IN VITRO METHOD FOR THE DIAGNOSIS AND/OR PROGNOSIS OF MULTIPLE SCLEROSIS

Abstract

The present invention refers to an in vitro method for the diagnosis and/or prognosis of multiple sclerosis.

Claims

1. In vitro method for the diagnosis and/or prognosis of multiple sclerosis (MS) in a subject, which comprises assessing the concentration level of sIFNAR2 in a biological sample obtained from the subject in combination with the obtention of a magnetic resonance imaging (MRI) to assess the presence/absence of dissemination in space (MRI_DS) and/or dissemination in time (MRI_DT), wherein the identification of a lower level of SIFNAR2 as compared with a pre-established threshold level, and the presence of MRI_DS and/or MRI_DT, are indications that the subject is suffering from MS.

2. In vitro method, according to claim 1, which comprises assessing the concentration level of sIFNAR2 in a biological sample obtained from a patient, wherein the patient shows clinical and/or radiologic features suggestive of MS and is characterized by the presence of MRI_DS and/or MRI_DT identified by MRI.

3. In vitro method, according to any of the previous claims, further comprising the assessment of visual evoked potential (VEP) values.

4. In vitro method, according to any of the previous claims, further comprising the assessment of the presence or absence of oligoclonal IgG bands (BOC_IgG) in CSF.

5. In vitro method, according to any of the previous claims, which further comprises processing the concentration level of sIFNAR2, the information obtained from MRI images regarding the presence of MRI_DS and/or MRI_DT, VEP values and information regarding the presence of BOC_IgG in CSF in order to define a risk score, wherein if a deviation or variation of the risk score value is identified, as compared with a reference value, this is indicative that the patient is suffering from MS.

6. In vitro method, according to any of the previous claims, wherein the biological sample is a biological fluid selected from blood, plasma, serum, cerebrospinal fluid, urine or tears.

7. In vitro method, according to any of the previous claims, wherein the determination of SIFNAR2 concentration level is carried out by means of an immunoassay, preferably by a technique selected from: immunoblot, enzyme-linked immunosorbent assay (ELISA), linear immunoassay (LIA), radioimmunoassay (RIA), immunofluorescence, x-map, protein chips or aptamer-based ELISA, or any technique for the determination of SIFNAR2 concentration level in a biological sample.

8. In vitro method, according to any of the previous claims, further comprising: a. Receiving by a computer program the concentration level of sIFNAR2, information regarding the presence of MRI_DS and/or MRI_DT, VEP values and information regarding the presence of BOC_IgG in CSF. b. Processing the information received according to the step a) for finding substantial variations or deviations with respect to the information obtained from a healthy subject, and c. Providing an output through a terminal display when a variation or deviation is found which indicates that the subject might be suffering from MS.

9. In vitro use of sIFNAR2, in combination with MRI measured variables MRI_DS and/or MRI_DT, for the diagnosis and/or prognosis of MS.

10. In vitro use, according to claim 9, of sIFNAR2 in combination with MRI measured variables MRI_DS and/or MRI_DT; and in combination with VEP values.

11. In vitro use, according to any of the claim 9 or 10, of sIFNAR2 in combination with MRI measured variables MRI_DS and/or MRI_DT, VEP values and BOC_IgG in CSF.

12. Kit-of-parts comprising reagents for assessing the concentration level of sIFNAR2 in combination with MRI to assess the presence/absence of MRI_DS and/or MRI_DT.

13. Kit-of-parts, according to claim 12, comprising reagents for assessing the concentration level of sIFNAR2 in combination with MRI to assess the presence/absence of MRI_DS and/or MRI_DT, surface recording electrodes for recording VEP and isoelectric focusing followed by staining for detecting BOC_IgG values in CSF.

14. In vitro use of the kit of claim 12 or 13 for the diagnosis and/or prognosis of MS.

Description

DESCRIPTION OF THE FIGURES

[0045] FIG. 1. It shows ROC curves representing the AUC value for each of the following signatures. X axis (Specificity). Y axis (Sensitivity).

[00001]$\begin{array}{cc}\left[\mathrm{IFNAR}2\right]>\mathrm{AUC}=0.82.& A)\end{array}$$\begin{array}{cc}\left[\mathrm{IFNAR}2+\mathrm{MRI\_DS}\right]>\mathrm{AUC}=0.91.& B)\end{array}$$\begin{array}{cc}\left[\mathrm{IFNAR}2+\mathrm{MRI\_DS}+\mathrm{VEP}\right]>\mathrm{AUC}=0.95.& C)\end{array}$$\begin{array}{cc}\left[\mathrm{IFNAR}2+\mathrm{MRI\_DS}+\mathrm{MRI\_DT}+\mathrm{VEP}\right]>\mathrm{AUC}=0.95.& D)\end{array}$$\begin{array}{cc}\left[\mathrm{IFNAR}2+\mathrm{MRI\_DS}+\mathrm{VEP}+\mathrm{BOC}\right]>\mathrm{AUC}=0.99.& E)\end{array}$$\begin{array}{cc}[\mathrm{IFNAR}2+\mathrm{MRI\_DS}+\mathrm{MRI\_DT}+\mathrm{VEP}+\mathrm{BOC}>\mathrm{AUC}=0.99.& F)\end{array}$$\begin{array}{cc}\left[\mathrm{IFNAR}2+\mathrm{VEP}\right]>\mathrm{AUC}=0.93.& G)\end{array}$

DETAILED DESCRIPTION OF THE INVENTION

[0046] The present invention is illustrated by means of the Examples set below without the intention of limiting its scope of protection.

Example 1. Material and Methods

Example 1.1. Sample Selection

[0047] These have been selected by random resampling of a data subsample from 43 non-treated patients and 43 healthy controls who participated in a previous referenced study, seeking data samples that have similar mean sIFNAR2 values as those reported in the reference publication [rpez-Zafra T, Pavia J, Hurtado-Guerrero I, Pinto-Medel M J, Rodriguez Bada J L, Urbaneja P, Suardiaz M, Villar L M, Comabella M, Montalban X, Alvarez-Cermeo J C, Leyva L, Fernndez , Oliver-Martos B. Decreased soluble IFN- receptor (sIFNAR2) in multiple sclerosis patients: A potential serum diagnostic biomarker. Mult Scler. 2017 June; 23 (7):937-945].

[0048] A database including demographic and clinical data was constructed. Data was obtained from medical records and analysis of paraclinical examinations (MRI, VEP and CSF) of the 43 patients and 43 controls. The sIFNAR2 data of selected patients and controls were obtained from the reference study [rpez-Zafra T, Pavia J, Hurtado-Guerrero I, Pinto-Medel M J, Rodriguez Bada J L, Urbaneja P, Suardiaz M, Villar L M, Comabella M, Montalban X, Alvarez-Cermeo J C, Leyva L, Fernndez , Oliver-Martos B. Decreased soluble IFN- receptor (sIFNAR2) in multiple sclerosis patients: A potential serum diagnostic biomarker. Mult Scler. 2017 June; 23 (7):937-945].

Example 1.2. Statistics

[0049] A multivariate analysis by logistic and discriminant regression of the demographic, clinical and paraclinical variables (MRI, CSF, VEP) included in the study was performed to obtain the best predictors from sIFNAR2 and the rest of markers (sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV)), each of them alone and in various combinations. The area under the curve (AUC)ROC curve was used to select the best classifier models, in order to improve the diagnostic SE and SP of sIFNAR2 as a biomarker in the serum of treatment-nave MS patients versus healthy controls. Results are shown in Table 2.

[0050] For MRI sensitivity and specificity data were obtained from a reference publication [Barkhof F, Filippi M, Miller D H et al. Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis. Brain 1997; 120(Pt11):2059-2069] and for BOC-IgG in CSF [Abraira V, Alvarez-Cermeo J C, Arroyo R, Cmara C, Casanova B, Cubillo S, et al. Utility of oligoclonal IgG band detection for MS diagnosis in daily clinical practice. J Immunol Methods. 2011 Aug. 31; 371(1-2):170-173]. For VEP values in controls, data was obtained from 43 healthy subjects used as normality reference values by the Clinical Neurophysiology Laboratory at HRUM and from a reference publication [Thabit1 M N, Farouk M M, Awni M, Mohamed A B. Early disability in ambulatory patients with multiple sclerosis: optical coherence tomography versus visual evoked potentials, a comparative study. Egyptian J Neurol, Psychiat and Neurosurg (2020) 56:70]. For all variables, all values obtained at HRUM were maintained, including extreme values and avoiding any manipulation of the data.

Example 1.3. Ethical Considerations Treatment of Personal Data

[0051] This study was conducted with the prior approval of the study site's IRB (Malaga Regional University HospitalHRUM), following the guidelines of the Declaration of Helsinki, 1964, Belmont Report, 1978, EU Regulation 2016/679 on Data Protection (GDPR), the Organic Law 3/2018, of 5 December, on the Protection of Personal Data and Guarantee of Digital Rights (LOPDGDD) and Royal Decree 1716/2011, of 18 November, which establishes the basic requirements for the authorization and operation of biobanks for biomedical research purposes and the treatment of biological samples of human origin, and regulating the operation of the National Register of Biobanks for biomedical research, the Law 41/2002, of 14 Nov. 2002, which regulates patient autonomy and the rights and obligations regarding information and clinical documentation, the Law 14/2007, of 3 July, on Biomedical Research, the Standards of Good Clinical Practice (GCP) of the International Conference on Harmonization (ICH), as well as any other applicable standard and/or law.

Example 2. Results

Example 1. Demographical, Clinical and Paraclinical Results

[0052] Demographical, clinical and paraclinical results are shown in Table 1 (see below). Data included in Table 1 show a typical population of MS patients in terms of demographics: age 37.8 (13.9), female 60.5%. Baseline EDSS 1.6 (1.4), range 0.0-6.5.

TABLE-US-00001 TABLE 1 Variable PATIENTS (N = 43) CONTROLS (N = 43) Total (N = 86) p value Age N-Miss 0.0 43.0 43.0 Mean (SD) 37.8 (13.9) N/A 37.8 (13.9) Range 14.0-66.0 N/A 14.0-66.0 gender N-Miss 0.0 43.0 43.0 Male 17.0 (39.5%) 0.0 17.0 (39.5%) Female 26.0 (60.5%) 0.0 26.0 (60.5%) Flare-ups N-Miss 2.0 43.0 45.0 Mean (SD) 1.0 (0.9) N/A 1.0 (0.9) Range 0.0-3.0 N/A 0.0-3.0 Baseline EDSS N-MISS 7.0 43.0 50.0 Mean (SD) 1.6 (1.4) N/A 1.6 (1.4) Range 0.0-6.5 N/A 0.0-6.5 <0.0011 VEP_HIGHEST Mean (SD) 114.9 (11.0) 100.8 (6.6) 107.8 (11.5) Range 91.0-150.0 88.7-112.1 88.7-150.0 <0.0011 MVEP N-Miss 5.0 0.0 5.0 Mean (SD) 111.0 (10.0) 98.0 (6.5) 104.1 (10.5) Range 81.5-135.0 86.7-110.0 81.5-135.0 <0.0012 VEP_PC111.1 N-Miss 5.0 0.0 5.0 1 16.0 (42.1%) 42.0 (97.7%) 58.0 (71.6%) 2 Abnormal 22.0 (57.9%) 1.0 (2.3%) 23.0 (28,4%) <0.0012 MRI_SD 0.0 6.0 (14.0%) 34.0 (79.1%) 40.0 (46.5%) 0.8 13.0 (30.2%) 0.0 (0.0%) 13.0 (15.1%) 1.0 Yes 24.0 (55.8%) 9.0 (20.9%) 33.0 (38.4%) <0.0012 MRI_TD 0.000 13.0 (30.2%) 26.0 (60.5%) 39.0 (45.3%) 0.581 12.0 (27.9%) 0.0 (0.0%) 12.0 (14.0%) 1,000 Yes 18.0 (41.9%) 17.0 (39.5%) 35.0 (40.7%) <0.0012 BOC_IgG 1.000 Positive 37.0 (86.0%) 3.0 (7.0%) 40.0 (46.5%) 1.075 3.0 (7.0%) 0.0 (0.0%) 3.0 (3.5%) 2.000 3.0 (7.0%) 40.0 (93.0%) 43.0 (50.0%) 1. Linear Model ANOVA. 2. Pearson's Chi-squared test

[0053] For VEP values in controls, data from 43 healthy subjects used as reference by the Clinical Neurophysiology Laboratory at HRUM and a reference publication were used to set the normality threshold [Thabit1 M N, Farouk M M, Awni M, Mohamed A B. Early disability in ambulatory patients with multiple sclerosis: optical coherence tomography versus visual evoked potentials, a comparative study. Egyptian J Neurol, Psychiat and Neurosurg (2020) 56:70], which was set as the mean2SD, as specified in the literature. The highest VEP latency (including RE and LE) for patients was 114.9 (11.0), and for controls was 100.8 (6.6). The mean VEP latency for patients was 111.0 (10.0), and for controls was 98.0 (6.5). Patients showed an abnormal VEP_PC111_1 threshold (latency 1112SD) in 57.9% of cases, while controls showed an abnormal threshold only in 2.3% of cases.

[0054] For MRI and CSF data of controls, SE and SP data from two published reference studies were used [Barkhof F, Filippi M, Miller D H et al. Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis. Brain 1997; 120(Pt11):2059-2069] and [Abraira V, Alvarez-Cermeo J C, Arroyo R, Cmara C, Casanova B, Cubillo S, et al. Utility of oligoclonal IgG band detection for MS diagnosis in daily clinical practice. J Immunol Methods. 2011 Aug. 31; 371(1-2):170-173].

[0055] All values obtained at HRUM for all variables have been maintained, including extreme values, avoiding any manipulation of the data.

Example 2. Results of the Logistic Regression Analysis of Paraclinical Variables

[0056] The results of the logistic regression analysis of paraclinical variables are shown in Table 2.

TABLE-US-00002 TABLE 2 Variable AUC Sensitivity Specificity PPV NPV IFNAR2 0.82 76.74 76.74 76.74 76.74 MRI_DS 0.79 86.05 79.07 80.43 85 MRI_DS + MRI_DT 0.80 86.05 79.07 80.43 85 IFNAR2 + MRI DS 0.91 90.70 76.74 79.59 89.19 IFNAR2 + MRI DT 0.91 93.02 79.07 81.63 91.89 IFNAR2 + MRI DS + MRI DT 0.95 86.05 93.02 92.50 86.96 IFNAR2 + MRI DS + VEP 0.95 83.72 95.35 94.74 85.42 IFNAR2 + MRI_DT + VEP 0.93 90.70 88.37 88.64 90.48 IFNAR2 + MRI DS + MRI DT + VEP 0.95 86.05 93.02 92.50 86.96 IFNAR2 + MRI DS + MRI_DT + VEP + BOC_IgG 0.99 97.67 93.02 93.33 97.56 IFNAR2 + MRI_DS + VEP + BOC_IgG 0.99 100 93.02 93.48 100 IFNAR2 + VEP 0.93 90.69 88.37 88.64 90.58

[0057] Clinical and sIFNAR2 data did not show significant differences for SE/SP/PPV/NPV versus the reference study, [rpez-Zafra T, Pavia J, Hurtado-Guerrero I, Pinto-Medel M J, Rodriguez Bada J L, Urbaneja P, Suardiaz M, Villar L M, Comabella M, Montalban X, Alvarez-Cermeo J C, Leyva L, Fernndez , Oliver-Martos B. Decreased soluble IFN- receptor (sIFNAR2) in multiple sclerosis patients: A potential serum diagnostic biomarker. Mult Scler. 2017 June; 23(7):937-945], confirming that the correct subsample was selected by random resampling. All SE/ES/PPV/NPV values ranged around 77%.

[0058] For VEP values, the cut-off point was set as the mean2SD of healthy controls (latency of VEP_PC111.1=111.1 milliseconds), as described in the literature. This value is consistent with data reported in the literature [Thabit1 M N, Farouk M M, Awni M, Mohamed A B. Early disability in ambulatory patients with multiple sclerosis: optical coherence tomography versus visual evoked potentials, a comparative study. Egyptian J Neurol, Psychiat and Neurosurg (2020) 56:70].

[0059] When serum sIFNAR2 and VEP_PC111.1 were jointly evaluated with this cut-off point, SE/ES/PPV/NPV values increased to around 90%.

[0060] SE/ES/PPV/NPV values increased with the number of variables included in the predictive model, and as might be expected, inclusion of BOC-IgG values and MRI spatial dissemination criteria (MRI_SD) was particularly useful.

[0061] Such as it is shown in Table 2, the combination of IFNAR2 with any of the following variables MRI_DS, MRI_DT or [MRI_DS+MRI_DT] always gave rise to an improved AUC value (between 0.90 and 0.95) as compared with both the AUC associated with the use of IFNAR2 alone (0.82) and the AUC associated with MRI_DS (0.79) or [MRI_DS+MRI_DT] (0.80).

[0062] So, an unexpected synergistic effect was observed when IFNAR2 was combined at least with MRI_DS and/or MRI_DT. These results are of utmost clinical relevance since, although MRI is the gold standard test for the diagnosis of MS in clinical practice, it has a sensitivity of around 80% and is, in turn, the cause of much of the diagnostic errors associated with the disease. However, the combination of MRI with sIFNAR2 levels offers a synergistic effect able to improve the individual AUC values of both MRI or sIFNAR2, and can be the basis of a specific test for the diagnosis of MS.

[0063] Moreover, when VEP values were combined with IFNAR2 and [MRI_DS and/or MRI_DT], high AUC values were also obtained. See for example the combinations of IFNAR2 with [MRI_DS+VEP], [MRI_DT+VEP] or [MRI_DS+MRI_DT+VEP] in Table 2, showing AUC values between 0.93 and 0.95.

[0064] Finally, when the assessment of the presence of BOC_IgG in CSF was included in the combinations, even higher AUC values were obtained. See for instance the combinations of IFNAR2 with [MRI_SD+VEP+BOC_IgG] or [MRI_SD+MRI_TD+VEP+BOC_IgG] in Table 2 showing an AUC=0.99).