Optimizing multiple sclerosis diagnosis: gene expression and genomic association

Abstract

Objective: The diagnosis of multiple sclerosis (MS) at disease onset is sometimes masqueraded by other diagnostic options resembling MS clinically or radiologically (NonMS). In the present study we utilized findings of large-scale Genome-Wide Association Studies (GWAS) to develop a blood gene expression-based classification tool to assist in diagnosis during the first demyelinating event.

Methods: We have merged knowledge of 110 MS susceptibility genes gained from MS GWAS studies together with our experimental results of differential blood gene expression profiling between 80 MS and 31 NonMS patients. Multiple classification algorithms were applied to this cohort to construct a diagnostic classifier that correctly distinguished between MS and NonMS patients. Accuracy of the classifier was tested on an additional independent group of 146 patients including 121 MS and 25 NonMS patients.

Results: We have constructed a 42 gene-transcript expression-based MS diagnostic classifier. The overall accuracy of the classifier, as tested on an independent patient population consisting of diagnostically challenging cases including NonMS patients with positive MRI findings, achieved a correct classification rate of 76.0 ± 3.5%.

Interpretation: The presented diagnostic classification tool complements the existing diagnostic McDonald criteria by assisting in the accurate exclusion of other neurological diseases at presentation of the first demyelinating event suggestive of MS.

Figure 1

Flowchart of study design. Samples from 257 patients including 137 patients at first demyelinating event and 120 RRMS patients were subjected to gene expression microarray analysis and randomly divided into a training set (n = 111) and test set (147). training set was used for diagnostic classifier generation and then classifier performance was validated on independent test set. Resampling (n = 43) was done to demonstrate classifier consistency.

Figure 2

Principal component analysis (PCA) based on 42 gene-transcripts of the diagnostic classifier. This difference between MS and NonMS patients from training set is presented. Each dot represents patient sample principal components derived from expression of 42 diagnostic classifier gene-transcripts. The distance between any pair of points is related to the similarity between the two observations in high-dimensional (3D) space. Blue dots represent NonMS patients, Red dots represent MS patients.

Figure 3

Functional regulatory network of classifier genes. Classifier gene network reconstructed based on literature-known relationships according to IPA software database. Each node in the regulation tree represents a regulating gene, arrows indicate literature confirmed regulatory interactions. Over-expressed genes are depicted in red, down-expressed in green.