76.8 (15.2)	93.8 ( 7.7)	105.5 ( 9.7)	101.9 ( 9.5)
ADOS-T Social Affect Score, mean (s.d.)	14.8 ( 4.9)d	10.7 ( 3.2)d	6.8 ( 4.7)	4.0 ( 3.5)	1.8 ( 1.5)	2.5 ( 1.8)
ADOS-T Restricted/Repetitive Score, mean (s.d.)	3.1 ( 1.6)e	2.2 ( 1.5)e	1.4 ( 1.6)	0.9 ( 1.0)	0.6 ( 1.1)	0.5 ( 1.4)
ADOS-T Total Score, mean (s.d.)	17.9 ( 5.8)f	13.0 ( 3.2)	8.2 ( 5.4)	4.9 ( 3.8)	2.3 ( 1.8)	3.0 ( 2.5)

Abbreviations: AD = autistic disorder; ADOS-T = Autism Diagnostic Observation Schedule–Toddler; DD = developmental delay; LD = language delay; PDD = pervasive developmental disorder not otherwise specified; TD = typically developing; TIE = type-I error.

^aAge: significantly different (p<0.05) from AD, DD, PDD, and TD groups

^bMullen: significantly different (p<0.05) from TD and TIE groups

^cVineland: significantly different (p<0.05) from LD, TD, and TIE groups

^dADOS-T Social Affect: significantly different (p<0.05) from all other diagnostic groups, including each other

^eADOS-T Restricted/Repetitive: significantly different (p<0.05) from all other diagnostic groups, but not each other

^fADOS-T Total: significantly different (p<0.05) from all other diagnostic groups, including each other

Microarray Data Analyses

The ASD sample was split into two independent subgroups: a discovery sample and a replication sample. The two subsamples were drawn to be equivalent in sample size, age, sex, and diagnostic composition (AD vs. PDD). The CNT sample was similarly split into discovery and replication samples drawn to be equivalent in size, age, sex, and diagnostic composition (TD vs. TIE). The basic analytic model for identifying candidate biomarkers in the discovery samples was an analysis of covariance (ANCOVA) with each gene's expression intensity value as the dependent measure, diagnostic group (ASD vs. CNT) and sex as fixed between-subjects factors, and age in months as a continuous covariate. Genes whose expression levels were influenced by at least a nominally significant main effect of diagnostic group (p<0.05) and whose log2 fold-change in the ASD group was ≥ |1.2| were identified as putative candidate ASD biomarkers and advanced to the next phase of analysis. These fairly liberal criteria were used to cast a wide net to catch all potentially informative genes, while false-positives would be pared off the putative biomarker set by subsequent model-building, -optimization, and replication steps. These genes were then used to build a support vector machine (SVM) classifier of the same discovery sample in which they were initially discovered. The SVM was configured as cost-based, with costs varied from 1 to 1001 in intervals of 100. The tolerance (termination criterion) of the SVM was set at 0.001. The kernel for the SVM was a radial basis function, with gamma equal to the inverse of the number of evaluated markers. The optimal (i.e., most accurate and parsimonious) SVM was derived by shrinking centroids (which prunes highly correlated or redundant features) and 10-fold cross-validation. The optimal SVM classifier was then tested for classification accuracy in the fully independent ASD and CNT replication samples, and then re-evaluated in the presence of the LD and DD samples as affected but non-autistic comparators. The list of genes comprising the optimal classifier was subjected to the Database for Annotation, Visualization, and Integrated Discovery (DAVID)²² algorithm to determine if it was enriched with genes having particular functional, ontological, or structural annotations. Fold-enrichments of such annotations were only declared statistically significant if they surpassed a Bonferroni-corrected p-value threshold of α=0.05/the number of terms evaluated in a particular category.

RESULTS

Discovery Samples

Putative Biomarker Identification by ANCOVA

We found 2540 probes whose expression levels were at least nominally significantly different (0.05>p>3.0e⁻⁶) between the ASD and CNT groups. When limiting this probe-set to only those that showed significant dysregulation in ASD (p<0.05) and a log2 fold-change ≥ |1.2|, 154 probes remained (Table 2). The log2 fold-change in these probes ranged from 2.1-fold up-regulation to 1.7-fold down-regulation, corresponding to actual changes of 4.3-fold up-regulation and 3.2-fold down-regulation, respectively.

Table 2.

Probes Significantly Dysregulated (p<0.05, Fold-Change≥|1.2|) in the autism spectrum disorder (ASD) Discovery Sample Compared to the unaffected control group (CNT) Discovery Sample

Probe IDa	Gene Symbol	Gene Product	Log2 Fold-Changeb	p
ILMN_2261600	FCGR1A	Fc fragment of IgG, high affinity Ia, receptor (CD64)	−1.69	1.29E-02
ILMN_1799848	ANKRD22	Ankyrin repeat domain 22	−1.65	2.63E-02
ILMN_2114568	GBP5	Guanylate binding protein 5	−1.65	3.89E-03
ILMN_2132599	ANKRD22	Ankyrin repeat domain 22	−1.64	2.77E-02
ILMN_1743145	L-RAP	Homo sapiens l-rap mRNA for leukocyte-derived arginine aminopeptidase long form variant, complete cds.	−1.62	3.91E-02
ILMN_2391051	FCGR1B	Fc fragment of IgG, high affinity Ib, receptor (CD64)	−1.62	8.29E-03
ILMN_2148785	GBP1	Guanylate binding protein 1, interferon-inducible, 67kDa	−1.60	1.04E-02
ILMN_2176063	FCGR1A	Fc fragment of IgG, high affinity Ia, receptor (CD64)	−1.59	2.31E-02
ILMN_1687306	LGALS2	Lectin, galactoside-binding, soluble, 2	−1.52	6.16E-03
ILMN_1694548	ANXA3	Annexin A3	−1.51	2.80E-03
ILMN_1786303	LILRA3	Leukocyte immunoglobulin-like receptor, subfamily A (without TM domain), member 3	−1.51	3.83E-02
ILMN_1701114	GBP1	Guanylate binding protein 1, interferon-inducible, 67kDa	−1.49	4.02E-02
ILMN_1708934	ADM	Adrenomedullin	−1.47	5.90E-03
ILMN_1745242	PLSCR1	Phospholipid scramblase 1	−1.47	3.18E-02
ILMN_1753111	NAMPT	Nicotinamide phosphoribosyltransferase	−1.46	4.22E-02
ILMN_1666078	HLA-H	Major histocompatibility complex, class I, H (pseudogene)	−1.41	2.66E-02
ILMN_1720771	STX11	Syntaxin 11	−1.41	2.77E-03
ILMN_1782487	GBP1	Guanylate binding protein 1, interferon-inducible, 67kDa	−1.40	3.14E-02
ILMN_1795336	PTER	Phosphotriesterase related	−1.40	1.33E-03
ILMN_1689734	IL1RN	Interleukin 1 receptor antagonist	−1.40	4.15E-02
ILMN_1771385	BC050625	Hs.409925	−1.39	2.35E-02
ILMN_2050911	SLC22A4	Solute carrier family 22 (organic cation/ergothioneine transporter), member 4	−1.39	4.21E-03
ILMN_1700067	BTN3A2	Butyrophilin, subfamily 3, member A2	−1.39	1.11E-02
ILMN_1748915	S100A12	S100 calcium binding protein A12	−1.38	2.58E-02
ILMN_1755843	SLC26A8	Solute carrier family 26, member 8	−1.37	7.40E-03
ILMN_1756806	DQ893795	Hs.719112	−1.35	1.92E-02
ILMN_1653871	NAMPT	Nicotinamide phosphoribosyltransferase	−1.35	1.85E-02
ILMN_1652003	GNG10	Guanine nucleotide binding protein (G protein), gamma 10	−1.35	4.91E-03
ILMN_1757074	GNG10	Guanine nucleotide binding protein (G protein), gamma 10	−1.35	9.47E-03
ILMN_1721116	USP10	Ubiquitin specific peptidase 10	−1.34	3.20E-03
ILMN_1731224	PARP9	Poly (ADP-ribose) polymerase family, member 9	−1.34	2.60E-02
ILMN_1679826	CST7	Cystatin F (leukocystatin)	−1.33	3.12E-02
ILMN_2335813	GCH1	GTP cyclohydrolase 1	−1.33	3.73E-02
ILMN_1676528	BTN3A2	Butyrophilin, subfamily 3, member A2	−1.33	3.01E-02
ILMN_1707979	CARD17	Caspase recruitment domain family, member 17	−1.33	3.26E-02
ILMN_1851599	BX110640	Unknown	−1.33	1.83E-02
ILMN_1696419	STOM	Stomatin	−1.33	2.07E-02
ILMN_2224486	C3orf14	Chromosome 3 open reading frame 14	−1.33	2.65E-03
ILMN_2053527	PARP9	Poly (ADP-ribose) polymerase family, member 9	−1.32	3.68E-02
ILMN_2357419	LILRA5	Leukocyte immunoglobulin-like receptor, subfamily A (with TM domain), member 5	−1.31	1.92E-02
ILMN_2294762	AMY1A	Amylase, alpha 1A (salivary)	−1.31	8.77E-03
ILMN_1685057	SLC22A4	Solute carrier family 22 (organic cation/ergothioneine transporter), member 4	−1.31	9.80E-03
ILMN_2256050	SERPINA1	Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 1	−1.31	3.21E-02
ILMN_1709233	F5	Coagulation factor V (proaccelerin, labile factor)	−1.31	8.64E-04
ILMN_2126706	LMNB1	Lamin B1	−1.31	5.47E-03
ILMN_1810289	MYOF	Myoferlin	−1.30	4.60E-02
ILMN_1668865	SLC2A14	Solute carrier family 2 (facilitated glucose transporter), member 14	−1.30	3.54E-04
ILMN_1666742	C9orf72	Chromosome 9 open reading frame 72	−1.30	1.99E-02
ILMN_1767809	GNG10	Guanine nucleotide binding protein (G protein), gamma 10	−1.29	4.62E-03
ILMN_1726545	LILRA5	Leukocyte immunoglobulin-like receptor, subfamily A (with TM domain), member 5	−1.29	2.12E-02
ILMN_1684585	ACSL1	Acyl-CoA synthetase long-chain family member 1	−1.29	1.28E-02
ILMN_2393296	DQ894773	Hs.1466	−1.28	4.47E-02
ILMN_1725471	DQ894773	Hs.1466	−1.28	3.62E-02
ILMN_2109197	EPB41L3	Erythrocyte membrane protein band 4.1-like 3	−1.27	3.26E-02
ILMN_1726591	CARD16	Caspase recruitment domain family, member 16	−1.27	4.53E-02
ILMN_2219712	HMGB2	High-mobility group box 2	−1.26	1.55E-02
ILMN_1806015	SLC2A3	Solute carrier family 2 (facilitated glucose transporter), member 3	−1.26	5.79E-04
ILMN_1659047	HIST2H2AA4	Histone cluster 2, H2aa4	−1.26	3.13E-02
ILMN_1798270	C11orf75	Chromosome 11 open reading frame 75	−1.26	3.07E-02
ILMN_2232478	APOBEC3G	Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3G	−1.26	2.62E-02
ILMN_1741727	QPCT	Glutaminyl-peptide cyclotransferase	−1.26	8.82E-03
ILMN_1772036	STEAP4	STEAP family member 4	−1.26	3.66E-02
ILMN_1710937	IFI16	Interferon, gamma-inducible protein 16	−1.25	3.15E-02
ILMN_1845037	TRIM69	Tripartite motif-containing 69	−1.25	3.98E-02
ILMN_1741881	C9orf72	Chromosome 9 open reading frame 72	−1.25	6.62E-03
ILMN_1795715	DPYD	Dihydropyrimidine dehydrogenase	−1.25	1.24E-02
ILMN_1737964	HIATL1	Hippocampus abundant transcript-like 1	−1.25	1.30E-02
ILMN_1680109	COL4A3BP	Collagen, type IV, alpha 3 (Goodpasture antigen) binding protein	−1.25	3.03E-02
ILMN_2412172	APOBEC3F	Apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3F	−1.25	2.16E-02
ILMN_1780546	OSM	Oncostatin M	−1.25	1.63E-02
ILMN_1779486	FAM126B	Family with sequence similarity 126, member B	−1.25	3.36E-02
ILMN_1674574	VNN1	Vanin 1	−1.24	3.86E-02
ILMN_2186061	IPFK-2	Homo sapiens inducible 6-phosphofructo-2-kinase/fructose 2,6-bisphosphatase (IPFK-2) mRNA, complete cds.	−1.23	1.66E-02
ILMN_1672124	C4orf18	Chromosome 4 open reading frame 18	−1.23	1.91E-02
ILMN_1788002	MAPK14	Mitogen-activated protein kinase 14	−1.23	6.14E-03
ILMN_1652680	NCF1B	Hs.647047	−1.23	4.82E-02
ILMN_1705663	DMXL2	Dmx-like 2	−1.23	4.59E-02
ILMN_1804735	CBS	Cystathionine-beta-synthase	−1.22	4.38E-02
ILMN_1674789	Unknown	Unknown	−1.22	4.52E-02
ILMN_1701914	CD274	CD274 molecule	−1.22	3.25E-02
ILMN_1713058	PSTPIP2	Proline-serine-threonine phosphatase interacting protein 2	−1.22	3.08E-02
ILMN_2061318	TAF13	TAF13 RNA polymerase II, TATA box binding protein (TBP)-associated factor, 18kDa	−1.22	2.48E-02
ILMN_1683250	LOC440731	Similar to hCG1817424	−1.22	4.22E-02
ILMN_1659255	RP2	Retinitis pigmentosa 2 (X-linked recessive)	−1.22	4.25E-02
ILMN_2061327	TAF13	TAF13 RNA polymerase II, TATA box binding protein (TBP)-associated factor, 18kDa	−1.22	1.90E-02
ILMN_1751079	TAP1	Transporter 1, ATP-binding cassette, sub-family B (MDR/TAP)	−1.22	2.42E-02
ILMN_1687201	APOL6	Apolipoprotein L, 6	−1.22	4.05E-02
ILMN_1667711	DQ895783	Unknown	−1.22	4.45E-02
ILMN_1801767	ABHD3	Abhydrolase domain containing 3	−1.22	3.78E-02
ILMN_1776649	LRRK2	Leucine-rich repeat kinase 2	−1.22	4.93E-02
ILMN_1781700	IL18R1	Interleukin 18 receptor 1	−1.21	1.31E-02
ILMN_2078264	C3orf38	Chromosome 3 open reading frame 38	−1.21	3.14E-02
ILMN_1654268	HMGB2	High-mobility group box 2	−1.21	1.93E-02
ILMN_2319077	FAS	Fas (TNF receptor superfamily, member 6)	−1.21	3.08E-02
ILMN_1761941	C4orf18	Chromosome 4 open reading frame 18	−1.21	2.23E-02
ILMN_1778059	CASP4	Caspase 4, apoptosis-related cysteine peptidase	−1.21	4.30E-02
ILMN_2226015	LRRK2	Leucine-rich repeat kinase 2	−1.21	3.97E-02
ILMN_1729115	UBE2S	Ubiquitin-conjugating enzyme E2S	−1.21	3.42E-02
ILMN_2230862	GYG1	Glycogenin 1	−1.21	1.06E-02
ILMN_2374340	PLAUR	Plasminogen activator, urokinase receptor	−1.21	4.33E-02
ILMN_1678766	DYNLT1	Dynein, light chain, Tctex-type 1	−1.21	4.65E-02
ILMN_1756953	GBP6	Guanylate binding protein family, member 6	−1.21	4.23E-02
ILMN_1792682	MCTP2	Multiple C2 domains, transmembrane 2	−1.21	1.96E-02
ILMN_1707286	PLBD1	Phospholipase B domain containing 1	−1.20	2.52E-02
ILMN_1736327	CDC42EP3	CDC42 effector protein (Rho GTPase binding) 3	−1.20	2.54E-02
ILMN_1716704	NLRC5	NLR family, CARD domain containing 5	−1.20	3.87E-03
ILMN_1806651	PARP8	Poly (ADP-ribose) polymerase family, member 8	−1.20	2.83E-02
ILMN_2189037	WDR52	WD repeat domain 52	−1.20	4.38E-02
ILMN_1691104	PGAM4	Phosphoglycerate mutase family member 4	−1.20	1.23E-02
ILMN_1685824	B4GALT5	UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 5	−1.20	3.47E-02
ILMN_1745686	MFHAS1	Malignant fibrous histiocytoma amplified sequence 1	1.20	2.24E-03
ILMN_2320330	MAL	Mal, T-cell differentiation protein	1.20	3.54E-02
ILMN_2074860	KRT4	Keratin 4	1.21	1.54E-02
ILMN_1795639	MGMT	O-6-methylguanine-DNA methyltransferase	1.21	4.50E-03
ILMN_1713731	ALOX12	Arachidonate 12-lipoxygenase	1.21	3.71E-02
ILMN_1658624	UBXN6	UBX domain protein 6	1.21	4.99E-03
ILMN_1727183	ZNF763	Zinc finger protein 763	1.21	1.81E-02
ILMN_1772876	ZNF395	Zinc finger protein 395	1.21	4.67E-03
ILMN_1728107	GNG7	Guanine nucleotide binding protein (G protein), gamma 7	1.22	3.06E-03
ILMN_1658957	MGC13005	Hypothetical LOC84771	1.22	7.00E-03
ILMN_2402131	TAF15	TAF15 RNA polymerase II, TATA box binding protein (TBP)-associated factor, 68kDa	1.23	2.83E-02
ILMN_1662587	PNPLA7	Patatin-like phospholipase domain containing 7	1.23	1.48E-02
ILMN_1662451	FCER2	Fc fragment of IgE, low affinity II, receptor for (CD23)	1.23	1.95E-02
ILMN_1676042	Unknown	Unknown	1.23	4.56E-02
ILMN_2337928	CXCR5	Chemokine (C-X-C motif) receptor 5	1.23	1.01E-02
ILMN_1753782	ZNF266	Zinc finger protein 266	1.23	1.24E-02
ILMN_2409384	SIGLEC7	Sialic acid binding Ig-like lectin 7	1.23	1.45E-02
ILMN_1762764	SH3BGRL2	SH3 domain binding glutamic acid-rich protein like 2	1.23	3.96E-02
ILMN_1666089	OLIG1	Oligodendrocyte transcription factor 1	1.24	3.21E-02
ILMN_2374352	DBNDD1	Dysbindin (dystrobrevin binding protein 1) domain containing 1	1.24	2.44E-02
ILMN_1659075	HLA-DOA	Major histocompatibility complex, class II, DO alpha	1.24	1.75E-02
ILMN_1773567	LAMA5	Laminin, alpha 5	1.24	4.66E-04
ILMN_2307025	RBM12	RNA binding motif protein 12	1.25	1.43E-02
ILMN_1735712	KRT1	Keratin 1	1.26	2.42E-02
ILMN_1657996	AK098012	Uncharacterized protein ENSP00000383353.	1.27	4.34E-03
ILMN_2143314	SPIB	Spi-B transcription factor (Spi-1/PU.1 related)	1.28	2.23E-02
ILMN_1695812	KRT72	Keratin 72	1.29	6.15E-03
ILMN_1685122	COL9A2	Collagen, type IX, alpha 2	1.29	8.81E-04
ILMN_1743911	SLC25A39	Solute carrier family 25, member 39	1.30	6.34E-03
ILMN_1752755	VWF	Von Willebrand factor	1.31	5.65E-03
ILMN_1703326	GPR44	G protein-coupled receptor 44	1.31	2.92E-02
ILMN_1814397	EPB42	Erythrocyte membrane protein band 4.2	1.32	3.25E-03
ILMN_1678707	TAF15	TAF15 RNA polymerase II, TATA box binding protein (TBP)-associated factor, 68kDa	1.32	3.94E-02
ILMN_1763322	CCR3	Chemokine (C-C motif) receptor 3	1.32	2.97E-02
ILMN_1804350	LOC644852	Hypothetical LOC644852	1.33	2.42E-02
ILMN_1792323	HDC	Histidine decarboxylase	1.34	3.46E-02
ILMN_1751814	BC032353	Homo sapiens cDNA FLJ36366 fis, clone THYMU2007824.	1.35	2.06E-02
ILMN_1654875	CLC	Charcot-Leyden crystal protein	1.37	2.10E-02
ILMN_1796216	VASH1	Vasohibin 1	1.39	2.22E-04
ILMN_1705605	BC032353	Homo sapiens cDNA FLJ36366 fis, clone THYMU2007824.	1.39	4.81E-03
ILMN_1696512	ERAF	Erythroid associated factor	1.43	1.34E-02
ILMN_1815527	HBD	Hemoglobin, delta	1.51	8.33E-03
ILMN_1796678	HBG1	Hemoglobin, gamma A	2.04	9.24E-03
ILMN_2084825	HBG2	Hemoglobin, gamma G	2.07	7.00E-03

Note:

^aThe 48 probe IDs in bold font are those that comprised the optimal support vector machine (SVM) classifier of ASD.

^bRows are sorted by ascending fold-change in the ASD discovery sample compared to the CNT discovery sample, with largest negative fold-change at the top and largest positive fold-change at the bottom of the Table.

Construction of Optimal SVM Classifier

The 154 probes meeting our significance and fold-change criteria were utilized to construct, optimize, and test the classification accuracy of various SVMs. The most accurate SVM (cost 901, gamma 0.001) utilized the expression intensities of 48 probes (Table 2) to correctly classify 71% of ASD and CNT subjects across ten subsets of the discovery sample into their appropriate diagnostic categories. Twenty-two of 30 ASD subjects were correctly classified by this SVM as having an ASD, while 23 of 34 CNT subjects were correctly classified as CNT subjects. The corresponding sensitivity and specificity of this SVM after ten-fold cross-validation was 0.73 and 0.68, respectively.

Replication Samples

Evaluation of Optimal SVM Classifier

The identical 48-probe SVM classifier optimized in the discovery sample performed almost perfectly in the replication sample, with an accuracy of 91%. Just 2 of 30 ASD subjects were incorrectly identified as CNT subjects, while only 4 of 34 CNT subjects were misclassified as ASD subjects. The corresponding sensitivity and specificity of this SVM was 0.93 and 0.88, respectively. The area under the receiver-operating characteristic (ROC) curve generated from these classifications was 0.91.

When the optimized 48-probe SVM diagnostic classifier of ASDs was deployed to classify the ASD and CNT replication samples in the presence of LD and DD samples, its accuracy was only slightly attenuated to 87%. Only 2 of 30 ASD subjects in the replication sample were misclassified (1 as CNT and 1 as LD), and only 8 of 36 individuals classified by the SVM as ASD group members were not actually diagnosed as such (including 4 CNT subjects, 1 DD subject, and 3 LD subjects). Though not designed or optimized for this purpose, the 48-probe SVM classifier of ASD also fared quite well in distinguishing LD and DD subjects from CNT subjects and from each other, with only 8 errors made among the total of 80 subjects assigned to these categories.

The list of 48 probes comprising the optimal SVM classifier of ASDs was most significantly enriched with genes related to immune responses in general (nine genes: APOBEC3F, APOBEC3G, FCGR1B, FCGR1C, GBP1, GBP4, GBP5, GCH1, and TAP1; six-fold enrichment compared to chance expectation; Bonferroni-corrected p=0.014), and genes of the hemoglobin complex (four genes: AHSP, HBD, HBG1, and HBG2; 142-fold enrichment; Bonferroni-corrected p=1.8E⁻⁴) and genes with guanine- or guanylate-binding affinity (three genes: GBP1, GBP4, and GBP5; 140-fold enrichment; Bonferroni-corrected p=3.7E⁻³) in particular. Although no known canonical pathways were disproportionally represented among the 48 genes, some links between the genes along various biological pathways were observed through Ingenuity Pathway Analysis (see Figure S1, available online).

DISCUSSION

In this study we identified a blood-based gene expression profile that reliably identified subjects with ASDs near the age of first clinical signs. In fact, somewhat surprisingly, the optimal classifier attained nominally higher accuracy (though not statistically significantly higher) in the test sample than in the training sample. More typically, a classifier will perform best in the sample in which it was trained, and a drop-off in performance will be encountered in subsequent test sets. In the present case, the apparent difference in classification accuracy may stem from the fact that the accuracy in the training sample actually represents the average of ten smaller cross-validation samples (each with just 4–5 cases and 4–5 comparison subjects), whereas the value in the test set is derived from measurement in the full test sample without additional cross-validation. Or the spike in accuracy may represent a random fluctuation, and additional test samples may very well show a decrement in accuracy, which would not be unexpected. In either event, further analyses and outside replications of the optimal gene-expression-based classifier of ASDs are needed.

This promising work, while far from being definitive, gives further proof to the recently emerging principle that peripheral blood is a potentially useful source of biomarkers for disorders of the brain and other inaccessible tissues.²³ The optimal ASD classifier panel of 48 mRNA biomarkers, which was derived to maximize differences between ASD subjects and those in the two unaffected control groups (TD and TIE), might ultimately have clinical utility in the context of high-risk screening where a heightened risk of autism is feared or expected (i.e., in multiply affected families). However, this same model was not necessarily expected to provide maximal classification accuracy for distinguishing ASD subjects from those with other developmental delays or disorders, such as the LD and DD subjects in our sample. Nevertheless, when we performed these comparisons, the 48-probe model of ASDs still retained a very high degree of classification accuracy. This model, in contrast, may ultimately have utility (and could be further optimized) for a general population screen for ASDs at an early age, or in the form of a test for autism among children who show at least some early warning signs of developmental delay, whether or not those warning signs ultimately manifest as a diagnosable disorder (e.g., ASDs, DD, or LD) or not (e.g., TIE).

Since we first demonstrated the potential utility of transcriptome profiling in peripheral blood cells as a source of biomarkers for mental illnesses in 2005,²⁴ we and others have sought to identify those select biomarkers that also might shed light on the pathophysiology underlying such disorders.^{20, 25} Further work will be needed to establish if these dysregulated biomarkers are truly indicative of the early developmental pathogenesis of autism in the brain, and if the dysregulation of these genes is governed by one or more expression quantitative trait loci; however, neither is a prerequisite of an effective biomarker. Yet, one of the 48 biomarkers in our optimized SVM (IFI16, which encodes interferon, gamma inducible 16) was previously found to be dysregulated in postmortem temporal cortex of subjects with autism.²⁶ In turn, although some of the identified biomarkers may highlight potential pathophysiological processes, our work is no replacement for studies utilizing animal models, human postmortem brain tissue, induced pluripotent stem cells, and neuroimaging studies of children living with autism.

Our analyses of gene-expression data at just one early timepoint in the lives of actively developing children preclude us from determining if the identified biomarkers of ASDs are or will be stable over time. A recent study of longitudinal gene expression in healthy individuals,²⁷ as well as earlier small-scale studies,^28–31 found remarkable temporal stability in the expression of most genes within individuals, with only 1–2% of genes showing significant variability over periods on the order of one month or longer; however, these studies have not routinely examined children, nor individuals affected with neurodevelopmental disorders such as ASDs. Thus, the stability of the particular biomarker signatures we identified remains an empirical question to be answered in due course. Toward that objective, we continue to track the subjects reported on here, as well as ascertain new subjects in this study, which will provide an invaluable source of replication since other similar sample resources are not yet available elsewhere. As we are tracking these subjects longitudinally, we can observe how these putative biomarker profiles change over time as symptoms also change naturally or in response to intensive behavioral treatment. Further evaluation of LD and DD subjects will also shed light on the unique biomarker signatures of these important conditions. Perhaps most importantly, the peripheral blood gene expression data (and linked genetic polymorphism and structural and functional neuroimaging data) generated in our sample of children with a broad range of clinical symptoms both within and beyond the autistic spectrum could be harnessed for the purpose of “reverse engineering” a new nosology of childhood neurodevelopmental disorders based on clusters of biological homogeneity rather than clinical symptoms which may reflect common endpoints of diverse etiologies.

The putative ASD biomarkers found here were initially identified using an ANCOVA that accounted for the age differences in the groups, and the marker set advancing to the stage of classifier-construction was filtered for genes showing a significant age effect. Thus, the age difference between diagnostic groups is not expected to be a major driver of classification accuracy. We recognize, however, that in real-world applications a child's age—in conjunction with a particular gene-expression profile—may be critically important for making an ASD diagnosis. Therefore, with the addition of more subjects in this study across a range of ages, we continue working to refine distinct ASD-biomarker signatures for children of different ages, and to incorporate age itself (along with the expression levels of both age-dependent and -independent genes) as a factor influencing the performance of the diagnostic classifier.

Much work remains to be done, but even at this stage the currently characterized biomarkers—if replicated—could be harnessed by efforts to expedite and standardize the process of primary and differential diagnosis, which presently involves considerable time, effort, and uncertainty. Biomarkers such as these, if deployed in a clinical testing environment, should allow for earlier identification of affected individuals, in turn hastening their receipt of effective (perhaps personalized) treatment and improving prognoses.² Furthermore, biomarkers could form the basis for early intervention and prevention efforts targeting at-risk infants and toddlers, which might significantly improve clinical outcomes for affected infants.³² The identified biomarkers may, in turn, suggest “treatable targets” for future waves of medicinal chemistry and drug-discovery research. Collectively, these advances would translate into an enormous improvement in global public health.