Patent Application
20140329242
The presently-disclosed subject matter relates to the characterization of multiple sclerosis (MS) in a subject, including diagnosis of MS and exclusion of a diagnosis of MS.
Detection of brain lesions disseminated in space and time by magnetic resonance imaging (MRI) with gadolinium contrast is a cornerstone in the diagnosis of multiple sclerosis (MS)1-3. Laboratory and clinical findings include detection of immunologic abnormalities in cerebrospinal fluid and evoked potential testing4, 5, 31, 32. Clinically isolated syndrome (CIS) is a first neurologic episode lasting at least 24 hours possibly caused by focal inflammation or demyelination33, 34. Approximately 10,000-15,000 new diagnoses of MS are made in the United States each year35. Approximately 2-3 times that number experience a CIS each year indicating that a far greater number of subjects experience a CIS than develop MS36, 37, 38, 39. The cost to healthcare of determining if a subject with a CIS has MS is significant considering the cost of MRI and additional testing that is performed and the fact that many more subjects have a CIS than develop MS.
Therefore, improved tests that can effectively, efficiently, and noninvasively characterize MS are needed, including tests to diagnose MS and/or to exclude a diagnosis of MS.
The presently-disclosed subject matter meets some or all of the above-identified needs, as will become evident to those of ordinary skill in the art after a study of information provided in this document.
This Summary describes several embodiments of the presently-disclosed subject matter, and in many cases lists variations and permutations of these embodiments. This Summary is merely exemplary of the numerous and varied embodiments. Mention of one or more representative features of a given embodiment is likewise exemplary. Such an embodiment can typically exist with or without the feature(s) mentioned; likewise, those features can be applied to other embodiments of the presently-disclosed subject matter, whether listed in this Summary or not. To avoid excessive repetition, this Summary does not list or suggest all possible combinations of such features.
The presently-disclosed subject matter includes methods useful for characterizing an auto-immune disease, and more particularly, for characterizing multiple sclerosis. The presently-disclosed subject matter further includes kits and devices useful for characterizing an auto-immune disease.
In some embodiments, a method for characterizing multiple sclerosis (MS) in a subject involves providing a biological sample from the subject; determining expression levels of at least two genes in the biological sample; calculating one or more ratios of the expression levels of the at least two genes; and comparing each ratios to a reference, wherein the is multiple sclerosisis characterized based on a difference in the ratios of the expression values of the at least two genes in the biological sample from the subject as compared to the references.
The at least two genes can be selected from those represented by SEQ ID NOs: 1-47, those corresponding to the genes set forth in Table A, or those corresponding to the genes set forth in Table B. In embodiments of the method, the expression levels of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes can be determined. In some embodiments, expression levels of the genes corresponding to CD55, FOS, JUN, PMAIP1, SPIB, TAF11, and TBP are determined. In some embodiments, expression levels of the genes corresponding to ACTB, CDKN1B, CTSS, GAPDH-1, KRAS, PGK1, and TBP are determined.
In accordance with the presently-disclosed subject matter, ratios of expression levels of genes are used to characterize an auto-immune disease. In this regard, ratios of interest for use in characterizing MS in a subject include the one or more ratios of expression levels of genes corresponding to those set forth in Table A, wherein each ratio is calculated by dividing the expression level of a first gene in Table A by the expression level of a second gene in Table A. In some embodiments, the at least one ratio is selected from the ratios set forth in Table B. In some embodiments, the one or more ratios consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, or 83 ratios set forth in Table B. In some embodiments, the one or more ratios consist of the ratios set forth in Column 1 (MS vs. CTRL) of Table B. In some embodiments, the one or more ratios consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, or 42 ratios set forth in Column 1 (MS vs. CTRL) of Table B. In some embodiments, the one or more ratios consist of the ratios set forth in Column 2 (MS vs. OND) of Table B. In some embodiments, the one or more ratios consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, or 41 ratios set forth in Column 2 (MS vs. OND) of Table B.
Various references can be selected for use in accordance with the presently-disclosed subject matter. In some embodiments, the reference is a standard reference ratio or a threshold value. For another example, in some embodiments, the reference is a reference ratio of a comparator group. In some embodiments, a “comparator group” or “reference group” includes individuals having a common characterization, for example, healthy control individuals, individuals who have been diagnosed with a condition often confused with an auto-immune disease of interest in the context of clinical diagnosis, individuals who have been diagnosed with an auto-immune disease of interest, or individuals who have another common characterization of interest. Expression values of biomarkers obtained from biological samples of individuals in a comparator group can be used to calculate reference ratios.
Methods of the presently-disclosed subject matter and also include comparing each subject ratio to a second reference. For example, in some embodiments, the reference can be a healthy control, and the second reference is not a healthy control. In some embodiments, the second reference comprises other neurologic disorders (OND).
Characterizing MS in a subject is inclusive of providing a diagnosis, prognosis and/or theranosis of the condition. As such, in some embodiments, characterization comprises diagnosing or prognosticating MS. In some embodiments, MS is predicted. In some embodiments, MS is not predicted. In some embodiments, the characterization comprises an exclusion of a diagnosis of MS. In some embodiments, the method also includes providing a series of biological sample obtained from the subject over a period of time. A change in the ratios in each of the biological samples from the subject can be useful for characterizing MS in the subject.
The presently-disclosed subject matter further includes kits and devices useful for detecting and/or determining expression levels of at least two genes in a biological sample.
The kits of the presently-disclosed subject matter can include primer pairs for determining expression levels of at least two genes, which can be useful for calculating ratios as disclosed herein. In some embodiments, the kit includes primer pairs for determining expression levels of at least two genes represented by SEQ ID NOs: 1-47. In some embodiments, the kit includes primer pairs for determining expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes represented by SEQ ID NOs: 1-47. In some embodiments, the kit includes primer pairs for determining expression levels of at least two genes corresponding to those set forth in Table A. In some embodiments, the kit includes primer pairs for determining expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes corresponding to those set forth in Table A. In some embodiments, the kit includes primer pairs for determining expression levels of the genes corresponding to CD55, FOS, JUN, PMAIP1, SPIB, TAF11, and TBP. In some embodiments, the kit includes primer pairs for determining expression levels of the genes corresponding to ACTB, CDKN1B, CTSS, GAPDH-1, KRAS, PGK1, and TBP. In some embodiments, the kit includes primer pairs for determining expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, or 34 genes corresponding to those set forth in Table B.
The devices of the presently-disclosed subject matter can include a probe for selectively binding each of at least two gene expression products to detect at least two genes, which can be useful for determining expression levels of the genes and for calculating ratios as disclosed herein. Such probes can selectively bind the gene products, for example, by hybridization of the probe and a nucleotide gene product. In some embodiments, the device includes probes for detecting each of at least two genes represented by SEQ ID NOs: 1-47. In some embodiments, the device includes probes for detecting each of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes represented by SEQ ID NOs: 1-47. In some embodiments, the device includes probes for detecting each of at least two genes corresponding to those set forth in Table A. In some embodiments, the device includes probes for detecting each of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes corresponding to those set forth in Table A. In some embodiments, the device includes probes for detecting each of the genes corresponding to CD55, FOS, JUN, PMAIP1, SPIB, TAF11, and TBP. In some embodiments, the device includes probes for detecting each of the genes corresponding to ACTB, CDKN1B, CTSS, GAPDH-1, KRAS, PGK1, and TBP. In some embodiments, the device includes probes for detecting each of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, or 34 genes corresponding to those set forth in Table B.
The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are used, and the accompanying drawings of which:
a. Ability of the radioscore method to discriminate between MS and combined CTRL plus OND subjects. Columns represent individual ratios. Rows represent individual subjects within the MS cohort. Black/dark grey in the heatmap denotes individual subject with the value of the individual ratio greater than the value of the ratio in all subjects within the CTRL cohort. Light grey/white denotes individual subjects with the value of the individual ratio less than or equal to the highest ratio value in all subjects within the CTRL cohort. b. Results from inputting independent CIS→MS subjects into the ratioscore alcorithm.
The details of one or more embodiments of the presently-disclosed subject matter are set forth in this document. Modifications to embodiments described in this document, and other embodiments, will be evident to those of ordinary skill in the art after a study of the information provided in this document. The information provided in this document, and particularly the specific details of the described exemplary embodiments, is provided primarily for clearness of understanding and no unnecessary limitations are to be understood therefrom. In case of conflict, the specification of this document, including definitions, will control.
The presently-disclosed subject matter includes methods, devices, and kits useful for characterizing an auto-immune disease in a subject and, more particularly, for characterizing multiple sclerosis (MS) in a subject. In some embodiments, the method involves providing a biological sample from the subject; determining expression values of at least two genes in the biological sample; calculating one or more ratios of the expression values of the at least two genes; and comparing each ratios to a reference, wherein the MS is characterized based on a difference in the ratios of the expression values of the at least two genes in the biological sample from the subject as compared to the references. In some embodiments, the biological sample is blood obtained from the subject or another biological sample containing a cell obtained from the subject, e.g., a subject suspected of having MS. The method can be used, in some embodiments, to diagnose the subject with MS. In some embodiments, the method can be used to exclude the subject from a diagnosis of MS.
Methods of the presently-disclosed methods include determining expression values of genes in biological samples. As such, nucleic acid molecules or nucleotides are relevant to the disclosed subject matter. Nucleotides or genes, the expression of which is desired to be determined for characterizing an auto-immune disease, include, but are not limited to those identified in Table A, the isolated nucleic acid molecules of any one of SEQ ID NOs: 1-47, fragments of the isolated nucleic acid molecules of any one of SEQ ID NOs: 1-47 where detection of such fragments are indicative of expression of an associated gene, e.g., as identified in Table A, complementary nucleic acid molecules, isolated nucleic acid molecules capable of hybridizing to any one of the SEQ ID NOs: 1-47 under conditions disclosed herein, and corresponding RNA and/or DNA molecules.
As used herein, “nucleic acid” and “nucleic acid molecule” refer to any of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), oligonucleotides, fragments generated by the polymerase chain reaction (PCR), and fragments generated by any of ligation, scission, endonuclease action, and exonuclease action. The term “isolated”, when used in the context of an isolated DNA molecule or an isolated polypeptide, is a DNA molecule or polypeptide that, by the hand of man, exists apart from its native environment and is therefore not a product of nature.
Unless otherwise indicated, a particular nucleotide sequence also implicitly encompasses complementary sequences, subsequences, elongated sequences, as well as the sequence explicitly indicated. The terms “nucleic acid molecule” or “nucleotide sequence” can also be used in place of “gene”, “cDNA”, or “mRNA”. Nucleic acids can be derived from any source, including any organism. In one embodiment, a nucleic acid is derived from a biological sample isolated from a subject.
The terms “complementary” and “complementary sequences”, as used herein, refer to two nucleotide sequences that comprise antiparallel nucleotide sequences capable of pairing with one another upon formation of hydrogen bonds between base pairs. As used herein, the term “complementary sequences” means nucleotide sequences which are substantially complementary, as can be assessed by the same nucleotide comparison set forth herein, or is defined as being capable of hybridizing to the nucleic acid segment in question under conditions such as those described herein. In one embodiment, a complementary sequence is at least 80% complementary to the nucleotide sequence with which is it capable of pairing. In another embodiment, a complementary sequence is at least 85% complementary to the nucleotide sequence with which is it capable of pairing. In another embodiment, a complementary sequence is at least 90% complementary to the nucleotide sequence with which is it capable of pairing. In another embodiment, a complementary sequence is at least 95% complementary to the nucleotide sequence with which is it capable of pairing. In another embodiment, a complementary sequence is at least 98% complementary to the nucleotide sequence with which is it capable of pairing. In another embodiment, a complementary sequence is at least 99% complementary to the nucleotide sequence with which is it capable of pairing. In still another embodiment, a complementary sequence is at 100% complementary to the nucleotide sequence with which is it capable of pairing. A particular example of a complementary nucleic acid segment is an antisense oligonucleotide.
“Stringent hybridization conditions” in the context of nucleic acid hybridization experiments are both sequence- and environment-dependent. Longer sequences hybridize specifically at higher temperatures. Generally, highly stringent hybridization and wash conditions are selected to be about 5° C. lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength and pH. The Tm is the temperature (under defined ionic strength and pH) at which 50% of the target sequence hybridizes to a perfectly matched probe. Very stringent conditions are selected to be equal to the Tm for a particular probe. Typically, under “stringent conditions” a probe hybridizes specifically to its target sequence, but to no other sequences. An extensive guide to the hybridization of nucleic acids is found in Tijssen 1993, which is incorporated herein by this reference. In general, a signal to noise ratio of 2-fold (or higher) than that observed for a negative control probe in a same hybridization assay indicates detection of specific or substantial hybridization.
It is understood that in order to determine a gene expression level by hybridization, a full-length cDNA need not be employed. To determine the expression level of a gene represented by one of SEQ ID NOs: 1-47, any representative fragment or subsequence of the sequences set forth in SEQ ID NOs: 1-47 can be employed in conjunction with the hybridization conditions disclosed herein. As a result, a nucleic acid sequence used to assay a gene expression level can comprise sequences corresponding to the open reading frame (or a portion thereof), the 5′ untranslated region, and/or the 3′ untranslated region. It is understood that any nucleic acid sequence that allows the expression level of a reference gene to be specifically determined can be employed with the methods and compositions of the presently disclosed subject matter.
As used herein, the terms “corresponding to” and “representing”, “represented by” and grammatical derivatives thereof, when used in the context of a nucleic acid sequence corresponding to or representing a gene, refers to a nucleic acid sequence that results from transcription, reverse transcription, or replication from a particular genetic locus, gene, or gene product (for example, an mRNA). In other words, a partial cDNA, or full-length cDNA corresponding to a particular reference gene is a nucleic acid sequence that one of ordinary skill in the art would recognize as being a product of either transcription or replication of that reference gene (for example, a product produced by transcription of the reference gene). One of ordinary skill in the art would understand that the partial cDNA, or full-length cDNA itself is produced by in vitro manipulation to convert the mRNA into a cDNA, for example by reverse transcription of an isolated RNA molecule that was transcribed from the reference gene. One of ordinary skill in the art will also understand that the product of a reverse transcription is a double-stranded DNA molecule, and that a given strand of that double-stranded molecule can embody either the coding strand or the non-coding strand of the gene. The sequences presented in the Sequence Listing are single-stranded, however, and it is to be understood that the presently claimed subject matter is intended to encompass the genes represented by the sequences presented in SEQ ID NOs: 1-47, including the specific sequences set forth as well as the reverse/complement of each of these sequences.
The term “gene expression” generally refers to the cellular processes by which a biologically active polypeptide is produced from a DNA sequence. Generally, gene expression comprises the processes of transcription and translation, along with those modifications that normally occur in the cell to modify the newly translated protein to an active form and to direct it to its proper subcellular or extracellular location.
The terms “gene expression level” and “expression level” as used herein refer to an amount of gene-specific RNA or polypeptide that is present in a biological sample. When used in relation to an RNA molecule, the term “abundance” can be used interchangeably with the terms “gene expression level” and “expression level”.
Determination of expression levels of genes of interest can be achieved using any technique known the skilled artisan. For example, in some embodiments, RNA can be purified from the biological sample, converted to the more-stable complementary DNA (cDNA), before the gene expression products of genes of interest are detected. As will be recognized by the skilled artisan, where amplification of the sample is desired, polymerase chain reaction amplification can be employed. Determining the expression levels can be achieved, for example, using reverse transcription-polymerase chain reaction (RT-PCR), microarray analysis, or other techniques known to the skilled artisan.
In some embodiments, determining the expression levels of genes in the biological sample includes determining the expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, or 47 genes represented by SEQ ID NOs: 1-47. In some embodiments, determining the expression levels of genes in the biological sample includes determining the expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes corresponding to those set forth in Table A.
In some embodiments, determining the expression levels of genes in the biological sample includes determining the expression levels of the genes corresponding to CD55, FOS, JUN, PMAIP1, SPIB, TAF11, and TBP. In some embodiments, determining the expression levels of genes in the biological sample includes determining the expression levels of the genes corresponding to ACTB, CDKN1B, CTSS, GAPDH-1, KRAS, PGK1, and TBP. In some embodiments, determining the expression levels of genes in the biological sample includes determining the expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, or 34 genes corresponding to those set forth in Table B.
As used herein, a “ratio” or “expression ratio” is the expression value of a first biomarker (numerator) divided by the expression value of a second biomarker (denominator), e.g., Gene A/Gene B. As such, once the expression levels of at least two genes are determined, a ratio can be calculated. Ratios can be calculated using expression levels of genes in a biological sample obtained from a subject. In some embodiments, a reference can be a ratio calculated using expression levels of genes from another source. As such, the term “subject ratio” can used herein to refer to a ratio calculated using expression values of a gene pair in a biological sample obtained from a subject, while the term “reference ratio” can be used to refer to a ratio of the same biomarker pair in a reference sample, which serves as a reference to which the subject ratio is compared.
In embodiments of the presently-disclosed subject matter, the method involves calculating one or more ratios of expression levels of genes corresponding to those set forth in Table A, wherein each ratio is calculated by dividing the expression level of a first gene in Table A by the expression level of a second gene in Table A.
In embodiments of the presently-disclosed subject matter, the method involves calculating one or more ratios set forth in Table B. In some embodiments, the method includes calculating 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, or 83 ratios set forth in Table B.
In embodiments of the presently-disclosed subject matter, the method involves calculating one or more ratios set forth in Column 1 (MS vs. CTRL) of Table B. In some embodiments, the method includes calculating 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, or 42 ratios set forth in Column 1 (MS vs. CTRL) of Table B.
In embodiments of the presently-disclosed subject matter, the method involves calculating one or more ratios set forth in Column 2 (MS vs. OND) of Table B. In some embodiments, the method includes calculating 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, or 41 ratios set forth in Column 2 (MS vs. OND) of Table B.
Various references are appropriate for use in connection with the presently-disclosed subject matter, with non-limiting examples described herein. In some embodiments, the reference comprises a reference ratio calculated using of the expression level of two genes in a biological sample taken from one or more individuals, which two genes are the same two genes used to calculate the subject ratio. The expression levels of genes in biological samples from one or more individuals can be a expression levels from a reference group or comparator group.
In some embodiments, a “comparator group” or “reference group” includes individuals having a common characterization, for example, healthy control individuals, individuals who have been diagnosed with a condition often confused with an auto-immune disease of interest in the context of clinical diagnosis, individuals who have been diagnosed with an auto-immune disease of interest, or individuals who have another common characterization of interest. Expression values of biomarkers obtained from biological samples of individuals in a comparator group can be used to calculate reference ratios. Data associated with one or more comparator groups can be stored, for example, in a database that can be accessed when practicing a method in accordance with the presently-disclosed subject matter.
With reference to Table B, for example, ratios-of-interest are provided for use with a healthy control comparator group (CTRL, column 1) or a comparator group of individuals having other neurologic disorders (OND, column 2). Examples of comparator groups relevant to characterization of MS include, but are not limited to: healthy control (CTRL), clinically isolated syndrome (CIS), CIS later developing MS (CIS→MS), MS diagnosed, newly diagnosed with MS who have not yet begun treatment (MS Naïve), established MS (>1 year), other neurologic disorders (OND), e.g., Alzheimer's disease, ataxia, Bell's palsy, cerebellar ataxia, cerebral bleed, cervical radiculopathy, Charcot-Marie tooth disease, CNS Lupus, dizziness/pituitary, drug-induced movement disorder, drug-induced tremor, dystonia, epilepsy, essential tremor, Huntington's disease, hydrocephalus, median neuropathy, meningioma, meningitis, migraine, Parkinson's disease, peripheral neuropathy, pituitary adenoma, pseudotumor cerebri, RLS, seizures, spasmodic torticollis, stroke, tension headache, Tourette's syndrome, transient ischemic attack, tremor, and trigeminal tremor. For some comparator groups, ONDs can be grouped by those typically considered inflammatory (OND-I) and those typically considered non-inflammatory (OND-NI).
Because a comparator group can include data from multiple individuals, as will be recognized by one of ordinary skill in the art, it is expected that the expression values of biomarkers in biological samples obtained from different individuals in the same comparator group might differ. As such, identification of a reference ratio for a particular gene pair can be made with reference to a “threshold reference ratio” for the gene pair within the comparator group. In some embodiments, for example, the threshold expression ratio could be a median, an average, a value based on statistical analysis of the distribution of ratios of expression levels of the gene pair within the comparator group, or another threshold value, e.g., top value in the group, second highest value in the group, third highest value in the group, etc.
In some embodiments, the reference comprises a reference ratio calculated using a standard sample containing standard biomarker amounts, which can be analyzed in the same manner or even concurrently with the biological sample. In some embodiments, the reference comprises ratio values, such as standard threshold values. Such values can be published in a format useful for the practitioner, such as in a list, table, database, or incorporated into a software or system for use in connection with the presently-disclosed subject matter. Such values can in some cases be based, for example, on information obtained from a comparator group.
Ratios of interest, or ratios of gene pairs that are useful for characterizing MS, have the ability to distinguish to groups, e.g., MS group and health control group. Table B includes examples of ratios of interest for MS vs. healthy control (CTRL) and MS vs. other neurologic disorders (OND). In this regard, an auto-immune disease can be characterized based on a difference in the ratios of the expression values of at least two genes in a biological sample from the subject as compared to a reference ratio.
In some embodiments, it can be useful to compare one or more subject ratios to one or more first reference ratios, e.g., from a first comparator group, and also to compare the one or more subject ratios to one or more second reference ratios, e.g., from a second comparator group. Such a multi-tiered approach can improve the efficacy of the characterization of the MS, as will be explained further in the Examples section.
Characterizing can include providing a diagnosis, prognosis, and/or theragnosis of an auto-immune disease in a subject.
“Making a diagnosis” or “diagnosing,” as used herein, are further inclusive of making a prognosis, which can provide for predicting a clinical outcome (with or without medical treatment), selecting an appropriate treatment (or whether treatment would be effective), or monitoring a potential auto-immune disease, based on calculated ratios of expression levels of genes. Diagnostic testing that involves treatment, such as treatment monitoring or decision making can be referred to as “theranosis.” Further, in some embodiments of the presently disclosed subject matter, multiple determinations of ratios of expression levels of genes over time can be made to facilitate diagnosis (including prognosis), evaluating treatment efficacy, and/or progression of a potential auto-immune disease or auto-immune disease. A temporal change in one or more ratios can be used to predict a clinical outcome, monitor the progression of the condition, and/or efficacy of administered therapies. In such an embodiment for example, one could observe a change in a particular ratio in a biological sample over time during the progression of a condition and/or during the course of a therapy.
The presently disclosed subject matter further provides in some embodiments a method for theranostic testing, such as evaluating progression of a condition and/or treatment efficacy in a subject. In some embodiments, the method comprises providing a series of biological samples over a time period from the subject; determining expression values of at least two genes in each of the biological samples; calculating one or more ratios of the expression values of the at least two genes for each of the biological samples; and determining any measurable change in the ratios in each of the biological samples from the series to thereby evaluate progression of the condition and/or treatment efficacy.
Any changes in the ratios, and changes in the ratios relative to references, over the time period can be used to make a diagnosis, predict clinical outcome, determine whether to initiate or continue the therapy, and whether a current therapy is effectively.
The phrase “determining the prognosis” as used herein refers to methods by which the skilled artisan can predict the course or outcome of a condition in a subject. The term “prognosis” can refer to the ability to predict the course or outcome of a condition with up to 100% accuracy, or predict that a given course or outcome is more or less likely to occur based on the ratios of expression values of genes of interest. The term “prognosis” can also refer to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a subject when compared to individuals in a comparator group. For example, in individuals exhibiting subject ratios-of-interest that are higher than reference ratio-of-interest, the chance of a given outcome (e.g., MS diagnosis) may be very high. In certain embodiments, a prognosis is about a 5% chance of a given expected outcome, about a 7% chance, about a 10% chance, about a 12% chance, about a 15% chance, about a 20% chance, about a 25% chance, about a 30% chance, about a 40% chance, about a 50% chance, about a 60% chance, about a 75% chance, about a 90% chance, or about a 95% chance.
The skilled artisan will understand that associating a prognostic indicator with a predisposition to an adverse outcome can be performed using statistical analysis. For example, subject ratios that are higher than reference ratios in some embodiments can signal that a subject is more likely to suffer from an auto-immune disease than subjects with ratios that are substantially equal to reference ratios, as determined by a level of statistical significance. Statistical significance is often determined by comparing two or more populations, and determining a confidence interval and/or a p value. See, e.g., Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York, 1983, incorporated herein by reference in its entirety. Exemplary confidence intervals of the present subject matter are 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% and 99.99%, while exemplary p values are 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, and 0.0001. When performing multiple statistical tests, p values can be corrected for multiple comparisons using techniques known in the art.
Further with respect to the methods of the presently disclosed subject matter, a preferred subject is a vertebrate subject. A preferred vertebrate is warm-blooded; a preferred warm-blooded vertebrate is a mammal. A mammal is most preferably a human. As used herein, the term “subject” includes both human and animal subjects. Thus, veterinary therapeutic uses are provided in accordance with the presently disclosed subject matter.
As such, the presently disclosed subject matter provides for the diagnosis of mammals such as humans, as well as those mammals of importance due to being endangered, such as Siberian tigers; of economic importance, such as animals raised on farms for consumption by humans; and/or animals of social importance to humans, such as animals kept as pets or in zoos. Examples of such animals include but are not limited to: carnivores such as cats and dogs; swine, including pigs, hogs, and wild boars; ruminants and/or ungulates such as cattle, oxen, sheep, giraffes, deer, goats, bison, and camels; and horses. Also provided is the treatment of birds, including the treatment of those kinds of birds that are endangered and/or kept in zoos, as well as fowl, and more particularly domesticated fowl, i.e., poultry, such as turkeys, chickens, ducks, geese, guinea fowl, and the like, as they are also of economic importance to humans. Thus, also provided is the treatment of livestock, including, but not limited to, domesticated swine, ruminants, ungulates, horses (including race horses), poultry, and the like.
The presently-disclosed subject matter further includes kits and devices useful for detecting and/or determining expression levels of at least two genes in a biological sample.
The kits of the presently-disclosed subject matter can include primer pairs for determining expression levels of at least two genes, which can be useful for calculating ratios as disclosed herein. In some embodiments, the kit includes primer pairs for determining expression levels of at least two genes represented by SEQ ID NOs: 1-47. In some embodiments, the kit includes primer pairs for determining expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes represented by SEQ ID NOs: 1-47. In some embodiments, the kit includes primer pairs for determining expression levels of at least two genes corresponding to those set forth in Table A. In some embodiments, the kit includes primer pairs for determining expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes corresponding to those set forth in Table A. In some embodiments, the kit includes primer pairs for determining expression levels of the genes corresponding to CD55, FOS, JUN, PMAIP1, SPIB, TAF11, and TBP. In some embodiments, the kit includes primer pairs for determining expression levels of the genes corresponding to ACTB, CDKN1B, CTSS, GAPDH-1, KRAS, PGK1, and TBP. In some embodiments, the kit includes primer pairs for determining expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, or 34 genes corresponding to those set forth in Table B.
The devices of the presently-disclosed subject matter can include a probe for selectively binding each of at least two gene expression products to detect at least two genes, which can be useful for determining expression levels of the genes and for calculating ratios as disclosed herein. Such probes can selectively bind the gene products, for example, by hybridization of the probe and a nucleotide gene product. In some embodiments, the device includes probes for detecting each of at least two genes represented by SEQ ID NOs: 1-47. In some embodiments, the device includes probes for detecting each of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes represented by SEQ ID NOs: 1-47. In some embodiments, the device includes probes for detecting each of at least two genes corresponding to those set forth in Table A. In some embodiments, the device includes probes for detecting each of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes corresponding to those set forth in Table A. In some embodiments, the device includes probes for detecting each of the genes corresponding to CD55, FOS, JUN, PMAIP1, SPIB, TAF11, and TBP. In some embodiments, the device includes probes for detecting each of the genes corresponding to ACTB, CDKN1B, CTSS, GAPDH-1, KRAS, PGK1, and TBP. In some embodiments, the device includes probes for detecting each of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, or 34 genes corresponding to those set forth in Table B.
Some of the gene sequences disclosed herein are cross-referenced to GENBANK® accession numbers. The sequences cross-referenced in the GENBANK® database are expressly incorporated by reference as are equivalent and related sequences present in GENBANK® or other public databases. Also expressly incorporated herein by reference are all annotations present in the GENBANK® database associated with the sequences disclosed herein. Unless otherwise indicated or apparent, the references to the GENBANK® database are references to the most recent version of the database, as of the filing date of this application.
While the terms used herein are believed to be well understood by one of ordinary skill in the art, definitions are set forth to facilitate explanation of the presently-disclosed subject matter.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the presently-disclosed subject matter belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice or testing of the presently-disclosed subject matter, representative methods, devices, and materials are now described.
Following long-standing patent law convention, the terms “a”, “an”, and “the” refer to “one or more” when used in this application, including the claims. Thus, for example, reference to “a cell” includes a plurality of such cells, and so forth.
Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term “about”. Accordingly, unless indicated to the contrary, the numerical parameters set forth in this specification and claims are approximations that can vary depending upon the desired properties sought to be obtained by the presently-disclosed subject matter.
As used herein, the term “about,” when referring to a value or to an amount of mass, weight, time, volume, concentration or percentage is meant to encompass variations of in some embodiments ±20%, in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, in some embodiments ±0.5%, and in some embodiments ±0.1% from the specified amount, as such variations are appropriate to perform the disclosed method.
As used herein, ranges can be expressed as from “about” one particular value, and/or to “about” another particular value. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. It is also understood that each unit between two particular units are also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.
The presently-disclosed subject matter is further illustrated by the following specific but non-limiting examples. The following examples may include compilations of data that are representative of data gathered at various times during the course of development and experimentation related to the present invention.
Expression patterns of a common set of genes assayed using a common platform in control subjects and subjects with different neurologic conditions, including autoimmune diseases, were measured. Expression levels of individual genes were determined by quantitative RT-PCR by normalization to GAPDH expression levels. A heatmap was employed to depict those genes differentially expressed in individual disease cohorts relative to the control cohort, P <0.05 (after Bonferroni correction for multiple testing) (
Discrimination of MS from Homogeneous Comparator Groups: Identification of an Optimum Panel of Gene Expression Ratios
Healthy control subjects, subjects with MS, and subjects with other inflammatory neurologic disorders (OND-I), and subjects with neurologic disorders typically considered non-inflammatory (OND-NI) were recruited from multiple U.S. and European sites (Table 1-A and Table 1-B). Demographic characteristics of the different disease groups, MS, OND-I, or OND-NI were matched to the CTRL cohort (compilation of patient characteristics data not shown). Subjects with MS included subjects with clinically isolated syndrome (CIS), newly diagnosed MS subjects who were treatment naïve and subjects with established disease (>1 yr duration) on different therapies. Expression levels of test and control genes in blood were determined by quantitative reverse transcription polymerase chain reaction (RT-PCR) (Table 1-C).
A search algorithm was employed to identify those ratios of gene expression levels in which the greatest number of subjects in the test group possessed a ratio value greater than the highest ratio value in the comparator group. A second algorithm was employed to perform permutation testing of one subject group to identify the optimum set of discriminatory ratios.
It was reasoned that examination of expression levels of ratios of genes rather than individual genes would serve the following purposes. First, calculation of ratios normalized for differences in mRNA or cDNA template quantity and quality among different samples. Second, they obviated the need for inclusion of a ‘housekeeping’ gene in the analysis and the assumption that expression levels of ‘housekeeping’ genes did not vary among different subject populations. Third, comparisons of ratios or combinations of ratios may more accurately identify cellular phenotypes that may contribute to disease. For example, a ratio containing one gene in the numerator that is over-expressed in the test group relative to the comparator group and one gene in the denominator that is under-expressed in the test group relative to the comparator group should produce a greater ratio value difference between individuals in the two groups than a single expression value. A point system was employed to award one point to a subject if a ratio value of the test subject was greater than the ratio values of all subjects in the comparator group.
This approach was applied to determine how accurately it would distinguish subjects with MS from healthy control subjects. First, ratios capable of discriminating MS subjects from control subjects were identified. The single ratio with the greatest discriminatory power was ANAPC1/CHEK2 (
Discrimination of MS from Homogeneous Comparator Groups: Validation and Analysis
The analyses depended upon determination of multiple ratios, which may create Type 1 errors. Various methods are available to correct for false discovery rates. Rather than relying upon these methods, which all make underlying assumptions, a second evaluation was performed using an independent cohort of 40 new MS subjects and 40 new CTRL subjects to validate results obtained from the initial training set. These subjects were recruited separately and the PCR analyses were performed separately. The same ratio values were used, as defined from the original CTRL and MS test set to award points to subjects in the validation cohort. All 40 controls were awarded a score of 0 while 4% of MS subjects received a score of 0. The remaining 96% of MS subjects achieved a score of 1-6 and the distribution of scores was similar to that observed in the training set (
The point system was applied to OND-I and OND-NI subjects. In contrast to CTRL subjects, 90% of OND-I and 59% of OND-NI subjects scored ≧1 (
Further, follow-up clinical information on 8 CIS subjects >2 yr. after the initial consent and blood draw were able to be obtained. Of these subjects, the 7 CIS subjects who achieved a score >0 in the analysis now have documented MS. The 1 CIS subject who achieved a score of 0 does not have a documented case of MS.
NMO and TM are inflammatory neurologic diseases that scored positive in the analysis. Therefore, it was determined whether a similar approach could be employed to discriminate MS from TM and MS from NMO. A series of ratios were identified that, when combined using the point system, were able to discriminate TM from MS and NMO from MS with similar overall accuracy to the MS and CTRL comparisons (
Above results demonstrate it is possible to distinguish MS from either a control cohort or even a related inflammatory disease cohort if the disease cohort is a single disease.
Next, it was determined whether MS could be discriminated from Parkinson's disease, a disorder typically considered non-inflammatory. To test this hypothesis in it was determined if subjects with Parkinson's disease (N=24) segregated from MS (N=182) and from CTRL (N=109) using the ratio and point system. Ten (10) ratios capable of discriminating 97% of MS subjects from 100% of Parkinson's subjects and 9 ratios capable of discriminating 88% of Parkinson's patients from 100% of CTRL subjects were identified (
Discrimination of MS from Heterogeneous Comparator Groups
Next, it was determined whether MS could be distinguished from more heterogeneous groups of subjects. To do so, subjects with neurologic conditions typically considered as inflammatory (other neurologic disorders-inflammatory, OND-I in Table 1-B) were combined into one group. Subjects with neurologic conditions typically considered non-inflammatory (other neurologic disorders-non-inflammatory, OND-NI, OND in Table 1-B) were combined into a second group. A third group consisting of CTRL+OND-I+OND-NI subjects (ALL) was prepared. The 15 best ratios were determined using permutation testing for each comparison. Overall, comparison of MS to these heterogeneous comparator groups resulted in a marked reduction in overall discrimination ability (
Discrimination of MS from OND-I: Identification of Optimum Panels of Gene Expression Ratios
For additional analysis, OND-I was combined into one group of non-MS inflammatory neurologic disorders and investigated the ability of the approach to discriminate this combination of diseases from MS. The conditions were relaxed somewhat to identify ratios with the ability to detect 0 or 1 non-MS subjects. The best results were obtained with 10 ratios (
Discrimination of MS from OND-I: Validation and Analysis
Additional analyses were performed with 40 new MS subjects and 40 new OND-I subjects (20 NMO and 20 TM) not included in the training set. In the validation set, 88% of MS subjects achieved a score ≧1 and 12% of OND-I subjects achieved a score of 1 (
Discrimination of MS from OND-NI: Identification of Optimum Panels of Gene Expression Ratios
Next, gene expression differences between MS and OND-NI subjects were compared, which included Parkinson's disease, essential tremors, migraines, and strokes. The same search strategy used to compare MS and OND-I subjects was employed and identified 10 expression ratios to construct the point system. ABOBEC3F, CSF3R, and ANAPC1 were each in the numerators of two ratios and TAF11 was in the denominator of two ratios. Each ratio alone detected >10% of MS subjects relative to OND-NI subjects (
Discrimination of MS from OND-NI: Validation and Analysis
Additional analyses were performed with 40 new MS subjects and 40 new OND-NI subjects not included in the training set as outlined above. In the validation set, 88% of MS subjects achieved a score ≧1 (
All comparisons in these analyses were binary. Therefore, exclusion of a specific disorder by the analysis may be more accurate than inclusion of a specific disorder (see flow chart).
Analysis 1: MS versus control
Analysis 2A: MS versus OND-I
Analysis 2B: MS versus OND-NI
Analysis 3A: MS versus NMO
Analysis 3B: MS versus TM
Thus, a score of 0 in the MS versus CTRL test decreased the probability that a subject had MS. A second analysis comparing MS to OND-I and MS to OND-NI would be interpreted similarly. Scores of 0 decreased the probability of MS and favored the probability of OND-I or OND-NI, respectively. Finally, specific inflammatory neurologic disorders, NMO or TM, were distinguished from MS with high degrees of accuracy. Thus, results from this single platform can be analyzed in a tiered approach to provide meaningful disease classification.
Discussion
Although the focus was on MS and other inflammatory and non-inflammatory neurologic disorders, the results support the notion that this approach could be applicable to an array of diseases. First, discrimination between MS and healthy controls or subjects with individual diseases can be achieved with a relatively high degree of accuracy. However, subjects with OND-I and OND-NI also scored positive in MS-CTRL comparisons. As such, this single comparison has greater utility as an exclusionary test rather than a test of MS inclusion. Second, it is possible to discriminate MS from groups of diseases, such as inflammatory or non-inflammatory neurologic diseases, and validate results in independent cohorts, although overall accuracy is somewhat compromised. Third, discrimination of MS from a diverse comparator group including CTRL, OND-I, and OND-NI causes a further reduction in overall accuracy. Nevertheless, a score >0 in this analysis is highly predictive of the presence of MS. Fourth, it is possible to identify small numbers of ratios with high degrees of discriminatory power whose accuracy can be validated in independent cohorts analyzed separately.
One interpretation of the results is that many individual diseases express unique but overlapping gene expression signatures in whole blood. Given the attention paid to analyses of autoimmune diseases, it is not surprising that inflammatory neurologic diseases such as NMO and TM also express unique gene expression signatures. Perhaps somewhat surprising is that Parkinson's disease, a disorder typically considered non-inflammatory, also possesses a unique gene expression signature distinguishing it from both CTRL and MS. Implications may be that the immune system can sense specific neurologic damage caused by Parkinson's via responses to cytokine mediators, adhesion molecules, neurotransmitters, or other mediators read by immune cells. Alternatively, genetic risk factors associated with Parkinson's disease may contribute to altered gene expression signatures by either direct or indirect mechanisms.
Mechanisms underlying gene expression differences among study groups or relationships to MS disease mechanism are not altogether clear. However, defects in DNA damage repair, cellular responses to DNA damage, and regulation of cell cycle progression and arrest are common properties of lymphocytes in certain autoimmune diseases, including MS, and ANAPC1, CHEK2, CDKN1B, ACTB, FOSL1, LLGL2, and NRAS encode proteins playing key roles in these fundamental cellular processes23-27. These genes are highly represented in the ratios used to distinguish MS from comparator groups. Genes, such as ADAMTSL4, B2M, IL11RA, TXK and POU6F1, encode proteins playing key functions in regulating cells of both innate and adaptive arms of the immune system28, 29. As such, alterations in expression of these genes may contribute to pathogenesis of MS or may represent an altered response by the immune system to MS pathogenesis.
The follow-up analysis of CIS patients supports the idea that initial scores >0 will correlate with progression to MS. Future longitudinal studies are planned to better evaluate utility of these tests in this setting. Further, the binary analysis is also predicated on the fact that MS is best represented by a single set of gene expression ratios and this may not be the case. Additional analyses, such as analyses of gene expression ratios in multi-dimensional space, will address this possibility. Several different combinations of gene expression ratios were identified, which performed equivalently in their ability to discriminate among subject groups. In conclusion, these minimally invasive and relatively inexpensive tests may have utility to either exclude the diagnosis of MS or to contribute to establishing a diagnosis of MS.
Materials and Methods
Patients.
Blood samples in PAXgene tubes were obtained from patients with a) clinically isolated syndrome (CIS), b) an initial diagnosis of MS before onset of therapy, and c) established relapsing-remitting MS on medication. Blood samples were also obtained from healthy control subjects (CTRL) and subjects with different inflammatory (OND-I) or non-inflammatory (OND-NI) neurologic conditions. MS samples were obtained from a total of 9 different sites in the U.S. and Europe. Samples from subjects with OND-I and OND-NI were obtained from 7 sites in the U.S. CTRL samples were obtained from 3 U.S. sites. Inclusion criteria for MS and other neurologic conditions were diagnosis by a neurologist using established methods and ability to provide informed consent, thus providing an un-biased study cohort. Age, race and gender were not statistically different among the different study groups. Time of the blood draw, e.g. morning/afternoon clinics, was also not statistically different among the different study groups. Relevant institutional review board approval from all participating sites was obtained.
Procedures.
Total RNA, purified using Qiagen's isolation kits by standard protocols, was reverse-transcribed using SuperScript III (Invitrogen). A TaqMan Low Density Array (TLDA) was designed to analyze expression levels of 44 genes previously identified from the microarray analysis and of 4 “housekeeping” genes in 300 ng cDNA per sample. Patient diagnosis was blinded for all experimental procedures. Relative expression levels were determined directly from the observed threshold cycle (CT), the cycle number at which fluorescence generated within reactions crosses an assigned threshold reflecting the point where sufficient amplicons have accumulated to be statistically significant above baseline. Linear expression values were determined using the formula, 2(40-CT).
Identification of Discriminatory Gene Expression Ratios.
A computational algorithm was designed to identify the most discriminatory combinations of ratios22. All possible gene expression ratios were computed (e.g. ACTR1A/BRCA1, TAF11/ACTR1A, etc). To analyze individual results, Ri,jcontrol was used to denote the ith ratio for the jth control and let Ri,kMS was used to denote the ith ratio for the kth MS patient. Here, j=1, . . . , Ncontrol and k=1, . . . , NMS, where Ncontrol equals the total number of controls and NMS equals the total number of MS patients in the data set. The second largest member of each data set of ratios was calculated first by {Ri,1control, Ri,2control, . . . , Ri,N
Search Algorithm for Best Ratios.
Let D denote the set of 44 gene-expression levels associated with the disease group and C denote the set of gene-expression levels associated with the control group. For example, when D is the set of MS patients, then D is a set of 182 44-tuples; if C is associated with the Controls, then C is a set of 51 44-tuples. The algorithm that searches for the “best” set of gene ratios is the following:
The Welch's corrected T-test not assuming equal variances was used to calculate P values in two-way comparisons. The Chi-squared test for independence was used to calculate P values in three-way comparisons. The Bonferroni method was employed to correct for multiple testing30.
Patients.
A total of 562 subjects were included in the study: 199 with clinically definite MS, 203 with OND segregated into 84 OND-I subjects and 119 OND-NI subjects, 114 healthy control subjects and 46 subjects whose blood sample was obtained at the time of their CIS but who now have progressed to clinically definite MS, CIS→MS (Table 2-A). MS patients were divided into two additional categories: those at their initial diagnosis of MS but before initiation of therapies; MS-naïve, and those ≧1 year after diagnosis of MS and on different therapies; MS-established. The overall laboratory and analytic processes are summarized in
Transcript Profiles.
The transcript level in blood was determined for each target gene relative to GAPDH in the three study groups, CIS→MS, MS-naïve, MS-established and the CTRL group using TLDA plates. Target genes were selected from previous microarray studies.17-19 The ratio, log2, of the expression level of each gene in each study group was calculated relative to CTRL and results are presented in a heatmap. Numerical ratios, log2, are displayed within each box (
Ratioscore Algorithm.
The previously described ratioscore method was used to compute all gene expression ratios and permutation testing to identify the set best able to discriminate the MS cohort, naïve and established combined, from the CTRL cohort40. A heatmap was generated to depict which ratios (columns) were positive for each MS subject (rows) (
Using a similar approach, the ratioscore algorithm was used to compute ratios to discriminate MS, combined MS-naïve and MS-established from OND. As above, a heatmap was generated to depict which ratios (columns) were positive for each MS subject (rows) (
The rationale for performing this two-tier analysis rather than combining the CTRL and OND subjects into one cohort was that previous studies demonstrated that accuracy was severely compromised. To confirm that this was the case in this analysis the MS cohort was compared to the combined CTRL plus OND cohort and these data were inputted into the ratioscore algorithm. As expected, overall ability to discriminate MS from this combined cohort was compromised. Only 58% of MS subjects were assigned to the MS category while 100% of subjects in the combined CTRL plus OND cohort were excluded from the MS category (
The OND cohort was also subdivided into OND-I and OND-NI (Table 2-A) and the ratioscore algorithm was repeated to assess how well these sub-groups could be distinguished from MS (
Accuracy of Ratioscore and SVM Methods.
A support vector machine (SVM) was also trained with ratios identified by the ratioscore method using 60% of CTRL subjects and 60% of cases (see Methods). SVM was validated with the remaining 40% of CTRLs and cases. Subjects within the CIS→MS cohort were input into the SVM to ascertain if the SVM would identify them as controls or cases. New SVMs were created using 60% of OND, OND-NI, and OND-I cohorts as controls, respectively and 60% of MS subjects as the case cohort. SVMs were validated with the remaining 40% of the respective control cohort and remaining 40% of the case cohort20. As above, subjects within the CIS→MS cohort were input into each SVM to ascertain if the SVM would identify them as controls or cases. Results from the SVM method were compared to results from the ratioscore method by calculating sensitivity and specificity (Table 2-B). Overall, ratioscore and SVM produced comparable sensitivity and specificity in control: case comparisons. More relevant, subjects within the CIS→MS cohort were identified as MS by both methods with a high degree of specificity. Thus, this tiered approach, MS:CTRL then MS:OND, could be employed to predict if a subject with CIS will develop MS with a reasonable level of overall accuracy.
To summarize, overall transcript profiles in the CIS→MS, MS-naïve, and MS-established were markedly different and these dynamic transitions may reflect differences in underlying etiology. Studying the molecular origins of the robust transcript signature in CIS→MS subjects may produce insights into the origins of MS. In spite of the differences in overall transcript profiles in these three subject groups, ratioscore and SVM methods were able to assign CIS→MS subjects to the MS category with a high degree of accuracy. This is due, in part, to the fact that the ratioscore method does not require that all subjects within these three cohorts representing three distinct stages of disease progression possess identical gene expression signatures. In contrast, many other standard methods of analysis of gene expression signatures are dependent upon identification of overall differences between or among groups.
This study did not include subjects with an initial CIS that did not develop MS. The rationale for not including this parameter is three-fold. First, there is not a uniform clinical definition of CIS. Second, subjects with a CIS may or may not have MRI findings indicating inflammation or demyelination and the probability that a subject with CIS will develop MS is greater if MRI lesions are also detected. Third, with the current knowledge, it is uncertain if it is experimentally possible to absolutely conclude that a person with CIS will not develop MS. In fact, the period of time between an initial CIS and diagnosis of clinically definite MS is quite variable and can exceed 5 years.
Methods
Patients.
Blood samples in PAXgene tubes were obtained from CTRL, MS, OND-I and OND-NI subjects. Demographic characteristics of these cohorts have been previously described. Age, race and gender were not statistically different among the different study groups. Time of blood draw, for example, morning/afternoon clinics, was also not statistically significant among the different study groups. Relevant institutional review board approval was obtained from all participating sites.
Samples were also obtained from subjects with a clinically isolated syndrome (CIS) at the time of the blood draw. All of these subjects have gone on to develop MS according to the McDonald's criteria for the diagnosis of MS.
Transcript Determinations.
Total RNA purification, cDNA synthesis, and analysis using the 384-well Taqman Low Density Array (TLDA) were as previously described (FIG. 8).40 Patient diagnosis was blinded for all experimental procedures. Relative expression levels were determined directly from the observed threshold cycle (CT). Linear expression levels were determined using the formula, 2(40-CT).
Ratioscore and Support Vector Machine Algorithms.
The identification of the gene expression ratios and permutation testing strategy employed to identify discriminatory combinations of ratios to create the ratioscore have been previously described.40 and Example 1 Briefly, all possible gene-expression ratios of the 35 genes were computed. Ratios in which the greatest number of subjects in case groups possessed a ratio value greater than the highest ratio value in the control group were saved. Permutation testing was performed by randomly selecting 80% of the control group to compare with the case group and repeating this process 200 times producing 200 subsets of ratios. From these subsets of ratios, the smallest number of ratios to identify the ratioscore with maximum separation between case groups and control groups were identified. For example, MS versus CTRL, MS versus OND, etc. were compared. Each comparison produced a unique set of ratios that were used to define the ratioscore algorithm for that pairing of the case-control groups.
A support vector machine (SVM) was created from each set of ratioscores using LS-SVMLab software (http://www.esat.kuleuven.be/sista/Issvmab). For example, the gene-expression ratios from the MS versus CTRL were used to create a SVM for this type of comparison. The SVM was trained with L-fold cross-validation using 60% of the data. In this type of training a certain fraction of the training set was omitted from training and the remaining portion of the partial training set was used to estimate the parameters in the SVM. Once the SVM was trained, the SVM was applied to the total data set. Numbers of correct and incorrect classifications were tabulated for total sets (training and validation), training sets and validation sets. As expected, the overall accuracy in the training sets was greater than overall accuracy of the validation sets.
The following are complementary DNA (cDNA) sequences of genes-of-interest identified in Table A. The portion of the sequences bolded and underlined are Applied BioSystems context sequences, the region of that can be amplified in some embodiments of the presently-disclosed subject matter. ABI assay numbers for the sequences are provided in Table A.
CTCCTGGGGTGGAAGCAGGGAAA
GGCCTGGAGATGAGGAAGCTGGTTCTCTCGGGGTTCTTGGCCAGCGA
CTGTTA
CCTATCCATAAACCCCCAAAAGGATGAGACGCTAGAGACAGAGAAAGCTCAGTACTACCTGCCT
CAA
CCGGTATCATCTCCAGGCTCT
CCGGCACCTCTATGTGCTGGCCGCGGAGCCCAGGCTTCTAGTGCCT
G
CAGCATCATGGAGGTTTGAAGATGCCGCATTTGGATTGGATGAATTCCAAATTCTGCTTGCTTGCTTTT
CCA
GAAGAATTTATGCTCGTGT
ACAAGTTTGCCAGAAAACACCACATCACTTTAACTAATCTAATTACTG
G
AGAGCTGTTTGACAAAGTGGTGGGGAATAAACGCCTGAAAGAAGCTACCTGCAAGCTCTATTTTTACCA
TCT
GCACGGACCTG
GCCGTCTCCAGTGCCAACTTCATTCCCACGGTCACTGCCATCTCGACCAGTCCGGA
T
CCCTCTAATGAGACTGACCATATTGTGCTTCACAGTAGAGCCAGCTTGGGGCCACCAAAGCTGCCCACT
CCC
GGCAGGGAGTGGAGGATG
CCTTCTACACGTTGGTGCGTGAGATCCGGCAGCACAAGCTGCGGAAGCT
CTG
GCCAGGATTGCTACAGTT
GTGATTGGAGGAGTTGTGGCCATGGCGGCTGTGCCCATGGTGCTCAGTG
CCT
GACCC
ACCCCAGGGCCTGCGGGTAGAGTCAGTACCAGGTTACCCCCGACGCCTGCGAGCCAGCTGGA
GGG
TGTTGAT
GATGCCTTCTATACATTAGTTCGAGAAATTCGAAAACATAAAGAAAAGATGAGCAAAGAT
CG
CCACCGGCGTGTGTTCGAGATGGTGGAGGCACTGCAGGAGCACCCTCGAGACCCCAACCAGATCCTGA
ACA
GGGTGTTGA
AGATGCTTTTTACACACTGGTAAGAGAAATACGCCAGTACCGAATGAAAAAACTCAAC
GGA
AGTCGA
GTGTGCTACTCAACTCAGGAGATTTGGAGACAAACTGAACTTCCGGCAGAAACTTCTGAAT
TGC
AGCCCATCCA
GCCCACACCAACCGTGCCCCAGCCTGCTGTGGTCATTGCCAGCCCAGCTCCAGCCGC
CCC
TGATCTCACTAG
GGGAAGGACTCATCACAGCTGGGGCTCAGCTGGTGGAGCTGGACTTAAGCGACAA
TGT
CTGTACCCAG
ATGGCGTCTTCTATGACCTGGACAGCTGCAAGCATTCCAGCTACCCTGATTCAGAGG
ACT
TGCACGTACTCCCCTG
CCCTCAACAAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGT
AGTGTATGCCAGGATATATGTGAAGGAATGGAATATCTGGAGAGGAATGGCTATATTCATAG
GGATTTGG
CGG
CAAGGAATTGTTTG
GTCAGTTCAACATGCATAGTAAAAATTTCAGACTTTGGAATGACAAGGTACGT
Throughout this document, various references are mentioned. All such references are incorporated herein by reference, including the references set forth in the following list:
This application claims priority from U.S. Provisional Application Ser. No. 61/533,599 filed Sep. 12, 2011, the entire disclosure of which is incorporated herein by this reference.
This invention was made with U.S. government support under contract numbers AI53984 and AI044924 awarded by the National Institutes of Health. The U.S. government has certain rights in the invention.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/US12/54903 | 9/12/2012 | WO | 00 | 7/7/2014 |
| Number | Date | Country | |
|---|---|---|---|
| 61533599 | Sep 2011 | US |
