Blood molecular biomarkers and methods for the diagnosis of acute Kawasaki disease

Abstract

The present invention relates to a combination of biomarkers used in the diagnosis of Kawasaki disease (KD) in clinical settings and a method for characterizing them. In particular, the present invention provides a method for calculating a KD score by combining RNA / DNA-derived molecular biomarkers and their copy number (concentration), and utilizing this score for the diagnosis, prognosis, risk assessment, treatment, and monitoring of KD. Furthermore, a kit containing a group of reagents, such as primers and polymerase, that specifically detect and amplify these biomarkers can be combined with a testing system such as the Applied Biosystems QuantStudio 6 to measure biomarker concentrations and generate a KD score, enabling KD to be distinguished from other febrile childhood illnesses.

Description

[Technical Field]

[0001] FIELD OF THE INVENTION The present invention relates to a biomarker combination for the purpose of diagnosing Kawasaki disease (hereinafter referred to as KD) in a clinical setting and a method for characterizing the same. In particular, the present invention is characterized by calculating a KD score by combining RNA / DNA-derived molecular biomarkers and their copy number (concentration), thereby supporting diagnosis, prognosis, risk assessment, and even treatment and monitoring. Furthermore, the present invention provides a kit containing reagents (e.g., primers, polymerase, etc.) that specifically amplify or detect these biomarkers. This kit can be used to measure biomarker concentrations in combination with a detection system such as Applied Biosystems QuantStudio 6, and the resulting KD score can be used to distinguish KD from other pediatric febrile diseases. [Background technology]

[0002] Kawasaki disease (KD) is a rare, acute inflammatory disorder specific to children characterized by vasculitis and persistent fever. KD is the leading cause of acquired heart disease in children in the United States, and its incidence is increasing significantly in developing countries. Without early treatment, approximately 25% of children can develop serious complications, including coronary artery disease. While fatalities are rare, vascular remodeling of damaged coronary arteries can lead to stenotic lesions later in life. Long-term follow-up studies have shown that 50% of children with a first coronary aneurysm due to KD require revascularization surgery or are at high risk for myocardial infarction.

[0003] The etiology of KD remains unclear, but it is widely believed to involve an abnormal and persistent immune response to some infectious agent in certain genetically predisposed individuals. (9)Unfortunately, a consistent infectious agent has yet to be identified, compounding diagnostic difficulties. Furthermore, there are clear racial and ethnic differences in the incidence of KD. KD has the highest incidence in East Asia, with approximately 1 in 150 people affected in Japan. Furthermore, approximately 1–2% of hospitalized children in Korea have KD. Meanwhile, the incidence of KD in Caucasians is significantly lower, at 9–17 / 100,000, compared with the incidence of 265 / 100,000 among children under 5 years of age in Japan. The average incidence in other Asian countries has been reported to be 51–194 / 100,000.

[0004] Currently, KD is diagnosed primarily based on clinical symptoms, and no objective molecular testing method is available. Clinical diagnosis is difficult because KD shares many symptoms with other pediatric febrile illnesses, including fever, rash, mucocutaneous symptoms, lymphadenopathy, and inflammation. (17) Furthermore, 15–36.2% of patients have "incomplete KD," which means they do not meet all of the major clinical signs of KD (four or more) defined by the American Heart Association. This makes diagnosis more likely to be delayed. Delayed diagnosis of KD increases the risk of permanent cardiovascular disorders and coronary artery aneurysms. Because patients with incomplete KD do not meet all clinical criteria, early and accurate diagnosis and IVIG (intravenous immunoglobulin) treatment are particularly important. Treatment within 10 days of the onset of fever is known to significantly reduce the incidence of coronary artery disease.

[0005] However, diagnosis of KD is often delayed due to complex diagnostic criteria and the difficulty physicians unfamiliar with KD have in following the complex diagnostic algorithm based on clinical findings and persistent fever. Another problem is the current lack of useful, readily available, objective molecular biomarker tests to aid in the diagnosis of KD. Therefore, there is an urgent need to develop objective diagnostic tools based on blood biomarkers to help identify patients with incomplete KD and determine whether or not to treat them.

[0006] We searched for molecular biomarker candidates associated with KD through the GEO database, literature, and multiplex protein platforms (see Figure 1). We identified 61 genes as candidates: ABCC1, ADM, C10ORF59, C1S, CAMK4, CD274, CD55, CD59, CLEC4D, CR1, CRTAM, CTGF, FCGR1B, FKBP1A, FKBP5, FKBP6, FUT7, IFI30, LCN2, LGALS2, LILRA5, MAPK14, MMP8, MPO, MYD88, NKTR, NOTCH4, OLFM4, PCOLCE2, and PPARG. We measured the RNA copy numbers of these biomarkers using quantitative PCR (qPCR) platforms and assay systems, including the Applied Biosystems QuantStudio, 7500 Real-Time PCR System, and Bio-Rad CFX System. Based on these results, we developed a KD diagnostic model and KD score. This KD score significantly differentiates KD from other febrile pediatric illnesses. Summary of the Invention [Means for solving the problem]

[0007] (Summary of the Invention) The present invention discloses methods for diagnosing, risk assessment, and treatment / monitoring Kawasaki disease (KD) using protein biomarkers present in blood, plasma, and serum. Specifically, the inventors have discovered that a biomarker panel consisting of genes (RNA, DNA, or protein) can be used to calculate a KD risk score, which can be used for KD diagnosis, KD risk assessment, KD treatment monitoring, and differentiation from other febrile diseases. These biomarkers can be quantified using kits with appropriate assay systems, such as the Applied Biosystems QuantStudio, 7500 Real-Time PCR System, or Bio-Rad CFX System. By deriving a KD score from RNA copy number, a KD diagnosis can be confirmed either alone or in combination with additional clinical findings.

[0008] The present inventors have identified several proteins, including ABCC1, ADM, C10ORF59, C1S, CAMK4, CD274, CD55, CD59, CLEC4D, CR1, CRTAM, CTGF, FCGR1B, FKBP1A, FKBP5, FKBP6, FUT7, IFI30, LCN2, LGALS2, LILRA5, MAPK14, MMP8, MPO, MYD88, NKTR, NOTCH4, OLFM4, PCOLCE2, PPARG, PVRL2, S100A12, S100A8, S100A9, SLC11A1, and SLC We found that the concentrations of RNA / DNA biomarkers, such as 11A2, TLR7, TREML4, VEGFA, HGF, ZBTB20, CASP5, CACNA1E, CLIC3, IFI27, KLHL2, PYROXD2, RTN1, S100P, SMOX, ZNF185, C11ORF82, PlGF, CXCL16, OSM, NPPB, TNFSF14, CXCL6, CKM, CKB, and IL1R1, are associated with the onset and diagnosis of KD. Combining these biomarkers and accurately measuring their blood copy numbers using a quantitative PCR (qPCR) system (e.g., Applied Biosystems QuantStudio, 7500 Real-Time PCR System, Bio-Rad CFX System) enabled the calculation of a KD score, which significantly distinguished KD patients from other febrile patients (see Figure 1).

[0009] The present invention discloses a method using, as non-limiting examples, IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7, and ZHF185 as biomarkers (proteins, full-length RNA / DNA sequences, or portions thereof).

[0010] In certain embodiments, panels using these biomarkers can be used to diagnose KD. Biomarker panels for diagnosing KD can include a combination of as few as two biomarkers (one pair) and as many as 18 biomarkers (nine pairs). A panel with a small number of biomarkers can be sufficient and cost-effective for distinguishing KD from other febrile illnesses. However, panels containing a larger number of biomarkers can provide more detailed information and be useful for addressing differences in regional populations.

[0011] The binary scoring system distinguishes patients from other febrile illnesses by calculating a KD score, where a low KD score indicates a patient is unlikely to have KD, and a high KD score indicates a patient is likely to have KD (rule in).

[0012] Another approach based on the biomarkers and methods described above uses a KD score to assess the risk of developing KD in three stages: a score below the low cutoff value indicates a low risk of KD, a score above the high cutoff value indicates a high risk of KD, and scores between these two indicate an intermediate risk of KD.

[0013] In some cases, clinical parameters can be combined with these biomarkers to diagnose KD. For example, the present invention encompasses a method for measuring at least seven clinical parameters, including fever duration, blood hemoglobin concentration, C-reactive protein concentration, white blood cell count, eosinophil percentage, monocyte percentage, and immature neutrophil percentage, as standard clinical practice in patients suspected of KD who have had a fever for five consecutive days.

[0014] KD scores can be calculated from blood biomarker values ​​using geometric mean, multiple regression linear discriminant analysis (LDA), or distributed gradient boosting decision tree (GBDT) machine learning methods such as XGBoost. In binary models, receiver operating characteristic (ROC) curves can be derived from biomarker combinations or pairings to classify KD scores as low, intermediate, or high risk.

[0015] Another aspect of the present invention includes a diagnostic method in which a biological sample (blood, plasma, serum, etc.) is obtained from a patient using the above biomarker panel and the KD score calculation method, the RNA / DNA copy number or concentration of biomarkers is measured, and each biomarker value is compared with a reference value range. Thus, if the biomarker value is significantly different from that of a control group, it is suggested that the patient has KD. In one embodiment, the KD score is calculated to more reliably diagnose KD in patients presenting with a febrile illness.

[0016] Biomarker copy numbers can be measured using specific primers and reporting systems. Specific examples include qPCR, RT-qPCR, gene microarrays, RNA / DNA sequencing (RNAseq / DNAseq), sandwich assays, magnetic capture, microsphere capture, electrophoresis blots, surface-enhanced Raman spectroscopy (SERS), flow cytometry, and mass spectrometry. In certain embodiments, biomarker-specific DNA primers bind to the biomarker RNA, followed by reverse transcription and PCR amplification to determine copy number using a detectable reporter (e.g., BYBR Green dye). The primers specifically bind to the biomarker or a fragment thereof.

[0017] The present invention also encompasses a method for evaluating the effectiveness of KD therapeutic agents (intervention agents) in patients. Specifically, the biomarker copy number or concentration can be measured using the biomarker panel in patient-derived samples before and after treatment, and the results can be compared with the reference range or KD score to evaluate the therapeutic effect.

[0018] The present invention also includes a method for selecting patients suspected of having KD for intravenous immunoglobulin (IVIG) administration. Specifically, if a patient is diagnosed as KD-positive using the methods described herein, the patient can be selected for IVIG administration. In this embodiment, a patient is selected for IVIG treatment if their KD score is in the high-risk or intermediate-risk range and the biomarker panel has an expression profile indicative of KD development.

[0019] Furthermore, the present invention also encompasses a method for diagnosing a patient suspected of having KD and administering a therapeutically effective amount of IVIG if a positive KD diagnosis is obtained. In one embodiment, the method comprises determining a patient's KD clinical score, and administering a therapeutically effective amount of IVIG to the subject if the patient exhibits a high-risk or intermediate-risk KD clinical score and the biomarker panel indicates a positive KD diagnosis.

[0020] The present invention also encompasses kits for quantitative qPCR systems for measuring RNA / DNA biomarkers. The kits include a container for holding a biological sample isolated from a human patient suspected of having KD. The kits can include reagents for measuring KD biomarkers and printed instructions for reacting the sample to measure the biomarkers. The reagents can be packaged in separate containers and can include a control reference sample and qPCR reagents for detecting biomarker copy number.

[0021] The present invention encompasses assays in which copy numbers of a panel of biomarkers in a blood, plasma, or serum sample collected from a subject are measured and compared to control biomarker values. Differences in biomarker expression levels indicate the presence or absence of KD. In one embodiment, the invention also includes a step of determining a KD score from the concentrations of these biomarkers.

[0022] In some cases, to measure the biomarker, the copy number can be calculated by PCR reaction using primers that specifically bind to the biomarker or its fragment.In certain embodiments, the primers are selected from the coding region (exon) sequence of the biomarker.Examples include primer pairs that specifically bind to IFI27, C19ORF19, S100A12, S100A9, CACNA1E, SLC11A1, NKTR, CAMK4, CLIC3, LGALS2, etc.

[0023] These and other embodiments described herein are within the scope of those skilled in the art that will readily occur to those skilled in the art from the disclosure herein. DETAILED DESCRIPTION OF THE INVENTION

[0024] (Detailed explanation) The present invention provides KD (Kawasaki Disease) biomarkers, KD biomarker panels, and methods for obtaining indicators representing KD biomarker levels in a sample. These compositions and methods can be used for a variety of applications, including diagnosing KD, assessing the risk of developing KD, monitoring KD patients, and determining KD treatments. The present invention also encompasses systems, devices, and kits used in carrying out these methods. Objects, advantages, and features of the present invention will be apparent to those skilled in the art upon review of the specification and claims.

[0025] It is to be understood that the methods and compositions described below are not limited to the particular methods or compositions described, which may, of course, vary, and the terminology used herein is for the purpose of describing particular embodiments, and is intended to be limited in scope only by the claims.

[0026] All technical or scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art. Publications cited herein are incorporated by reference to disclose and describe the methods and / or materials described therein. In the event of a conflict between the disclosure of this specification and the disclosure of a cited reference, the disclosure of this specification shall control.

[0027] As one skilled in the art will appreciate upon reading this disclosure, the individual embodiments described and illustrated herein may have features separated or combined with other embodiments within the scope of the description without departing from the scope or spirit of the invention, and any method described can be carried out in the order of events recited or in any other order that is logically possible.

[0028] As used herein and in the appended claims, the singular forms "a," "an," or "the" include the plural unless the context clearly dictates otherwise. For example, reference to "a cell" includes a plurality of cells, and reference to "the RNA sequence" includes one or more peptides and their corresponding polypeptides.

[0029] As described in the Abstract, one aspect of the present invention provides methods, compositions, systems (e.g., qPCR systems such as the AB QuantStudio 6), and kits used to evaluate (diagnose, risk assessment, monitoring, and / or treat) KD. "Kawasaki disease (KD)" is a multisystem inflammatory and febrile illness characterized by rash, swelling of the hands and feet (edema), conjunctival congestion, cervical lymphadenopathy, and inflammation of the mouth, lips, and pharynx. KD primarily affects children under the age of 5, but can also affect older children, adolescents, and adults. If not treated within 10 days of the onset of fever, KD can lead to cardiovascular disease and aneurysms. "Diagnosis of KD" includes determining whether a subject currently has KD, subtyping KD, and assessing the severity of KD. "KD risk assessment" refers to risk assessment as one of the clinical manifestations associated with KD, such as predicting the likelihood of a subject having or developing KD, disease progression, and treatment response. "KD monitoring" includes tracking a subject's condition and providing information useful for KD diagnosis, KD risk assessment, and treatment effectiveness evaluation. "KD treatment" refers to administering treatment to prevent the progression of KD and to suppress or reduce symptoms and the risk of developing coronary artery aneurysms.

[0030] In the following description of the present invention, compositions useful for KD assessment will first be described, followed by a description of the method, system, and kit.

[0031] (Kawasaki Disease Biomarkers and Panels) In one embodiment of the present invention, KD biomarkers and KD biomarker panels are provided. "KD biomarker" refers to a molecular entity associated with the KD phenotype in a sample. For example, the level of a particular biomarker may be significantly increased or decreased in a sample derived from a KD patient compared to a non-KD healthy control. Biomarkers such as C10ORF59 are associated with the KD phenotype and may be increased in copy number in KD patient samples. Meanwhile, IFI27 may be decreased in association with the KD phenotype. qPCR measurements tend to show a decrease in C(t) value with an increase in copy number, and an increase in C(t) value with a decrease in copy number.

[0032] KD biomarkers include proteins and peptides associated with KD and their gene sequences (RNA, DNA, etc.). A "gene" refers to a nucleic acid having an open reading frame encoding a corresponding protein, and may include a promoter, regulatory sequences, and untranslated regions.

[0033] The boundaries of the coding sequence are defined by a start codon at the 5' end and a stop codon at the 3' end. A gene may include its natural promoter and associated regulatory sequences.

[0034] From the examples shown herein, the present inventors have identified multiple molecular entities associated with KD and found that their combination (panel) use enables evaluation of KD for purposes such as KD diagnosis, KD risk assessment, KD monitoring, and KD treatment decisions. These include IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7, and ZHF185.

[0035] The present invention also provides a "panel" of KD biomarkers. A "panel" is a combination of two or more (e.g., 2, 4, 6, 8, 10, 12, 14, 16, or 18) KD biomarkers, and KD assessment is performed by comprehensively considering their copy numbers or expression levels. In particular, a panel including IFI27, C19ORF59, S100A12, S100A9, CACNHA1E, and CLIC3 is useful. In one embodiment, a panel in which a KD score is calculated using the delta C(t) values ​​of C19ORF59 and IFI27 is exemplified.

[0036] Those skilled in the art can identify other KD biomarker panels applicable to the present invention using appropriate statistical methods (including those described herein or in technical literature). For example, by combining genetic algorithms (GA) and all-paired (AP) support vector machines (SVM), KD classification analysis can be performed to select a compact group of biomarkers with optimal characteristics for KD assessment. Even if different classifier sets have only a small overlap of characteristic genes, KD assessment can be performed with high accuracy.

[0037] (method) In one aspect of the present invention, a method for obtaining a KD biomarker level representation of a subject is provided. As used herein, "KD biomarker level representation" refers to a representation of the concentration or amount of one or more KD biomarkers (e.g., a KD biomarker panel) present in a biological sample from a subject. The term "biological sample" encompasses various types of samples obtained from an organism, including samples that can be used for diagnostic, prognostic, or monitoring assays. Specifically, the term "biological sample" encompasses blood and other biological fluids, as well as cells and progeny derived therefrom. It also encompasses samples that have been manipulated after collection, such as by treatment with reagents, lysis, or enrichment for specific components. Clinical samples include cell supernatants, cell lysates, serum, plasma, biological fluids, and tissue samples. Clinical samples that can be used in the methods of the present invention are often obtained, particularly blood samples.

[0038] Particularly preferred sample sources include blood samples (whole blood, serum, plasma, etc.). In many embodiments, a suitable initial sample source is a blood sample, and therefore, the samples used in the assays of the present invention are generally blood-derived samples. Blood-derived samples can be prepared from whole blood or fractions thereof (serum, plasma, etc.). In some cases, blood is collected and allowed to clot, and the resulting serum is separated and collected for use in the assay.

[0039] In some embodiments, the sample is serum or a serum-derived sample. The method used to obtain a fluid serum sample is not particularly limited, and typically involves collecting venous blood (e.g., fingerstick, venipuncture, etc.) into a clot tube or serum separator tube, allowing the blood to clot, and then collecting the serum by centrifugation to separate it from the clot. The collected serum is stored until assayed. The patient-derived sample is then used to measure the level of a KD biomarker.

[0040] Test samples are typically obtained during a clinical visit when a patient presents with persistent fever. KD primarily affects children under the age of five, but can occur at any age, including teenagers and adults.

[0041] Once obtained, samples may be used directly, cryopreserved, or maintained in an appropriate culture medium for short periods. Typically, samples are derived from human patients, but samples from animal models (e.g., horses, cows, pigs, dogs, cats, mice, rats, hamsters, monkeys) may also be useful. Any source that has been shown to differentially express or present KD biomarkers can be evaluated in this method. Generally, samples derived from fluids in which proteins, peptides, and RNA are free and available for evaluation are preferred.

[0042] If desired, samples can be treated to improve the sensitivity of detection of one or more KD biomarkers. For example, in the case of blood samples, red blood cells can be removed by centrifugation or other methods to reduce nonspecific background during biomarker detection with affinity reagents. Biomarkers in samples can also be concentrated using well-known concentration techniques, such as acid, alcohol, or salt precipitation, hydrophobic precipitation, or filtration. In some cases, adjusting the pH of the sample (e.g., urine) to near-neutral can inhibit complex formation and improve the accuracy of biomarker quantification.

[0043] In carrying out the methods of the present invention, the level of one or more KD biomarkers is assessed in a biological sample from a subject. Assessing the level of a KD biomarker can be performed by any applicable method. For example, for RNA biomarkers, the amount of one or more oligonucleotides (RNA or DNA fragments) can be measured to determine the RNA expression level. Expression levels of KD-associated genes can be determined by measuring the amount of nucleic acid transcripts, such as mRNA, of the gene. The terms "evaluating," "assaying," "measuring," "assessing," and "determining" refer to any form of measurement, including qualitative and quantitative evaluations, and encompass everything from determining the presence or absence of a target to quantitative evaluations.

[0044] For example, when assessing at least one KD biomarker, the copy number can be determined by measuring the amount or level of the RNA / DNA or a fragment thereof. The terms "RNA," "nucleic acid," and "oligonucleotide" are used broadly and interchangeably herein and include RNA, DNA, and modifications and derivatives thereof.

[0045] Furthermore, the amount of nucleic acid biomarkers can be determined by quantifying gene-derived RNA transcripts or their fragments. Gene expression levels can be measured by a variety of well-known methods, including array-based gene expression profiling, hybridization assays, target nucleic acid labeling and hybridization detection, qPCR, RT-qPCR, and gene sequencing.

[0046] Reference may be made to specific hybridization techniques described in U.S. Patent Nos. 5,143,854, 5,288,644, 5,324,633, 5,432,049, 5,470,710, 5,492,806, 5,503,980, 5,510,270, 5,525,464, 5,547,839, 5,580,732, 5,661,028, 5,800,992, and WO 95 / 21265, WO 96 / 31622, WO 97 / 10365, WO 97 / 27317, EP 373 203, EP 785 280, and the like. In these methods, labeled target nucleic acids are hybridized to an array containing complementary probes for each gene to be analyzed, and after non-specific binding is removed by washing, the hybridized nucleic acids are detected qualitatively and quantitatively.

[0047] The resulting hybridization pattern provides information on the expression of each gene (presence / absence, expression level, qualitative and quantitative information).

[0048] Alternatively, non-array-based nucleic acid quantification techniques (methods based on amplification reactions, e.g., PCR, quantitative PCR, RT-qPCR, real-time PCR, etc.) are also available.

[0049] When protein levels need to be detected, standard techniques such as ELISA can be used to quantify the concentration of one or more proteins in a sample. In ELISA, an antibody against a specific protein is immobilized on a solid support and allowed to form an immune complex with an antigen (biomarker) in the sample. After washing away unbound components, the immune complex is detected using a second antibody or label, and the protein amount is measured using a colorimetric (dye) assay.

[0050] This format can be varied in many ways, such as by first fixing the sample, then adding a primary antibody, followed by detection with a secondary antibody.

[0051] The carrier used for immobilizing the antibody may be any carrier such as a microtiter plate, beads, dipstick, or resin particles, and can be selected taking into consideration the signal-to-noise ratio, background reduction, ease of handling, and cost.

[0052] Non-ELISA-based methods (mass spectrometry, proteomics arrays, xMAP) TM Microsphere technology, flow cytometry, Western blot, immunohistochemistry, etc. are also available for protein level quantification.

[0053] The data obtained by these methods provide qualitative and quantitative information, such as the presence or absence of a biomarker and its quantitative value. Qualitative assessment determines the presence or absence of a target biomarker (nucleic acid or protein), while quantitative assessment allows for the evaluation of the actual or relative values ​​of the biomarker being measured.

[0054] After the levels of one or more KD biomarkers are determined, these measurements are analyzed to obtain a representation of the KD biomarker levels.

[0055] For example, one or more KD biomarker measurements can be analyzed individually to derive a KD score. A "KD score" is a measure of the normalized level of one or more KD biomarkers (e.g., serum protein concentration) in a patient sample. For example, the expression level of each biomarker can be log2 transformed and normalized to a housekeeping gene or normalized to the signal of the entire panel.

[0056] It is also possible to derive a single KD score by analyzing a panel (multiple types) of KD biomarkers together. The "KD score" is a single numerical index obtained by combining the weighted levels of each KD biomarker group (panel). Methods and algorithms for determining biomarker scores are known. For example, a single value can be obtained by log2-transforming each biomarker value, multiplying it by an appropriate weight, and then summing or averaging the values.

[0057] Weights can be assigned to reflect the importance of each biomarker to KD specificity, sensitivity, and diagnostic accuracy. For example, biomarkers whose expression levels increase during KD can be weighted +1, and those whose expression levels decrease can be weighted -1, and a KD-specific signature can be calculated from the ratio of increase to decrease. Additionally, statistical and machine learning techniques such as PCA, linear regression, SVM, and random forest can be used to determine weights based on the importance of each biomarker. Weights can be defined based on datasets derived from patient samples or reference datasets (training datasets).

[0058] Those skilled in the art can easily perform the above analysis using a computer-based system (hardware, software, data storage medium, etc.) and known algorithms. Data mining techniques can also be applied through cloud computing, smartphone apps, and client-server based platforms.

[0059] In some cases, only a single biomarker expression can be assessed to generate a biomarker-level representation, while in other cases multiple biomarkers (panels) can be assessed to obtain a more comprehensive representation, allowing for KD assessment (e.g., diagnosis, risk assessment, monitoring, and treatment selection) that takes into account the entire expression profile.

[0060] An additional step can be to report the KD biomarker level representation (KD score or KD profile). That is, the methods of the present invention can include outputting the resulting KD biomarker level representation as a report. This report can be provided in any form, including electronic (displayed on a computer screen) or tangible (printed report on paper or other tangible medium).

[0061] (Usefulness) The KD biomarker level representations obtained herein can be used for a variety of purposes. For example, they can be used to diagnose KD, i.e., determine whether a subject has KD, the type of KD (complete KD, incomplete KD), and the severity of KD (normal coronary arteries, dilated arteries, aneurysms, etc.). They are also useful when a subject already exhibits clinical symptoms of KD, such as fever, rash, swelling of the hands and feet, conjunctival congestion, cervical lymphadenopathy, and inflammation of the oral, labial, and pharyngeal mucosa.

[0062] KD biomarker level expression can also be used to assess KD risk in patients with incomplete KD. Even if a patient does not meet the AHA guidelines for KD diagnosis (i.e., has fewer than four clinical signs), the KD biomarker level expression and KD score can be used as additional clinical signs to suggest the possibility of KD. This allows for estimation of KD risk, confirmation of KD diagnosis, prediction of KD progression and prognosis (e.g., cardiovascular phenotype), and prediction of KD treatment response (positive, negative, or no response).

[0063] Furthermore, KD biomarker levels can be used for KD monitoring, which involves tracking and observing the patient's condition to obtain information useful for confirming KD diagnosis, assessing KD prognosis, and evaluating the effectiveness of KD treatment.

[0064] KD biomarker levels can also be used to guide patient treatment. "Treatment" or "treating" refers to achieving a desired pharmacological or physiological effect, including completely or partially preventing, slowing the progression of, or alleviating a disease or its symptoms. Treatment may occur before disease onset, during disease progression, or after symptoms appear. KD treatments may include bed rest, hydration, a low-salt diet, blood pressure control medications, corticosteroids, or IVIG (intravenous immunoglobulin) administration.

[0065] In the methods of the present invention for KD risk assessment, KD diagnosis, KD treatment monitoring, etc., the obtained KD biomarker level expression can be compared with a KD phenotype determination element, which is an element representative of the KD phenotype (tissue sample, biomarker profile, value, value range, etc.) and can be used to determine whether a subject has KD, the possibility of transitioning from incomplete KD to complete KD, treatment response, etc.

[0066] For example, KD phenotyping elements can be reference controls, such as samples from KD patients or non-KD controls. Alternatively, KD phenotyping elements can be biomarker level expressions (biomarker profiles or scores) representative of the KD state. Positive references (derived from KD patients) and negative references (derived from non-KD control patients) can be established, and these can be used to compare biomarker levels in subject samples to predict KD onset, risk of KD progression, and treatment response.

[0067] In one embodiment, the acquired biomarker level expression can be compared to a single phenotypic element to obtain subject information. In another embodiment, it can be compared to multiple phenotypic elements. For example, by comparing with both a negative reference (non-KD subjects) and a positive reference (KD subjects), the likelihood of a subject developing KD can be more reliably assessed. Furthermore, by comparing with reference values ​​for KD treatment responsiveness (biomarker profiles of treatment-effective and treatment-unresponsive populations), it is possible to predict whether a patient will respond to treatment.

[0068] Comparison of biomarker level expression with phenotypic factors is performed using well-known techniques (e.g., normalization of C(t) values ​​in qPCR, comparison with known amounts of RNA, comparison between expression profiles, comparison of digital image data, database search, etc.). The comparison determines the degree to which the test sample is similar to or different from the reference profile, and this information can be used to predict the onset of KD, diagnose KD, monitor KD patients, etc. For data comparison methods, reference can be made to techniques described in patent documents such as US 6,308,170 and US 6,228,575.

[0069] In some embodiments, biomarker level expressions can be used directly for KD diagnosis, KD risk assessment, and KD treatment monitoring without comparison to phenotypic factors.

[0070] The methods of the present invention are applicable to a variety of subjects (mammals, including humans), and embodiments directed to human patients are particularly contemplated.

[0071] Furthermore, the KD assessment method according to the present disclosure can present diagnostic results, prognosis, monitoring results, etc., as a report. That is, the method may include a step of reporting the results of the KD assessment (whether the subject is KD-positive, the type and severity of KD, the risk of developing KD, predicted response to treatment, etc.) electronically (displayed on a computer screen) or in tangible media (such as paper). Any reporting format can be used, and methods described herein and in the well-known art can be applied.

[0072] (report) As used herein, a "report" is an electronic or physical document containing information related to a subject assessment and its results. In one embodiment, the subject report includes at least a KD biomarker level representation (e.g., a KD profile or KD score). In another embodiment, the subject report includes at least a trained KD assessment (e.g., KD diagnosis, clinical indications for KD prognosis, analysis of KD monitoring results, treatment recommendations, etc.). The subject report can be generated entirely or partially electronically. The subject report can further include one or more of the following information: 1) laboratory information, 2) service provider information, 3) patient data, 4) sample data, 5) assessment report (e.g., including baseline values ​​used, test data (e.g., protein level measurements), and 6) other characteristics.

[0073] The report may include information about the testing facility, i.e., the hospital, clinic, or laboratory where the sample was collected and / or the data was generated. For example, the name and address of the testing facility, the name of the lab technician who performed the assay or registered the input data, the date and time the assay was performed, the location of the sample and result data, and the lot number of any reagents (e.g., kits) used. These report fields can be populated based on information provided by the user.

[0074] The report may also include information about service providers, who may be located within or outside the user's medical facility. Examples include the name and location of the service provider, the name of the reviewer, and the name of the individual who collected the sample or generated the data. Report fields are populated by data entered by the user or predefined options selected from pull-down menus. The report may also include contact information for interpretive reports and technical inquiries about results.

[0075] The report may include a patient data section that may describe the patient's clinical history (e.g., age, race, serotype, previous KD episodes, etc.) and may also include administrative patient data such as patient identifiers (name, date of birth, sex, address, medical record number (MRN), hospital room and / or bed number), insurance information, and the name of the ordering or attending physician.

[0076] The report can include a sample data section that contains information about the biological sample analyzed (such as the source of the sample (e.g., patient-derived blood, saliva, or specific tissue type), the sample storage temperature and pre-processing procedure, and the date and time of collection), which can be reflected by user-entered data or pull-down options.

[0077] The report may include an assessment report section, which includes information obtained after data analysis as described herein. The interpretation report may include a prediction of the subject's likelihood of developing KD, a diagnosis of KD, or an assessment of the nature (phenotype) of KD. The assessment report may also include recommendations. For example, if the results indicate a possibility of KD, literature or medically recommended therapeutic actions, such as dietary changes or antihypertensive medication, may be provided.

[0078] Reports can include additions or modifications as needed. Electronic reports can also include hyperlinks to internal or external databases to provide access to more information. For example, patient data can include a hyperlink to an electronic patient record, providing access to the patient record from a confidential database. These electronic reports can be stored on physical media such as computer memory, flash drives, CDs, or DVDs.

[0079] The report may include all or some of the elements listed above, but should contain enough elements to address the user's needs (e.g., calculated KD biomarker level representations, KD onset prediction, diagnosis, and phenotypic characteristics).

[0080] (reagents, systems, kits) The present invention also provides reagents, systems, and kits for use in carrying out the above-described methods. These reagents, systems, and kits are diverse. For example, they include specialized reagents (e.g., detection oligonucleotides, protein-detecting antibodies, and peptides) for obtaining biomarker level representations of KD biomarkers in a sample. Detection elements vary from specialized reagents for detecting a single KD biomarker to dipsticks, plates, arrays, and cocktails (e.g., multiple oligonucleotides, primer sets, and antibody groups) capable of simultaneously detecting multiple KD biomarkers.

[0081] Other examples of reagents include arrays containing probe nucleic acids containing KD-associated genes. Many formats are known, including dot blot arrays and microarrays. Related U.S. and international patent literature is available for reference as described above.

[0082] Another example is a KD gene-specific primer set, which is designed to selectively amplify a gene of interest, and can be used to generate a KD biomarker level representation using PCR, RT-PCR, real-time RT-PCR, etc. U.S. Patent No. 5,994,076 describes gene-specific primers and methods for their use.

[0083] For protein biomarker quantification, specific antibodies are used as reagents. These include ELISA, xMAP, TM They can be used in microspheres, proteomics arrays, flow cytometry, Western blots, dot blots, immunohistochemistry, etc. Antibodies can be provided in solution or pre-bound to a solid support such as the bottom of a multiwell plate or the surface of an xMAP microsphere.

[0084] In particular, arrays, primer sets, and antibody groups containing probes, primers, and antibodies corresponding to IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7, ZHF185, etc., or biochemical substrates specifically associated with these, are useful. The probes, primers, and antibody groups of the present invention can also include those specialized for KD-related genes, proteins, lipids, and cofactors other than those mentioned above.

[0085] The present invention may provide a system such as a qPCR system (e.g., AB QuantStudio 6). A "system" refers to a collection of reagents in any combination, which can be purchased or assembled from the same or different suppliers. As used herein, a "kit" refers to a set of the above-mentioned reagents provided (sold). For example, when nucleic acids or antibodies are used to detect target nucleic acids or proteins in a sample, combining this with an electrochemical biosensor platform enables multiplex measurement of KD biomarkers, which can be utilized for personalized KD treatment.

[0086] The systems and kits of the present invention may include the above-mentioned arrays, gene-specific primer collections, and protein-specific antibody collections, as well as primers for target nucleic acid synthesis, dNTPs, rNTPs, labeled dNTPs / rNTPs, enzymes (e.g., reverse transcriptase, DNA polymerase, RNA polymerase), hybridization and washing buffers, probe arrays, purification reagents such as spin columns, and signal generation and detection reagents (e.g., labeled secondary antibodies, streptavidin-alkaline phosphatase conjugates, chemiluminescent or chemiluminescent substrates).

[0087] The systems and kits of the invention can also include a KD phenotyping element, which can be a reference sample or a database of biomarker level expressions (e.g., reference profiles or scores) from individuals with or without KD, against which the biomarker level expressions can be compared to predict or diagnose KD.

[0088] Additionally, the kits will contain instructions for practicing the methods of the invention, which may be provided in any format, such as paper, a package insert, a computer readable medium (diskette, CD, etc.), a website address, etc.

[0089] The following examples are presented to enable those skilled in the art to understand and use the present invention, but are not intended to limit the scope of the invention as contemplated by the inventors. [Example]

[0090] The following examples are provided to demonstrate to one of ordinary skill in the art how to utilize the present invention, but are not intended to limit the scope of the invention. Nor are they intended to imply that the experiments presented are exhaustive or exclusive. While careful attention has been paid to quantitative descriptions (amounts, temperatures, etc.), experimental error and deviation should be considered. Unless otherwise noted, parts are parts by weight, molecular weight is weight average molecular weight, temperature is in degrees Celsius, and pressure is near atmospheric.

[0091] (material and method) KD Patient Validation Cohort, Demographic Information, and Clinical Criteria: All protocols for this study were approved by the Institutional Review Board (IRB) of Chang-Gen Memorial Hospital. Blood samples were collected from confirmed KD patients and from febrile controls within the same cohort who were later determined not to have KD. KD patients met the American Heart Association (AHA) clinical criteria for complete and incomplete KD and received IVIG treatment within 10 days of fever onset (Reference 3). Patient blood samples were collected and analyzed in the initial state, before IVIG or medication administration. All samples for this study were collected from Chang-Gen Memorial Hospital, and informed consent was obtained from the parents of participating children.

[0092] Identification by meta-analysis of vasculitis and KD microarrays: Differentially expressed genes (DEGs) were extracted from seven datasets of peripheral blood mononuclear cell (PBMC) microarray experiments. These seven datasets included PBMC microarray experiments on primary vasculitis (including KD) obtained from the NCBI Gene Expression Omnibus (GEO). Four datasets were KD-related (GSE15297 (KD vs. FC), GSE18606 (KD vs. normal control), GSE9864 (KD vs. normal control), and GSE9863 (KD vs. normal control)), and three datasets were related to other vasculitis (GSE33910 (Takayasu's arteritis vs. normal control), GSE17114 (Behçet's disease vs. normal control), and GSE16945 (Takayasu's arteritis vs. normal control)). After identifying DEGs from each dataset, we used Ingenuity Pathway Analysis (IPA) to extract DEGs associated with pathways commonly enriched across the seven datasets. Gene biomarkers that were present in at least one dataset and involved in pathways commonly enriched across the seven datasets formed a vasculitis meta-signature. Further, biomarker candidates were matched against human biofluid proteome databases (HUPO Plasma Proteome Project (24), Plasma Proteome Institute Nonredundant List (25), MAPU Proteome Database (26), and Urinary Exosome Database (27, 28)) to narrow down the candidates to genes with known proteins detectable in serum and urine.

[0093] Literature search: We mined literature abstracts in the PubMed database using the Ge´nie tool. The analysis included 20,396 genes and 1,183,931 gene-abstract links (551,555 unique PMIDs) (Reference 24). Using Ge´nie, we ranked genes using machine learning classification with keywords such as "kawasaki disease," "myocardial dysfunction," "vascular inflammation," "vascular leakage," "coronary artery aneurysms," "ischemia," and "multisystem inflammatory syndrome." For each keyword search, we set the significance level at p<0.05 (abstract-based) and false discovery rate <0.05 (gene-based), and ranked significant genes using the Fisher statistic. Gene ranks under the seven topics were normalized and compared, and 12 genes were selected for further validation. We also added nine antigen targets from the cardiac-related discovery panel, and examined the differential expression of a total of 61 genes in patients with KD and other febrile illnesses.

[0094] Biomarker validation using qPCR platform: Blood samples from the KD and fever control cohorts were stored fresh or frozen at -80°C for later preparation. RNA was extracted directly from the blood using an RNA extraction kit. qPCR assays for 61 protein targets were performed using the QuantStudio 6 IVD platform and commercially available one-step or two-step qPCR kits according to the reagent manufacturer's recommended conditions. The linearity, limit of quantification (LOQ) / limit of detection (LOD), and minimum C(t) values ​​of each primer were determined.

[0095] Statistical Analysis: This study included 200 cases (100 confirmed KD cases and 100 febrile control cases) and measured the RNA copy number of the target gene. Patient characteristics were compared between KD patients and febrile control groups using Fisher's exact test for gender and rank-sum test for age. Univariate analysis of each analyte was performed between KD patients and febrile control groups, and receiver operating characteristic (ROC) analysis was performed to determine specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC). Wilcoxon rank-sum test and fold-change analysis were used to compare analyte concentrations between KD patients and febrile control groups. Age differences between the confirmed KD group and febrile control group were also assessed using rank-sum tests. Comprehensive analysis was performed using extrapolation correction, with values ​​below the LOQ being half the LOQ and values ​​above the ULQ (upper limit of quantification) being twice the ULQ. A p<0.05 was considered significant, and 19 genes with a fold change greater than 1.5 or less than 0.67 and an AUC greater than 0.6 were selected as differentially expressed genes.

[0096] For each analyte concentration, all 10 analyte combinations were iteratively explored to determine the maximum ROC AUC value. For each combination, the geometric mean was calculated, log-transformed, scaled to 0–10, and then resubmitted to ROC analysis. In the first iteration, the optimal analytes with an AUC > 0.5 were selected one at a time in the univariate analysis. In the next iteration, new analytes were added to the retained analytes; if they improved the AUC, they were retained in the panel; if they did not, they were removed. This process was repeated until the maximum AUC was reached to determine the final panel.

[0097] KD biomarker panel and diagnostic score: Performance of the resulting model was evaluated using the area under the receiver operating characteristic curve (AUC). The KD score was calculated using the geometric mean of the final panel, and the optimal cutoff was determined using the optimal Youden index in a binary model. Sensitivity, specificity, PPV, and NPV were calculated based on the optimal cutoff, with 95% confidence intervals. Statistical analysis was performed using R software (version 4.1, R Foundation for Statistical Computing). A two-sided p<0.05 was considered significant.

[0098] (result) Demographic and Characteristics of Patients: This study focused on a pediatric population with persistent high fever and included 100 cases of confirmed KD and 100 children with fever (Table 1a). Thirty of these patients had five clinical signs, and 52 had four clinical signs, meeting the AHA guidelines for the diagnosis of complete KD at the time of blood collection (Table 1b). The remaining 18 patients were diagnosed with incomplete KD at the time of blood collection. The age of the confirmed KD group (median 1.4 years, range 0.8-2.4 years) was significantly younger than that of the febrile control group (median 3.0 years, range 1.7-4.0 years), p<0.001. For IRB approval, clinical information on the control group was limited, but the febrile control patients did not meet the diagnostic criteria for KD. [Table 1a] [Table 1b]

[0099] Meta-analysis identified a qPCR discovery panel for KD: A meta-analysis of seven vasculitis and KD microarray datasets identified 13 common pathways. A total of 82 genes were identified and refined by matching with a human biofluid proteome database, resulting in a meta-signature of 40 vasculitis-specific gene markers (meta-signature) that are potentially differentially expressed in blood (Figure 1). VEGF, MMP8, and HGF were included in this vasculitis meta-signature, and significant expression variations in serum between KD and febrile controls (FCs) and between KD and normal controls (p<0.0001 and p<0.001, respectively) were reported. This finding supports the validity of our hypothesis and methodology that meta-analysis of vasculitis PBMC microarray data can identify biomarkers for KD diagnosis. This method is also consistent with the successful identification of novel biomarkers for preeclampsia in a previous study. Furthermore, literature mining methods (such as those of G▲e´▼nie et al.) [1]) was used. Currently, 20,396 genes and 1,183,931 gene-abstract links (551,555 unique PMIDs) are analyzed. Using the Ge´nie method, genes were ranked using machine learning classification for keywords such as "kawasaki disease," "myocardial dysfunction," "vascular inflammation," "vascular leakage," "coronary artery aneurysms," "ischemia," and "multisystem inflammatory syndrome." For each keyword search, a cutoff of p<0.05 for abstracts and a false discovery rate <0.05 for genes were used, and significantly enriched genes were ranked using Fisher's statistics. Gene ranks were normalized across seven topics, with the top genes assigned a relative rank of 1 and the bottom genes assigned a relative rank of 0. Based on their ranks for each keyword, 12 candidate genes were selected for further validation. Additionally, nine antigen targets from a Luminex-based cardiac-related discovery panel that may be correlated with KD symptoms were also included.

[0100] A two-step process of discovery and validation was used to narrow down the final gene expression panel for validation studies. Nineteen genes were selected from the initial 5x5 cohort, and then a 15x15 cohort was used to determine the optimal KD scoring panel. The delta C(t) values ​​of two genes, C19ORF59 and IFI27, were found to be the optimal combination for KD discrimination.

[0101] Performance of the binary and risk classification models: For each gene biomarker in the exploratory discovery panel, qPCR results from the KD and fever control cohorts were examined using univariate analysis (Table 2). An optimal panel achieving maximum AUC was then constructed using a linear approach. The two-gene pair C19ORF59 and IFI27 was constructed as a panel using the delta C(t) difference between the expression of both genes after the initial discovery and validation cohort studies (Figure 1). This two-gene panel was further validated in a validation cohort consisting of 80 KD cases and 80 fever controls, achieving an overall ROC AUC of 0.930 with an optimal cutoff of 4.558. [Table 2]

[0102] The diagnostic model demonstrated high performance in KD diagnosis, with an AUC of 0.930 (Figure 2b). AUC is an index of a diagnostic test's ability to discriminate between the presence and absence of disease, with AUC = 1.00 representing perfect discrimination and AUC = 0.5 representing random discrimination. The optimal cutoff, calculated using the Youden index, was a KD score (4.558), yielding a sensitivity of 84% (77-91%), specificity of 91% (85-96%), PPV of 90.3%, and NPV of 85% (Figure 2c).

[0103] Furthermore, we performed a three-level risk classification (low risk: <3.558, intermediate risk: 3.558–4.958, high risk: >4.958) based on two thresholds based on the KD score, and examined KD risk stratification as an aid to clinical diagnosis (Figure 3a). The high-risk group had a PPV of 91.8% and the low-risk group had an NPV of 89.5% (Figure 3b). In the high-risk group, more than 9 in 10 patients were KD-positive, while in the low-risk group, less than 1 in 10 were KD-positive (Figure 3c). This KD score is designed to aid in the diagnosis of incomplete KD and to assist physicians in making decisions as an in vitro diagnostic test.

[0104] We also examined the correlation between coronary artery abnormalities and KD score in the KD cohort. Z scores were calculated for each KD patient and classified into three categories: no coronary artery abnormalities (z<2), dilatation only (2≦z<2.5), and aneurysm (z≧2.5). This panel captured the majority of aneurysm cases (24 of 26, 92.3%) in the high-risk group and was also able to identify KD patients with normal coronary arteries (Figure 4). This suggests that this panel can identify KD without the need for myocardial stress signals or a distinct cardiac phenotype.

[0105] In this study, we developed a diagnostic panel to aid in the diagnosis of KD, achieving a receiver operating characteristic curve (ROC) area under the normalized control (AUC) of 0.930. This serological test accurately identified KD patients and distinguished them from other febrile illnesses. The delta C(t) model for C19ORF59 and IFI27 showed an AUC of 0.930 using a linear geometric mean-based statistical model. This simple model avoids overfitting and reduced generalizability, making it easily transferable to patient cohorts in different regions.

[0106] Previous studies have attempted to identify specific or multiple biomarkers (serum proteins, cytokines, gene expression profiles) using LC-MS techniques or microarrays. One recent study achieved a ROC AUC of 0.82 for a serum biomarker panel combining MRP8 / 14, human neutrophil elastase (HNE), and C-reactive protein, but the NPV was somewhat lower. Another study analyzed 16 clinically available proteins using a random forest model and achieved an AUC similar to our four-analyte panel. However, random forests are difficult to interpret and implement, and are difficult to apply to regional differences. On the other hand, simple equal-weight linear models have a low risk of overfitting and are more practical.

[0107] Wright et al. used microarray data to develop a panel of 13 gene transcripts capable of distinguishing between KD and fever, reporting an AUC of 0.946 in the validation set. However, the training and validation cohorts were based solely on microarray data, and validation of the 13 genes using more quantitative qPCR remains a challenge. Furthermore, a recent analysis found that KD exhibits a host immune response similar to that observed in pediatric multisystem inflammatory syndrome (MIS) from COVID-19 without a cardiac phenotype (Ref. 25). This suggests that the gene signature primarily captures the host immune response to KD and does not necessarily reflect cardiac events. It will be interesting to examine whether our serum biomarkers are similarly elevated in whole blood gene expression analysis via quantitative PCR analysis.

[0108] This study used a quantitative PCR (qPCR) assay and IVD-compatible equipment (QuantStudio 6) to enable rapid clinical application of KD diagnosis. The panel model's simple design, based on copy number differences between two molecular targets, avoids complex statistical models and machine learning, improving transferability across cohorts and preventing overfitting. A limitation of this study is the lack of additional, larger validation cohorts to verify its applicability to other regional patient populations. The US Food and Drug Administration considers KD a rare disease, making rapid patient enrollment difficult. The greatest clinical need in KD is in patients with incomplete KD, who are prone to misdiagnosis for other febrile illnesses. Failure to administer IVIG treatment within 10 days of fever onset significantly increases future cardiovascular risk. A KD diagnostic panel that can be developed using currently available clinical tests and standard statistical methods could significantly contribute to the rapid and accurate diagnosis of KD.

[0109] References: 1. Wood LE, Tulloh RM. Kawasaki disease in children. Heart 2009;95:787-92 2. Jiao F, Jindal AK, Pandiarajan V, Khubchandani R, Kamath N, Sabui T, et al. The emergence of Kawasaki disease in India and China. Glob Cardiol Sci Pract 2017;2017:e201721 3. McCrindle BW, Rowley AH, Newburger JW, Burns JC, Bolger AF, Gewitz M, et al. Diagnosis, Treatment, and Long-Term Management of Kawasaki Disease: A Scientific Statement for Health Professionals From the American Heart Association. Circulation 2017;135:e927-e99 4. Newburger JW, Takahashi M, Burns JC. Kawasaki Disease. J Am Coll Cardiol 2016;67:1738-49 5. Singh S, Vignesh P, Burgner D. The epidemiology of Kawasaki disease: a global update. Arch Dis Child 2015;100:1084-8 6. Kim GB. Reality of Kawasaki disease epidemiology. Korean J Pediatr 2019;62:292-6 7. Suda K, Iemura M, Nishiono H, Teramachi Y, Koteda Y, Kishimoto S, et al. Long-term prognosis of patients with Kawasaki disease complicated by giant coronary aneurysms: a single-institution experience. Circulation 2011;123:1836-42 8. Daniels LB, Gordon JB, Burns JC. Kawasaki disease: late cardiovascular sequelae. Current Opinion in Cardiology 2012;27:572-7 9. Rowley AH. Kawasaki disease: novel insights into etiology and genetic susceptibility. Annu Rev Med 2011;62:69-77 10. Makino N, Nakamura Y, Yashiro M, Sano T, Ae R, Kosami K, et al. Epidemiological observations of Kawasaki disease in Japan, 2013-2014. Pediatr Int 2018;60:581-7 11. Kim GB, Eun LY, Han JW, Kim SH, Yoon KL, Han MY, et al. Epidemiology of Kawasaki Disease in South Korea: A Nationwide Survey 2015-2017. Pediatr Infect Dis J 2020;39:1012-6 12. Harnden A, Mayon-White R, Perera R, Yeates D, Goldacre M, Burgner D. Kawasaki Disease in England: Ethnicity, Deprivation, and Respiratory Pathogens. The Pediatric Infectious Disease Journal 2009;28:21-4 13. Holman RC, Belay ED, Christensen KY, Folkema AM, Steiner CA, Schonberger LB. Hospitalizations for Kawasaki syndrome among children in the United States, 1997-2007. Pediatr Infect Dis J 2010;29:483-8 14. Makino N, Nakamura Y, Yashiro M, Ae R, Tsuboi S, Aoyama Y, et al. Descriptive Epidemiology of Kawasaki Disease in Japan, 2011-2012: From the Results of the 22nd Nationwide Survey. Journal of Epidemiology 2015;25:239-45 15. Du Z-D, Zhao D, Du J, Zhang Y-L, Lin Y, Liu C, et al. EPIDEMIOLOGIC STUDY ON KAWASAKI DISEASE IN BEIJING FROM 2000 THROUGH 2004. The Pediatric Infectious Disease Journal 2007;26:449-51 16. Lue HC, Chen LR, Lin MT, Chang LY, Wang JK, Lee CY, et al. Estimation of the incidence of Kawasaki disease in Taiwan. A comparison of two data sources: nationwide hospital survey and national health insurance claims. Pediatr Neonatol 2014;55:97-100 17. Newburger JW, Takahashi M, Gerber MA, Gewitz MH, Tani LY, Burns JC, et al. Diagnosis, treatment, and long-term management of Kawasaki disease: a statement for health professionals from the Committee on Rheumatic Fever, Endocarditis and Kawasaki Disease, Council on Cardiovascular Disease in the Young, American Heart Association. Circulation 2004;110:2747-71 18. Rowley AH. Incomplete (atypical) Kawasaki disease. Pediatr Infect Dis J 2002;21:563-5 19. Sonobe T, Kiyosawa N, Tsuchiya K, Aso S, Imada Y, Imai Y, et al. Prevalence of coronary artery abnormality in incomplete Kawasaki disease. Pediatr Int 2007;49:421-6 20. Anderson MS, Todd JK, Glode MP. Delayed diagnosis of Kawasaki syndrome: an analysis of the problem. Pediatrics 2005;115:e428-33 21. Yu JJ. Diagnosis of incomplete Kawasaki disease. Korean J Pediatr 2012;55:83-7 22. Wilder MS, Palinkas LA, Kao AS, Bastian JF, Turner CL, Burns JC. Delayed diagnosis by physicians contributes to the development of coronary artery aneurysms in children with Kawasaki syndrome. Pediatr Infect Dis J 2007;26:256-60 23. Newburger JW, Takahashi M, Beiser AS, Burns JC, Bastian J, Chung KJ, et al. A single intravenous infusion of gamma globulin as compared with four infusions in the treatment of acute Kawasaki syndrome. N Engl J Med 1991;324:1633-9 24. Fontaine JF, Priller F, Barbosa-Silva A, Andrade-Navarro MA. Genie: literature-based gene prioritization at multi genomic scale. Nucleic Acids Res 2011;39:W455-61 25. Sahoo D, Katkar GD, Shimizu C, Kim J, Khandelwal S, Tremoulet AH, et al. An AI-guided signature reveals the nature of the shared proximal pathways of host immune response in MIS-C and Kawasaki disease. bioRxiv 2021 [Brief explanation of the drawings]

[0110] The invention will be best understood when read in conjunction with the accompanying tables and drawings, as set forth below. The specification or patent application file contains at least one drawing, copies of which will be provided by the Patent Office upon request and payment of the necessary fee. It should be noted that, according to common practice, the various features in the drawings are not necessarily to scale. Rather, the dimensions of the features have been arbitrarily increased or reduced to facilitate understanding.

[0111] Table 1a: Patient demographic information. KD patients were slightly younger than febrile control patients. b: Summary of KD findings in the study cohort.

[0112] Table 2: Nineteen genetic biomarkers were tested using qPCR assays on the QuanStudio 6 qPCR instrument. These biomarkers are ranked from top to bottom by their area under the receiver operating characteristic curve (AUC) values ​​based on univariate analysis.

[0113] [Figure 1] We present the biomarker discovery and validation process for Kawasaki disease (KD). Potential molecular biomarkers were identified through GEO analysis, literature review, and multiplex Luminex protein biomarker studies. Sixty-one gene targets were identified, which were narrowed down to 18 genes based on the discovery cohort. The final panel was validated in a validation cohort of KD patients (n=80) and febrile controls (n=80). [Figure 2] a: The panel was constructed based on the delta C(t) values ​​of IFI27 and C19ORF59. The serum copy number of each biomarker was measured using the corresponding primers on a QuantStudio 6 qPCR instrument. b: ROC analysis achieved an AUC of 0.930, with an optimal cutoff of 3.274. c: Model performance at the optimal cutoff is shown. [Figure 3] a: A three-level KD risk score classification model (high, intermediate, and low risk) using two thresholds was used to separate the cohort into three risk groups. b: The cohort was classified based on the risk scoring system. c: The high-risk group achieved a positive predictive value (PPV) of 91.8% with an optimal cutoff of 4.958, while the low-risk group showed a negative predictive value (NPV) of 89.5% with a cutoff of <3.558. [Figure 4] Coronary artery Z-scores were classified based on KD risk classification. This panel captures most aneurysm cases. It also identifies KD patients with normal or mildly dilated coronary arteries.

Claims

1. 1. A method for determining a Kawasaki Disease (KD) biomarker level expression in a subject, the method comprising: a. evaluating a panel of KD biomarkers in a sample, such as blood, serum, or plasma, obtained from the subject to determine the expression level of each KD biomarker; b. obtaining a KD biomarker level representation based on the level of each KD biomarker in the panel; c. The KD biomarker panel comprises one or more biomarkers selected from the group consisting of IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7 and ZHF185.

2. The method of claim 1, characterized in that the oligonucleotide, full length or its nucleotide sequence, RNA or DNA level of each KD biomarker is measured.

3. The method of claim 1, wherein the biomarker expression level is represented by a cycle threshold value (C(t) value).

4. 2. The method of claim 1, wherein the panel includes C19ORF59 and IFI27.

5. 2. The method of claim 1, wherein the panel includes C19ORF59, IFI27, S100A12 and S100A9.

6. 2. The method of claim 1, wherein the panel includes C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1.

7. 2. The method of claim 1, wherein the panel comprises C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

8. 2. The method of claim 1, wherein the panel includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4.

9. 10. The method of claim 1, further comprising providing the KD biomarker level representation in a report format as absolute concentration or median times the C(t) value (MoM).

10. 10. The method of claim 1, wherein a KD score is derived from the KD biomarker level representation, the KD score being calculated in one of the following ways: a. Calculation based on the difference in C(t) value between two genes, such as C19ORF59 and IFI27; b. Calculated from the measured blood biomarker values ​​by GBDT machine learning methods such as geometric mean, multivariate linear discriminant analysis (LDA) or XGBoost; c. The C(t), concentration, or MoM level of each biomarker is normalized to a scale of 0 to 10, for example, using the following formula: [C(t)1 - C(t)2] x [C(t)3 - C(t)4] x [C(t)5 - C(t)6] / (normalization factor) x 10 = KD score.

11. A method for diagnosing a subject with KD based on a KD biomarker level representation obtained from a sample from the subject, wherein the subject is diagnosed with KD if the KD score is greater than 4.

558.

12. The method of claim 11, characterized in that the oligonucleotide, full length or its nucleotide sequence, RNA or DNA level of each KD biomarker is measured.

13. 12. The method of claim 11, wherein the biomarker expression level is expressed as a C(t) value.

14. 12. The method of claim 11, wherein the panel includes C19ORF59 and IFI27.

15. 12. The method of claim 11, wherein the panel comprises C19ORF59, IFI27, S100A12 and S100A9.

16. 12. The method of claim 11, wherein the panel comprises C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1.

17. 12. The method of claim 11, wherein the panel comprises C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

18. 12. The method of claim 11, wherein the panel comprises C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4.

19. 12. The method of claim 11, further comprising providing the KD biomarker level expression in the report as an absolute concentration or a C(t) value MoM.

20. 12. The method of claim 11, wherein the KD score is calculated by one of the following methods: a. Based on the C(t) difference between two genes, e.g., C19ORF59 and IFI27; b. Computed using GBDT methods such as geometric mean, multivariate LDA, or XGBoost; c. Normalize each biomarker level to a range of 0-10 and calculate as follows: [C(t)1 - C(t)2] x [C(t)3 - C(t)4] x [C(t)5 - C(t)6] / (normalization factor) x 10.

21. 1. A method for assessing KD risk based on KD biomarker level representations obtained from a subject, comprising: a. The KD score is divided into three levels to assess the risk of developing KD; b. Low risk (less than 3.558, value is population adjusted); c. High risk (>4.958, values ​​are population adjusted); d. A method characterized by considering the range between the two (3.558 to 4.958) as intermediate risk.

22. The method of claim 21, characterized in that the RNA or DNA levels of each KD biomarker are measured.

23. 22. The method of claim 21, wherein the biomarker expression level is expressed as a C(t) value.

24. 22. The method of claim 21, wherein the panel includes C19ORF59 and IFI27.

25. 22. The method of claim 21, wherein the panel comprises C19ORF59, IFI27, S100A12 and S100A9.

26. 22. The method of claim 21, wherein the panel comprises C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1.

27. 22. The method of claim 21, wherein the panel comprises C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

28. 22. The method of claim 21, wherein the panel comprises C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4.

29. 22. The method of claim 21, further comprising providing the KD biomarker level expression in the report as absolute concentration or MoM of C(t) values.

30. 22. The method of claim 21, wherein a KD score is derived from the KD biomarker level representation, wherein the KD score is calculated in one of the following ways: a. Calculation based on the difference in C(t) values ​​between two genes, for example, C19ORF59 and IFI27; b. Calculation of measured blood biomarker values ​​using geometric mean, multivariate linear discriminant analysis (LDA), or GBDT machine learning methods such as XGBoost; c. Normalize the C(t), concentration, or MoM of each biomarker to 0-10 and calculate using the formula: [C(t)1 - C(t)2] × [C(t)3 - C(t)4] × [C(t)5 - C(t)6] / (normalization factor) × 10 = KD score.

31. A method for monitoring KD treatment in a subject, the method comprising obtaining a KD biomarker level representation from a sample from the subject, wherein the subject is determined to have a KD score of greater than 4.558 (based on population adjustment) prior to treatment, indicating KD, and the KD score is significantly reduced to less than 3.558 after treatment.

32. The method of claim 31, characterized in that the oligonucleotide, full length or its nucleotide sequence, RNA or DNA level of each KD biomarker is measured.

33. 32. The method of claim 31, wherein the biomarker expression level is expressed as a C(t) value.

34. 32. The method of claim 31, wherein the panel includes C19ORF59 and IFI27.

35. 32. The method of claim 31, wherein the panel comprises C19ORF59, IFI27, S100A12 and S100A9.

36. The method of claim 31, wherein the panel includes C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1.

37. 32. The method of claim 31 , wherein the panel comprises C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

38. 32. The method of claim 31 , wherein the panel comprises C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4.

39. 32. The method of claim 31, further comprising providing the KD biomarker level expression in the report as absolute concentration or MoM of C(t) values.

40. 32. The method of claim 31, wherein a KD score is derived from the KD biomarker level representation, wherein the KD score is calculated in one of the following ways: a. Calculation based on the C(t) difference between two genes, for example, C19ORF59 and IFI27; b. Calculated from measured biomarker values ​​using GBDT machine learning methods such as geometric mean, multivariate linear discriminant analysis (LDA), or XGBoost; c. The level of each biomarker (C(t), concentration, MoM) is normalized to 0-10 and calculated using the following formula: [C(t)1 - C(t)2] × [C(t)3 - C(t)4] × [C(t)5 - C(t)6] / (normalization factor) × 10 = KD score.

41. A diagnostic system product for generating a KD score from a sample such as blood, serum, or plasma, comprising one or more detection reagents for measuring the amount of one or more KD biomarkers selected from the group consisting of IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7, and ZHF185.

42. 42. The diagnostic system of claim 41, a. Platform system for measuring KD biomarkers (e.g., QuantStudio 6); b. Calculation sheet for KD score calculation, c. The system further comprising instructions for determining whether the patient has KD.

43. This is a method for selecting patients suspected of having KD as candidates for treatment with IVIG administration. a. Determine the patient's KD score; b. Diagnosing a patient using the methods described herein and selecting them for IVIG administration if they are KD positive; c. Patients are candidates for IVIG administration if they have a high-risk or intermediate-risk KD score.

29. The method of any of claims 10, 19 or 28.

44. A method for monitoring the effectiveness of treatment in patients with KD, a. Determine the patient's KD score; b. Diagnosing a patient using the methods described herein and selecting IVIG administration if KD is positive; c. Patients with high or intermediate risk scores are candidates for IVIG treatment; d. Effective treatment is characterized by a decrease in KD score; 29. The method of any of claims 10, 19 or 28.

45. 1. A method for assaying a sample processing procedure for KD, comprising: a. measuring the concentration of each biomarker in a biomarker panel described herein; b. Measured in blood, plasma, or serum from a patient suspected of having KD; c. comparing the measured value to a reference value for a control subject, and a differential expression indicating that the patient has KD; The method further comprises a step of determining a KD score from the concentrations of these biomarkers.

46. A method for measuring at least a pair of KD biomarkers with oligonucleotide primers and calculating a KD score, the primers specifically binding to a biomarker or a fragment thereof comprising an RNA / DNA sequencing region; a. the primers are selected from sequences comprising a portion of the complementary DNA sequence of the target biomarker target; b. Accurately measure the C(t) value of the target biomarker using a reporter dye or dye / quencher tag; c. A method characterized by accurately measuring the reporter / dye signal based on the amount of amplification using a qPCR instrument (e.g., QuantStudio 6).

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