Patent Application
20130123124
The invention relates to methods and reagents for characterizing and diagnosing autism spectrum disorder.
Autism Spectrum Disorders (ASD) cover a broad spectrum of neurocognitive and social developmental delays with typical onset before 3 years of age including Autistic Disorder, Pervasive Developmental Disorder-Not Otherwise Specified and Asperger's Disorder as sub classified in the Diagnostic and Statistical Manual of Psychiatric Disorders, 4th edition, Text Revision (DSM-IV-TR). Prevalence of ASD has been increasing during last decades, and current estimation is 1 in 90 (Kogan, M. D., et al. Prevalence of parent-reported diagnosis of autism spectrum disorder among children in the US, 2007. Pediatrics 124, 1395-1403 (2009)) to 3.7 in 10002. There are waiting lists for evaluation by most centers with expertise, and despite the progress made in adopting instruments such as the Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS) there remains significant debate regarding the prognostic value and accuracy of existing instruments3. Thus, improved diagnostic approaches are needed.
It has been discovered that a variety of genes are differentially expressed in individuals having autism spectrum disorder compared with individuals free of autism spectrum disorder. Such genes are identified as autism spectrum disorder-associated genes. It has also been discovered that the autism spectrum disorder status of an individual can be classified with a high degree of accuracy, sensitivity, and specificity based on expression levels of these autism spectrum disorder-associated genes. Accordingly, methods and related kits are provided herein for characterizing and diagnosing autism spectrum disorder in an individual.
According to some aspects of the invention, methods of characterizing the autism spectrum disorder status of an individual in need thereof are provided. In some embodiments, the methods comprise: (a) obtaining a clinical sample from the individual; (b) determining expression levels of a plurality of autism spectrum disorder-associated genes in the clinical sample using an expression level determining system, wherein the autism spectrum disorder-associated genes comprise at least ten genes selected from Table 4, 5, 6, 7 or 10; and (c) comparing each expression level determined in (b) with an appropriate reference level, wherein the results of comparing in (c) characterize the autism spectrum disorder status of the individual. In some embodiments, the methods further comprise diagnosing autism spectrum disorder in the individual based on the autism spectrum disorder status. In some embodiments, the autism spectrum disorder-associated genes comprise at least one of: ARRB2, AVIL, BTBD14A, CCDC50, CD180, CD300LF, CPNE5, CXCL1, CYP4F3, FAM101B, FAM13A1OS, HAL, KCNE3, LOC643072, LTB4R, MAN2A2, MSL-1, MYBL2, NBEAL2, NFAM1, NHS, PLA2G7, PNOC, RASSF6, REM2, SIRPA, SLC45A4, SPIB, SULF2, TMEM190, ZNF516, and ZNF746. In some embodiments, a higher level of at least one autism spectrum disorder-associated gene selected from: ARRB2, AVIL, BTBD14A, CD300LF, CXCL1, CYP4F3, FAM101B, FAM13A1OS, HAL, KCNE3, LOC643072, LTB4R, MAN2A2, MSL-1, NBEAL2, NFAM1, NHS, PLA2G7, REM2, SIRPA, SLC45A4, SULF2, and ZNF746, compared with an appropriate reference level, characterizes the individual's autism spectrum disorder status as having autism spectrum disorder. In some embodiments, a lower level of at least one autism spectrum disorder-associated gene selected from: CCDC50, CD180, CPNE5, MYBL2, PNOC, RASSF6, and SPIB, compared with an appropriate reference level, characterizes the individual's autism spectrum disorder status as having autism spectrum disorder. In some embodiments, the autism spectrum disorder-associated genes comprise at least one of: BCL11A, BLK, C5orf13, CCDC50, CD180, CENPM, CPNE5, CTBP2, EBF1, EIF1AY, FAM105A, FCRL2, HEBP2, IGL@, LOC401233, LRRC6, PLA2G7, PMEPA1, PNN, PNOC, POU2AF1, PRICKLE1, RBP7, SPIB, SULF2, TCF4, TUBB2A, ZNF117, ZNF20, ZNF763, and ZNF830. In some embodiments, the autism spectrum disorder-associated genes comprise at least one of: TSNAX, SH3BP5L, PPIF, CCDC6, CTSD, IL18, UFM1, MTRF1, LPAR6, TWSG1, MAPKSP1, CD180, NFYA, TTRAP, ZNF92, CAPZA2, BLK, OSTF1, HSDL2, ATP6V1G1, DCAF12, and NOTCH1. In some embodiments, the clinical sample is a sample of peripheral blood, brain tissue, or spinal fluid. In some embodiments, each expression level is a level of an RNA encoded by an autism spectrum disorder-associated gene of the plurality. In some embodiments, the expression level determining system comprises a hybridization-based assay for determining the level of the RNA in the clinical sample. In some embodiments, the hybridization-based assay is an oligonucleotide array assay, an oligonucleotide conjugated bead assay, a molecular inversion probe assay, a serial analysis of gene expression (SAGE) assay, or an RT-PCR assay. In some embodiments, each expression level is a level of a protein encoded by an autism spectrum disorder-associated gene of the plurality. In some embodiments, the expression level determining system comprises an antibody-based assay for determining the level of the protein in the clinical sample. In some embodiments, the antibody-based assay is an antibody array assay, an antibody conjugated-bead assay, an enzyme-linked immuno-sorbent (ELISA) assay, or an immunoblot assay.
According to some aspects of the invention, the methods of characterizing the autism spectrum disorder status in an individual in need thereof comprise (a) obtaining a peripheral blood sample from the individual; (b) determining expression levels of a plurality of autism spectrum disorder-associated genes in the clinical sample using an expression level determining system, wherein the autism spectrum disorder-associated genes comprise at least ten genes selected from Table 4, 5, 6, 7 or 10; and (c) applying an autism spectrum disorder-classifier to the expression levels, wherein the autism spectrum disorder-classifier characterizes the autism spectrum disorder status of the individual based on the expression levels. In some embodiments, the methods further comprise diagnosing autism spectrum disorder in the individual based on the autism spectrum disorder status. In some embodiments, the autism spectrum disorder-classifier comprises an algorithm selected from logistic regression, partial least squares, linear discriminant analysis, quadratic discriminant analysis, neural network, naïve Bayes, C4.5 decision tree, k-nearest neighbor, random forest, and support vector machine. In some embodiments, the autism spectrum disorder-classifier has an accuracy of at least 75%. In some embodiments, the autism spectrum disorder-classifier has an accuracy in a range of about 75% to 90%. In some embodiments, the autism spectrum disorder-classifier has a sensitivity of at least 70%. In some embodiments, the autism spectrum disorder-classifier has a sensitivity in a range of about 70% to about 95%.
In some embodiments, the autism spectrum disorder-classifier has a specificity of at least 65%. In some embodiments, the autism spectrum disorder-classifier has a specificity in range of about 65% to about 85%. In some embodiments, the autism spectrum disorder-classifier is trained on a data set comprising expression levels of the plurality of autism spectrum disorder-associated genes in clinical samples obtained from a plurality of individuals identified as having autism spectrum disorder, wherein the interquartile range of ages of the plurality of individuals identified as having autism spectrum disorder is from about 2 years to about 10 years. In some embodiments, the autism spectrum disorder-classifier is trained on a data set comprising expression levels of the plurality of autism spectrum disorder-associated genes in clinical samples obtained from a plurality of individuals identified as not having autism spectrum disorder, wherein the interquartile range of ages of the plurality of individuals identified as not having autism spectrum disorder is from about 2 years to about 10 years. In some embodiments, the autism spectrum disorder-classifier is trained on a data set consisting of expression levels of the plurality of autism spectrum disorder-associated genes in clinical samples obtained from a plurality of male individuals. In some embodiments, the autism spectrum disorder-classifier is trained on a data set comprising expression levels of the plurality of autism spectrum disorder-associated genes in clinical samples obtained from a plurality of individuals identified as having autism spectrum disorder based on DSM-IV-TR criteria. In some embodiments, the autism spectrum disorder-associated genes comprise at least one of: BCL11A, BLK, C5orf13, CCDC50, CD180, CENPM, CPNE5, CTBP2, EBF1, EIF1AY, FAM105A, FCRL2, HEBP2, IGL@, LOC401233, LRRC6, PLA2G7, PMEPA1, PNN, PNOC, POU2AF1, PRICKLE1, RBP7, SPIB, SULF2, TCF4, TUBB2A, ZNF117, ZNF20, ZNF763, and ZNF830. In some embodiments, the autism spectrum disorder-associated genes comprise: TSNAX, SH3BP5L, PPIF, CCDC6, CTSD, IL18, UFM1, MTRF1, LPAR6, TWSG1, MAPKSP1, CD180, NFYA, TTRAP, ZNF92, CAPZA2, BLK, OSTF1, HSDL2, ATP6V1G1, DCAF12, and NOTCH1. In some embodiments, the autism spectrum disorder-associated genes comprise at least one of: ARRB2, AVIL, BTBD14A, CCDC50, CD180, CD300LF, CPNE5, CXCL1, CYP4F3, FAM101B, FAM13A1OS, HAL, KCNE3, LOC643072, LTB4R, MAN2A2, MSL-1, MYBL2, NBEAL2, NFAM1, NHS, PLA2G7, PNOC, RASSF6, REM2, SIRPA, SLC45A4, SPIB, SULF2, TMEM190, ZNF516, and ZNF746. In some embodiments, the clinical sample is a sample of peripheral blood, brain tissue, or spinal fluid. In some embodiments, each expression level is a level of an RNA encoded by an autism spectrum disorder-associated gene of the plurality. In some embodiments, the expression level determining system comprises a hybridization-based assay for determining the level of the RNA in the clinical sample. In some embodiments, the hybridization-based assay is an oligonucleotide array assay, an oligonucleotide conjugated bead assay, a molecular inversion probe assay, a serial analysis of gene expression (SAGE) assay, or an RT-PCR assay. In some embodiments, each expression level is a level of a protein encoded by an autism spectrum disorder-associated gene of the plurality. In some embodiments, the expression level determining system comprises an antibody-based assay for determining the level of the protein in the clinical sample. In some embodiments, the antibody-based assay is an antibody array assay, an antibody conjugated-bead assay, an enzyme-linked immuno-sorbent (ELISA) assay, or an immunoblot assay.
According to some aspects of the invention, arrays are provided that comprise, or consist essentially of, oligonucleotide probes that hybridize to nucleic acids having sequence correspondence to mRNAs of at least ten autism spectrum disorder-associated genes selected from Table 4, 5, 6, 7 or 10. According to other aspects of the invention, arrays are provided that comprise, or consist essentially of, antibodies that bind specifically to proteins encoded by at least ten autism spectrum disorder-associated genes selected from Table 4, 5, 6, 7 or 10.
According to some aspects of the invention, methods of monitoring progression of an autism spectrum disorder in an individual in need thereof are provided. In some embodiments, the methods comprise: (a) obtaining a clinical sample from the individual; (b) determining expression levels of a plurality of autism spectrum disorder-associated genes in the clinical sample using an expression level determining system, (c) comparing each expression level determined in (b) with an appropriate reference level, wherein the results of the comparison are indicative of the extent of progression of the autism spectrum disorder in the individual.
In some embodiments, the methods of monitoring progression of an autism spectrum disorder comprise: (a) obtaining a first clinical sample from the individual, (b) determining expression levels of a plurality of autism spectrum disorder-associated genes in the first clinical sample using an expression level determining system, (c) obtaining a second clinical sample from the individual, (d) determining expression levels of the plurality of autism spectrum disorder-associated genes in the second clinical sample using an expression level determining system, (e) comparing the expression level of each autism spectrum disorder-associated gene determined in (b) with the expression level determined in (d) of the same autism spectrum disorder associated-gene, wherein the results of comparing in (e) are indicative of the extent of progression of the autism spectrum disorder in the individual. In some embodiments, the autism spectrum disorder-associated genes comprise at least ten genes selected from Table 4, 5, 6, 7 or 10.
In some embodiments, the methods of monitoring progression of an autism spectrum disorder comprise: (a) obtaining a first clinical sample from the individual, (b) obtaining a second clinical sample from the individual, (c) determining the expression level of an autism spectrum disorder-associated gene in the first clinical sample using an expression level determining system, (d) determining the expression level of the autism spectrum disorder-associated gene in the second clinical sample using an expression level determining system, (e) comparing the expression level determined in (c) with the expression level determined in (d), and (f) repeating (c)-(e) for at least one other autism spectrum disorder-associated gene, wherein the results of comparing in (e) for the at least two autism spectrum-associated genes are indicative of the extent of progression of the autism spectrum disorder in the individual.
In some embodiments, the methods of monitoring progression of an autism spectrum disorder comprise: (a) obtaining a first clinical sample from the individual, (b) obtaining a second clinical sample from the individual, (c) determining a first expression pattern comprising expression levels of at least two autism spectrum disorder-associated genes in the first clinical sample using an expression level determining system, (d) determining a second expression pattern comprising expression levels of at least two autism spectrum disorder-associated genes in the second clinical sample using an expression level determining system, (e) comparing the first expression pattern with the second expression pattern, wherein the results of comparing in (e) are indicative of the extent of progression of the autism spectrum disorder in the individual.
In some embodiments of the methods of monitoring progression of an autism spectrum disorder, the time between obtaining the first clinical sample and obtaining the second clinical sample is a time sufficient for a change in the severity of the autism spectrum disorder to occur in the individual. In some embodiments, the individual is treated for the autism spectrum associated disorder between obtaining the first clinical sample and obtaining the second clinical sample.
According to some aspects of the invention, methods of assessing the efficacy of a treatment for an autism spectrum disorder in an individual in need thereof are provided. In some embodiments, the methods comprise: (a) obtaining a clinical sample from the individual, (b) administering a treatment to the individual for the autism spectrum disorder, (c) determining an expression pattern comprising expression levels of at least two autism spectrum disorder-associated genes in the clinical sample, (e) comparing the expression pattern with an appropriate reference expression pattern, wherein the appropriate reference expression pattern comprises expression levels of the at least two autism spectrum disorder-associated genes in a clinical sample obtained from a individual who does not have the autism spectrum disorder, wherein the results of the comparison in (c) are indicative of the efficacy of the treatment.
According to some aspects of the invention, the methods of assessing the efficacy of a treatment for an autism spectrum disorder comprise: (a) obtaining a first clinical sample from the individual, (b) administering a treatment to the individual for the autism spectrum disorder, (c) obtaining a second clinical sample from the individual after having administered the treatment to the individual, (d) determining a first expression pattern comprising expression levels of at least two autism spectrum disorder-associated genes in the first clinical sample, (e) comparing the first expression pattern with an appropriate reference expression pattern, wherein the appropriate reference expression pattern comprises expression levels of the at least two autism spectrum disorder-associated genes in a clinical sample obtained from a individual who does not have the autism spectrum disorder, (f) determining a second expression pattern comprising expression levels of at least two autism spectrum disorder-associated genes in the second clinical sample, and (g) comparing the second expression pattern with the appropriate reference expression pattern, wherein a difference between the second expression pattern and the appropriate reference expression pattern that is less than the difference between the first expression pattern and the appropriate reference pattern is indicative of the treatment being effective.
According to some aspects of the invention, methods for selecting an appropriate dosage of a treatment for an autism spectrum associated disorder in an individual in need thereof are provided. In some embodiments, the methods comprise: (i) administering a first dosage of a treatment for an autism spectrum associated disorder to the individual, (ii) assessing the efficacy of the first dosage of the treatment, in part, by determining at least one expression pattern comprising expression levels of at least two autism spectrum disorder-associated genes in a clinical sample obtained from the individual, (iii) administering a second dosage of a treatment for an autism spectrum associated disorder in the individual, (iv) assessing the efficacy of the second dosage of the treatment, in part, by determining at least one expression pattern comprising expression levels of at least two autism spectrum disorder-associated genes in a clinical sample obtained from the individual, wherein the appropriate dosage is selected as the dosage administered in (i) or (iii) that has the greatest efficacy. In some embodiments, the efficacy is assessed in (ii) and/or (iv) according to the methods disclosed herein.
According to some aspects of the invention, methods for selecting an appropriate dosage of a treatment for an autism spectrum associated disorder in an individual in need thereof are provided. In some embodiments, the methods comprise: (i) administering a dosage of a treatment for an autism spectrum associated disorder to the individual; (ii) assessing the efficacy of the dosage of the treatment, in part, by determining at least one expression pattern comprising expression levels of at least two autism spectrum disorder-associated genes in a clinical sample obtained from the individual, and (iii) selecting the dosage as being appropriate for the treatment for the autism spectrum associated disorder in the individual, if the efficacy determined in (ii) is at or above a threshold level, wherein the threshold level is an efficacy level at or above which a treatment substantially improves at least one symptom of an autism spectrum disorder.
According to some aspects of the invention, methods for identifying an agent useful for treating an autism spectrum associated disorder in an individual in need thereof are provided. In some embodiments, the methods comprise: (i) contacting an autism spectrum disorder-associated cell or tissue with a test agent, (ii) determining at least one expression pattern comprising expression levels of at least two autism spectrum disorder-associated genes in the autism spectrum disorder-associated cell or tissue, (iii) comparing the at least one expression pattern with a test expression pattern, and (iv) identifying the agent as being useful for treating the autism spectrum associated disorder based on the comparison in (iii). In some embodiments, the test expression pattern is an expression pattern indicative of an individual who does not have the autism spectrum disorder, and a decrease in a difference between the at least one expression pattern and the test expression pattern resulting from contacting the autism spectrum disorder-associated cell or tissue with the test agent identifies the test agent as being useful for the treatment of the autism spectrum associated disorder. In some embodiments, the autism spectrum disorder-associated cell or tissue is contacted with the test agent in (i) in vivo. In some embodiments, the autism spectrum associated disorder-cell or tissue is contacted with the test agent in (i) in vitro.
Autism Spectrum Disorder (ASD) is a common pediatric cognitive disorder with high heritability although no single gene or locus has been identified to date that explains a majority of cases diagnosed. Earlier diagnosis and behavioral intervention changes the outcome4, thus to distinguish the patients with ASD based on a molecular signature from unaffected children would be of great utility in diagnosis and in underpinning the genetic and molecular basis of ASD. No single causative gene or chromosomal locus, however, has been identified to date that explains a majority of cases diagnosed. Current consensus is that the inherited component of ASD is a result of mutations in multiple genes associated with the etiopathology of this heterogeneous developmental condition. Not surprisingly then, the rubric of idiopathic autism is only very gradually shrinking with the discovery of mutations in genes that individually account for 1% or less of all cases in any given ASD cohort (e.g., SHANKS, NLGN3, and NLGN4X)(reviewed in ref.2) and total no more than 2-4%. Copy-number variations appear to account for up to another 10% of genetic contributions to ASD5. These multiple mutations can result in a small number of characteristic gene expression signatures in two ways: ASD may result from gene interactions, in which case its essential signature may reflect changes in expression of many genes or ASD may be a constellation of single gene disorders. Present evidence suggests that many of these single gene disorders converge on common mechanisms, so that even for multiple, single gene disorders, there may be a convergent signature in gene expression.
Studies of expression in the brain have been limited to postmortem samples6-8 and these have been notable for gene and protein expression of immune-related pathways (e.g., TNFα, IL1R, and NF-κB systems) in ASD. Numerous lines of evidence suggest that measurements in tissue that are not primarily involved in a disease can also reveal disease signatures and several investigators have demonstrated differential expression of genes in peripheral white blood cells in disorders of the central nervous system9-12. To this point, Sullivan et al.13 have established a shared expression profile between different CNS tissues and the blood suggesting the use of peripheral blood expression as a surrogate for the brain. Moreover individual gene expression variations of multiple brain regions were correlated well with those of blood in non-human primate14. Recently, gene expression profiles of lymphoblastoid cell lines were shown to distinguish between different forms of ASD caused by defined genetic lesions (Fragile X syndrome and chromosome 15q duplication) and normal controls15, and small studies of patients phenotypically defined with ASD have shown differential expression of genes in their peripheral blood cells16 and in the function of T cell subsets17. These results are mirrored by proteomic studies of serum, which suggest systematic differences between patients with ASD and controls18. Thus fresh peripheral blood cells might serve as diagnostic and prognostic surrogate for gene expression in the developing nervous system.
Applicants disclose herein methods that accurately classify patients diagnosed with ASD using gene expression patterns (profiles). Gene expression profiles were obtained from 196 patients with ASDs and 182 controls enrolled in Boston area hospitals. A 330-gene expression signature (ASD330) was developed on one sample cohort (P1) using a machine-learning algorithm, and tested the performance with independently collected second population (P2). Next the gene expression profiles from postmortem brain samples of 11 patients with ASD and 11 controls were prepared to test the possibility of using the blood gene expression signature as a surrogate.
Disclosed herein are the results of a profiling study with peripheral blood gene expression data from 196 patients with ASD and 182 controls enrolled in Boston area hospitals. Applicants developed an expression signature containing 330 genes that achieves 88% cross-validation accuracy on one sample cohort of 97 ASDs and 73 controls. Moreover, this model achieves 78% in an independent population of 99 ASDs and 109 controls. Certain dominant molecular themes for 330 genes used for classification are noteworthy for their association with long-term potentiation and inflammatory pathways heterogeneously distributed across the subjects. This signature also distinguishes postmortem brain gene expression profiles of 11 ASDs from 11 controls.
Methods for characterizing and diagnosing autism spectrum disorder are disclosed herein. The term “autism spectrum disorder” (which may also be referred to herein by the acronym, “ASD”) refers to a spectrum of psychological conditions that cause severe and pervasive impairment in thinking, feeling, language, and the ability to relate to others. Autism spectrum disorder is usually first diagnosed in early childhood and may range in severity from a severe form, called autistic disorder, or autism, through pervasive development disorder not otherwise specified (PDD-NOS), to a much milder form, Asperger syndrome. Autism spectrum disorder may also include two rare disorders, Rett syndrome and childhood disintegrative disorder. As used herein, the phrase “diagnosing autism spectrum disorder” refers to diagnosing, or aiding in diagnosing, an individual as having autism spectrum disorder.
As described herein, a variety of genes are differentially expressed in individuals having autism spectrum disorder compared with individuals not having autism spectrum disorder. An “autism spectrum disorder-associated gene” is a gene whose expression levels are associated with autism spectrum disorder. Examples of autism spectrum disorder-associated genes include, but are not limited to, the genes listed in Table 7. In some embodiments, the autism spectrum disorder associated gene is a gene of Table 4, Table 5, Table 6 or Table 10. As used herein, the term “autism spectrum disorder-associated cell” refers to a cell that expresses one or more autism spectrum disorder-associated genes. In some embodiments, an autism spectrum disorder-associated cell expresses at least two autism spectrum disorder associated genes. As used herein, the term “autism spectrum disorder-associated tissue” is a tissue comprising an autism spectrum disorder-associated cell.
The term “individual”, as used herein, refers to any subject, including, but not limited to, humans and non-human mammals, such as primates, rodents, and dogs. Typically, an individual is a human subject. A human subject may of any appropriate age for the methods disclosed herein. For example, methods disclosed herein may be used to characterize the autism spectrum disorder status of a child, e.g., a human in a range of about 1 to about 12 years old. An individual may be a non-human subject that serves as an animal model of autism spectrum disorder.
Methods are provided herein for characterizing the autism spectrum disorder status of an individual in need thereof. An individual in need of a characterization of autism spectrum disorder status is any individual at risk of, or suspected of, having autism spectrum disorder. An individual's “autism spectrum disorder status” may be characterized as having autism spectrum disorder or as not having autism spectrum disorder.
An individual in need of diagnosis of autism spectrum disorder is any individual at risk of, or suspected of, having autism spectrum disorder. An individual at risk of having autism spectrum disorder may be an individual having one or more risk factors for autism spectrum disorder. Risk factors for autism spectrum disorder include, but are not limited to, a family history of autism spectrum disorder; elevated age of parents; low birth weight; premature birth; presence of a genetic disease associated with autism; and sex (males are more likely to have autism than females). Other risk factors will be apparent to the skilled artisan. An individual suspected of having autism spectrum disorder may be an individual having one or more clinical symptoms of autism spectrum disorder. A variety of clinical symptoms of Autism Spectrum Disorder are known in the art. Examples of such symptoms include, but are not limited to, no babbling by 12 months; no gesturing (pointing, waving goodbye, etc.) by 12 months; no single words by 16 months; no two-word spontaneous phrases (other than instances of echolalia) by 24 months; any loss of any language or social skills, at any age.
The methods disclosed herein may be used in combination with any one of a number of standard diagnostic approaches, including, but not limited to, clinical or psychological observations and/or ASD-related screening modalities, such as, for example, the Modified Checklist for Autism in Toddlers (M-CHAT), the Early Screening of Autistic Traits Questionnaire, and the First Year Inventory to facilitate or aid in the diagnosis of ASD. In some embodiments, methods disclosed herein are used to identify subgroups of ASD.
The methods disclosed herein typically involve determining expression levels of at least one autism spectrum disorder-associated genes in a clinical sample obtained from an individual. The methods may involve determining expression levels of at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, or more autism spectrum disorder-associated genes in a clinical sample obtained from an individual. The methods may involve determining expression levels in a range of 1 to 10, 10 to 20, 20 to 30, 30 to 40, 40 to 50, 50 to 60, 60 to 70, 70 to 80, 80 to 90, 90 to 100, 100 to 200, 200 to 300, or 300 to 400 autism spectrum disorder-associated genes in a clinical sample obtained from an individual.
An expression level determining system may be used in the methods. The term “expression level determining system”, as used herein, refers to a set of components, e.g., equipment, reagents, and methods, e.g., assays, for determining the expression level of a gene in a sample. As will be appreciated by the skilled artisan, the components of an expression level determining system will vary depending on the nature of the method used to determining the expression levels.
The expression level of an autism spectrum disorder-associated gene may be determined as the level of an RNA encoded by the gene, in which case, the expression level determining system will typically comprise components and methods useful for determining levels of nucleic acids. The expression level determining system may comprises, for example, a hybridization-based assay, and related equipment and reagents, for determining the level of the RNA in the clinical sample. Hybridization-based assays are well known in the art and include, but are not limited to, oligonucleotide array assays (e.g., microarray assays), cDNA array assays, oligonucleotide conjugated bead assays (e.g., Multiplex Bead-based Luminex® Assays), molecular inversion probe assay, serial analysis of gene expression (SAGE) assay, RNase Protein Assay, northern blot assay, an in situ hybridization assay, and an RT-PCR assay. Multiplex systems, such as oligonucleotide arrays or bead-based nucleic acid assay systems are particularly useful for evaluating levels of a plurality of nucleic acids in simultaneously. RNA-Seq (mRNA sequencing using Ultra High throughput or Next Generation Sequencing) may also be used to determine expression levels. Other appropriate methods for determining levels of nucleic acids will be apparent to the skilled artisan.
The expression level of an autism spectrum disorder-associated gene may be determined as the level of a protein encoded by the gene, in which case, the expression level determining system will comprise components and methods useful for determining levels of proteins. The expression level determining system may comprises, for example, antibody-based assay, and related equipment and reagents, for determining the level of the protein in the clinical sample. Antibody-based assays are well known in the art and include, but are not limited to, antibody array assays, antibody conjugated-bead assays, enzyme-linked immuno-sorbent (ELISA) assays, immunofluorescence microscopy assays, and immunoblot assays. Other methods for determining protein levels include mass spectroscopy, spectrophotometry, and enzymatic assays. Still other appropriate methods for determining levels of proteins will be apparent to the skilled artisan.
As used herein, a “level” refers to a value indicative of the amount or occurrence of a molecule, e.g., a protein, a nucleic acid, e.g., RNA. A level may be an absolute value, e.g., a quantity of a molecule in a sample, or a relative value, e.g., a quantity of a molecule in a sample relative to the quantity of the molecule in a reference sample (control sample). The level may also be a binary value indicating the presence or absence of a molecule. For example, a molecule may be identified as being present in a sample when a measurement of the quantity of the molecule in the sample, e.g., a fluorescence measurement from a PCR reaction or microarray, exceeds a background value. Similarly, a molecule may be identified as being absent from a sample (or undetectable in the sample) when a measurement of the quantity of the molecule in the sample is at or below background value.
The methods frequently involve obtaining a clinical sample from the individual. As used herein, the phrase “obtaining a clinical sample” refers to any process for directly or indirectly acquiring a clinical sample from an individual. For example, a clinical sample may be obtained (e.g., at a point-of-care facility, e.g., a physician's office, a hospital) by procuring a tissue or fluid sample (e.g., blood draw, spinal tap) from a individual. Alternatively, a clinical sample may be obtained by receiving the clinical sample (e.g., at a laboratory facility) from one or more persons who procured the sample directly from the individual.
The term “clinical sample” refers to a sample derived from an individual, e.g., a patient. Clinical samples include, but are not limited to, tissue, e.g., brain tissue, cerebrospinal fluid, blood, blood fractions such as serum including fetal serum (e.g., SFC) and plasma, blood cells (e.g., white blood cells), sputum, tissue or fine needle biopsy samples, urine, peritoneal fluid, and pleural fluid, or cells there from. A clinical sample comprises a tissue, a cell, and/or a biomolecule, e.g., an RNA, protein. Frequently, the clinical sample is a sample of peripheral blood, brain tissue, or spinal fluid.
It is to be understood that a clinical sample may be processed in any appropriate manner to facilitate determining expression levels of autism spectrum disorder-associated genes. For example, biochemical, mechanical and/or thermal processing methods may be appropriately used to isolate a biomolecule of interest, e.g., RNA, protein, from a clinical sample. A RNA sample may be isolated from a clinical sample by processing the clinical sample using methods well known in the art and levels of an RNA encoded by an autism spectrum disorder-associated gene may be determined in the RNA sample. A protein sample may be isolated from a clinical sample by processing the clinical sample using methods well known in the art and levels of a protein encoded by an autism spectrum disorder-associated gene may be determined in the protein sample. The expression levels of autism spectrum disorder-associated genes may also be determined in a clinical sample directly.
The methods disclosed herein also typically comprise comparing expression levels of autism spectrum disorder-associated genes with an appropriate reference level. An “appropriate reference level” is an expression level of a particular autism spectrum disorder gene that is indicative of a known autism spectrum disorder status. An appropriate reference level can be determined or can be pre-existing. An appropriate reference level may be an expression level indicative of autism spectrum disorder. For example, an appropriate reference level may be representative of the expression level of an autism spectrum disorder-associated gene in a reference (control) clinical sample obtained from a individual known to have autism spectrum disorder. When an appropriate reference level is indicative of autism spectrum disorder, a lack of a detectable difference between a expression level determined from an individual in need of characterization or diagnosis of autism spectrum disorder and the appropriate reference level may be indicative of autism spectrum disorder in the individual. Alternatively, when an appropriate reference level is indicative of autism spectrum disorder, a difference between an expression level determined from an individual in need of characterization or diagnosis of autism spectrum disorder and the appropriate reference level may be indicative of the individual being free of autism spectrum disorder.
Alternatively, an appropriate reference level may be an expression level indicative of an individual being free of autism spectrum disorder. For example, an appropriate reference level may be representative of the expression level of a particular autism spectrum disorder-associated gene in a reference (control) clinical sample obtained from a individual known to be free of autism spectrum disorder. When an appropriate reference level is indicative of an individual being free of autism spectrum disorder, a difference between an expression level determined from an individual in need of diagnosis of autism spectrum disorder and the appropriate reference level may be indicative of autism spectrum disorder in the individual. Alternatively, when an appropriate reference level is indicative of the individual being free of autism spectrum disorder, a lack of a detectable difference between an expression level determined from an individual in need of diagnosis of autism spectrum disorder and the appropriate reference level may be indicative of the individual being free of autism spectrum disorder.
For example, when a higher level, relative to an appropriate reference level that is indicative of an individual being free of autism spectrum disorder, of at least one autism spectrum disorder-associated gene selected from: ARRB2, AVIL, BTBD14A, CD300LF, CXCL1, CYP4F3, FAM101B, FAM13A1OS, HAL, KCNE3, LOC643072, LTB4R, MAN2A2, MSL-1, NBEAL2, NFAM1, NHS, PLA2G7, REM2, SIRPA, SLC45A4, SULF2, and ZNF746 is identified, the individual's autism spectrum disorder status may be characterized as having autism spectrum disorder. When a lower level, relative to an appropriate reference level that is indicative of an individual being free of autism spectrum disorder, of at least one autism spectrum disorder-associated gene selected from: CCDC50, CD180, CPNE5, MYBL2, PNOC, RASSF6, and SPIB is identified, the individual's autism spectrum disorder status may be characterized as having autism spectrum disorder.
The magnitude of difference between a expression level and an appropriate reference level may vary. For example, a significant difference that indicates an autism spectrum disorder status or diagnosis may be detected when the expression level of an autism spectrum disorder-associated gene in a clinical sample is at least 1%, at least 5%, at least 10%, at least 25%, at least 50%, at least 100%, at least 250%, at least 500%, or at least 1000% higher, or lower, than an appropriate reference level of that gene. Similarly, a significant difference may be detected when the expression level of an autism spectrum disorder-associated gene in a clinical sample is at least 2-fold, at least 3-fold, at least 4-fold, at least 5-fold, at least 6-fold, at least 7-fold, at least 8-fold, at least 9-fold, at least 10-fold, at least 20-fold, at least 30-fold, at least 40-fold, at least 50-fold, at least 100-fold, or more higher, or lower, than the appropriate reference level of that gene. Significant differences may be identified by using an appropriate statistical test. Tests for statistical significance are well known in the art and are exemplified in Applied Statistics for Engineers and Scientists by Petruccelli, Chen and Nandram 1999 Reprint Ed.
It is to be understood that a plurality of expression levels may be compared with plurality of appropriate reference levels, e.g., on a gene-by-gene basis, as a vector difference, in order to assess the autism spectrum disorder status of the individual. In such cases, Multivariate Tests, e.g., Hotelling's T2 test, may be used to evaluate the significance of observed differences. Such multivariate tests are well known in the art and are exemplified in Applied Multivariate Statistical Analysis by Richard Arnold Johnson and Dean W. Wichern Prentice Hall; 4th edition (Jul. 13, 1998).
The methods may also involve comparing a set of expression levels (referred to as an expression pattern) of autism spectrum disorder-associated genes in a clinical sample obtained from an individual with a plurality of sets of reference levels (referred to as reference patterns), each reference pattern being associated with a known autism spectrum disorder status; identifying the reference pattern that most closely resembles the expression pattern; and associating the known autism spectrum disorder status of the reference pattern with the expression pattern, thereby classifying (characterizing) the autism spectrum disorder status of the individual.
The methods may also involve building or constructing a prediction model, which may also be referred to as a classifier or predictor, that can be used to classify the disease status of an individual. As used herein, an “autism spectrum disorder-classifier” is a prediction model that characterizes the autism spectrum disorder status of an individual based on expression levels determined in a clinical sample obtained from the individual. Typically the model is built using samples for which the classification (autism spectrum disorder status) has already been ascertained. Once the model (classifier) is built, it may be applied to expression levels obtained from a clinical sample in order to classify the autism spectrum disorder status of the individual from which the clinical sample was obtained. Thus, the methods may involve applying an autism spectrum disorder-classifier to the expression levels, such that the autism spectrum disorder-classifier characterizes the autism spectrum disorder status of the individual based on the expression levels. The individual may be further diagnosed, e.g., by a health care provider, based on the characterized autism spectrum disorder status.
A variety of prediction models known in the art may be used as an autism spectrum disorder-classifier. For example, an autism spectrum disorder-classifier may comprises an algorithm selected from logistic regression, partial least squares, linear discriminant analysis, quadratic discriminant analysis, neural network, naïve Bayes, C4.5 decision tree, k-nearest neighbor, random forest, and support vector machine.
The autism spectrum disorder-classifier may be trained on a data set comprising expression levels of the plurality of autism spectrum disorder-associated genes in clinical samples obtained from a plurality of individuals identified as having autism spectrum disorder. For example, the autism spectrum disorder-classifier may be trained on a data set comprising expression levels of a plurality of autism spectrum disorder-associated genes in clinical samples obtained from a plurality of individuals identified as having autism spectrum disorder based on DSM-IV-TR criteria. The training set will typically also comprise control individuals identified as not having autism spectrum disorder, e.g., identified as not satisfying the DSM-IV-TR criteria. As will be appreciated by the skilled artisan, the population of individuals of the training data set may have a variety of characteristics by design, e.g., the characteristics of the population may depend on the characteristics of the individuals for whom diagnostic methods that use the classifier may be useful. For example, the interquartile range of ages of a population in the training data set may be from about 2 years old to about 10 years old, about 1 year old to about 20 years old, about 1 year old to about 30 years old. The median age of a population in the training data set may be about 1 year old, 2 years old, 3 years old, 4 years old, 5 years old, 6 years old, 7 years old, 8 years old, 9 years old, 10 years old, 20 years old, 30 years old, 40 years old, or more. The population may consist of all males or may consist of males and females.
A class prediction strength can also be measured to determine the degree of confidence with which the model classifies a clinical sample. The prediction strength conveys the degree of confidence of the classification of the sample and evaluates when a sample cannot be classified. There may be instances in which a sample is tested, but does not belong, or cannot be reliable assign to, a particular class. This is done by utilizing a threshold wherein a sample which scores above or below the determined threshold is not a sample that can be classified (e.g., a “no call”).
Once a model is built, the validity of the model can be tested using methods known in the art. One way to test the validity of the model is by cross-validation of the dataset. To perform cross-validation, one, or a subset, of the samples is eliminated and the model is built, as described above, without the eliminated sample, forming a “cross-validation model.” The eliminated sample is then classified according to the model, as described herein. This process is done with all the samples, or subsets, of the initial dataset and an error rate is determined. The accuracy the model is then assessed. This model classifies samples to be tested with high accuracy for classes that are known, or classes have been previously ascertained. Another way to validate the model is to apply the model to an independent data set, such as a new clinical sample having an unknown autism spectrum disorder status.
As will be appreciated by the skilled artisan, the strength of the model may be assessed by a variety of parameters including, but not limited to, the accuracy, sensitivity and specificity. Methods for computing accuracy, sensitivity and specificity are known in the art and described herein (See, e.g., the Examples). The autism spectrum disorder-classifier may have an accuracy of at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 99%, or more. The autism spectrum disorder-classifier may have an accuracy in a range of about 60% to 70%, 70% to 80%, 80% to 90%, or 90% to 100%. The autism spectrum disorder-classifier may have an sensitivity of at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 99%, or more. The autism spectrum disorder-classifier may have an sensitivity in a range of about 60% to 70%, 70% to 80%, 80% to 90%, or 90% to 100%. The autism spectrum disorder-classifier may have an specificity of at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 99%, or more. The autism spectrum disorder-classifier may have an specificity in a range of about 60% to 70%, 70% to 80%, 80% to 90%, or 90% to 100%.
Described herein are oligonucleotide (nucleic acid) arrays that are useful in the methods for determining levels of multiple nucleic acids simultaneously. Such arrays may be obtained or produced from commercial sources. Methods for producing nucleic acid arrays are well known in the art. For example, nucleic acid arrays may be constructed by immobilizing to a solid support large numbers of oligonucleotides, polynucleotides, or cDNAs capable of hybridizing to nucleic acids corresponding to mRNAs, or portions thereof. The skilled artisan is also referred to Chapter 22 “Nucleic Acid Arrays” of Current Protocols In Molecular Biology (Eds. Ausubel et al. John Wiley and #38; Sons NY, 2000), International Publication WO00/58516, U.S. Pat. No. 5,677,195 and U.S. Pat. No. 5,445,934 which provide non-limiting examples of methods relating to nucleic acid array construction and use in detection of nucleic acids of interest. In some embodiments, the nucleic acid arrays comprise, or consist essentially of, binding probes for mRNAs of at least 2, at least 5, at least 10, at least 20, at least 50, at least 100, at least 200, at least 300, or more genes selected from Table 7. Kits comprising the oligonucleotide arrays are also provided. Kits may include nucleic acid labeling reagents and instructions for determining expression levels using the arrays.
Prior studies15-17,24,25 have found differential expression of genes in brain and blood samples. The examples disclosed herein demonstrate patients with ASD may be distinguished from “normal” controls with accuracies of greater than 80% across a population and greater than 67% in a second validation population (and 78% accuracy when going from P2 to P1). The odds ratios entailed by this classification are also high (Table 8). The robustness of this classification across these populations is remarkable, particularly as the two groups were heterogeneous and relatively small. With wishing to be bound by theory, the results suggest that the predictors are either capturing a multiplicity of effects from an equally large number of etiologies or encompassing a smaller number of pathophysiologies that constitute a partially shared end point in ASD in a much larger set of etiologies. This contrasts with the small percentage of ASD cases characterized through genetic mutations to date. The classifying performance of the ASD330 is also intriguing in that it is based on measurements in peripheral blood mononuclear cells (PBMC)'s rather than tissues of the central nervous system. Moreover, these PBMC-borne measures are congruent with those of cerebellar expression and can also be used to accurately classify those brain samples. This congruence is echoed in the concordance of genes with decreased methylation in a separate study to genes with increased expression in this study. The pathways that were found to be enriched include those that are classically thought of as neurodevelopmental (e.g. the Notch signaling pathways)20, and genes in involved long-term potentiation26 and including several genes in the calmodulin pathways such as CREBBP (p-value <0.0001, q-value 0.0028 in P1; p-value 0.14, q-value 0.19 in P2) and MEF2C (p-value 0.0054, q-value 0.016 in P1; p-value <0.0001, q-value <0.0001 in P2). Among the latter group was CREBBP which has been implicated in Rubinstein-Taybi syndrome (mental retardation—sometimes with autistic features—and skeletal abnormalities) and was recently implicated in a candidate gene study of autism although the finding was not replicated in a second population27. The MEF2 target genes such as PCDH10 and C3orf58 (also known as deleted in autism1 (DIA1)) have been implicated in ASD28,29, and PCDH10 was up-regulated in P1 (p-value <0.0001, q-value 0.016). Again, without wishing to be bound by theory, the fact that these expression perturbations were found in PBMC's suggests that there are broad transcriptional changes across multiple tissues in ASD even if the pathology is only apparent in the CNS. The pathways related to the brain neural/synaptic activities identified by others (e.g. Purcell and colleagues) that include GABA and Glutamate receptors such as GABRA5 and AMPA and NMDA receptors, and Reelin (Rein) to be specifically and highly expressed in multiple parts of brain such as cortex, hippocampus, and striatum, were not found significantly differentially regulated here except for RAF1. Dominant themes across all the data sets are those of immune signaling, including the B-cell and natural killer T cell signaling pathways. Although immunological and/or “inflammatory” pathways have only recently been implicated in neurodevelopment30-32, there is evidence of immunological changes in patients with ASD including in the CNS (e.g. microglial proliferation6, up regulation of cytokines and other messengers typically related to inflammation at the level of mRNA and protein expression7), as well as in the peripheral blood (e.g. differences in NK cell, TH1 and TH2 subsets, and serum markers17,18). That is, there is overlap in the immunological “themes” differentially expressed in the PBMC's and those reported by others in proteomic and gene expression profiling in the central nervous system. Moreover, the evidence for autoimmune processes in ASD and epidemiological overlap with other autoimmune disorders is growing 33. Gene sets related to endosomal trafficking were also enriched (Table 1). Among these genes, lysosome-associated membrane protein-2 (LAMP2) mutation has been reported with a rare case of Danon disease with autism34.
Two data sets (P1 and P2) used in this study were obtained at different times and the methods for RNA acquisition in P1 differed in part from those in P2. Also, the control population in P2 different in differed in ethnicity and in the clinics from which they were drawn. This heterogeneity adds noise to the case vs. control comparison and conversely if the analysis utilized more homogeneous data sets, we would have expected improved accuracy. Further, because the numbers of patients were relatively small it was not possible to achieve large enough subsamples of ASD endophenotypes that might have a more homogenous etiology. The data were collected after diagnosis and not as part of a longitudinal study of individuals. The application of these predictors to a prospective cohort would permit further assess their validity as a diagnostic and prognostic tool. The results obtained from groups with ASD were compared to normal controls not to individuals with other neurodevelopmental disorders.
The examples disclosed herein demonstrate that the use of peripheral blood with expression studies offers significant clinical utility for the diagnosis of ASD. The role of the pathways implicating long-term potentiation and immunological mechanisms in the etiology or effect of ASD appear increasingly prominent across multiple tissues.
Summary of Aspects of the Methods
Patients and control samples. Total of 378 blood and 22 postmortem cerebella samples were collected and interrogated using oligonucleotides microarrays. Affymetrix HG-U133 plus 2 (97 ASDs and 73 controls) and Gene 1.0 ST arrays (99 ASDs and 109 controls) were used for two sets of blood samples, and Exon 1.0 ST arrays (11 ASDs and 11 controls) were used for the brain samples. Microarray data with sample characteristics are available at the Gene Expression Omnibus database (GSE18123).
Prediction Analysis.
Gene expression profiles were subject to a machine-learning method for distinguishing ASD from controls. Two independently collected datasets served as a training set (P1) and a validation set (P2). Informative genes were selected using a cross validation method from the training set (P1) to build the prediction model. The prediction model was tested for classification accuracy with the validation set (P2) and 22 postmortem brain samples. Partial least squares and logistic regression methods were used to select genes for prediction models. See Full Methods for detailed description of procedures.
Patients with ASD were recruited from the Developmental Medicine Center (DMC), the Division of Genetics, and the Department of Neurology at the Children's Hospital Boston (CHB) with additional samples obtained from Boston Medical Center (BMC), Cambridge Health Alliance, Tufts Medical Center, and Mass General Hospital (MGH) in collaboration with the Autism Consortium of Boston. Patients recruited for this study have undergone diagnostic assessment, using the Autism Diagnostic Observation Schedule (ADOS) and the Autism-Diagnostic Interview-Revised (ADI-R), as well as comprehensive clinical genetic testing. Inclusion criteria comprised a diagnosis of ASD by DSM-IV-TR criteria, positive ADOS and ADI-R, and an age >24 months (see Methods). Collection of control samples was performed through partnerships with both the Department of Endocrinology (12 individuals from the P1 group) and Children's Hospital Primary Care Center (CHPCC) (61 individuals from P1 and all 109 from P2). Patients seen in the Endocrine department were identified as healthy children with idiopathic short stature, including genetic short stature and constitutional delay of growth, and were having clinical blood draws. The clinical blood draw results were examined to confirm they were within normal limits (those that were not were withdrawn from the study). Patients seen in the CHPCC for a well-child visit that involved a routine blood draw (for example to obtain lead levels) were offered enrollment. A diagnosis of a chronic disease, mental retardation, autism spectrum disorder, or neurological disorder acted as exclusion criteria from our control group. Postmortem cerebella samples from 11 patients with ASD and 11 controls were obtained from the Brain and Tissue Bank at the University of Maryland and the Harvard Brain Tissue Resource Center under IRB approval.
The first sample cohort served as a training set (P1), which encompassed blood gene expression profiles from 97 patients with ASD and 73 controls. Subsequently, 99 patients and 109 controls were recruited for the second sample cohort (P2). To reduce the gender specific gene expression changes that possibly confounds with ASD related gene expression changes, only male samples were recruited in our training set (P1) by study design (
Although the two data sets differ from one another in the time they were acquired, in gender ratios, and in the RNA extraction method, global gene expression profiles of 378 samples did not segregate appreciably by the training and validation sets, or by diagnosis (
A leave-group out cross-validation strategy was used (see Methods) on P1 to determine the number of genes for the best performing prediction model. The highest accuracy was achieved when top 330 probesets ranked by the partial Area Under the receiver operating characteristic (ROC) Curve (pAUC) scores were used to build the prediction model using a logistic regression or a partial least squares methods. These 330 probesets are designated as ASD330 hereafter (330 probesets are listed in Table 7). The ROC curve for P1 using logistic regression showed overall performance of ASD330 classifier (
Although the P1 population recruitment and assays preceded that of P2, for completeness a classifier was developed in the reciprocal fashion starting with P2. The highest accuracy was achieved with a 370-gene classifier (ASD370) with overall accuracy of 77.3% (range, 67.3% to 82.2%) with AUC 0.85 (range, 0.714 to 0.902) (PPV 76.3% (range, 67.0% to 81.7%), NPV 78.7% (range, 67.5% to 83.5%), OR 13.6 (range, 4.2 to 21.3)). When ASD370 was then applied (without retraining) to P1, the overall accuracy was 78.2% (PPV 80%, NPV 75.7%) with OR 12.47 (95% CIs 5.99 to 25.98), as summarized in Table 8.
The prediction model from peripheral blood gene expression of our sample cohort was evaluated for its ability to discriminate brain samples from patients with ASD and from controls. 11 postmortem cerebella samples from ASD and 11 samples from controls were obtained from the Brain and Tissue Bank at the University of Maryland and the Harvard Brain Tissue Resource Center under IRB approval, and hybridized to the Affymetrix Human Exon 1.0 ST microarrays. There are 95 common genes between 2847 differentially expressed genes of P1 and 537 genes from brain samples (uncorrected p-value <0.01, Welch's t-test)(
22 genes were differentially expressed in P1, P2, and brain datasets as shown in
To understand which molecular themes were overrepresented, a gene set enrichment analysis was performed using ASD330 probesets (330 probesets and statistical scores are listed in Table 7). Among the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significant pathways included Gap junction (KEGG pathway identifier: hsa04540) and Long-term potentiation (hsa04720) (hypergeometric test p-value 0.0035 and 0.0037 respectively)(Table 1). The long-term potentiation pathway includes NMDA and AMPA glutamate receptors and secondary messenger systems such as calcium and MAPK signaling pathways that converge at cyclic AMP response element-binding protein (CREB) transcriptional pathway. Among the genes of this pathway, CREB binding protein (CREBBP), guanine nucleotide binding protein (G protein), alpha q polypeptide (GNAQ), mitogen activated kinases (MAPK1 and MAP2K1), protein phosphatase 1 subunit (PPP1R12A) and ribosomal protein S6 kinase, polypeptide 3 (RPS6KA3) that interacts with CREBBP were included in ASD330. Moreover 10 genes (CAMK2G, GNAZ, IGF1R, LPAR1, PLA2G4A, PLCB2, PPP1R12A, RAF1, TUBB2A, TUBB6) from Gap junction or Long-term potentiation pathways are differentially expressed in both P1 and P2 (q-value <0.05). In addition, genes involved in the immune system responses and particularly innate immunity were enriched). Chemokine/cytokine related genes (CCR2, CMTM2, CXCL1, IL8RB, and TLR8), receptor for complement C5a (C5AR1(CD88)), second messengers of the chemokine signaling pathway (MAPK1 and MAP2K1), formyl peptide receptor 1(FPR1), and lymphocyte antigens (CD180, ITGB2(CD18), and LY75) recurrently appeared in these Gene Ontology (GO) gene sets, to enriched genesets were clustered into larger categories if significant proportion of genes were found in between GO genesets (Cohen's Kappa >0.5). Among the enriched GO Biological Process (GO-BP) genesets, the immune system process was the most significant (hypergeometric test p-value 0.0007, corresponding q-value 0.013). To determine whether the pathways identified were uniformly or heterogeneously enriched among the subjects, each sample's distance from the multivariate centroid (using T2 statistics, see Methods) was calculated for each the functional categories listed in
Potential confounding factors with regard to the ASD330 classifier genes were assess. Among the demographic and clinical features, age at the time of blood drawing and time since last calorie intake were two factors that changed several genes expression. Gene expression level of the Insulin-like growth factor 1 receptor (IGF1R) was marginally correlated with the blood collection time since calorie intake (Spearman's rank correlation coefficient −0.20, p-value 0.0147, N=170). Within the ASD group, the age at the blood collection was correlated with 14 probesets (13 genes) at the significance level of q-value <0.01 (corresponding p<0.00073 using Fisher's r to z transformation of Pearson's correlation coefficients, N=97). These genes are related to transcriptional activities (EBF1, POU2AF1, TCF4, and TOX2), inflammatory response (CD180 and CMTM2), cell growth (NOV), and other functions (CPNE5, CYBASC3, LOC100131043, LOC731484, PMEPA1, and SH3GLP1). The histories of learning, emotional, neurological, autoimmune, and gastrointestinal disorders, and prescribed medication were not significantly correlated with the blood gene expression changes in ASD (N=96, q-value <0.05). 14 probesets were differentially expressed by the history of language disorder (N=96, Positive History N=84, Negative History N=12, q-value <0.05). Interestingly, none of these differentially expressed probesets was found in the ASD330 classifier. 14 probesets, differentially expressed with the history of language disorder, included Chemokine (C-C motif) ligand 23 (CCL23), serine protease 33 (PRSS33), LOC145783, acidic chitinase (CHIA), sphingomyelin phosphodiesterase 3 (SMPD3), the gene encodes Islet-Brain-1 (MAPK81P1), arachidonate 15-lipoxygenase (ALOX15), Tubulin-tyrosine ligase-like protein 9 (TTLL9). The other probesets were not matched with known genes.
Expression profiling may also indicate chromosomal abnormalities, DNA methylation, and epigenetic modifications. For example, an affected male was identified who had high level of X-inactive-specific transcript (XIST) that was comparable to that of females. Subsequent karyotyping of the sample confirmed Klinefelter syndrome, and the case was excluded in this study for further analysis. Epigenetic changes were also reflected in gene expression profiling. Genome wide DNA methylation profiles from 5 patients with ASD and their unaffected siblings were compared to the affected individual's blood gene expression profile. DNA methylation levels were negatively correlated with gene expression (Spearman's rank correlation coefficients, −0.206 to −0.189, p-value <2.2×10−16), and 367 genes were associated with differentially methylated CpG islands (paired t-test uncorrected p-value <0.01). Among these differentially methylated genes, 37 genes were also found by gene expression profiling in the P1 blood data set (Welch's t-tests, q-value <0.05). Moreover, comparison of significant genes from methylation studies of 110 pairs of affected and unaffected siblings, and the differentially expressed genes from P1 dataset revealed 323 unique genes in common (paired t-test controlled for the family effect for DNA methylation data, and Welch's t-test for P1, q-value <0.05 for both datasets). Additionally, ASD330 genes classified 110 pairs of DNA methylation profiles (AUC 0.73 from leave-group out cross validations) independently obtained (personal communication with Dr. Warren at Emory University).
Patients with ASD and Control Samples.
The clinical characteristics of ASD and control samples in the training and validation sets are summarized in Tables 2 and 3. Each proband recruited into the examples disclosed herein underwent an extensive diagnostic evaluation by our trained study staff including the ADI-R, ADOS and cognitive testing. Phenotype information specific to a patient's diagnosis was obtained from these measures. All medical history information obtained on the proband and family members was collected through an interview with the family by a genetic counselor during study enrollment and may include some medical record review. This allows for collection of data regarding co-morbid conditions such as autoimmune disease or neurological disorders including convulsive disorders. In one example, a patient reported by the family to have expressive language disorder would be considered to have a language disorder. However, limited sample size prevents in-depth analysis of endophenotypes and subsets of patients. There was no significant difference in clinical characteristics of ASD between the training and validation set except for gender ratios (p<0.001). Control samples were younger than ASD samples in the training and validation sets (p<0.001), however there was no significant difference between the control samples of the training and validation sets (p=0.98) (Table 3). In the validation set, the proportion of female samples in ASD group (24%) was lower than that of control group (45%) (p=0.002).
Samples and Gene Expression Profiling.
ASD patients were recruited from the Developmental Medicine Center (DMC), the Division of Genetics, and the Department of Neurology at the Children's Hospital Boston (CHB) with additional samples obtained from Boston Medical Center (BMC), Cambridge Health Alliance, Tufts Medical Center, and Mass General Hospital (MGH) in collaboration with the Autism Consortium of Boston. Patients recruited for this study have undergone diagnostic assessment, using the Autism Diagnostic Observation Schedule (ADOS) and the Autism-Diagnostic Interview-Revised (ADI-R), as well as comprehensive clinical genetic testing. Inclusion criteria comprised a diagnosis of ASD by DSM-IV-TR criteria and an age >24 months. Independent data sets consisted of 97 (P1) and 99 (P2) ASD individuals (
Gene expression profiling of RNA from dataset P1 was conducted using the Human Genome U133 Plus 2.0 microarray platform (U133p2) (Affymetrix, Santa Clara, Calif.) and profiling of RNA from dataset P2 was conducted using the GeneChip Human Gene 1.0 ST arrays (GeneST). Postmortem brain samples were prepared with Affymetrix Exon 1.0 ST arrays (ExonST). A total of 1 μg RNA (U133p2) or 250 ng (GeneST and ExonST) was processed using established Affymetrix protocols for the generation of biotin-labeled cRNA and the hybridization, staining, and scanning of arrays as outlined in the Affymetrix technical manuals. Briefly, total RNA was converted to double stranded cDNA using an oligo(DT) (U133p2) or T7 primer (GeneST and ExonST). Biotin labeled cRNA was then generated from the cDNA by in vitro transcription. The cRNA was quantified using A260 and fragmented. Fragmented cRNA was hybridized to the appropriate Affymetrix array and scanned on an Affymetrix GeneChip scanner 3000. cRNA from both affected and normal control population groups was prepared in batches of a randomized assortment of the two comparison groups. Microarray data with sample characteristics are available at the Gene Expression Omnibus database (GSE18123).
Real Time Quantitative PCR Validation.
A subset of the gene expression data in 55 ASD and 61 control samples from P1 and 20 ASD and 20 control samples from P2 was further validated using nanoliter reactions and the Universal Probe Library system (Roche Indianapolis, Ind.) on the Biomark real time PCR system (Fluidigm, South San Francisco, Calif.). Following the Biomark protocol, real time quantitative PCR (RT-qPCR) amplifications were carried out in 9 nanoliter reaction volume containing 2× Universal Master Mix (Taqman), hydrolysis Universal Probe library (UPL, Roche), probe-specific primers and preamplified cDNA. Pre-amplification reactions were done in a PTC-200 thermal cycler from MJ Research, per Biomark protocol. Reactions and analysis were performed using a Biomark system (Fluidigm, South San Francisco, Calif.). The cycling program consisted of an initial cycle of 50° C. for 2 minutes and a 10 min incubation at 95° C. followed by 40 cycles of 95° C. for 15 seconds, 70° C. for 5 seconds, and 60° C. for 1 minute. Data was normalized to the housekeeping gene GAPDH, and expressed relative to control samples.
Preprocessing of Microarray Data.
The gene expression levels were calculated using the Probe Log Iterative ERorr (PLIER) algorithm after the normalizing the probe intensities using a quantile method. To match the probeset identifiers from two different platforms used in this study, we used the Best Match subset (affymetrix.com/Auth/support/downloads/comparisons/U133PlusVsHuGene_BestMatch.zip) between two as described in the Affymetrix technical note (affymetrix.com/support/technical/manual/comparison_spreadsheets_manual.pdf). 29,129 out of 54,613 total probesets on U133p2 were best-matched to 17,984 unique probesets of Gene 1.0 ST array, and these matched probesets were used for the cross-platform prediction analysis. The same strategy was used for Affymetrix Exon 1.0 ST array probes. For the genes represented by more than two probesets in U133p2 arrays used for the Training set (P1), genes of which all probesets changed to the same direction were included. Differentially expressed genes of combined three datasets and of each dataset were selected using Welch's t-test and the false discovery rate (FDR, q-value) calculation according to Storey and Tibshirani's35. Multivariate analysis was performed using the Hotelling T2 test as previously described in Kong et al19. Permutation test was used where applicable by randomizing the sample labels to generate background distribution, and the number of permutation was listed. The exact test for categorical data was used. All statistical analysis performed using the R statistical language (http://cran.r-project.org) and prediction analysis was performed using the caret R library package36.
Prediction Analysis.
The ability of blood gene expression changes to predict clinical diagnosis was using the logistic regression with five fold cross validations. The prediction analysis was performed in sequential steps; 1) gene selection, 2) setting up a cross-validation strategy in the training set, 3) prediction algorithm selection and build a prediction model, 4) predict the test set, and 5) evaluation of prediction performance (illustrated in
Prediction Performance Measurements.
For each prediction instance, the result are summarized as a 2×2 contingency table with the numbers of True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) predictions. Overall prediction accuracy was calculated as (TP+TN)/N, where N was the total number of samples in a dataset. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were presented as standard measures of prediction performance with the AUC, Matthews Correlation Coefficient (MCC), and Odds Ratio (OR). In a prediction analysis, the output of the prediction methods is continuous probability of being classified as ASD (i.e., threshold), thus there is a trade-off of the amount of false positives among true positives at different threshold. ROC curve summarizes the result at different threshold. AUC was calculated from the ROC curve, i.e., sensitivity (also True Positive Rate (TRP) vs. (1-specificity) (also False Positive Rate (FPR)) as y and x-axis.
For all TP, TN, FP, and FN is related to the performance of any prediction procedure, two metrics were used; MCC and OR that did not discard any of these 4 information. MCC was defined as
MCC can range from −1 to 1 where 1 is perfect, 0 is random, and −1 is a total opposite prediction. For the MCC is related to the Chi-square distribution as χ2=N×MCC2, p-value was calculated from MCC and average MCC. 95% Confidence Intervals of ln(OR) was calculated as
where ln is the natural logarithm.
Functional Enrichment Analysis.
Selected genes for classifiers and the differentially expressed genes were checked for enriched biological theme using the Bioconductor GOstats package39 and DAVID/EASE functional annotation system40. Comparative GO analysis was performed at the detailed branch level of GO biological processes using a cytoscape plug-in, the ClueGO41, and visualized in the Cytoscape42 (
This invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways. Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having,” “containing,” “involving,” and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.
Having thus described several aspects of at least one embodiment of this invention, it is to be appreciated various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be part of this disclosure, and are intended to be within the spirit and scope of the invention. Accordingly, the foregoing description and drawings are by way of example only.
This application claims priority under 35 U.S.C. §119 from U.S. provisional application Ser. No. 61/313,565, filed Mar. 12, 2010. The entire teachings of the referenced provisional application is expressly incorporated herein by reference.
This invention was made with United States Government support under grants R01 MH085143 and P30HD018655 awarded by the National Institutes of Health. The United States government has certain rights in the invention.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/US11/28142 | 3/11/2011 | WO | 00 | 1/24/2013 |
| Number | Date | Country | |
|---|---|---|---|
| 61313565 | Mar 2010 | US |
