TW202409297A - Molecular biomarkers and methods of analysis for acute diagnosis of kawasaki disease - Google Patents

Abstract

The present invention provides a biomarker combination and a characterization method for the diagnosis of Kawasaki disease (KD) in a clinical setting. In particular, the invention requires the use of combinations of RNA/DNA molecular biomarkers and their copy numbers (concentrations) to derive KD scores for aiding diagnosis, prognosis, risk evalluation, and treatment/monitoring. More specifically, these biomarker characterizing reagents (such as primers and polymerases) will be packaged in a kit in conjunction with a testing system (such as Applied Biosystems QuantStudio 6) that measures biomarker concentrations to generate a KD score, which can be used to distinguish KD from other febrile pediatric indications.

Description

Molecular biomarkers and analytical methods for rapid diagnosis of Kawasaki disease

The present invention relates to a biomarker combination and characterization method for the diagnosis of Kawasaki disease (KD) in a clinical setting. Specifically, the present invention advocates using a combination of RNA/DNA molecular biomarkers and their copy number (concentration) to derive a KD score to assist diagnosis, prognosis, risk assessment, and treatment/monitoring of KD. More specifically, these biomarker characterization reagents, such as primers and polymerases, will be packaged in kits along with a test system, such as the Applied Biosystems QuantStudio 6, which can measure the concentration of the biomarker, generate a KD score, and Distinguish KD from other febrile pediatric symptoms.

Kawasaki disease (KD) is a rare, acute inflammatory disease of children associated with vasculitis and persistent fever. KD is the leading cause of congenital heart disease in children in the United States and is increasing in developing countries. If not treated promptly, serious complications can occur, with approximately 25% of children developing coronary artery damage. KD has a low mortality rate; however, stenotic lesions may develop late due to vascular remodeling of the damaged arteries. Long-term outcome studies have shown that 50% of children who initially develop a coronary aneurysm due to KD require revascularization surgery or are at high risk for myocardial infarction.

The etiology of KD is currently unknown. However, it is widely believed to be an abnormal and persistent immune response to an infectious agent in a specific genetically susceptible individual. Unfortunately, no consistent infectious agent has been identified to date, further increasing the difficulty of diagnosis. In addition, different ethnic distributions of KD have been observed. KD has the highest incidence in East Asia, affecting 1 in 150 children in Japan, and it accounts for approximately 1-2% of pediatric hospitalizations in South Korea. In the white population, KD is significantly lower, with a prevalence of 9-17/100,000, while in children under 5 years of age, the prevalence in Japan is 265/100,000. The average prevalence in other Asian countries is 51-194/100,000.

Currently, the diagnosis of KD mainly relies on clinical symptoms, and there are currently no available objective molecular tests to aid diagnosis. Because KD has many symptoms and characteristics of common self-limited febrile diseases in children, such as fever, rash, mucosal manifestations, lymphadenopathy, and inflammation, clinical diagnosis is difficult. About 15% to 36.2% of KD cases are considered incomplete KD, which do not show complete clinical features and lack more than four KD clinical manifestations according to the American Heart Association standards, which can seriously affect the accuracy of diagnosis. For patients who present with “incomplete” KD and do not meet all clinical criteria, timely diagnosis is crucial. Early diagnosis and treatment with intravenous immune globulin (IVIG) within ten days of febrile onset can significantly reduce the incidence of coronary artery disease, thereby reducing the risk of permanent heart damage and coronary artery aneurysms.

Timely identification, diagnosis, and treatment are often challenging for many clinicians. Guidelines for diagnosing KD based on persistent fever and clinical criteria often delay diagnosis for physicians who are unfamiliar with or confused by complex clinical algorithms. Furthermore, there are currently no clinically useful and readily available objective molecular biomarker tests to help physicians diagnose KD. Therefore, there is an urgent need for an objective detection method based on blood biomarkers to help physicians identify KD patients who do not meet the full clinical criteria but require treatment to prevent cardiovascular damage.

We studied potential molecular biomarkers related to Kawasaki disease through GEO, literature, and multiplex protein platforms (Figure 1). We screened out 61 candidate genes, 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, PVRL2, S100A12, S100A8, SLC11a1, SLC11a2, TRR7, Treml4, VEGFA, HGF, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZB, ZBs TB20, CASP5, CACNA1E, CLIC3, IFI27, KLHL2, PYROXD2, RTN1, S100P, SMOX, ZNF185, C11ORF82, PlGF, CXCL16, OSM, NPPB, TNFSF14, CXCL6, CKM, CKB, IL1R1. We use quantitative polymerase chain reaction (qPCR) platforms and assay systems, such as the Applied Biosystem QuantStudio, 7500 Real-Time PCR System, or Bio-Rad CFX System, to measure the RNA copy number of these biomarkers in a sample. Then, based on the RNA copies of these key biomarkers, we developed a KD diagnostic model and a KD score capable of distinguishing KD from other febrile patients.

The present invention discloses a method for diagnosis, risk assessment and treatment/monitoring of KD using blood/plasma/serum protein biomarkers. In particular, the inventors discovered and used a set of genetic biomarkers (RNA, DNA, or protein) to calculate a KD risk score that can be used to diagnose, determine risk, monitor KD treatment, and distinguish KD from other febrile diseases. Biomarkers can be determined using a suite of appropriate detection systems (e.g. Applied Biosystems QuantStudio, 7500 Real-Time PCR System, or Bio-Rad CFX System) to determine RNA copy number for derivation either alone or in combination with other KD clinical criteria Use the KD score to determine and confirm the KD diagnosis.

The concentration of RNA/DNA biomarkers, such as but not limited to 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, SL C11A2, TLR7, TREML4, VEGFA, HGF, ZBTB20, CASP5, CACNA1E, CLIC3, IFI27, KLHL2, PYROXD2, RTN1, S100P, SMOX, ZNF185, C11ORF82, PlGF, CXCL16, OSM, NPPB, TNFSF14, CXCL6, CKM, CKB, IL1R1, the combination of these biomarkers in blood/plasma/serum is associated with the development and diagnosis of KD. The copy number of blood biomarkers can be measured by quantitative PCR system (such as Applied Biosystems QuantStudio, 7500 Real-Time PCR System or Bio-Rad CFX System) to accurately separate KD patients from other febrile patients (Figure 1).

The present invention discloses a method of using biomarkers that can be used in the practice of the present invention, including protein biomarkers or RNA/DNA sequences from biomarkers, including but not limited to IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7 and ZHF185, etc.

In certain embodiments, a combined panel of these biomarkers is used for diagnosing KD. Biomarker panels for KD diagnosis can include a minimum of 2 biomarkers (pairs) and a complete panel of up to 18 biomarkers (9 pairs of genes), as described above. This would include any combination of any of the biomarkers described in paragraph 1 above, including at least 2, 4, 6, 8, 10, 12, 14, 16 and 18 biomarkers. Smaller biomarker panels are sufficient to differentiate KD from other febrile diseases and are more cost-effective. However, larger groups may provide more detailed information and may be used in the practice of the present invention in different regional populations.

In a binary scoring system, a method based on calculating a KD score is used to distinguish patients from other febrile diseases. A low KD score means that the patient is unlikely to have KD. A high KD score means that the patient is likely to have KD (rule).

A KD score is calculated based on the above biomarkers and methods, and a method for determining KD is determined with three different ranges of KD risk. A low KD risk below the low score cutoff value indicates that the patient is at very low risk of developing KD. A high KD risk above the high score cutoff value indicates that the patient is at very high risk of developing KD. Scores between the low and high KD score cutoff values indicate an intermediate KD risk.

In some cases, clinical parameters are used in conjunction with the biomarkers described in this article to diagnose KD. For example, the present invention includes a method for determining the KD score of a patient suspected of having KD who has had a fever for 5 consecutive days. The method involves measuring at least seven clinical parameters based on standard care for patients, including duration of fever, hemoglobin concentration in the blood, C-reactive protein concentration in the blood, white blood cell count, eosinophil percentage in the blood, monocyte percentage in the blood and the percentage of immature neutrophils in the blood.

The KD score can be calculated from the measured blood biomarker values by geometric averaging, multivariate linear discriminant analysis (LDA), or distributed gradient boosting decision tree (GBDT) machine learning, such as XGBoost. In a binary model, a receiver operating characteristic (ROC) curve can be derived from the biomarker combination or pairing. The KD score can then be classified into a low-risk KD clinical score, a medium-risk KD clinical score, or a high-risk KD clinical score by the methods described herein.

In another aspect, the present invention includes a method for diagnosing a patient with KD using the described biomarker combination and methods and procedures for calculating a KD score. The method includes: 1) obtaining a biological sample from a patient, 2) measuring the number of RNA/DNA copies or concentration of each biomarker in the biological sample, and 3) comparing the level of each biomarker to a corresponding reference value range for the biomarker. The reference value range can represent the level of the biomarker from one or more samples from subjects without KD (i.e., normal samples), or represent the level of the biomarker from one or more subjects with KD. The differential level of the blood biomarker of the biomarker combination compared to the reference value of the biomarker of the control group subject indicates that the patient has KD. In one embodiment, the method also includes a method for calculating a KD score to distinguish whether the patient's febrile illness is KD.

Biomarkers can be measured by using specific primers and reporter systems to determine the copy number of the biomarker in the biomarkers and methods described above. For example, but not limited to, performing quantitative PCR analysis (qPCR), reverse transcription quantitative PCR (RT-qPCR), genetic microarrays, RNA or DNA sequencing (RNAseq/DNAseq), sandwich assays, magnetic capture, microsphere capture, electrophoretic blotting , surface-enhanced Raman spectroscopy (SERS), flow cytometry, or mass spectrometry to determine the copy number of these biomarkers. In certain embodiments, biomarker copy number is measured starting from the binding of a specific DNA primer to the biomarker RNA, followed by reverse transcription and polymerase chain reaction to identify cells with a detectable reporter (e.g., BYBR Green dye, etc.) copy number of the biomarker. wherein the primer specifically binds to the biomarker or fragment thereof.

The present invention also includes a method for evaluating the efficacy of an interventional agent for treating a patient with KD. The method comprises: using the biomarker panel described herein to analyze the copy number or concentration of each biomarker before and after treatment from a patient's sample. The effectiveness of the treatment can be determined by the corresponding reference value range and the calculated KD score.

In particular, the present invention includes a method for selecting a patient suspected of having KD for intravenous immunoglobulin (IVIG) treatment, the process comprising: 1) diagnosing the patient according to the method described herein; b) if the patient is diagnosed with KD, selecting the patient for IVIG treatment. In another embodiment, the method comprises 1) determining a KD score for the patient; and b) selecting the patient for IVIG treatment based on a KD score in a high-risk area or an intermediate-risk area of an expression profile comprising a biomarker panel described herein and a confirmed KD.

In another aspect, the present invention provides a method for treating a patient suspected of having KD, comprising the steps of: 1) diagnosing the patient or receiving information regarding the patient's diagnosis according to the methods described herein; and 2) conducting a diagnosis on the patient Intravenous immune globulin (IVIG) therapy if the patient has confirmed KD. In one aspect, the method includes 1) determining the patient's KD clinical score; and 2) if the patient has a high-risk KD clinical score or an intermediate-risk KD clinical score and KD is confirmed based on the expression profile of the biomarker panel in paragraphs 1 to 11, A therapeutically effective dose of intravenous immune globulin (IVIG) is administered to the subject.

In another aspect, the present invention provides a kit for a quantitative qPCR system (i.e., a system for measuring RNA/DNA biomarkers in a sample). The kit may include a container for containing a biological sample collected and isolated from a human patient suspected of having KD. The kit contains at least one reagent for measuring KD biomarkers and printed instructions for reacting the reagent with the biological sample or a portion of the biological sample to measure at least one KD biomarker in the biological sample. The reagent may be packaged in a separate container. The kit may also include one or more control reference samples and reagents for performing qPCR to detect the number of biomarker copies, as described herein.

In another aspect, the present invention provides a detection method comprising: a) measuring the concentration of each biomarker in a panel of biomarkers (as described herein) in a blood, plasma or serum sample collected from a patient suspected of having KD copy number; and b) compare the measured value of each biomarker to a reference value for each biomarker in a control subject, where the difference in the biomarker in a blood, plasma, or serum sample compared to the reference value expression indicates that the patient has KD. In one aspect, the assay method further includes determining a KD score based on the patient's concentration of these biomarkers.

In some cases, at least one biomarker is measured by targeting the biomarker with a primer, wherein the primer specifically binds to the biomarker or a fragment of the biomarker, and the copy number of the biomarker is determined by a PCR reaction. In certain embodiments, the primer is selected from the coding region (exon) sequence of the biomarker. In one case, at least one pair of primers is selected from the group consisting of a primer pair that specifically binds to IFI27, a primer pair that specifically binds to C19ORF19, a primer pair that specifically binds to S100A12, a primer pair that specifically binds to S100A9, and a primer pair that specifically binds to S100A12. A primer pair that binds specifically to CACNA1E, a primer pair that specifically binds to SLC11A1, a primer pair that specifically binds to NKTR, a primer pair that specifically binds to CAMK4, a primer pair that specifically binds to CLIC3, and a primer pair that specifically binds to LGALS2 Select the right group.

These and other implementations will be readily apparent to those skilled in the art within the technical field of the disclosed scheme described herein.

Biomarkers for Kawasaki disease (KD), panels of KD biomarkers, and methods of obtaining a representation of the level of a KD biomarker in a sample are provided. These compositions and methods have uses in many applications, including, for example, diagnosing KD, assessing whether or not a subject has KD, monitoring a subject with KD, and determining a method for treating KD. In addition, systems, devices, and kits for implementing the methods are provided. These and other objects, advantages, and features of the present invention will become apparent to those skilled in the art as the details of these compositions and methods are described in more detail below.

Before present methods and compositions are described, it is to be understood that this invention is not limited to the particular methods or compositions described, as such may, of course, vary. Furthermore, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the invention, which is limited only by the appended claims.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood in the art to which the present invention belongs. Although any methods and materials similar or equivalent to the methods and materials described can be used to implement or test the present invention, some possible and preferred methods and materials are now described. All publications mentioned are cited and incorporated herein to disclose and describe the methods and/or materials related to the cited publications. It is understood that the content disclosed herein covers any content that conflicts with the disclosure of the incorporated publications.

As will be apparent to those skilled in the art, each of the specific embodiments described has discrete elements and features that may be readily separated or combined with features of any of the other several embodiments without exceeding the scope of this disclosure. The scope or spirit of the invention. Any described method may be performed in the sequence of events described, or in any other order that is logically possible.

It should be noted that, as used herein and in the appended claims, the singular forms "a", "a", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, a reference to "cell" includes a plurality of such cells, a reference to "RNA sequence" includes one or more peptides and equivalents, such as polypeptides known to those skilled in the art, and the like.

As stated above, the present invention relates to methods, compositions, systems (e.g., qPCR systems such as AB QuantStudio 6) and kits for providing assessment of Kawasaki disease (KD), including diagnostic, risk assessment, monitoring and/or treatment subjects KD in . Kawasaki disease, or KD, is a complication of a multisystem inflammatory and febrile illness that may be accompanied by a rash, swelling of the hands and feet (edema), redness and inflammation of the whites of the eyes, swollen lymph nodes in the neck, and mouth, lips, and throat Irritation and inflammation. KD occurs primarily in children under 5 years of age, but older children, adolescents, and adults can still be infected with KD. If left untreated within 10 days of the onset of fever, KD can lead to cardiovascular disease and aneurysms. The so-called "diagnosing" KD or "providing KD diagnosis" generally refers to providing a diagnosis of KD, such as determining whether a subject is affected by KD (such as a subject with complete or incomplete KD symptoms); classifying a subject's KD to determine its Subtype of disease or disorder; determining severity of KD, etc. The so-called "risk assessment" of KD or "providing KD risk assessment as one of the clinical signs" generally refers to providing the risk of KD as another clinical sign, such as diagnosing the risk or susceptibility of the subject in the presence of other clinical symptoms. ; Assess risk of disease progression and/or disease outcome; Predict subject's response to KD treatment, e.g. positive response, negative response, no response, etc. The so-called "monitoring" of KD generally refers to monitoring the situation of the subject, such as providing information for KD diagnosis, providing information for KD prognosis/risk, and other clinical signs, providing information on the effect or efficacy of KD treatment, etc. By "treating" KD we mean any treatment that prescribes or delivers KD in mammals, including: (a) preventing the development of KD and associated cardiovascular events in subjects who may be susceptible to KD but have not yet been diagnosed; (b) ) inhibit KD and cardiac events, i.e., prevent its symptoms and the development of cardiac aneurysms; or (c) alleviate KD, i.e., inhibit KD and degrade the cardiac arteries.

In describing the present invention, compositions for providing KD assessment will be described first, followed by methods, systems and kits for use thereof.

In certain aspects of the invention, Kawasaki disease biomarkers and groups of Kawasaki disease biomarkers are provided. By "Kawasaki disease biomarker," we mean the expression in a sample of a molecular entity associated with a Kawasaki disease phenotype. For example, a Kawasaki disease biomarker may have a different expression (i.e., be expressed at a different level) in a sample from an individual compared to a healthy individual. In some cases, an elevated level of the biomarker, such as C10ORF59, is associated with a Kawasaki disease phenotype. For example, in a sample associated with a Kawasaki disease phenotype, the number of RNA copies of the biomarker may be 1.5 times, 2 times, 2.5 times, 3 times, 4 times, 5 times, 7.5 times, 10 times, or more higher than in a sample not associated with a Kawasaki disease phenotype. In other cases, a decreased level of the biomarker is associated with a Kawasaki disease phenotype, such as IFI27. For example, in a sample associated with a Kawasaki disease phenotype, the RNA copy number of the biomarker may be 10%, 20%, 30%, 40%, 50% or more lower than in a sample not associated with a Kawasaki disease phenotype.

Kawasaki disease biomarkers may include proteins and peptides associated with Kawasaki disease and their corresponding genetic sequences, i.e., RNA, DNA, etc. The so-called "gene" or "recombinant gene" refers to a nucleic acid that includes an open reading frame for encoding a protein.

The boundaries of the coding sequence are determined by a start codon at the 5' end (amino group) and a translation stop codon at the 3' end (carboxyl group). A transcription termination sequence may be located 3' to the coding sequence. In addition, a gene may optionally include its natural promoter (i.e., the promoter to which the exons and introns of the gene are operably linked in a non-recombinant cell, i.e., a naturally occurring cell), as well as associated regulatory sequences, May have sequences upstream of the AUG start site and/or exclude untranslated leader sequences, signal sequences, downstream untranslated sequences, transcription initiation and termination sequences, polyadenylation signals, translation initiation and termination sequences , ribosome binding site and other sequences.

As shown in the examples described in this disclosure, the inventors have identified a number of molecular entities associated with KD and used them in combination (i.e., as a group) to provide KD assessments, e.g., diagnose KD, assess risk of developing KD, monitor patients. Subjects with KD, subjects determined to be treated for KD, etc. These include but are not limited to IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7 and ZHF185, etc.

In addition, this article also provides KD groups. The so-called "KD biomarker panel" refers to two or more KD biomarkers, such as 2, 4, 6, 8, 10, 12, 14, 16 and 18 biomarkers. When their Levels (i.e. copy number), when considered in combination, can be used to provide KD assessment, for example for KD diagnosis, risk assessment, monitoring and/or treatment. Of particular interest is the group including IFI27, C19ORF59, S100A12, S100A9, CACNHA1E and CLIC3. For example, in certain embodiments, the KD set may include ΔC(t) for C19ORF59 and IFI27.

Other KD biomarker sets for use as KD sets in the present methods can be readily determined by one of ordinary skill in the art using any convenient statistical method, e.g., as known herein or described in the working examples herein. For example, KD classification analysis can be performed by combining genetic algorithm (GA) and all-pairwise (AP) support vector machine (SVM) methods to automatically determine predictive features, e.g., by iteratively running GA/SVM, resulting in a very compact set of non-redundant KD-related analytes to achieve optimal classification performance. Although different sets of classifiers will typically have only a small amount of overlapping genetic features, they will have similar levels of accuracy in providing KD estimates as described above and in the working examples herein. Methods

In certain aspects, the present invention provides methods for obtaining a representation of the level of a KD biomarker in a subject. The so-called representation of the level of a KD biomarker refers to the representation of the level of one or more KD biomarkers in a biological sample of a subject, such as a KD biomarker panel. The term "biological sample" includes various types of samples obtained from an organism that can be used for diagnostic, prognostic or monitoring assays. The term includes blood and other liquid samples from biological sources or their derivatives and progeny. The term includes samples that have been processed in any way after collection, such as treatment with reagents, solubilization or enrichment of certain components. The term includes clinical samples, and also includes cell supernatants, cell lysates, serum, plasma, biological fluids and tissue samples. Clinical samples used in the methods of the present invention can be obtained from a variety of sources, in particular blood samples.

In particular, sample sources include blood samples or preparations thereof, such as whole blood, serum or plasma. In many embodiments, a suitable initial source for human samples is a blood sample. Therefore, samples used for subject assays are typically samples from blood. The blood sample can be derived from whole blood or a portion thereof, such as serum, plasma, etc. In some embodiments, the sample is derived from blood, allowed to clot, and the serum is separated and collected for measurement.

In certain embodiments, the sample is serum or a serum-derived sample. Any convenient method can be used to prepare a fluid serum sample. In many embodiments, the method uses a skin puncture (e.g., finger prick, venous puncture) to draw venous blood into a coagulation or serum separation tube, allowing the blood to coagulate, and centrifuging the serum to leave the coagulated blood. The serum is then collected and stored until the assay is performed. Once a sample from a patient is obtained, the sample can be assayed to determine the level of a KD biomarker.

The main body sample is usually obtained from an individual during a clinical visit when the patient has persistent, recurring fever. Kawasaki disease is most likely to occur in children under the age of five, but it can occur at any age, including teenagers and adults.

Once the sample is obtained, it can be used directly, frozen, or maintained for a short time in an appropriate culture medium. Typically, samples will be from human patients, although animal models may also be used, such as horses, cattle, pigs, dogs, cats, rodents (e.g., mice, rats, hamsters), and primates. Any convenient tissue sample that exhibits differential expression of one or more of the Kawasaki disease biomarkers disclosed herein in Kawasaki disease patients may be evaluated in the subject methods. Typically, suitable sample sources will originate from body fluids capable of analyzing the molecular entities of interest (e.g., proteins, peptides, and RNA) that have been released.

Subject samples may be processed in a variety of ways to enhance detection of one or more Kawasaki disease biomarkers. For example, if the sample is blood, red blood cells can be removed from the sample (e.g., by centrifugation) before testing. This treatment can reduce the level of non-specific background in detection of Kawasaki disease biomarker levels using affinity reagents. Samples can be concentrated to enhance detection of Kawasaki disease biomarkers using procedures well known in the art (e.g., acid precipitation, alcohol precipitation, salt precipitation, hydrophobic precipitation, filtration). In some embodiments, the pH of test and control samples will be adjusted and maintained at a near neutral pH. This pH adjustment will prevent complex formation, providing a more accurate quantification of biomarker levels in the sample. In embodiments where the sample is urine, the pH of the sample will be adjusted and the sample concentrated to enhance detection of the biomarker.

In performing the present method, KD biomarker levels in individual biological samples are assessed. Any convenient method can be used to assess the level of one or more KD biomarkers in a sample. For example, RNA biomarkers can be detected by measuring the level/amount of one or more oligonucleotides. KD gene expression levels can be detected by measuring the level/amount of nucleic acid transcripts (e.g., mRNA) of one or more KD genes. The terms "evaluate", "test", "measure", "evaluate" and "determine" are used interchangeably and are used to refer to any form of measurement, including determining the presence or absence of an element and including both quantitative and qualitative measurements. Assessments can be relative or absolute.

For example, the level of at least one KD biomarker can be assessed by detecting the amount or level of one or more RNA/DNA or fragments thereof in a sample to obtain a copy number representation. The terms "RNA" and "nucleic acid" are used interchangeably in this application. "Oligonucleotide" refers to a nucleic acid polymer (RNA or DNA sequence) rather than a specific length of the molecule. Therefore, RNA, DNA and their fragments are included in the definition of oligonucleotide. The term also encompasses modified oligonucleotides such as methylated DNA, DNA linked to fluorescent dyes/quenchers, modified RNA/DNA, DNA primers, etc. Included within the definition are oligonucleotides containing one or more hydroxyl analogs, oligonucleotides with alternative linkages, and other modifications known in nature and outside of nature.

As another example, the level of at least one kidney disease biomarker can be assessed by detecting the amount or level of one or more RNA transcripts, or fragments thereof, encoding a desired gene in a patient sample to derive a nucleic acid biomarker. Express. Nucleic acid levels in a sample can be detected using any convenient protocol. Although many different methods for detecting nucleic acids are known, such as those used in the field of differential gene expression analysis, a representative and convenient type of protocol for generating biomarker expression is the array-based gene expression analysis protocol. These applications are hybridization assays where biomarker expression is generated using "probe" nucleic acids displaying the genes to be measured/analysed. In these assays, a target nucleic acid sample is first prepared from the initial nucleic acid sample to be assayed, where the preparation may include labeling the target nucleic acid with a label, such as a member of a signal generating system. After the target nucleic acid sample is prepared, the sample is contacted with the array under hybridization conditions, thereby forming a target nucleic acid complex complementary to the probe sequence attached to the array surface. The presence of the hybridized complex is then detected qualitatively or quantitatively.

Specific hybridization techniques used to generate biomarker characterizations employed in the methods are included 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; 5,5 Described in 80,732; 5,661,028; 5,800,992 technology, the disclosures of which are incorporated herein by reference, and include WO 95/21265; WO 96/31622; WO 97/10365; WO 97/27317; EP 373 203; and EP 785 280. In these methods, an array of "probe" nucleic acids containing probes for each phenotype-determining gene whose expression is measured is contacted with a target nucleic acid as described above. Contacting is performed under hybridization conditions, for example under stringent hybridization conditions, and unbound nucleic acid is then removed. The term "stringent detection conditions" as used herein refers to conditions that are compatible with generating binding pairs of sufficient complementarity, e.g., surface binding and solution-phase nucleic acids, to provide the desired detection specificity while being incompatible with binding The formation of binding pairs between binding members where complementarity is insufficient to provide the required specificity. Stringent detection conditions are the sum or combination of hybridization and wash conditions.

Patterns generated by hybridized nucleic acids provide expression information about each detected gene. The expression information is based on whether the gene is expressed and its typical expression level. Expression data, such as biomarker representation in transcriptome format, may be qualitative or quantitative. of.

Alternatively, non-array-based methods can be employed to quantify the levels of one or more nucleic acids in a sample, including methods based on amplification protocols, such as polymerase chain reaction (PCR) based assays, including quantitative PCR, reverse transcriptase PCR (RT-PCR), real-time PCR, and the like.

When it is desired to detect protein levels, protein levels may be assessed using any convenient protocol in which the level of one or more proteins in a sample to be tested is determined. For example, one representative and convenient type of protocol for determining protein levels is the enzyme-linked immunosorbent assay (ELISA). In ELISA and ELISA-based assays, one or more antibodies specific for the target protein may be immobilized on a selected solid surface, preferably a surface that exhibits an affinity for the protein, such as the wells of a polystyrene microplate. After removal of incompletely adsorbed material, the wells of the assay plate are coated with a nonspecific "blocking" protein that is known to be antigenically neutral with the test sample, such as a solution of bovine serum albumin (BSA), casein, or milk powder. This blocks nonspecific adsorption sites on the immobilized surface, thereby reducing background caused by nonspecific binding of the antigen to the surface. After removing unbound blocking proteins, the sample to be tested is contacted with the fixed surface under conditions that are conducive to the formation of immune complexes (antigen/antibody). Such conditions include diluting the sample with a diluent (e.g., bovine serum albumin (BSA) or bovine gamma globulin (BGG) in PBS/Tween or PBSATriton-X 100), which also helps to reduce non-specific background, and allowing the sample to incubate for about 2-4 hours at a temperature of about 25°-27°C (although other temperatures may also be used). After incubation, the surface contacted by the antiserum is washed to remove materials that are not formed by immune complexes. An exemplary washing procedure includes washing with a solution such as PBS/Tween, PBS/Triton-X 100, or borate buffer. The presence and quantity of immune complexes are then determined by subjecting the bound immune complexes to a second antibody with a different target specificity than the first antibody and detecting the binding of the second antibody. In certain embodiments, the second antibody will have an associated enzyme, such as urease, peroxidase, or alkaline phosphatase, which will produce a color precipitate when incubated with an appropriate chromogenic substrate. For example, an anti-human IgG coupled to urease or peroxidase may be used and incubated for a period of time under conditions that are conducive to immune complex formation (e.g., incubated for 2 hours at room temperature in a PBS solution containing PBS/Tween). After incubation with a secondary antibody and washing to remove unbound material, the amount of labeling can be quantified by incubation with a chromogenic substrate (such as urea and bromocresol purple) or hydrogen peroxide and 2,2'-hydrazinobenzothiazoline-6-sulfonic acid (ABTS) in the case of peroxidase labels and then measuring the extent of color production (e.g. using a visible light spectrophotometer).

The previous method can be modified by first binding the sample to the test plate. Then, the primary antibody is incubated with the test plate and the bound primary antibody is subsequently detected using a labeled secondary antibody specific for the first antibody.

Antibodies or antibody-immobilized solid matrices can be made of various materials and in various shapes, such as microplates, microbeads, dip rods, resin particles, etc. The matrix can be selected to maximize signal-to-noise ratio, minimize background binding, and provide ease of separation and low cost. Cleaning can be performed in a manner most appropriate for the matrix being used, such as by removing beads or dip rods from reservoirs, draining or diluting microplate wells, or rinsing beads, particles, columns, or screens with cleaning solutions or solvents .

Alternatively, non-ELISA based methods can be used to measure the level of one or more proteins in a sample. Representative examples include, but are not limited to, mass spectrometry, proteomic arrays, xMAP™ microsphere technology, flow cytometry, Western blot, and immunohistochemistry.

The data obtained provide information about the levels in the sample of each biomarker that has been detected, where the information is typically both qualitative and quantitative. Thus, where the test is qualitative, the method provides a readout or assessment of whether the target biomarker being detected (e.g., nucleic acid or protein) is present in the sample being tested. In other embodiments, the method provides for quantitative detection of an assessment of whether a target biomarker being detected is present in a sample being detected or an assessment of the actual amount of a target analyte (e.g., nucleic acid or protein) present in a sample being detected or relative abundance. In such embodiments, quantitative detection may be absolute, or relative if the method is one that detects two or more different analytes (eg, target nucleic acids or proteins) in a sample. Therefore, when referring to the quantitative detection of a target analyte (such as a nucleic acid or protein) in a sample, the term "quantification" can refer to either absolute quantification or relative quantification. Absolute quantification can be accomplished by including known concentrations of one or more control analytes and referencing the detected levels of the target analyte to known control analytes (e.g., by generating a standard curve). Alternatively, relative quantification can be achieved by comparing detection levels or amounts between two or more different target analytes to provide relative quantification of each of the two or more different analytes, for example relative to each other.

Once the levels of one or more KD biomarkers are determined, the measurements can be analyzed by a variety of methods to obtain an indication of the KD biomarker levels.

For example, the measurement results of one or more KD biomarkers can be analyzed individually to develop a KD score. As described herein, a "KD score" is the normalized level of one or more KD biomarkers in a patient sample, such as the normalized level of serum protein concentration in a patient sample. The KD summary can be generated by a variety of known methods. For example, the level of each biomarker can be log2 transformed and normalized relative to the signal of the selected gene expression or the entire group. Other methods of calculating the KD summary are also common knowledge to ordinary technicians.

As another example, the measurements of a set of KD biomarkers can be analyzed collectively to yield a single KD score. By "KD score" is meant a single metric that represents the weighted level of each KD biomarker in the KD set. Thus, in some embodiments, the method includes detecting the level of a KD set of biomarkers in a sample and calculating a KD score based on the weighted levels of the KD biomarkers. The KD score for a patient sample can be calculated using a variety of known methods and algorithms. For example, weighted biomarker levels (e.g., each normalized biomarker level multiplied by a weighting factor) can be added and, in some cases, averaged to yield a single value representing the analyzed KD biomarker set.

In some cases, for each biomarker in the KD panel, its weight factor, or simply "weight", may reflect the change in the level of the analyte in the sample. For example, the analyte level of each KD biomarker may be log-transformed and weighted to 1 (for those biomarkers whose levels increase in KD) or -1 (for those biomarkers whose levels decrease in KD), respectively, and then the ratio of the sum of the increased biomarkers to the sum of the decreased biomarkers is determined to derive the KD signature. In other cases, the weight may reflect the importance of each biomarker to the specificity, sensitivity and/or accuracy of the biomarker panel in making a diagnostic, prognostic or monitoring assessment. These weights may be determined by any convenient statistical machine learning method, such as principal component analysis (PCA), linear regression, support vector machines (SVM), and/or random forests, and may be calculated using a dataset obtained from a dataset of the resulting samples. In some cases, the weights for each biomarker are defined by a dataset obtained from patient samples. In other cases, the weights for each biomarker may be defined based on a reference dataset or "training dataset."

These analysis methods can be easily performed by a person of ordinary skill by using a computer system, such as using any known hardware, software and data storage media, and adopting any convenient algorithm to perform such analysis. For example, the data acquisition algorithm can be applied through "cloud computing", based on smartphones or client server platforms, etc.

In some embodiments, the expression of only one biomarker (e.g., oligonucleotide level) is assessed to produce a representation of the biomarker level. In other embodiments, the levels of two or more biomarkers (i.e., groups) are assessed. Thus, in this method, the expression of at least one biomarker in a sample is assessed. In some embodiments, the assessment performed can be considered an assessment of a proteome, as that term is used in the art.

In some cases, the method of determining or obtaining a KD biomarker representation (e.g., a KD score or a KD profile) for a subject further comprises providing the KD biomarker representation as a report. Thus, in some cases, the method may also comprise generating or outputting a report providing the results of the KD biomarker assessment in the sample, which report may be provided in the form of an electronic medium (e.g., an electronic display on a computer monitor) or a tangible medium (e.g., a report printed on paper or other tangible medium). Any form of the report may be provided, for example, as known in the art or as described below.

Application

The obtained characterization of KD biomarker levels has many uses. For example, biomarker levels can be used to characterize the diagnosis of KD, i.e., to determine whether a subject is affected by KD, the type of KD (complete KD and incomplete KD), and the severity of KD (normal cardiac phenotype, dilation, or aneurysm). wait). In some cases, subjects may develop clinical symptoms of KD, such as fever, rash, swelling of the hands and feet, irritation and redness of the whites of the eyes, swollen lymph nodes in the neck, and irritation and inflammation of the mouth, lips, and throat.

Another example is that when patients develop incomplete KD, KD biomarker level characterization can be used to assess KD risk, i.e., the clinical characterization of KD can be used as a risk indicator. For example, KD biomarker level characterization can be utilized as a KD diagnosis for a subject in place of additional clinical characterization. By "adding biomarker signatures if the individual has KD", it is meant that the likelihood of an individual having KD can be determined even when less than four clinical signatures are present according to the AHA guidelines. Characterization of KD biomarker levels and KD scores can be used as clinical characterizations of disease progression and/or disease outcome, e.g., expected confirmation of the diagnosis of KD, expected duration of KD, whether KD will develop a cardiac phenotype, etc. Characterization of KD biomarker levels can be used to predict a subject's response to KD treatment, e.g., a positive response, a negative response, or no response.

As another example, KD biomarker level characterization can be used to monitor KD. By "monitoring" KD, it generally refers to monitoring the subject's condition, such as notifying KD diagnosis, notifying KD prognosis, providing information on the effect or efficacy of KD treatment, etc.

As another example, a KD biomarker level profile can be used to determine a treatment regimen for a subject. As used herein, "treatment" and similar terms generally refer to obtaining a desired pharmacological and/or physiological effect. The effect can be a preventative measure, which completely or partially prevents a disease or its symptoms, or a therapeutic measure, which partially or completely cures a disease and/or adverse effects associated with the disease. As used herein, "treatment" encompasses any treatment of a mammalian disease, including: (a) preventing the disease, i.e., preventing the diseased individual from being diagnosed with the disease; (b) inhibiting the disease, i.e., arresting its development; or (c) ameliorating the disease, i.e., causing regression of the disease. The therapeutic agent can be administered before, during, or after the onset of the disease or injury. Particularly, treatment of an ongoing disease in which the treatment stabilizes or reduces the patient's adverse clinical symptoms. Subject treatment may be given before and in some cases after the symptomatic stage of the disease. In this article, the terms "subject," "host," and "patient" are used interchangeably and refer to any mammalian subject, especially a human, who requires diagnosis, treatment, or therapy. KD treatment is well known and can include bed rest, drinking more water, a low-salt diet, medications to control blood pressure, corticosteroids, inducing pregnancy, etc.

In certain embodiments, a subject method for providing a KD risk assessment, such as diagnosing KD, assessing KD risk, monitoring KD treatment, etc., may include comparing the obtained KD biomarker level representation with a KD phenotype determining element to identify similarities or differences with the phenotype determining element, and then using the identified similarities or differences to provide a KD assessment, such as diagnosing KD, assessing KD risk, monitoring KD treatment, and determining the need for KD treatment, etc. By “phenotype-determining elements,” we mean elements that represent a phenotype (in this case, a KD phenotype), such as a tissue sample, biomarker profile, value (e.g., score), range of values, etc., that can be used to determine a subject’s phenotype, such as whether the subject is healthy or has KD, whether the subject has incomplete KD that may progress to complete/confirmed KD, whether the subject has KD that is responsive to therapy, etc.

For example, a KD phenotyping element can be a sample, from an individual with or without KD, that can be used as an experimentally determined reference/control for an expression of biomarker levels in a given individual. As another example, the KD phenotyping element may be a biomarker level representation representative of KD status, such as a biomarker profile or score, which may be used as a reference/control for interpreting the biomarker level representation for a given individual. The phenotypic identification element could be a positive reference/control, such as a sample from a child with KD or a child with KD that progresses from incomplete KD to complete KD or is controllable by known treatments or their biomarker levels Indicates, or indicates the level of, a KD of a sample or its biomarker that has been determined to be responsive to IVIG. Alternatively, the phenotypic identification element could be a negative reference/control, such as a sample from a child without KD or with other febrile illness or a representation of its biomarker levels. Preferably, the phenotyping elements are the same type of sample or if the biomarker level representation is obtained from a sample that generates a biomarker level representation from the individual being monitored, they should be the same type of sample. For example, if an individual's serum is evaluated, the phenotypic identification element is preferably plasma.

In certain embodiments, the resulting biomarker level representation is compared to a single phenotypic assay element to obtain information about whether the individual being tested has KD. In other embodiments, the resulting biomarker level representation is compared to two or more phenotypic assay elements. For example, the resulting biomarker level representation can be compared to a negative reference and a positive reference to obtain confirmatory information about whether the individual will develop KD. As another example, the resulting biomarker level representation can be compared to a reference representing KD that responds to treatment and a reference representing KD that does not respond to treatment to obtain information about whether the patient responds to treatment.

In certain embodiments, the obtained characterization of biomarker levels is compared to a single phenotypic assay element to obtain information about the individual being tested for whether or not he or she has KD. In other embodiments, the obtained characterization of biomarker levels will be compared to two or more phenotypic assay elements. For example, the obtained representation of biomarker levels can be compared to negative and positive references to obtain confirmatory information about whether an individual will develop KD. As another example, the obtained characterization of biomarker levels can be compared to a reference representing a KD that is sensitive to treatment and a reference that represents a KD that is insensitive to treatment to obtain information about whether the patient is responding to treatment. Comparing the obtained characterization of biomarker levels to one or more phenotypic assay elements may use any convenient method, various of which are known to the skilled person. For example, those skilled in the art of qPCR will know that qPCR data can be compared by, for example, normalizing the loop threshold C(t) for a known amount of RNA, comparing the normalized values, etc. The results of the comparison step indicate how similar or dissimilar the obtained biomarker level profile is to the control/reference profile. This similarity/dissimilarity information can be used, for example, to predict the onset of KD, diagnose KD, monitor KD patients, etc. Likewise, those skilled in the array art will know that array profiles can be compared, for example, by comparing digital images representing the profiles, comparing databases representing the profiles, and the like. Patents describing comparative expression profile methods include, but are not limited to, U.S. Patent Nos. 6,308,170 and 6,228,575, the contents of which are incorporated herein by reference. Methods for comparing biomarker level profiles are also described above. Similarity can be based on relative biomarker levels, absolute biomarker levels, or a combination of both. In some embodiments, the similarity determination is made using a computer stored with a program for receiving input from a monitored individual characterizing the biomarker levels, such as from a user, to one or more reference profiles or reference Score similarity and return KD clinical representation predictions e.g. back to the user (e.g. lab technician, doctor, pregnant woman, etc.). Further computer implementation aspects are described below. In certain embodiments, similarity determinations may be based on visual comparisons of biomarker levels, e.g., comparing KD scores to a series of phenotypic assay elements (e.g., a series of KD scores) to determine the most similar Similar reference KD score. Depending on the type and nature of the phenotypic assay elements to which the obtained biomarker level distribution is compared, the above comparison steps yield various types of information about the cells/fluids detected. Therefore, the above comparison steps can produce positive/negative predictions of KD onset, positive/negative diagnoses of KD, characterization of KD, information on KD response to treatment, etc.

In other embodiments, biomarker level representations are used directly for making KD diagnosis, KD risk assessment, or monitoring KD treatment without comparison to phenotypic defining elements.

This method can be applied to different types of objects under test. In many embodiments, the object being tested is of the order Mammalian, including Carnivora (e.g., dogs and cats), Rodents (e.g., mice, guinea pigs, and rats), Lagomorpha (e.g., rabbits), and Primates (e.g., rabbits). e.g. humans, chimpanzees, and monkeys). In some embodiments, the animal or host, the subject being tested (also referred to herein as the patient), is a human.

In certain embodiments, the method of providing a KD assessment includes providing a diagnosis, prognosis, or monitoring result. In certain embodiments, the KD assessment of the present disclosure is achieved by providing an assessment by a person skilled in the art, such as a person skilled in the art's judgment as to whether a patient currently suffers from KD, the type, stage, or severity of KD of the patient, etc. ("KD diagnosis"); a person skilled in the art's prediction of a patient's risk of disease, disease progression, response to treatment, etc. (i.e., a person skilled in the art's "KD prognosis"); or a person skilled in the art's monitoring result of KD. Thus, the method may also include the step of generating or outputting a report providing the results of the evaluation by a person skilled in the art, which may be in the form of an electronic medium (e.g., an electronic display on a computer display), or in the form of a tangible medium (e.g., a report printed on paper or other tangible medium). Any form of report may be used, for example, as known in the art or as described in more detail below.

report

As used herein, a "report" is an electronic or tangible document that includes reporting elements that provide information of interest related to a subject's assessment and its results. In some embodiments, the subject report includes at least a KD biomarker representation, such as a KD profile or KD score, as discussed in greater detail above. In some embodiments, the subject report includes at least a technician's KD assessment, such as KD diagnosis, KD prognosis as KD clinical signs, KD monitoring analysis, treatment recommendations, etc. Subject reports may be generated entirely or partially electronically. Subject reports may further include one or more of the following: 1) information about the testing facility; 2) provider information; 3) patient data; 4) sample information; 5) evaluation reports, which may include a variety of information , including a) the reference value used and b) test data, where the test data can include, for example, protein level determination; 6) other functions.

The report may include information about the testing facility and what information is associated with the hospital, clinic, or laboratory where the samples were collected and/or the data were generated. Sample collection may include obtaining fluid samples, such as blood, saliva, urine, etc. Tissue samples from subjects, such as tissue biopsies, etc. Data generation may include measuring biomarker concentrations in KD patients versus healthy individuals, i.e., individuals who do not have and/or do not develop KD. The information may include one or more details, such as the name and location of the testing facility, the identity of the laboratory technician who performed the analysis and/or entered the input data, the date and time the assay was performed and/or analyzed, the location where the samples and/or resulting data were stored, the lot number of the reagents (e.g., reagent kits, etc.) used in the assay, etc. Report fields containing this information are typically populated with user-supplied information.

The report may include information about the service provider, which may be located outside the user's health care facility or within the health care facility. Examples of such information may include the name and location of the service provider, the name of the reviewer, and, if necessary or required, the name of the individual who conducted sample collection and/or data generation. Report fields with this information can typically be populated with user-entered data that can be selected from specified selections (for example, using a drop-down menu). Other service provider information in the report may include contact information for technical information about the results and/or about the interpretive report.

Reports may include patient data sections, including patient history (e.g., age, race, serotype, previous KD episodes, and any other characteristics of the patient), as well as administrative patient data, such as information that identifies the patient (e.g., name, patient date of birth (DOB)) , gender, mailing and/or residential address, medical record number (MRN), medical facility room and/or bed number, insurance information, etc.), the name of the patient's physician or other health professional requesting surveillance evaluation, if Instead of asking for a doctor, the name of the doctor responsible for the patient's care (such as a primary care physician).

The report may include a sample information section that provides information about the biological sample analyzed in the monitoring evaluation, such as the source of the biological sample obtained from the patient (e.g., blood, saliva, or tissue type, etc.), how the sample was handled (e.g., storage temperature, preparation protocol) and the date and time of collection. Report fields with this information can typically be populated with user-entered data, some of which can be provided as pre-scripted selections (for example, using a drop-down menu). The report may include a results section.

The report may include an evaluation report portion, which may include information generated after processing the data described herein. The interpretive report may include a prediction of the likelihood that the subject will develop KD. The interpretive report includes a diagnosis of KD. The interpretive report may include characteristics of KD. The evaluation portion of the report may also optionally include recommendations. For example, in the event that the results indicate that KD may occur, the recommendations may include recommendations to change diet, take antihypertensive medications, etc., as recommended in the art.

It will also be readily appreciated that the report may include additional elements or modified elements. For example, in the case of an electronic version, the report may contain hyperlinks to internal or external databases that provide more detailed information about the selected elements of the report. For example, the patient data element of the report may include a hyperlink to an electronic patient record or a website for accessing such a patient record that is maintained in a confidential database. An example of the latter may be an interest in an inpatient system or clinic environment. When an electronic format is employed, the report is recorded on an appropriate physical medium, such as a computer readable medium, such as a computer memory, a flash memory drive, a CD, a DVD, etc.

It is readily understood that a report may include all or part of the above, provided that the report will generally include at least these elements sufficient to provide the analysis requested by the user (e.g., calculated expression of KD biomarker levels; prediction, diagnosis or characterization of KD).

Reagents, Systems and Kits

The present invention also provides reagents, systems and kits thereof for practicing one or more of the above methods. The reagents, systems and kits thereof mentioned can vary greatly. The reagents include the above-mentioned biomarker level representations designed specifically for producing KD biomarkers from samples, such as one or more detection elements, oligonucleotides for detecting nucleic acids, antibodies or peptides for detecting proteins, etc. In some cases, the detection element includes a reagent for detecting the abundance of a single KD biomarker; for example, the detection element can be a ruler, plate, array or mixture comprising one or more detection elements, one or more oligonucleotides, one or more sets of PCR primers, antibodies that can be used to simultaneously detect the abundance of one or more KD biomarkers, etc.

Another type of such reagents are probe nucleic acid arrays, in particular, in which genes (biomarkers) are represented. A variety of array formats are known in the art, with a variety of probe structures, substrate compositions, and connection techniques (e.g., dot-print arrays, microarrays, etc.). In particular, representative array structures include those described in U.S. Patent 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; the disclosures of which are incorporated herein by reference, and WO 95/21265; WO 96/31622; WO 97/10365; WO 97/27317; EP373203; and EP785280.

Another reagent tailored for generating biomarker level expression of genes (e.g., KD genes) is a collection of gene-specific primers designed to selectively amplify such genes (e.g., using PCR-based techniques, real-time RT-PCR). Gene-specific primers and methods of using them are described in U.S. Patent 5,994,076, the disclosure of which is incorporated herein by reference.

A reagent specifically tailored to produce biomarker-level characterization (e.g., KD biomarker-level characterization) is a collection of antibodies that specifically bind to a protein biomarker, e.g., in an ELISA format, in xMAP™ microsphere format, in proteomics On arrays, in suspension, and analyzed by flow cytometry, Western blot, dot blot, or immunohistochemistry. Methods using identical antibodies are well known in the art. These antibodies can be provided in solution. Alternatively, they can be pre-bound to a solid substrate, for example, the wells of a multi-well petri dish or the surface of xMAP microspheres.

In particular, the probe array, primer set or antibody set includes pairs selected from IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7 and ZHF185, or their related specific biochemical substrates. Subject probes, primers or antibody sets or reagents may include reagents specific only for the genes/proteins/lipids/cofactors listed above, or they may include reagents specific for other genes/proteins/lipids not listed above Reagents specific for a cofactor, such as probes, primers or antibodies specific for the gene/protein/lipid/cofactor it is expressed, are known in the art to be associated with KD, such as IFI27 and C19ORF59.

In some cases, such a qPCR instrument may be provided, such as the AB QuantStudio 6. As used herein, the term "system" refers to a collection of reagents, however, compiled by purchasing a collection of reagents from the same or different sources. In some cases, a kit may be provided. As used herein, the term "kit" refers to a collection of reagents provided, such as reagents sold together. For example, nucleic acid- or antibody-based detection of sample nucleic acids or proteins can be coupled to an electrochemical biosensor platform, respectively, which will allow for multiplexed determination of these biomarkers for personalized KD care.

Systems and kits of the invention may include arrays, gene-specific primer sets, or protein-specific antibody sets as described above. Systems and kits may further include one or more additional reagents for use in various methods, such as primers for generating target nucleic acids, dNTPs and/or rNTPs, which may be premixed or separate, one or more unique labels dNTPs and/or rNTPs, such as biotinylated or Cy3 or Cy5 labeled dNTPs, gold or silver particles with different scattering spectra, or other post-synthetic labeling reagents such as chemically active derivatives of fluorescent dyes, enzymes such as reverse Transcriptases, DNA polymerases, RNA polymerases, etc., various buffer media, such as hybridization and wash buffers, prefabricated probe arrays, labeled probe purification reagents and components, such as spin columns, etc., signal generation and detection reagents, such as labeled Secondary antibody, streptavidin alkaline phosphatase conjugate, chemifluorescent or chemiluminescent matrix, etc.

The subject systems and kits may also include one or more KD phenotype determination elements, which in many embodiments are reference or control samples or biomarker representations that can be used, for example, by appropriate experimental or computational means, to make a KD prognosis based on an "input" biomarker level profile, for example, that has been determined using the biomarker determination elements described above. Representative KD phenotype determination elements include samples from individuals known to have or not have KD, databases of biomarker level representations, such as reference or control profiles or scores, and the like, as described above.

In addition to the above components, the subject kit will also include instructions for practicing the subject method. These instructions may appear on the subject of multiple forms of kits, one or more of which may be present in the kit. These instructions may be in the form of printed information on a suitable medium or substrate, such as in the packaging of the kit, a packaging insert on one or more sheets of paper on which the information is printed, etc. Another method is computer-readable media, such as floppy disks, CDs, etc., on which information has been recorded. Another possible means is a website address that provides access to information about the removed website via the Internet. Any convenient method can be included in the kit.

The following examples are provided by way of illustration and not limitation.

Example

The following examples are presented to provide those of ordinary skill in the art with a description of how to make and use the present invention and are not intended to limit the scope of what the inventors believe to be their invention, nor are they intended to represent that the following experiments are all or The only experiment. Efforts have been made to ensure the accuracy of the figures used (e.g. quantities, temperatures, etc.), but some should account for experimental error and bias. Unless otherwise stated, parts are parts by weight, molecular weight is weight average, temperature is in degrees Celsius, and pressure is at or near atmospheric pressure. Materials and methods

KD patient validation cohort, demographic information, and clinical criteria. All study protocols were approved by the Institutional Review Board (IRB) of Chang Gung Memorial Hospital. Blood samples were obtained from patients with confirmed KD, and febrile control samples were obtained from the same cohort but were later determined not to be KD. KD patients met the American Heart Association clinical criteria for complete and incomplete KD and received IVIG treatment within 10 days of the first fever. Patient blood samples were collected and analyzed at baseline before the administration of IVIG or drug treatment.

Meta-analysis of vasculitis and identified trace cases of KD. Differentially expressed genes were extracted from seven data sets from PBMC microarray experiments. The seven datasets include PBMC microarray experiments for analysis of primary vasculitis, including KD, in subjects from the NCBI Gene Expression Omnibus (GEO) 20:4 KD dataset, GSE15297 (KD vs. FC), GSE18606 ( KD vs. normal control), GSE9864 (KD vs. normal control); GSE9863 (KD vs. normal control); 3 other vasculitis datasets, GSE33910 (Takayasu arteritis vs. normal control), GSE17114 (Behcet's disease vs. normal control) group) and GSE16945 (large artery arteritis vs normal control). After the DEGs were discovered in each dataset, an innovative pathway analysis (IPA) was performed to identify the pathways associated with the DEGs in each of the seven datasets. Gene biomarkers that are present in the DEG list of at least one dataset and participate in at least one common enriched pathway shared by up to seven datasets constitute a collective meta-signature of vasculitis. To identify potential biomarker candidates, gene markers were further filtered using a human biofluid proteome database with known serum and urine detectable proteins, which contains data from the HUPO Plasma Proteome Project, ( 24) Plasma Proteome Institute, (25) MAPU Proteome Database, (26) and Urinary Exosome Database. (27,28).

For literature retrieval, Génie was used to mine literature abstracts from the PubMed database. Currently, we have a total of 20,396 genes and 1,183,931 gene abstract links (551,555 unique PMIDs) for mining analysis. (24) Using Génie, genes were ranked according to different topics. Their PubMed citations were classified using machine learning and tested for topic keywords such as "Kawasaki disease", "myocardial dysfunction", "vascular inflammation", "vascular leak", "coronary aneurysm", "ischemia", and "multisystem inflammatory syndrome". For each topic keyword search, the significance cutoff for abstracts was set to p < 0.05, and the false discovery rate for genes was set to < 0.05. All significantly enriched genes were ranked using Fisher statistics. In order to compare the ranking differences of genes under different thematic keywords, the gene rankings of the seven themes were normalized. The relative ranking of the gene ranked first was 1, and the relative ranking of the last gene in the effective gene list was 0. As a result, 12 candidate genes were selected for further testing based on their ranking in each keyword. Nine antigenic phases of the cardiac discovery panel. A total of 61 genes were detected for differential expression in KD and other febrile shedding patients.

Biomarker detection via qPCR platform. Blood was collected from KD and fever control groups. The blood is quickly stored and prepared, either fresh or frozen at -80 degrees Celsius for later preparation. Extract RNA directly from blood using an RNA extraction kit. The qPCR assay was performed on the QuantStudio 6 IVD platform, using commercially available one- or two-step qPCR kits to extract RNA for 61 protein targets. Perform the assay according to the reagent manufacturer's recommended protocol and dilution, and determine the linearity, LOQ/LOD, and minimum C(t) values for each primer.

Statistical analysis. The entire study population consisted of 200 patients, including 100 confirmed KD and 100 febrile control cases. The RNA copy number of the targeted gene was measured for each target gene. Patient characteristics were examined between KD patients and febrile controls using fisher's exact check for sex and rank sum test for age. Univariate analysis was performed for individual analytes in KD patients versus analytes in febrile controls. Receiver operating characteristic (ROC) analysis was performed and specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) values were determined for each analyte. Wilcoxon rank sum check sum fold change analysis was also used to compare analyte concentration values for all KD patients with febrile controls. A similar rank-sum test was used to compare age differences between confirmed KD diagnoses and febrile controls. To aid statistical analysis, any measured value below the limit of quantification was extrapolated to half the limit of quantitation, and any analyte value above the upper limit of quantitation (ULQ) was extrapolated to twice the ULQ value. This ensures that data with values below the LOQ or above the ULQ are more inclusive. Setting values to neither LOQ nor ULQ affects analysis. Nineteen significant genes were selected for differential analysis using the standard rank sum test with p value less than 0.05, fold change greater than 1.5 or less than 0.67, and AUC greater than 0.6.

Analyte concentrations were analyzed by linearly significantly different analytes in an iterative search for all ten analyte combinations to find the maximum ROC AUC value. The geometric mean of each different combination of analytes was calculated and a logarithmic transformation was applied to the geometric mean, which was then scaled to a range of 0 and 10 and subjected to ROC analysis. For the first iteration, the best analyte with the highest AUC>0.5 based on the univariate analysis was selected once. The analytes were added to the previous best AUC combination in the next iteration. If a new analyte improved the performance of the model by maximizing the AUC, it was retained in the combination and vice versa. The process was repeated until the maximum AUC was achieved and the remaining features constituted the final combination.

KD biomarker panel and diagnostic score. The model was tested using in-sample validation to evaluate the performance of the area under the receiver operating characteristic curve. KD scores were generated using the geometric mean from the final group. In binary models, the optimal Youden index with a single cutoff was used to determine the optimal cutoff. Other operating characteristics, such as sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), were calculated based on the optimal cutoff values with 95% confidence intervals for all indicators. All statistics were performed using R software version 4.1 (R Foundation for Statistical Computing). Two-sided p values were calculated, and p < 0.05 was considered significant. result

Patient demographics and characteristics. This study focused on a pediatric population with persistent high fever, with a total of 100 subsequently confirmed KD cases and 100 febrile children (Table 1a). Thirty patients had five clinical symptoms and 52 KD patients had four clinical symptoms that met the AHA guideline criteria for a complete KD diagnosis at the time of blood sampling (Table 1b), whereas the remaining 18 were diagnosed as incomplete at the time of blood sampling. The age at diagnosis of KD was younger (1.4 years, 0.8-2.4) than the febrile controls (3.0 years, 1.7-4.0), p < 0.001. IRB approval restricted clinical information on the control group, but none of the febrile control patients met the KD diagnosis criteria.

Table 1a. Patient demographic information: KD patients were slightly younger than febrile controls. Features Cases(n=100) Control(n=100) test statistics P value Gender, N(%) Fisher's exact test 0.536 female 56 (56) 62 (62) male 43 (43) 36 (36) quit 1 (1) twenty two) Age, median (IQR) 1.4 (0.8,2.4) 3 (1.7,4.1) rank sum test ＜0.001

Table 1b. Summary of KD symptoms in the study queue. 1 2 3 4 5 clinical symptoms KD(n=1) KD(n=2) KD (n=15) KD (n=52) KD (n=30) lip 0 1 10 50 30 Eye 1 1 14 51 30 lymph 0 0 0 5 30 Edema 0 1 11 51 30 skin 0 1 13 50 30

Meta-analysis identifies panel of qPCR discoveries for KD. Through meta-analysis of 7 vasculitis and KD microarray data, 13 pathways overlapped. A. A total of 82 genes were identified and screened using the human biofluid proteome database. This led to the identification of potentially 40 vasculitis-specific genetic markers (meta-markers) that may be differentially expressed in blood (Figure 1). VEGF, MMP8, and HGF, belonging to our vasculiton signature, were reported to be different in serum between KD and FC (p-value < 0.0001) and between KD and normal controls (p-value p < 0.001) subjects Express. This observation provides direct evidence supporting the validity of our overall biomarker discovery approach and our hypothesis that meta-analysis of vasculitis PBMC microarray datasets can lead to specific KD diagnostic biomarkers. This observation is also consistent with our success in applying the same approach to identify new preeclampsia biomarkers. In addition, a literature thinking method was used based on the method of Génie et al. [1]. Currently, we have a total of 20,396 genes and 1,183,931 gene summary links (551,555 unique PMIDs) for mining analysis. Using Génie's method, machine learning was used to classify PubMed citations of genes using subject keywords (e.g., “kawasaki disease”, “myocardial dysfunction”, “vascular inflammation”, “vascular leakage”, “coronary arteries”) , "aneurysm", "ischemia" and "multisystem inflammatory syndrome". Each topic keyword search was tested in our study with the cutoff set to p<0.05 and the false discovery rate of the gene set to < 0.05. All significantly enriched genes were ranked using Fisher statistics. In order to compare the ranking differences of genes under different topic keywords, the gene rankings of the 7 topics were normalized. The relative ranking of the first-ranked gene was 1 , the relative ranking of the last gene in the valid gene list was 0. As a result, 12 candidate genes were selected for further testing based on their ranking in each keyword. In addition, the Luminex-based cardiac discovery panel with KD 9 antigenic targets related to symptoms.

A two-step discovery and validation process was performed to narrow down the final gene expression set for the validation study. 19 genes were selected from the initial 5x5 cohort, and an additional 15x15 cohort was then used to determine the best set of KD score groups. Two genes, C19ORF59 and IFI27, and their δC(t) were further identified as the best combination for KD identification. Final validation cohort.

Binary and risk classification model performance. The qPCR results for the KD and fever control cohorts for each gene biomarker in the exploratory discovery set were analyzed separately by univariate analysis (Table 2).

Table 2. 19 gene biomarkers detected by qPCR using the QuanStudio 6 qPCR instrument. Biomarkers are ranked from top to bottom based on the area under the receiver operating characteristic curve (AUC) in univariate analysis. Gene Source P -value Mean - ( ΔΔ Ct) IFI27 Literature (Wright et al) 0.027002 -4.61611 C19ORF59 GEO 0.003369 2.939104 PCOLCE2 GEO 0.185414 2.507202 CR1 GEO 0.003429 2.425294 CLIC3 Literature (Wright et al) 0.000155 -2.15071 TLR7 GEO 0.012507 -2.09338 S100A12 GEO 0.006337 2.087284 CACNA1E Literature (Wright et al) 0.005815 1.954435 SLC11A1 GEO 0.010753 1.828993 CASP5 Literature (Rahmati) 0.008873 1.788848 CRTAM GEO 0.031853 -1.74138 ILIRL1 Luminex 0.273124 1.710008 LGALS2 GEO 0.147196 -1.6982 FKBP5 GEO 0.031187 1.696485 S100A9 GEO 0.013157 1.601009 KLHL2 Literature (Wright et al) 0.013108 1.492222 MAPK14 GEO 0.019355 1.203032 NKTR GEO 0.05526 -1.11142 ZNF185 Literature (Wright et al) 0.03499 1.101665

Subsequently, a linear method is used to construct the best combination with the largest AUC. The first two pairs of δC(t) values, C19ORF59 and IFI27, after initial discovery and validation queue studies, resulted in a subgroup generated by differences in δC(t) values between the expression of the two genes (Fig. 1). Both genomes were further validated in the 80KD validation queue and the 80 fever control queue. Both genomes achieved an overall ROC ACU of 0.930 at the optimal cutoff of 4.558.

The diagnostic model has a robust AUC of 0.930 for diagnosing KD (Fig. 2, b). AUC (area under the ROC curve) quantifies the ability of a diagnostic test to differentiate between individuals with and without a disease. A perfect test with no false positives or false negatives has an AUC of 1.00; a test that is no better than random chance at identifying true positives has an AUC of 0.5. Based on the optimal cutoff value of 4.558 for the Kawasaki disease score (KD score) determined using the Youden index, the diagnostic cutoff value was determined to optimize the sensitivity and specificity of effective KD diagnosis. Overall binary classification model performance for the total population had a sensitivity of 84% (77%-91%), a specificity of 91% (85%-96%), a positive predictive value (PPV) of 90.3%, and a negative predictive value (NPV). ) is 85% (c in Figure 2).

In addition, a two-threshold-based scale was created to stratify patients into a three-level risk score system for effective KD risk stratification to aid in the clinical diagnosis of KD: low risk (KD score <3.558), moderate risk (3.558-4.958), and high risk (KD score >4.958). As shown in Figure 3a. For the high-risk KD group, the PPV was 91.8%, and the NPV was 89.5% for the low-risk KD (febrile disease) group (Figure 3b). This result indicates that more than 90% of patients in the high-risk group and less than 1/10 of patients in the low-risk group were positive for KD (Figure 3c). The KD score for diagnostic tests is intended as an in vitro diagnostic test to aid physician decision making, especially for incomplete KD.

We also examined the association of coronary artery abnormalities with the KD scoring system in a cohort of studies diagnosed with KD. Z scores were calculated for individual KD patients and were classified into three categories: no coronary artery involvement (Z score <2), dilatation only (Z score 2 to <2.5), and aneurysm (Z score >2.5). Our test captured the majority of KD patients with aneurysms in the high-risk group, and 24 of 26 (92.3%) could identify KD patients with normal coronary arteries (Figure 4). The data also showed that our study group did not require obvious cardiac stress signs or phenotypes to be positively identified as KD.

We developed a diagnostic marker panel to aid in the diagnosis of Kawasaki disease with a ROC AUC of 0.930. Serological testing can accurately identify KD patients and distinguish them from other febrile cases. A delta C(t) model of C19ORF59 and IFI27 was constructed using a linear geometric mean-based statistical model with an AUC of 0.930. A simple model based on the linear geometric mean of the combined biomarkers avoids training bias, leading to overfitting and poor prediction results in the validation set. The model is also more likely to divert different patient groups from other regions.

Previously, several studies aimed to discover a specific biomarker or a multi-biomarker panel containing serological protein analytes, cytokines, or gene expression profiles to identify potential KD biomarkers by LC-MS-based technologies or gene microarray approaches. A recent study targeting three serological biomarkers, myeloid-related protein 8/14 (MRP8/14), human neutrophil elastase (HNE), and C-reactive protein as a prospective biomarker panel achieved a high ROC AUC value of 0.82, but the negative predictive value was relatively poor. Another study used a random forest model using the geometric mean of analyte concentrations and the best Youden index to determine the cutoff value to achieve similar AUC values to our simple four-analyte panel. Complex statistical models like random forests are more difficult to interpret and implement than simple linear models of equal weights. Random forest models are also difficult to transfer from the original cohort to another cohort from a different region, which may limit their usefulness as actual clinical analyses.

Using microarray data, Wright et al. also developed a blood gene expression signature panel of 13 transcripts that was able to distinguish KD cases from patients with fever. The study used a parallel regularized regression model search to distinguish KD cases. The team achieved an AUC of 0.946 in the final validation set. However, both the training and validation cohorts used only microarray data generated by the study, and the gene expression was further validated with more quantitative measurements, such as qPCR of the 13 genes. This team needs to be re-examined and validated in a separate cohort using quantitative qPCR analysis of a focused gene panel. Gene expression signature analysis recently showed that KD has similarities to COVID-19 infection in children with multisystem inflammatory syndrome caused by the new coronavirus, but without the commonly associated cardiac phenotype. (25) This suggests that the gene signature primarily captures the host immune response to KD rather than cardiac events. It will be interesting to observe whether gene expression of our serum biomarkers is also upregulated from whole blood gene expression analysis by quantitative PCR analysis.

In this study, we used quantitative PCR assays and IVD-qPCR instrumentation (QuantStudio 6) in combination with available clinical tests to rapidly adapt the diagnosis of KD for clinical use. The combined model was also based on a simple difference (RNA copy number) in two molecular targets, significantly improving the transferability of the model between cohorts without overfitting the data. Overfitting of the data often occurs if the data are processed by complex statistical models or artificial intelligence machine learning algorithms. A limitation of this study is that there was no additional larger validation cohort to determine whether the group could be easily transferred to different patient populations. The U.S. Food and Drug Administration considers KD to be a rare orphan disease. Therefore, timely registration may be difficult. The most significant medical need for KD is for patients who suffer from incomplete KD and are often misdiagnosed with other febrile illnesses. Patients who do not receive IVIG treatment within ten days of onset of fever have a much higher likelihood of experiencing a cardiac event later in life. A KD diagnostic test panel that can be deployed with currently available clinical tests and standard statistical algorithms could greatly improve the speed and accuracy of KD diagnosis.

The present invention includes, but is not limited to, the following technical solutions: A method of determining the presence of Kawasaki disease biomarker levels in a subject, the method comprising: a. Evaluate a panel of Kawasaki disease biomarkers in a sample from a subject, such as blood, serum, or plasma, to determine the expression level of each Kawasaki disease biomarker in the sample; b. Obtain a level representation of the Kawasaki disease biomarker based on the level of each Kawasaki disease biomarker in the group; c. wherein the group of Kawasaki disease biomarkers includes 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 one or more biomarkers.

Further, wherein the full length of the oligonucleotide or its nucleotide sequence, RNA or DNA level for each Kawasaki disease biomarker is measured.

Further, the expression level of the biomarker is expressed by a cycle threshold C(t).

Further, wherein the group includes C19ORF59 and IFI27.

Further, the group includes C19ORF59, IFI27, S100A12 and S100A9.

Further, the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1.

Further, wherein said group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

Further, the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4.

Further, reports providing a representation of Kawasaki disease biomarker levels, such as absolute concentrations or multiples of medium (MoM) of the cycle threshold C(t), were also included.

Further, wherein the Kawasaki disease biomarker level is derived from a Kawasaki disease score, wherein the Kawasaki disease score: a. It can be derived from the difference in loop threshold C(t) between two different genes such as C19ORF59 and IFI27; b. Can be derived from measured blood biomarker values by geometric mean, multivariate linear discriminant analysis (LDA), or decentralized gradient boosted decision tree (GBDT) machine learning (such as XGBoost); or c. Can be a multiple of each biomarker level, such as C(t), concentration or MoM of C(t), normalized to fit a scale of 0-10, e.g. can be derived by 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=Kawasaki disease score .

A method for diagnosing Kawasaki disease in a subject includes obtaining a level representation of a Kawasaki disease biomarker in a sample from the subject, and when the Kawasaki disease score is greater than 4.558, the subject is diagnosed with Kawasaki disease.

Further, the full length of the oligonucleotide or the nucleotide sequence, RNA or DNA level of each Kawasaki disease biomarker is measured.

Further, the biomarker expression level is expressed by a cycle threshold C(t).

Further, wherein the group includes C19ORF59 and IFI27.

Further, the group includes C19ORF59, IFI27, S100A12 and S100A9.

Further, the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1.

Further, the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

Further, the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4.

Furthermore, reports providing an expression of KD biomarker levels, such as absolute concentrations or MoMs of the cycle threshold C(t), are also included.

Further, wherein the Kawasaki disease biomarker level is derived from a Kawasaki disease score, wherein the Kawasaki disease score: a. Can be derived from the loop threshold difference between gene #1 and gene #2, such as C19ORF59 and IFI27; b. Can be derived from measured blood biomarker values by geometric mean, multivariate linear discriminant analysis (LDA), or decentralized gradient boosted decision tree (GBDT) machine learning (such as XGBoost); or c. Can be a multiple of each biomarker level, such as concentration or MoM, normalized to fit a scale of 0-10, e.g. can be derived by 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=Kawasaki disease score.

A method for assessing the risk of Kawasaki disease in a subject, comprising obtaining a level of a Kawasaki disease biomarker in a sample from the subject indicating: a. For Kawasaki disease risk assessment, a Kawasaki disease score assesses the risk of Kawasaki disease within three different ranges to determine the risk of Kawasaki disease; b. For example, a low risk of Kawasaki disease with a low score cutoff value of less than 3.558 (adjusted for the population) indicates that the patient has a low risk of Kawasaki disease; c. For example, a high risk of Kawasaki disease with a high score cutoff value of more than 4.958 (adjusted for the population) indicates that the patient has a high risk of Kawasaki disease; d. Scores between the low score cutoff value and the high score cutoff value indicate that the patient has an intermediate risk of Kawasaki disease.

Further, wherein the full length of the oligonucleotide or its nucleotide sequence, RNA or DNA level for each Kawasaki disease biomarker is measured.

Further, the expression level of the biomarker is expressed by a cycle threshold C(t).

Further, wherein said group includes C19ORF59 and IFI27.

Further, the group includes C19ORF59, IFI27, S100A12 and S100A9.

Further, the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1.

Further, the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

Further, the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4.

Furthermore, reports providing an expression of KD biomarker levels, such as absolute concentrations or multiples of median (MoM) of the cycle threshold, were included.

Further, wherein the Kawasaki disease biomarker presents a Kawasaki disease score, wherein the Kawasaki disease score: a. Can be derived from the loop threshold difference between gene #1 and gene #2, such as C19ORF59 and IFI27; b. Can be derived from measured blood biomarker values by geometric mean, multivariate linear discriminant analysis (LDA), or decentralized gradient boosted decision tree (GBDT) machine learning (such as XGBoost); or c. Can be a multiple of each biomarker level, such as concentration or MoM, normalized to fit a scale of 0-10, which can be derived, for example, by 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=Kawasaki disease score.

A method for providing Kawasaki disease treatment monitoring for a subject, comprising obtaining a Kawasaki disease biomarker level representation in a sample from the subject; for Kawasaki disease treatment monitoring, the Kawasaki disease score should initially be greater than, for example, 4.558, but a population adjustment for Kawasaki disease is required before treatment; and after treatment the Kawasaki disease score should be significantly reduced to less than a Kawasaki disease score of 3.558.

Further, the full length of the oligonucleotide or the nucleotide sequence, RNA or DNA level of each Kawasaki disease biomarker is measured.

Furthermore, the biomarker expression level is represented by a cycle threshold C(t).

Further, wherein said group includes C19ORF59 and IFI27.

Further, the group includes S100A12 and S100A9.

Further, the group includes S100A12, S100A9, CLIC3 and SLC11A1.

Further, wherein said group includes S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

Further, wherein said group includes S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4.

Further, reports providing a representation of Kawasaki disease biomarker levels, such as absolute concentrations or multiples of medium (MoM), were also included.

Further, wherein the Kawasaki disease biomarker presents a Kawasaki disease score, wherein the Kawasaki disease score: a. Can be derived from the loop threshold difference between gene #1 and gene #2, such as C19ORF59 and IFI27; b. Can be derived from measured blood biomarker values by geometric mean, multivariate linear discriminant analysis (LDA), or decentralized gradient boosted decision tree (GBDT) machine learning (such as XGBoost); or c. Can be a multiple of each biomarker level, such as concentration or MoM, normalized to fit a scale of 0-10, which can be derived, for example, by 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=Kawasaki disease score.

A diagnostic system, including a kit, a reagent, and an instrument for generating a Kawasaki disease score from a sample from a subject, such as blood, serum, or plasma, includes: a detection reagent for measuring the amount of one or more Kawasaki disease biomarkers selected from the following biomarker group, wherein the Kawasaki disease biomarker group includes IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7 and ZHF185.

The diagnostic system further includes: a. Platform systems for measuring Kawasaki disease biomarkers, such as the QuantStudio 6 system; b. Calculation sheet for calculating Kawasaki disease score, and c. Indication to determine whether the patient has Kawasaki disease.

Further, wherein said group includes C19ORF59 and IFI27.

Further, wherein said group includes C19ORF59, IFI27, S100A12 and S100A9.

Further, the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1.

Further, wherein said group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

Further, the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4.

Further, including selecting patients suspected of having Kawasaki disease for intravenous immunoglobulin (IVIG) treatment, the method includes: a. Determine the patient’s Kawasaki disease score; b. Diagnose the patient according to the method described and, if the patient is diagnosed with Kawasaki disease, select the patient for IVIG administration; c. Select patients for IVIG administration if their Kawasaki disease score is in the high-risk range and moderate-risk range.

Further, it also includes monitoring the effectiveness of Kawasaki disease treatment in patients with Kawasaki disease, including: a. Determine the patient’s Kawasaki disease score; b. Diagnose the patient according to the method described and, if the patient is diagnosed with Kawasaki disease, select the patient for IVIG administration; c. Select the patient for IVIG administration if the patient's Kawasaki disease score is in the high-risk range and the moderate-risk range; and d. Effective treatment will result in a decrease in Kawasaki disease score.

A method, including a processing routine for a Kawasaki disease sample assay to retain necessary data, comprises: a. measuring the concentration of each biomarker of a biomarker panel in a blood, plasma or serum sample collected from a patient suspected of having Kawasaki disease, wherein the Kawasaki disease biomarker panel includes IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7 and ZHF185; and b. comparing the measured value of each biomarker to a reference value of each biomarker in a control subject, wherein differential expression indicates that the patient has Kawasaki disease; the assay further comprises determining a Kawasaki disease score for the patient based on the concentrations of these biomarkers.

A method of measuring at least one pair of Kawasaki disease biomarkers of a biomarker panel by oligonucleotide primers targeting the biomarker to calculate a Kawasaki disease score, wherein the biomarker panel includes IFI27, C19ORF59, CACNA1E, CASP5 , CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7 and ZHF185, the primer specifically binds to all RNA/DNA sequence determinants comprising the biomarker. The above biomarkers or fragments thereof: a. The primer is selected from a sequence consisting of a part of the complementary DNA sequence of the biomarker target; b. Reporting dye or dye/quencher reporting label that can accurately meter a loop threshold for a targeted biomarker, such as the amount of RNA or DNA in a sample; c.qPCR instruments, such as QuantStudio 6, are used to accurately determine reporter gene/dye signals based on amplification of biomarker templates.

Kawasaki disease biomarker panel, including one or more organisms selected from IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7, and ZHF185 markers.

Further, C19ORF59 and IFI27 are included.

Further, the Kawasaki disease biomarker panel includes C19ORF59, IFI27, S100A12 and S100A9.

Furthermore, the biomarker panel includes C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1.

Furthermore, the Kawasaki disease biomarker panel includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

Further, the Kawasaki disease biomarker panel includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4.

without

The invention is best understood in conjunction with the accompanying tables and figures. The patent or application contains at least one figure. Copies of the patent or patent application with the figure may be obtained from the Patent Office upon request and payment of the necessary fee. It is emphasized that, as is common practice, the features of the figures are not drawn to scale. Rather, the size of the features is arbitrarily expanded or reduced to more clearly present the size of the features. Figure 1. KD disease biomarker discovery and validation process. Potential molecular biomarkers were initially identified through GEO analysis, literature discovery, and multiplex Luminex protein biomarker studies. 61 gene targets were narrowed down to 18 genes in the discovery queue. The final group was validated in a validation cohort of one KD (n=80) and a fever control group (n=80). Figure 2 shows the model construction based on IFI27 and C19ORF59, where a is the delta C(t) construction of group based on IFI27 and C19ORF59. The serum copy number of each biomarker was measured using the qPCR instrument QuantStudio 6, using the corresponding primers. b is the model obtained according to ROC analysis AUC of 0.930, and the optimal cutoff value is 3.274. c is the performance of the model at the optimal cutoff value. Figure 3 shows the risk prediction based on the cutoff value, where a is the model with two thresholds and three risk level scores that divides the cohort population into high-risk, intermediate and low-risk groups. b shows the classification of the population based on the risk score system. c shows that at the optimal cutoff value of 4.958, the positive predictive value (PPV) of the high-risk group reached 91.8%, and the negative predictive value (NPV) of the low-risk patient population with a risk score below 3.558 was 89.5%. Figure 4. Classification of coronary artery Z scores according to KD risk classification. Our group captured most of the aneurysm cases. This group also identified KD patients with normal or dilated coronary arteries.

Claims (29)

A method for determining the level of Kawasaki disease biomarkers in a subject, comprising: a. evaluating a group of Kawasaki disease biomarkers in a sample from the subject, including blood, serum or plasma, to determine the expression level of each of the Kawasaki disease biomarkers in the sample; and b. obtaining the level of the Kawasaki disease biomarker based on the level of each of the Kawasaki disease biomarkers in the group, wherein the Kawasaki disease biomarkers include IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7 and ZHF185. The method for determining the level of Kawasaki disease biomarker in a subject as described in claim 1, further comprising measuring the full length of the oligonucleotide or its nucleotide sequence, RNA or DNA of each of the Kawasaki disease biomarkers. level. The method for determining the level presentation of a Kawasaki disease biomarker in a subject as described in claim 1, wherein the expression level of the Kawasaki disease biomarker is expressed by a cycle threshold C(t). The method for determining the level of Kawasaki disease biomarkers in a subject as described in claim 1, wherein the Kawasaki disease biomarkers include C19ORF59 and IFI27. The method of determining the level of Kawasaki disease biomarkers in a subject as described in claim 1, wherein the Kawasaki disease biomarkers include C19ORF59, IFI27, S100A12 and S100A9. The method for determining the level of Kawasaki disease biomarkers in a subject as described in claim 1, wherein the Kawasaki disease biomarkers include C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1. The method for determining the level of Kawasaki disease biomarkers in a subject as described in claim 1, wherein the Kawasaki disease biomarkers include C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2. The method for determining the level of Kawasaki disease biomarkers in a subject as described in claim 1, wherein the Kawasaki disease biomarkers include C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4. The method of determining the level of a Kawasaki disease biomarker in a subject as described in claim 1 further comprises providing a report on the level of the Kawasaki disease biomarker. A method for determining the level of Kawasaki disease biomarkers in a subject as described in claim 1, wherein the level of Kawasaki disease biomarkers exports a Kawasaki disease score, and the Kawasaki disease score: a. is exported from the difference in loop thresholds C(t) between two different genes; b. is exported from the value of the Kawasaki disease biomarker measured in the blood by geometric mean, multivariate linear discriminant analysis (LDA) or distributed gradient boosted decision tree (GBDT) machine learning; or c. is a multiple of the level of each biomarker. A diagnostic system including: test kit; a detection reagent for measuring the amount of one or more Kawasaki disease biomarkers selected from the group of Kawasaki disease biomarkers; and An apparatus for generating a Kawasaki disease score from a sample from a subject, including, for example, blood, serum or plasma, The Kawasaki disease biomarker panel includes IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7 and ZHF185. The diagnostic system as described in claim 11 further includes: a. A platform system for measuring said Kawasaki disease biomarkers; b. A calculation sheet for calculating said Kawasaki disease score; and c. Indication to determine whether the patient has Kawasaki disease. A diagnostic system as described in claim 11 or 12, wherein the Kawasaki disease biomarker panel includes C19ORF59 and IFI27. The diagnostic system of claim 11 or 12, wherein the Kawasaki disease biomarker panel includes C19ORF59, IFI27, S100A12 and S100A9. A diagnostic system as described in claim 11 or 12, wherein the Kawasaki disease biomarker panel includes C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1. A diagnostic system as described in claim 11 or 12, wherein the Kawasaki disease biomarker panel includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2. The diagnostic system of claim 11 or 12, the Kawasaki disease biomarker panel includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4. A Kawasaki disease biomarker panel comprising one or more 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 Biomarkers. The Kawasaki disease biomarker panel as described in claim 18, including C19ORF59 and IFI27. The Kawasaki disease biomarker panel as described in claim 18, comprising C19ORF59, IFI27, S100A12 and S100A9. The Kawasaki disease biomarker panel as described in claim 18, including C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1. The Kawasaki disease biomarker panel as described in claim 18, comprising C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2. The Kawasaki disease biomarker panel as described in claim 18 includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4. Use of a detection reagent for measuring the amount of one or more Kawasaki disease biomarkers selected from the following biomarker group in the preparation of a diagnostic system for diagnosing Kawasaki disease in a subject, wherein the Kawasaki disease biomarker group includes IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100A12, S100A9, TLR7 and ZHF185. A diagnostic system as described in claim 24, wherein the group includes C19ORF59 and IFI27. A diagnostic system as described in claim 24, wherein the group includes C19ORF59, IFI27, S100A12 and S100A9. A diagnostic system as described in claim 24, wherein the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3 and SLC11A1. A diagnostic system as described in claim 24, wherein the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2. A diagnostic system as described in claim 24, wherein the group includes C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4.