SET OF BIOMARKERS FOR THE DIAGNOSIS OF BRUGADA SYNDROME

Abstract

The present invention relates to a specific set of circulating biomarkers and related methods and kits for the diagnosis of Brugada Syndrome in a human being.

Claims

1. A set of biomarkers for the diagnosis of Brugada Syndrome in a human being comprising: i) the subset of 6 mRNA listed in the following Table: TABLE-US-00009 Name Ensemble ID Calpain 2 (CAPN2) ENSG00000162909 Dynactin Subunit 2 (DCTN2) ENSG00000175203 Diacylglycerol Kinase Delta ENSG00000077044 (DGKD) Major Histocompatibility ENSG00000231389 Complex, Class II, DP Alpha 1 (HLA-DPA1) Major Histocompatibility ENSG00000223865 Complex, Class II, DP Beta 1 (HLA-DPB1) WD Repeat Domain 86 ENSG00000187260 (WDR86) ii) the subset of the following 7 metabolites: 2-aminooctanoate 4-methoxyphenol sulfate hypotaurine lysine N-acetyl-2-aminooctanoate phenol sulfate γ-glutamylleucine; and iii) the lipid docecenedioate (C12:1-DC).

2. The set of biomarkers according to claim 1, further comprising one or more of the following additional biomarkers: i) RNA selected from the group consisting of: TABLE-US-00010 Name Ensemble ID Type Acyl-CoA Synthetase Family ENSG00000157426 mRNA Member 4 (AASDH) Ankyrin Repeat And Death ENSG00000166839 mRNA Domain Containing 1A (ANKDD1A) Ankyrin Repeat Domain 49 ENSG00000168876 mRNA (ANKRD49) Rho/Rac Guanine Nucleotide ENSG00000104880 mRNA Exchange Factor 18 (ARHGEF18) BRICHOS Domain Containing 5 ENSG00000182685 mRNA (BRICD5) BRCA1 Interacting Protein C- ENSG00000136492 mRNA Terminal Helicase 1 (BRIP1) BRO1 Domain And CAAX ENSG00000162819 mRNA Motif Containing (BROX) Glucocorticoid Receptor AF-1 ENSG00000184887 mRNA Coactivator-1 (BTBD6) Chromosome 19 Open Reading ENSG00000257242 Long non- Frame 79 (C12orf79) coding RNA Chromosome 19 Open Reading ENSG00000214212 mRNA Frame 38 (C19orf38) Calcium/Calmodulin Dependent ENSG00000110931 mRNA Protein Kinase Kinase 2 (CAMKK2) Chimerin I (CHN1) ENSG00000128656 mRNA Cyclin-M3 (CNNM3) ENSG00000168763 mRNA Cytochrome P450 Family 2 ENSG00000167600 mRNA Subfamily S Member 1 (CYP2S1) ERBB Receptor Feedback ENSG00000116285 mRNA Inhibitor 1 (ERRFI1) Retrotransposon Gag Like 8C ENSG00000134590 mRNA (FAM127A) Glycoprotein, Alpha- ENSG00000204136 mRNA Galactosyltransferase (GGTA1P) Glutathione-Disulfide Reductase ENSG00000104687 mRNA (GSR) HLA Complex Group 18 ENSG00000231074 mRNA (HCG18) Histone Cluster 1 H1 Family ENSG00000187837 mRNA Member C (HIST1H1C) Histone Cluster 1 H2B Family ENSG00000197903 mRNA Member K (HIST1H2BK) Insulin Like Growth Factor ENSG00000146674 mRNA Binding Protein 3 (IGFBP3) Ras GTPase-Activating-Like ENSG00000145703 mRNA Protein (IQGAP2) Lymphotoxin Beta (LTB) ENSG00000227507 mRNA Methionine Adenosyltransferase ENSG00000168906 mRNA 2A (MAT2A) Mitochondrial Ribosomal Protein ENSG00000175110 mRNA L22 (MRPS22) Nucleosome Assembly Protein 1 ENSG00000186462 mRNA Like 2 (NAP1L2) Peptidyl Arginine Deiminase 4 ENSG00000159339 mRNA (PADI4) PDZ And LIM Domain 7 ENSG00000196923 mRNA (PDLIM7) Platelet Activating Factor ENSG00000169403 mRNA Receptor (PTAFR) RAN Binding Protein 6 ENSG00000137040 mRNA (RANBP6) RNA, 7SL, Cytoplasmic 473, ENSG00000277452 mRNA Pseudogene (RN7SL473P) RP11-429E11.3 ENSG00000179253.3 mRNA RP11-4B16.1 ENSG00000279433 microRNA Ribosomal Protein S20 (RPS20) ENSG00000008988 mRNA Syntaxin 10 (STX10) ENSG00000104915 mRNA T Cell Receptor Beta Variable ENSG00000241657 mRNA 11-2 (TRBV11-2) Zwilch Kinetochore Protein ENSG00000174442 mRNA (ZWILCH) ii) a metabolite selected from the group consisting of: 5-methyluridine (ribothymidine) fibrinopeptide A, phosphono-ser(3) glyco-alpha-muricholate; 3-(4-hydroxyphenyl)lactate glucoronate Trans-4-hydroxyproline; and iii) a phosphatidylinositol (16:0/18:1); or a combination thereof.

3. The set of biomarkers according to claim 2, comprising: i) the subset of 44 RNAs listed in the following Table: TABLE-US-00011 Name Ensemble ID Type Acyl-CoA Synthetase Family ENSG00000157426 mRNA Member 4 (AASDH) Ankyrin Repeat And Death ENSG00000166839 mRNA Domain Containing 1A (ANKDD1A) Ankyrin Repeat Domain 49 ENSG00000168876 mRNA (ANKRD49) Rho/Rac Guanine Nucleotide ENSG00000104880 mRNA Exchange Factor 18 (ARHGEF18) BRICHOS Domain Containing 5 ENSG00000182685 mRNA (BRICD5) BRCA1 Interacting Protein C- ENSG00000136492 mRNA Terminal Helicase 1 (BRIP1) BRO1 Domain And CAAX Motif ENSG00000162819 mRNA Containing (BROX) Glucocorticoid Receptor AF-1 ENSG00000184887 mRNA Coactivator-1 (BTBD6) Chromosome 19 Open Reading ENSG00000257242 Long non- Frame 79 (C12orf79) coding RNA Chromosome 19 Open Reading ENSG00000214212 mRNA Frame 38 (C19orf38) Calcium/Calmodulin Dependent ENSG00000110931 mRNA Protein Kinase Kinase 2 (CAMKK2) Calpain 2 (CAPN2) ENSG00000162909 mRNA Chimerin I (CHN1) ENSG00000128656 mRNA Cyclin-M3 (CNNM3) ENSG00000168763 mRNA Cytochrome P450 Family 2 ENSG00000167600 mRNA Subfamily S Member 1 (CYP2S1) Dynactin Subunit 2 (DCTN2) ENSG00000175203 mRNA Diacylglycerol Kinase Delta ENSG00000077044 mRNA (DGKD) ERBB Receptor Feedback ENSG00000116285 mRNA Inhibitor 1 (ERRFI1) Retrotransposon Gag Like 8C ENSG00000134590 mRNA (FAM127A) Glycoprotein, Alpha- ENSG00000204136 mRNA Galactosyltransferase (GGTA1P) Glutathione-Disulfide Reductase ENSG00000104687 mRNA (GSR) HLA Complex Group 18 (HCG18) ENSG00000231074 mRNA Histone Cluster 1 H1 Family ENSG00000187837 mRNA Member C (HIST1H1C) Histone Cluster 1 H2B Family ENSG00000197903 mRNA Member K (HIST1H2BK) Major Histocompatibility ENSG00000231389 mRNA Complex, Class II, DP Alpha 1 (HLA-DPA1) Major Histocompatibility ENSG00000223865 mRNA Complex, Class II, DP Beta 1 (HLA-DPB1) Insulin Like Growth Factor ENSG00000146674 mRNA Binding Protein 3 (IGFBP3) Ras GTPase-Activating-Like ENSG00000145703 mRNA Protein (IQGAP2) Lymphotoxin Beta (LTB) ENSG00000227507 mRNA Methionine Adenosyltransferase ENSG00000168906 mRNA 2A (MAT2A) Mitochondrial Ribosomal Protein ENSG00000175110 mRNA L22 (MRPS22) Nucleosome Assembly Protein 1 ENSG00000186462 mRNA Like 2 (NAP1L2) Peptidyl Arginine Deiminase 4 ENSG00000159339 mRNA (PADI4) PDZ And LIM Domain 7 ENSG00000196923 mRNA (PDLIM7) Platelet Activating Factor ENSG00000169403 mRNA Receptor (PTAFR) RAN Binding Protein 6 (RANBP6) ENSG00000137040 mRNA RNA, 7SL, Cytoplasmic 473, ENSG00000277452 mRNA Pseudogene (RN7SL473P) RP11-429E11.3 ENSG00000179253.3 mRNA RP11-4B16.1 ENSG00000279433 microRNA Ribosomal Protein S20 (RPS20) ENSG00000008988 mRNA Syntaxin 10 (STX10) ENSG00000104915 mRNA T Cell Receptor Beta Variable 11-2 ENSG00000241657 mRNA (TRBV11-2) WD Repeat Domain 86 (WDR86) ENSG00000187260 mRNA Zwilch Kinetochore Protein ENSG00000174442 mRNA (ZWILCH) ii) a subset of the following metabolites: 2-aminooctanoate 3-(4-hydroxyphenyl)lactate 4-methoxyphenol sulfate hypotaurine lysine glucoronate Trans-4-hydroxyproline N-acetyl-2-aminooctanoate phenol sulfate γ-glutamylleucine 5-methyluridine (ribothymidine) fibrinopeptide A, phosphono-ser(3) glyco-alpha-muricholate; and iii) a subset of the following lipids: dodecenedioate (C12:1-DC) phosphatidylinositol (16:0/18:1).

4. An in vitro method for detecting the presence or measuring a concentration of the occurrence of one of the set of biomarkers according to claim 1 comprising comparing the biomarker to a biological sample of a human being for the identification of patients affected by Brugada Syndrome.

5. The in vitro method for detecting the presence of one of the set of biomarkers according to claim 4, comprising: a) detecting the presence of at least 6 up to 44 RNAs of subset i) by an assay; b) detecting at least 7 up to 13 of the metabolites of subset ii) and at least one of the lipids of subset iii) by an assay; wherein a positive detection of all the biomarkers compared to a normal control allows the identification of patients affected by Brugada Syndrome.

6. The in vitro method according to claim 4, comprising: a) quantitatively determining at least 6 up to 44 RNAs of subset i); b) quantitatively determining at least 7 up to 13 of the metabolites of subset ii); c) quantitatively determining at least or both the lipids of subset iii); wherein an increase of the concentration of the biomarkers compared to a normal control indicates a diagnosis of Brugada Syndrome.

7. The in vitro method according to claim 4, wherein the biological sample is selected from the group consisting of whole blood, plasma, serum, peripheral blood and PBMCs.

8. The in vitro method according to claim 4, wherein the human being is asymptomatic.

9. The in vitro method according to claim 4, wherein the human being is at high risk for Brugada Syndrome due to family history, previous events of heart atrial and/or ventricular fibrillation, diabetes or obesity.

10. The in vitro method according to claim 4, wherein the human being is about 40 years old or younger.

11. A kit comprising oligonucleotides that are at least 85, 90, 95, 96, 97, 98, 99 or 100% complementary to each of the RNAs of subset i) according to claim 1, wherein said oligonucleotides are primers or probes optionally attached to a solid support.

12. A computer-implemented method for indicating a diagnosis of Brugada syndrome, comprising: a) retrieving on a computer biomarker information for a human being, wherein the biomarker information comprises biomarker values for each of the biomarkers of the set according to claim 1; b) performing with the computer a classification of said biomarker values.

13. The computer-implemented method of claim 12, wherein the indication of a diagnosis of Brugada syndrome comprises indicating the likelihood of a Brugada syndrome diagnosis, determined from a plurality of classifications.

14. The in vitro method according to claim 5 where the assay in step a) is selected from the group consisting of mRNA microarray detection, Real-Time PCR, Droplet Digital PCR or hybridization-based assays, and branched DNA (bDNA) technology (i.e. QuantiGene® assay), or a combination thereof.

15. The in vitro method according to claim 5 where the assay in step b) is selected from the group consisting of MS, GC-MS, fluorimetric or colorimetric assays, NMR, spectroscopy with nanoprobes, Enzyme-Linked oligonucleotide assays, or a combination thereof.

16. The in vitro method according to claim 6, wherein the quantitative technique of step a) comprises quantitative PCR.

17. The in vitro method according to claim 6, wherein the quantitative technique of step b) is selected from the group consisting of HPLC, MS, NMR analysis, ELONA, or a combination thereof.

18. The in vitro method according to claim 6, wherein the quantitative technique of step c) is selected from the group consisting of HPLC, MS, NMR analysis, or a combination thereof.

19. The in vitro method according to claim 16, wherein the quantitative PCR is selected from the group consisting of Real Time-PCR, Droplet Digital PCR, Quantigene assay, or a combination thereof.

Description

[0087] The present invention will now be described, for non-limiting illustrative purposes, according to a preferred embodiment thereof, with particular reference to the attached figures, wherein:

[0088] FIG. 1 shows a scheme of the procedure to create a CMTRX technical replicate sample.

[0089] FIG. 2 shows the ROC curve of the multiomic Brugada Syndrome set of 15 features (14 biomarkers+patient age) of the invention.

[0090] FIG. 3 shows the specificity and sensitivity of the Brugada Syndrome multi-omics 15-feature panels (metabolomics, lipidomics, transcriptomics) of the invention.

[0091] FIG. 4 shows the ROC curve of the multiomic Brugada Syndrome set of 60 features of the invention (59 biomarkers+patient age).

[0092] FIG. 5 shows the specificity and sensitivity of the Brugada Syndrome multi-omics 60 features panels (metabolomics, lipidomics, transcriptomics) of the invention.

[0093] FIG. 6 shows the curve of the accuracy of the test carried out with less than 60 features.

[0094] FIG. 7 shows the plateau reached by the curve of the accuracy of the test when more than 60 features have been employed.

[0095] The following example is merely illustrative and should not be considered limiting the scope of the present invention.

EXAMPLE 1: MULTI-OMIC IDENTIFICATION OF THE SET OF BIOMARKERS OF BRUGADA SYNDROME OF THE INVENTION

Materials and Methods

Ethical Statement

[0096] All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the Helsinki declaration and its later amendments or comparable ethical standards. The study design was examined by the Ethical Committee of the San Raffaele Hospital (Protocol BASED-version Jul. 1, 2004).

Patients Enrollment

[0097] The population study has been constituted by two groups (300 subjects/group): the Brugada (BrG) and the Control (CG) Groups. The expected enrollment period was of 24 months.

[0098] Patients were evaluated for: [0099] baseline laboratory examinations, including total blood count, hepatic, thyroid and coagulation function. [0100] transthoracic ECG.

[0101] The inclusion/exclusion criteria for the two groups have been respectively the following.

[0102] Brugada Group: All consecutive Brugada Syndrome patients, aged >18 years, positive to the ajmaline test, or considered at high risk of SCD and undergoing epicardial ablation will be enrolled in this study.

[0103] Control Group: A population of patients with a structurally normal heart with a negative ajmaline test confirming the absence of Brugada Syndrome.

[0104] Inclusion criteria for Control Group: [0105] Age >18 years [0106] Absence of Brugada Syndrome confirmed with ajmaline test.

Exclusion Criteria for Control Group:

[0107] Ejection Fraction <55% [0108] Coronary artery disease with the need for revascularization [0109] Previous heart surgery [0110] Recent surgical/percutaneous intervention for valvular heart disease (<6 months) [0111] Life expectancy <1 year [0112] Impossibility to provide consent to the procedure and to the study

Transcriptomic Analysis

[0113] Transcriptomic analysis was performed at the Center for Translational Genomics and Bioinformatics at San Raffaele Hospital.

[0114] RNA quality was confirmed with a 2100 Bioanalyzer (Agilent) and RNA with a RIN above 7 were included in the analysis. To generate the libraries, the TruSeq stranded mRNA protocol was performed. This protocol allowed unbiased 5′/3′ library preparation starting from 100 ng of total RNA. Libraries were barcoded, pooled and sequenced on an Illumina Nova-Seq 6000 sequencer. RNA-Seq experiments were performed generating single-end 30M reads, 75 nt long, for each run. After trimming of adapters, sequences generated within RNA-Seq experiments were aligned to the genome using the STAR aligner (20) and counted with feature Counts (21) on the appropriate annotation: genes from the last Gencode (22) release for RNA-Seq.

Metabolomic Analysis on Plasma

[0115] Samples were prepared using the automated MicroLab STAR® system from Hamilton Company. Several recovery standards were added prior to the first step in the extraction process for QC purposes. To remove protein, dissociate small molecules bound to protein or trapped in the precipitated protein matrix, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for 2 min (Glen Mills GenoGrinder 2000) followed by centrifugation. The resulting extract was divided into five fractions: two for analysis by two separate reverse phase (RP)/UPLC-MS/MS methods with positive ion mode electrospray ionization (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, one for analysis by HILIC/UPLC-MS/MS with negative ion mode ESI, and one sample was reserved for backup. Samples were placed briefly on a TurboVap® (Zymark) to remove the organic solvent. The sample extracts were stored overnight under nitrogen before preparation for analysis.

QA/QC

[0116] Several types of controls were analyzed in concert with the experimental samples: a pooled matrix sample generated by taking a small volume of each experimental sample (or alternatively, use of a pool of well-characterized human plasma) served as a technical replicate throughout the data set; extracted water samples served as process blanks; and a cocktail of QC standards that were carefully chosen not to interfere with the measurement of endogenous compounds were spiked into every analyzed sample, allowed instrument performance monitoring and aided chromatographic alignment.

[0117] The following Tables 4 and 5 describe these QC samples and standards.

TABLE-US-00004 TABLE 4 Description of Metabolon QC Samples Type Description Purpose MTRX Large pool of human plasma Assure that all aspects of maintained by Metabolon that the Metabolon process are has been characterized operating within extensively. specifications. CMTRX Pool created by taking a small Assess the effect of a non- aliquot from every customer plasma matrix on the sample. Metabolon process and distinguish biological variability from process variability. PRCS Aliquot of ultra-pure water Process Blank used to assess the contribution to compound signals from the process. SOLV Aliquot of solvents used in Solvent Blank used to extraction. segregate contamination sources in the extraction.

TABLE-US-00005 TABLE 5 Metabolon QC Standards Type Description Purpose RS Recovery Standard Assess variability and verify performance of extraction and instrumentation. IS Internal Standard Assess variability and performance of instrument.

[0118] Instrument variability was determined by calculating the median relative standard deviation (RSD) for the standards that were added to each sample prior to injection into the mass spectrometers. Overall process variability was determined by calculating the median RSD for all endogenous metabolites (i.e., non-instrument standards) present in 100% of the pooled matrix samples. Experimental samples were randomized across the platform run with QC samples spaced evenly among the injections, as outlined in FIG. 1. FIG. 1 shows the preparation of client-specific technical replicates wherein a small aliquot of each client sample (colored cylinders) is pooled to create a CMTRX technical replicate sample (multi-colored cylinder), which is then injected periodically throughout the platform run. Variability among consistently detected biochemicals can be used to calculate an estimate of overall process and platform variability.

Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS)

[0119] All methods utilized a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. The sample extract was dried then reconstituted in solvents compatible to each of the four methods. Each reconstitution solvent contained a series of standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds. In this method, the extract was gradient eluted from a C18 column (Waters UPLC BEH C18-2.1×100 mm, 1.7 μm) using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was also analyzed using acidic positive ion conditions, however it was chromatographically optimized for more hydrophobic compounds. In this method, the extract was gradient eluted from the same afore mentioned C18 column using methanol, acetonitrile, water, 0.05% PFPA and 0.01% FA and was operated at an overall higher organic content. Another aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however with 6.5 mM Ammonium Bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2.1×150 mm, 1.7 μm) using a gradient consisting of water and acetonitrile with 10 mM Ammonium Formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slighted between methods but covered 70-1000 m/z. Raw data files are archived and extracted as described below.

Data Extraction and Compound Identification

[0120] Raw data was extracted, peak-identified and QC processed using Metabolon's hardware and software. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. Metabolon maintains a library based on authenticated standards that contains the retention time/index (RI), mass to charge ratio (m/z), and chromatographic data (including MS/MS spectral data) on all molecules present in the library. Furthermore, biochemical identifications are based on three criteria: retention index within a narrow RI window of the proposed identification, accurate mass match to the library+/−10 ppm, and the MS/MS forward and reverse scores between the experimental data and authentic standards. The MS/MS scores are based on a comparison of the ions present in the experimental spectrum to the ions present in the library spectrum. While there may be similarities between these molecules based on one of these factors, the use of all three data points can be utilized to distinguish and differentiate biochemicals. More than 3300 commercially available purified standard compounds have been acquired and registered for analysis on all platforms for determination of their analytical characteristics. Additional mass spectral entries have been created for structurally unnamed biochemicals, which have been identified by virtue of their recurrent nature (both chromatographic and mass spectral). These compounds have the potential to be identified by future acquisition of a matching purified standard or by classical structural analysis.

Curation

[0121] A variety of curation procedures were carried out to ensure that a high quality data set was made available for statistical analysis and data interpretation. The QC and curation processes were designed to ensure accurate and consistent identification of true chemical entities, and to remove those representing system artifacts, mis-assignments, and background noise. Library matches for each compound were checked for each sample and corrected if necessary.

Metabolite Quantification and Data Normalization

[0122] Peaks were quantified using area-under-the-curve. For studies spanning multiple days, a data normalization step was performed to correct variation resulting from instrument inter-day tuning differences. Essentially, each compound was corrected in run-day blocks by registering the medians to equal one (1.00) and normalizing each data point proportionately (termed the “block correction”. For studies that did not require more than one day of analysis, no normalization is necessary, other than for purposes of data visualization. In certain instances, biochemical data may have been normalized to an additional factor (e.g., cell counts, total protein as determined by Bradford assay, osmolality, etc.) to account for differences in metabolite levels due to differences in the amount of material present in each sample.

Lipidomic Analysis on Plasma

[0123] Metabolon, Inc., Morrisville. USA performed the lipidomic analysis. Lipids were extracted from plasma in the presence of deuterated internal standards using an automated BUME extraction according to the method of Lofgren et al. [19]. The extracts were dried under nitrogen and reconstituted in ammonium acetate dichloromethane:methanol.

[0124] The extracts were transferred to vials for infusion-MS analysis, performed on a Shimadzu LC with nano PEEK tubing and the Sciex SelexIon-5500 QTRAP. The samples were analyzed via both positive and negative mode electrospray. The 5500 QTRAP was operated in MRM mode with a total of more than 1,100 MRMs.

[0125] Individual lipid species were quantified by taking the ratio of the signal intensity of each target compound to that of its assigned internal standard, then multiplying by the concentration of internal standard added to the sample. Lipid class concentrations were calculated from the sum of all molecular species within a class, and fatty acid compositions were determined by calculating the proportion of each class comprised by individual fatty acids.

Sphingomyelin Determination in Plasma

[0126] Sphingomyelin contents in human plasma were measured using a fluorimetric sphingomyelin assay kit (Abcam). Briefly, human plasma (diluted at 1:10) was treated with sphingomyelinase for two hours at 37° C. obtaining ceramide and phosphocholine. Then, samples were incubated with the AbRed fluorogenic dye that bound phosphocholine and analyzed with a spectrofluorimeter. The total sphingomyelins concentration was determined through the interpolation of the fluorescent emission with a standard curve, defined with known concentrations of sphingomyelin.

Statistical Analysis

[0127] Biomarker assays routinely report tens of thousands of individual results, be they proteins, metabolites, or gene expressions. The multitudinous nature of these biomarker observations complicates their use in classification or assessment problems, when the biomarkers must be related to some response variable. This is especially true if no a priori information relating specific biomarkers (or categories of biomarkers) to the response variable is available.

[0128] To this end, feature selection algorithms are commonly employed to identify which biomarker observations are most relevant to the response variable.

[0129] A template-oriented genetic algorithm has been employed.

Results

[0130] Patients enrolled in this study were divided in two groups based on the result of the ajmaline test. The number of patients included in each group (Control Group and Brugada Group) was 293, for a total of 586 subjects. In Table 4 the demographic features of each group are shown.

TABLE-US-00006 TABLE 6 Parameters Control Group Brugada Group Male 58.36% 68.47% Female 41.63% 31.52% Age (years) 33.03 ± 0.9135 40.92 ± 0.8009

Metabolomics

[0131] The metabolomics dataset provided us with the normalized concentrations of 984 compounds in 586 subjects, including 293 cases and 293 controls. For the subsequent analysis, all the 169 xenobiotics were excluded. Thus, the metabolomics dataset was composed by 815 metabolites.

Lipidomics

[0132] The lipidomics dataset provided us with the normalized concentrations of 1021 compounds in 586 subjects, including 293 cases and 293 controls. None of the lipids was excluded from the subsequent analysis.

Transcriptomics

[0133] The transcriptomics analysis detected 15155 RNA molecules in 586 subjects, including 293 cases and 293 controls. None of the RNA was excluded from the subsequent analysis.

Biomarkers Determination

[0134] The biomarker discovery process was performed combining only the metabolomics lipidomics and trancriptomics data.

[0135] The application of the template-oriented genetic algorithm highlighted the most important 15 to 60 features (including the age of the patient) useful for the diagnosis of Brugada Syndrome.

[0136] The 14 biomarkers set comprises 6 mRNA, lipid and 7 metabolites as depicted in the following Table 7.

TABLE-US-00007 TABLE 7 Identified 14 multi-omics biomarkers dodecenedioate lipid (C12:1-DC) hypotaurine metabolite lysine metabolite γ-glutamylleucine metabolite 4-methoxyphenol metabolite sulfate phenol sulfate metabolite N-acetyl-2- metabolite aminooctanoate 2-aminooctanoate metabolite ‘CAPN2’ Calpain 2 (CAPN2) ENSG00000162909 mRNA ‘HLA-DPA1’ Major ENSG00000231389 mRNA Histocompatibility Complex, Class II, DP Alpha 1 (HLA-DPA1) ‘HLA-DPB1’ Major ENSG00000223865 mRNA Histocompatibility Complex, Class II, DP Beta 1 (HLA-DPB1) ‘DCTN2’ Dynactin Subunit 2 ENSG00000175203 mRNA (DCTN2) ‘WDR86’ WD Repeat Domain ENSG00000187260 mRNA 86 (WDR86) ‘DGKD’ Diacylglycerol ENSG00000077044 mRNA Kinase Delta (DGKD)

[0137] The 59 biomarkers set comprise 44 RNAs, 13 metabolites, 2 lipids as depicted in the following Table 8:

TABLE-US-00008 TABLE 8 Identified 59 multi-omics biomarkers Biomarker Ensemble ID Type Acyl-CoA Synthetase Family ENSG00000157426 mRNA Member 4 (AASDH) Ankyrin Repeat And Death ENSG00000166839 mRNA Domain Containing 1A (ANKDD1A) Ankyrin Repeat Domain 49 ENSG00000168876 mRNA (ANKRD49) Rho/Rac Guanine Nucleotide ENSG00000104880 mRNA Exchange Factor 18 (ARHGEF18) BRICHOS Domain ENSG00000182685 mRNA Containing 5 (BRICD5) BRCA1 Interacting Protein C- ENSG00000136492 mRNA Terminal Helicase 1 (BRIP1) BRO1 Domain And CAAX ENSG00000162819 mRNA Motif Containing (BROX) Glucocorticoid Receptor AF-1 ENSG00000184887 mRNA Coactivator-1 (BTBD6) Chromosome 19 Open ENSG00000257242 Long non- Reading Frame 79 (C12orf79) coding RNA Chromosome 19 Open ENSG00000214212 mRNA Reading Frame 38 (C19orf38) Calcium/Calmodulin ENSG00000110931 mRNA Dependent Protein Kinase Kinase 2 (CAMKK2) Calpain 2 (CAPN2) ENSG00000162909 mRNA Chimerin I (CHN1) ENSG00000128656 mRNA Cyclin-M3 (CNNM3) ENSG00000168763 mRNA Cytochrome P450 Family 2 ENSG00000167600 mRNA Subfamily S Member 1 (CYP2S1) Dynactin Subunit 2 (DCTN2) ENSG00000175203 mRNA Diacylglycerol Kinase Delta ENSG00000077044 mRNA (DGKD) ERBB Receptor Feedback ENSG00000116285 mRNA Inhibitor 1 (ERRFI1) Retrotransposon Gag Like 8C ENSG00000134590 mRNA (FAM127A) Glycoprotein, Alpha- ENSG00000204136 mRNA Galactosyltransferase 1 (GGTA1P) Glutathione-Disulfide ENSG00000104687 mRNA Reductase (GSR) HLA Complex Group 18 ENSG00000231074 mRNA (HCG18) Histone Cluster 1 H1 Family ENSG00000187837 mRNA Member C (HIST1H1C) Histone Cluster 1 H2B Family ENSG00000197903 mRNA Member K (HIST1H2BK) Major Histocompatibility ENSG00000231389 mRNA Complex, Class II, DP Alpha 1 (HLA-DPA1) Major Histocompatibility ENSG00000223865 mRNA Complex, Class II, DP Beta 1 (HLA-DPB1) Insulin Like Growth Factor ENSG00000146674 mRNA Binding Protein 3 (IGFBP3) Ras GTPase-Activating-Like ENSG00000145703 mRNA Protein (IQGAP2) Lymphotoxin Beta (LTB) ENSG00000227507 mRNA Methionine ENSG00000168906 mRNA Adenosyltransferase 2° (MAT2A) Mitochondrial Ribosomal ENSG00000175110 mRNA Protein L22 (MRPS22) Nucleosome Assembly ENSG00000186462 mRNA Protein 1 Like 2 (NAP1L2) Peptidyl Arginine Deiminase ENSG00000159339 mRNA 4 (PADI4) PDZ And LIM Domain 7 ENSG00000196923 mRNA (PDLIM7) Platelet Activating Factor ENSG00000169403 mRNA Receptor (PTAFR) RAN Binding Protein 6 ENSG00000137040 mRNA (RANBP6) RNA, 7SL, Cytoplasmic 473, ENSG00000277452 mRNA Pseudogene (RN7SL473P) RP11-429E11.3 ENSG00000179253.3 mRNA RP11-4B16.1 ENSG00000279433 microRNA Ribosomal Protein S20 ENSG00000008988 mRNA (RPS20) Syntaxin 10 (STX10) ENSG00000104915 mRNA T Cell Receptor Beta Variable ENSG00000241657 mRNA 11-2 (TRBV11-2) WD Repeat Domain 86 ENSG00000187260 mRNA (WDR86) Zwilch Kinetochore Protein ENSG00000174442 mRNA (ZWILCH) Hypotaurine Metabolite Lysine Metabolite Glucoronate Metabolite γ-glutamylleucine Metabolite 3-(4-hydroxyphenyl)lactate Metabolite Trans-4-hydroxyproline Metabolite Phenol sulfate Metabolite 5-methyluridine Metabolite (ribothymidine) 2-aminooctanoate Metabolite 4-methoxyphenol sulfate Metabolite Fibrinopeptide A, Metabolite phosphonoser(3) N-acetyl-2-aminooctanoate Metabolite Glyco-alpha-muricholate Metabolite PI(16:0/18:1) Lipid Dodecenedioate (C12:1-DC) Lipid

[0138] Then, a support vector machine classifier was employed to predict the Brugada Syndrome diagnosis in the study population, as described in the Material and Methods section.

[0139] FIGS. 2 and 3 show a representative example of the results obtained from one such classifier, using all 15 features (14 biomarker+patient age). In this representative example, the classifier gives the following results: [0140] overall accuracy: 72.0% [0141] specificity: 65.7% [0142] sensitivity: 77.6%

[0143] The training population was composed by the 75% of our dataset, and the remaining 25% was used for the validation process. The dataset was randomly partitioned 200 times according to this method, and each random partition was used to train an independent support-vector machine model. The results of these 200 randomized models were compared to ensure consistency.

[0144] FIGS. 4 and 5 show a representative example of the results obtained from one such classifier, using all 60 features (59 biomarkers+patient age). In this representative example, the classifier gives the following results: [0145] overall accuracy: 87.2% [0146] specificity: 82.2% [0147] sensitivity: 91.7%

[0148] The terms “sensitivity” and “specificity” define the ability to correctly classify an individual, based on the biomarkers values detected, as having Brugada Syndrome or not. “Sensitivity” indicates the performance of the biomarker (s) with respect to correctly classifying individuals that have Brugada Syndrome. “Specificity” indicates the performance of the biomarkers with respect to correctly classifying individuals who do not have Brugada Syndrome. Thus, 84% specificity and 91% sensitivity for the panel of markers defined to test a set of control samples and Brugada Syndrome patients indicates that 84% of the control samples were correctly classified as control samples by the panel, and 91% of the Brugada syndrome samples were correctly classified as Brugada syndrome samples by the panel.

[0149] Finally, a comparison between the accuracy of the test carried out with less than 60 features and more than 60 features has been carried out. In general, it has been observed a linear increase in predictive ability between 15 and 60 features used (see FIG. 6), and a plateau between 60 and 150 (see FIG. 7).

[0150] Thus, 60 is an optimal number of features to select, though at least 15 features may equivalently be used to make a meaningful diagnosis of the syndrome.

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