Researchers have developed a blood test for autism spectrum
disorders (ASDs) that outperforms existing genetic tests while presenting
evidence that abnormal immunologic activity affecting brain development may
help explain some of autism’s origins. The
findings also suggest a new direction for genetic research on autism and the
search for treatments.
The blood test, described December 5 in the online open-access
journal PLOS ONE and based on the largest gene-chip investigation ever done in
autism, could enable early diagnosis of autism in about two-thirds of patients
before clear symptoms start to appear (the average age of diagnosis in the U.S.
is 5 years).
Researchers led by Sek Won Kong, HMS instructor of
pediatrics at Boston Children’s Hospital, and also a member of the hospital’s
Informatics Program (CHIP), analyzed blood samples from 66 male patients with
ASDs (from Boston Children’s and several other hospitals in collaboration with
the Autism Consortium of Boston) and compared them with 33 age-matched boys
without ASDs. Using microarrays, they looked for RNA signatures reflecting
differences in gene activity, or expression, between the two groups.
“Since brain biopsy
isn’t a viable option for research, we asked whether blood could serve as a
proxy for gene expression in the brain,” said Isaac Kohane, HMS professor of
pediatrics at Boston Children’s and director of CHIP. “We found that it could,
though we and others were initially skeptical.”…
Analyzing the blood samples, Kong and colleagues flagged 489 genes
as having distinct expression patterns in the ASD group, then narrowed this to
a group of 55 genes that correctly identified or ruled out autism in 76 percent
of samples. They validated their findings in a second group of 104 male and
female patients with ASDs and 82 controls, achieving an overall classification
accuracy of 68 percent (73 percent for males and 64 percent for females)…
“It’s clear that no single mutation or even a
single pathway is responsible for all cases,” said Kohane, who is also senior
investigator on both studies. “By looking at this 55-gene signature, which can
capture disruptions in multiple pathways at once, we can say with about 70
percent accuracy that ‘this child does not have autism,’ or ‘this child could
be at risk,’ putting him at the head of the queue for early intervention and
evaluation. And we can do it relatively inexpensively and quickly.”…
The 55 genes whose expression was altered
also suggest more than one path to what we know as autism. Based on their
genetic signatures, subjects with ASDs clustered into four subgroups marked by
changes in different biological pathways:
• Synaptic pathways,
specifically long-term potentiation pathways (essential for memory and
learning), and neurotrophic pathways (signaling neurons to survive, develop and
grow)
• Immune/inflammatory
pathways
“In our sample, about half of the autism cases had some sort of
alteration on immune pathways, synaptic pathways or both,” said Kong.
http://hms.harvard.edu/news/better-early-blood-test-autism-12-5-12
Kong SW, Collins CD, Shimizu-Motohashi Y, Holm IA, Campbell MG, Lee IH,
ReplyDeleteBrewster SJ, Hanson E, Harris HK, Lowe KR, Saada A, Mora A, Madison K, Hundley R,
Egan J, McCarthy J, Eran A, Galdzicki M, Rappaport L, Kunkel LM, Kohane IS. Characteristics and predictive value of blood transcriptome signature in males with autism spectrum disorders. PLoS One. 2012;7(12):e49475.
Abstract
Autism Spectrum Disorders (ASD) is a spectrum of highly heritable neurodevelopmental disorders in which known mutations contribute to disease risk in 20% of cases. Here, we report the results of the largest blood transcriptome study to date that aims to identify differences in 170 ASD cases and 115 age/sex-matched controls and to evaluate the utility of gene expression profiling as a tool to aid in the diagnosis of ASD. The differentially expressed genes were enriched for the neurotrophin signaling, long-term potentiation/depression, and notch signaling pathways. We developed a 55-gene prediction model, using a cross-validation strategy, on a sample cohort of 66 male ASD cases and 33 age-matched male controls (P1). Subsequently, 104 ASD cases and 82 controls were recruited and used as a validation set (P2). This 55-gene expression signature achieved 68% classification accuracy with the validation cohort (area under the receiver operating characteristic curve (AUC): 0.70 [95% confidence interval [CI]: 0.62-0.77]). Not surprisingly, our prediction model that was built and trained with male samples performed well for males (AUC 0.73, 95% CI 0.65-0.82), but not for female samples (AUC 0.51, 95% CI 0.36-0.67). The 55-gene signature also performed robustly when the prediction model was trained with P2 male samples to classify P1 samples (AUC 0.69, 95% CI 0.58-0.80). Our result suggests that the use of blood expression profiling for ASD detection may be feasible. Further study is required to determine the age at which such a test should be deployed, and what genetic characteristics of ASD can be identified.