Gene expression patterns in distinct neurologic diseases
We measured 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. Genes for analysis were selected from prior microarray studies. Expression levels of individual genes were determined by quantitative RT-PCR by normalization to GAPDH expression levels. We employed a heatmap to depict those genes differentially expressed in individual disease cohorts relative to the control cohort, P < 0.05 (after Bonferroni correction for multiple testing) (, red = over-expressed gene, green = under-expressed gene). Ratios of expression levels of individual genes in the indicated disease cohort relative to the control cohort were calculated and depicted within each colored box. Each disease exhibited an underlying unique pattern of gene expression. However, these profiles were sufficiently overlapping to prohibit accurate discrimination of one disease from another disease using the expression profile alone. For example, LLGL2, RANGAP1, ACTB, and POU6F1 were under-expressed in 4, 3, 4, and 4 of 5 different conditions, respectively. In contrast, other genes, e.g., ANAPC1 in Parkinson’s disease, EXT2 and FOS in TM, HRAS in NMO, were only differentially expressed in a single disease cohort. Overall, individual genes were either over-expressed, e.g. B2M, CD55, PMAIP1, or under-expressed, e.g. LLGL2, RANGAP1, ACTB, across multiple disease cohorts. Thus, each gene was differentially expressed in at least one disease cohort relative to the CTRL cohort. However, each individual disease cohort did not possess a unique expression profile distinguishing it from all other disease cohorts.
Figure 1 Gene expression profiles across multiple autoimmune diseases. Expression levels of 44 target genes were determined by quantitative RT-PCR and normalized to expression of GAPDH. Expression levels of 31 genes are shown; expression levels of the remainder (more ...)
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 ( and Supplementary Table 1
). Demographic characteristics of the different disease groups, MS, OND-I, or OND-NI were matched to the CTRL cohort (). 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) (Supplementary Table 2
). We employed a search algorithm 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. We employed a second algorithm to perform permutation testing of one subject group to identify the optimum set of discriminatory ratios. We 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. We employed a point system 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 (Supplementary Figure 1
Demographic characteristics of the different subject populations.
We applied this approach to determine how accurately it would distinguish subjects with MS from healthy control subjects. First, we identified ratios capable of discriminating MS subjects from control subjects. The single ratio with the greatest discriminatory power was ANAPC1/CHEK2 (). Fifty % of MS subjects achieved a ratio value higher than all the CTRL subjects and were awarded one point. Second, we eliminated those ratios that identified fewer than 20% of MS subjects. Third, since many ratios identified the same MS subjects, we performed another reduction to preserve only one ratio with this characteristic. A total of 8 ratios remained after this minimization process (). Using the point system, the combination of these 8 ratios positively identified 97% of MS subjects and eliminated 100% of CTRL subjects (). The score distribution was 0–6 for MS subjects and 0 for CTRL subjects ().
Figure 2 Discrimination between MS and CTRL subjects with an 8 ratio scoring system. (a) Performance of the single ratio, ANAPC1/CHEK2 to discriminate MS and CTRL subjects. (b) Genes making up 8 unique discriminatory ratios. P values compare expression levels (more ...)
Discrimination of MS from homogeneous comparator groups: validation and analysis
Our 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, we performed a second evaluation 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. We used the same ratio values 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 (). Taken together, this demonstrates that results obtained in the training set can be replicated in an independent cohort of CTRL and MS subjects.
We applied the point system to OND-I and OND-NI subjects. In contrast to CTRL subjects, 90% of OND-I and 59% of OND-NI subjects scored ≥ 1 (). We compared scores among subjects with CIS, with newly diagnosed MS not yet on medications, and with established MS on different medications. Scores did not differ significantly among these three groups (). We also compared scores within the MS group as a function of geographic origin. Scores also did not vary significantly among MS subjects from different geographic sites (). Thus, subjects with CIS or subjects after their initial diagnosis of MS had a similar mean score to subjects with established MS on therapies. However, a high percentage of subjects with other neurologic conditions, especially inflammatory neurologic conditions, also scored > 0 in this analysis. Given its extremely high specificity and relatively low sensitivity, this test has greater application to exclude an individual from the diagnosis of MS rather than to establish a diagnosis of MS.
Further, we were able to obtain follow-up clinical information on 8 CIS subjects > 2 yr. after the initial consent and blood draw. 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 our analysis. Therefore, we determined if we could employ a similar approach to discriminate MS from TM and MS from NMO. We identified a series of ratios 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 (). Thus, using our approach, it was possible to distinguish MS from TM and MS from NMO with a similar degree of accuracy as obtained for the comparison of MS to CTRL. However, since each disease possessed a unique signature, it was necessary to employ separate combinations of ratios to accurately distinguish MS from NMO and MS from TM.
Figure 3 Discrimination of MS subjects from subjects with inflammatory neurologic diseases, TM or NMO. Most discriminatory gene expression ratios were identified that segregate MS subjects from TM and NMO subjects (CTRL is included for reference). The point system (more ...)
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, we asked if we could discriminate MS from Parkinson’s disease, a disorder typically considered non-inflammatory. To test this hypothesis in we 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. We identified 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 (). We interpret these results to demonstrate that subjects with Parkinson’s disease express unique gene expression signatures in blood distinguishing them from CTRL and MS subjects.
Figure 4 Discrimination of subjects with Parkinson’s disease from MS and CTRL. Most discriminatory gene expression ratios were identified that segregate Parkinson’s disease subjects from MS subjects and CTRL subjects. Using the point system, we (more ...)
Discrimination of MS from heterogeneous comparator groups
Next, we determined if we could distinguish MS from more heterogeneous groups of subjects. To do so, we combined subjects with neurologic conditions typically considered as inflammatory (other neurologic disorders-inflammatory, OND-I in Table S1
) into one group. We also combined subjects with neurologic conditions typically considered non-inflammatory (other neurologic disorders-non-inflammatory, OND-NI, OND in Table S1
) into a second group. We produced a third group consisting of CTRL + OND-I + OND-NI subjects (ALL). We determined the 15 best ratios using permutation testing for each comparison. Overall, comparison of MS to these heterogeneous comparator groups resulted in a marked reduction in overall discrimination ability (). We conclude that a binary comparison such is this exhibits much reduced accuracy as the heterogeneity of the comparator group is increased.
Figure 5 Discrimination of MS subjects from heterogeneous comparator groups. We identified the top 15 gene expression ratios with the greatest ability to discriminate MS from OND-I, OND-NI, or ALL (OND-I, OND-NI, and CTRL). Using the point system, we determined (more ...)
Discrimination of MS from OND-I: identification of optimum panels of gene expression ratios
For additional analysis, we combined OND-I into one group of non-MS inflammatory neurologic disorders and investigated the ability of our approach to discriminate this combination of diseases from MS. We relaxed conditions somewhat to identify ratios with the ability to detect 0 or 1 non-MS subjects. Our best results were obtained with 10 ratios (). The combination of which identified 86% of MS subjects with a score > 0 and only 8% of OND-I subjects with a score > 0 (). Scores ranged from 0–7 for MS subjects and 0–1 for OND-I subjects ().
Figure 6 Discrimination between MS and OND-I subjects using 10 gene expression ratios. (a) Genes making up 10 unique discriminatory ratios. P values compare individual ratio values between MS and OND-I subjects. (b) Increasing number of ratios increases sensitivity (more ...)
Discrimination of MS from OND-I: Validation and analysis
We performed additional analyses 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 (), which was similar to the score distribution observed in the training set. We determined mean scores among subjects with CIS, subjects with newly diagnosed MS prior to onset of therapies, and subjects with established MS on therapies using the 10 ratios identified above. Mean scores were significantly higher in the CIS and MS-naïve groups than in the MS group with established disease (). We also determined mean scores based upon geographic origins of MS subjects. Subjects from Nashville and Europe had mean scores significantly greater than U.S. subjects from locations other than Nashville (). These results are consistent with results comparing CIS, MS-naïve, and MS-established. The majority of subjects from U.S. sites outside Nashville had established MS and were on therapies (76 of 80 subjects) while all European subjects were either CIS or newly diagnosed MS subjects not yet on therapies (N=101). The Nashville site also provided more samples with established disease (N=37) compared to CIS or treatment naïve MS (N=16) (P < 0.0001, Chi-squared test for independence among three geographic locations). The distribution of scores in the CIS and newly diagnosed MS group was also higher than that found in the established MS group. Greater than 50% of subjects with established MS achieved scores of 0 or 1 while 48% of CIS and newly diagnosed MS subjects achieved scores ≥ 3 (). Thus, subjects with CIS, newly diagnosed MS, and established MS from different geographic sites can be distinguished from subjects with OND-I with reasonable accuracy based upon gene expression profiles in whole blood.
Discrimination of MS from OND-NI: identification of optimum panels of gene expression ratios
Next, we compared gene expression differences between MS and OND-NI subjects, which included Parkinson’s disease, essential tremors, migraines, and strokes. We employed the same search strategy used to compare MS and OND-I subjects 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 (). Combining ratios using the point system improved overall ability to discriminate MS subjects from OND-NI subjects (). Using the point system, 79% of MS subjects achieved a score ≥ 1 and 91% of OND-NI subjects achieved a score of 0, 9% achieved a score of 1 ().
Figure 7 Discrimination between MS and OND-NI subjects using 10 gene expression ratios. (a) Identification of genes making up the 10 unique discriminatory ratios. P values compare individual ratio values between MS and OND-NI subjects. (b) Increasing the number (more ...)
Discrimination of MS from OND-NI: Validation and analysis
We performed additional analyses 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 (), which was a similar frequency to that observed in the training set, and 90% of OND-I subjects achieved a score of 0, 10% achieved a score of 1. As above, we determined mean scores of subjects with CIS, newly diagnosed MS and established MS and these were not statistically different among the three MS groups (). Similarly, mean scores of MS subjects from different geographic sites were not statistically different (). Using the point system, ~80% of MS subjects achieved a score ≥ 1 and 9% of OND-NI subjects achieved a score = 1 in the test set. These results demonstrate that expression in whole blood of a different set of gene ratios discriminated subjects with MS from subjects with OND-NI with reasonable accuracy.
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, Supplemental Figure 2
). 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.