CN116287220B

CN116287220B - Molecular biomarkers and assay methods for rapid diagnosis of kawasaki disease

Info

Publication number: CN116287220B
Application number: CN202310544514.2A
Authority: CN
Inventors: 陈利民
Original assignee: Tianjin Yunjian Medical Instrument Co ltd; Tianjin Yunjian Medical Lab Co ltd
Current assignee: Tianjin Yunjian Medical Instrument Co ltd; Tianjin Yunjian Medical Lab Co ltd
Priority date: 2022-08-26
Filing date: 2023-05-16
Publication date: 2023-08-01
Anticipated expiration: 2043-05-16
Also published as: CN116287220A; WO2024041348A1; CN117305433A

Abstract

The present invention relates to biomarker combinations and characterization methods for diagnosing Kawasaki Disease (KD) in a clinical setting. In particular, the invention calls for the use of RNA/DNA molecular biomarkers and combinations of their copy numbers (concentrations) to derive KD scores for aiding diagnosis, prognosis, risk assessment and treatment/monitoring. More specifically, these biomarker characterization reagents (e.g., primers and polymerase) will be packaged in a kit along with a test system (e.g., applied Biosystems QuantStudio) that measures biomarker concentrations to generate a KD score that can be used to distinguish KD from other febrile pediatric indications.

Description

Molecular biomarkers and assay methods for rapid diagnosis of kawasaki disease

Technical Field

The present invention relates to biomarker combinations and characterization methods for diagnosing Kawasaki Disease (KD) in a clinical setting. In particular, the present invention claims to use a combination of RNA/DNA molecular biomarkers and their copy numbers (concentrations) to derive KD scores to aid diagnosis, prognosis, risk assessment, and treatment/monitoring of KD. More specifically, these biomarker-characterizing reagents, such as primers and polymerase, will be packaged in a kit with a test system, such as Applied Biosystems QuantStudio 6, which can measure the concentration of the biomarker, generating a KD score to distinguish KD from other febrile pediatric symptoms.

Background

Kawasaki Disease (KD) is a rare acute inflammatory disease in children, associated with vasculitis and persistent fever. KD is the leading cause of acquired heart disease in children in the united states and has a trend to increase markedly in developing countries. If not treated in time, serious complications can occur, with about 25% of children suffering from coronary artery injury. KD mortality is low, however, stenotic lesions may develop late due to vascular remodeling of the damaged artery. Long-term results studies indicate that 50% of children with early stage coronary neoplasia due to KD require revascularization surgery or are at high risk of myocardial infarction.

The etiology of KD is currently unknown. However, it is widely recognized as an abnormal and sustained immune response to infectious agents in a specific genetically susceptible individual. Unfortunately, no consistent infectious agents have been identified so far, further increasing diagnostic difficulties. In addition, different KD population distributions were also observed. KD has the highest incidence in eastern asia, with 1 affected in japan for every 150 children, which accounts for about 1-2% of the hospitalizations of korean children. KD is significantly lower in caucasian populations with prevalence of 9-17/10 tens of thousands, while in children under 5 years of age, prevalence in japan is 265/10 tens of thousands. The average prevalence in other asian countries is 51-194/10 ten thousand people.

Currently, diagnosis of KD relies mainly on clinical symptoms, and no objective molecular detection is currently available to aid diagnosis. Clinical diagnosis is difficult because KD has symptoms and features of many common self-limiting febrile diseases in children, such as fever, rash, mucosal manifestations, lymphadenectasis, and inflammation. About 15% to 36.2% of KD cases are considered to be incomplete KD, do not exhibit complete clinical features, and lack of more than four KD clinical manifestations according to american heart association standards, which can seriously affect diagnostic accuracy. For patients exhibiting an "incomplete" KD and not meeting all clinical criteria, timely diagnosis is critical. Early diagnosis and treatment within ten days after the onset of fever, the use of intravenous immunoglobulin (IVIG) can significantly reduce the incidence of coronary artery disease, thereby reducing the risk of permanent heart injury and coronary aneurysms.

Timely identification, diagnosis, and treatment is often challenging for many clinicians. For those physicians who are unfamiliar or confused with complex clinical algorithms, diagnosis is often delayed according to guidelines for KD diagnosis based on sustained fever and clinical criteria. Furthermore, there is currently no clinically useful and readily available objective molecular biomarker test to aid doctors in diagnosing KD. Thus, there is an urgent need for an objective detection method based on blood biomarkers to help doctors identify KD patients who do not meet all clinical criteria but need treatment to prevent cardiovascular damage.

We studied potential molecular biomarkers associated with kawasaki disease through GEO, literature, and multiple protein platforms, etc. (figure 1). We screened 61 candidate genes, including ABCC1, ADM, C10ORF59, C1S, CAMK, 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, SLC11A2, TLR7, TREML4, VEGFA, HGF, ZBTB20, CASP5, CACNA1E, CLIC3, IFI27, KLHL2, PYROXD2, RTN1, S100P, SMOX, ZNF185, C11ORF82, plGF, CXCL16, OSM, NPPB, TNFSF, CXCL6, CKM, CKB, IL R1. We used a quantitative polymerase chain reaction (qPCR) platform and an assay system, such as Applied Biosystem QuantStudio, 7500 real-time PCR system or Bio-Rad CFX system, to determine the RNA copy number of these biomarkers in the sample. Then, based on RNA copies of these key biomarkers, we developed KD diagnostic models and KD scores that can distinguish KD from other febrile patients.

Disclosure of Invention

Methods of diagnosis, risk assessment and treatment/monitoring of KD using blood/plasma/serum protein biomarkers are disclosed. In particular, the inventors have discovered and used a set of gene biomarkers (RNA, DNA, or proteins) to calculate KD risk scores, which can be used to diagnose, determine risk, monitor KD therapy, and distinguish KD from other febrile diseases. Biomarkers can be determined using a kit of appropriate detection systems (e.g., using the biological system quantsudio, 7500 real-time PCR system, or Bio-Rad CFX system) to determine RNA copy numbers to derive KD scores that can be used alone or in combination with other KD clinical standards to determine and confirm KD diagnosis.

The concentration of RNA/DNA biomarkers such as, but not limited to ABCC1, ADM, C10ORF59, C1S, CAMK, 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, SLC11A2, TLR7, TREML4, VEGFA, HGF, ZBTB, CASP5, CACNA1E, CLIC, IFI27, KLHL2, PYROXD2, RTN1, S100P, SMOX, ZNF, C11 gf, CXCL16, OSM, NPPB, TNFSF, CXCL6, CKM, CKB, IL R1, the combination of these biomarkers in/serum has been correlated with the development of diagnostic systems in blood/plasma and with the development of a quantitative and diagnostic system (for example, a quantitative and a system for the use of the health of the biological system such as a red, a biological system, a biological amplification system (cfud 1, a PCR system, a real-time PCR 1, a system) is used to measure the other biological system, a biological amplification system, b.g. A1, b.k-b. a. And b.m. a.1, b. a.k.1, b.t. b.1, b.t.1, b.t.t.t..

The present invention discloses a method of using a biomarker, which may 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, ZHF185, and the like.

In certain embodiments, a combination set of these biomarkers is used to diagnose KD. The biomarker panel for KD diagnosis can include a complete panel of at least 2 biomarkers (one pair) and 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 panel sets are sufficient to distinguish KD from other febrile diseases and are more economical. However, larger groups may provide more detailed information and may be used in the practice of the invention in different regional populations.

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

A method for calculating KD scores based on the biomarkers and methods described above, and determining risk-determining KD with three distinct ranges of KD risk. A low KD risk below the low score cut-off indicates that the patient is at low risk of developing KD. A high KD risk above the high score cut-off indicates that the patient is at high risk for KD. A score between low and high KD score cutoff indicates a medium KD risk.

In certain instances, clinical parameters are used in combination with the biomarkers described herein to diagnose KD. For example, the invention includes a method for determining KD scores for a patient suspected of having KD who is continuously feverish for 5 days. The method includes measuring at least seven clinical parameters including fever duration, hemoglobin concentration in blood, C-reactive protein concentration in blood, white blood cell count, eosinophil percentage in blood, monocyte percentage in blood, and immature neutrophil percentage in blood according to standard care of the patient.

KD scores may be calculated from measured blood biomarker values by geometric mean, multivariate Linear Discriminant Analysis (LDA), or distributed Gradient Boost Decision Tree (GBDT) machine learning, such as XGBoost. In a bigram, a subject operating characteristic (ROC) curve may be derived from a biomarker combination or pairing. KD scores can then be classified as low risk KD clinical scores, medium risk KD clinical scores, or high risk KD clinical scores by the methods described herein.

In another aspect, the invention includes methods and programs for diagnosing patients with KD using the biomarker combinations and calculating KD scores. The method comprises the following steps: 1) obtaining a biological sample from a patient, 2) measuring the RNA/DNA copy number or concentration of each biomarker in the biological sample, 3) comparing the level of each biomarker to a corresponding reference range of biomarkers. The range of reference values may represent the level of a biomarker from one or more samples from a subject without KD (i.e., a normal sample), or from one or more subjects with KD. Differential levels of blood biomarkers of the biomarker combinations compared to reference values of biomarkers for control subjects indicate that the patient has KD. In one embodiment, the method further comprises a method of calculating a KD score to distinguish whether the febrile disease of the patient is KD.

Biomarkers can be measured by using specific primers and reporting systems to determine the copy number of the biomarkers in the above biomarkers and methods. For example, but not limited to, quantitative PCR analysis (qPCR), reverse transcription quantitative PCR (RT-qPCR), gene microarrays, RNA or DNA sequencing (RNAseq/dnaeq), sandwich assays, magnetic capture, microsphere capture, electrophoresis blotting, surface Enhanced Raman Spectroscopy (SERS), flow cytometry or mass spectrometry, etc. methods are performed to determine the copy number of these biomarkers. In certain embodiments, the copy number of the biomarker is measured starting with the binding of a particular DNA primer to the biomarker RNA, followed by reverse transcription and polymerase chain reaction to determine the copy number of the biomarker with a detectable reporter (e.g., BYBR green dye, etc.). Wherein the primer specifically binds to the biomarker or fragment thereof.

The invention also includes a method for assessing the efficacy of an intervention agent for treating a patient suffering from KD. The method comprises the following steps: the copy number or concentration of each biomarker before and after treatment was analyzed from patient samples using the biomarker panel described herein. The effectiveness of the treatment can be determined by the corresponding reference value range and the calculated KD score.

In particular, the invention includes a method for selecting patients suspected of having KD for intravenous immunoglobulin (IVIG) treatment, the process comprising: 1) Diagnosing a patient according to the methods described herein; b) If the patient has a positive diagnosis of KD, the patient is selected to receive 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 the KD score and the confirmed KD of the high or medium risk region comprising the expression profile of the biomarker panels described herein.

In another aspect, the invention relates to a method for treating a patient suspected of having KD, comprising the steps of: 1) Diagnosing a patient or receiving information regarding a patient's diagnosis according to the methods described herein; and 2) intravenous immunoglobulin (IVIG) treatment of the patient if the patient is diagnosed with KD. In one aspect, the method comprises 1) determining a KD clinical score for the patient; and 2) administering a therapeutically effective dose of intravenous immunoglobulin (IVIG) to the subject if the patient has a high or medium risk KD clinical score and KD is confirmed based on the expression profile of the biomarker panel of paragraphs 1 to 11.

In another aspect, the invention relates to a kit for use in 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 comprises at least one reagent for measuring KD biomarkers and printed instructions for reacting the reagent with a biological sample or a portion of a biological sample to measure at least one KD biomarker in the biological sample. The reagents may be packaged in separate containers. The kit may also include one or more reagents for controlling the reference sample and for performing qPCR to detect the number of biomarker replications, as described herein.

In another aspect, the invention relates to a method of detection comprising: a) Measuring the copy number of each biomarker in a biomarker panel (as described herein) in a blood, plasma or serum sample taken from a patient suspected of having KD; and b) comparing the measured value of each biomarker to a reference value controlling each biomarker in the subject, wherein differential expression of the biomarker in a blood, plasma or serum sample compared to the reference value indicates that the patient has KD. In one aspect, the detection method further comprises determining a KD score based on the biomarker concentrations for the patient.

In certain instances, at least one biomarker is measured by a primer targeting biomarker, 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 primer pair 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, a primer pair that specifically binds 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 LGALS 2.

The present invention provides the use of a detection reagent for measuring the amount of one or more kawasaki disease biomarkers selected from the following biomarker groups, characterized in that the kawasaki disease biomarker groups comprise one or more of IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100a12, S100A9, TLR7 and ZHF185, in the manufacture of a kit for determining the presence of a kawasaki disease biomarker level in a subject, the method of determining the presence of a kawasaki disease biomarker level in a subject comprising:

a. Assessing a set of kawasaki disease biomarkers in a sample from a subject, the sample being blood, serum or plasma, to determine the expression level of each kawasaki disease biomarker in the sample;

b. a level representation of kawasaki disease biomarkers is obtained based on the level of each kawasaki disease biomarker in the group.

In some embodiments, the full length of the oligonucleotide or its nucleotide sequence, RNA or DNA level of each kawasaki disease biomarker is measured.

In some embodiments, the biomarker expression level is expressed as a circulation threshold C (t).

In some embodiments, the panel includes C19ORF59 and IFI27.

In some embodiments, the panel includes C19ORF59, IFI27, S100a12, and S100A9.

In some embodiments, the panel includes C19ORF59, IFI27, S100a12, S100A9, CLIC3, and SLC11A1.

In some embodiments, the panel includes C19ORF59, IFI27, S100a12, S100A9, CLIC3, SLC11A1, CACNA1E, and LGALS2.

In some embodiments, the panel includes C19ORF59, IFI27, S100a12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS, ABCC1, and CAMK4.

In some embodiments, further comprising providing a report representative of kawasaki disease biomarker levels.

In some specific embodiments, the kawasaki disease biomarker level appears to derive a kawasaki disease score, wherein the kawasaki disease score:

a. derived from the cycle threshold C (t) difference between two different genes;

b. deriving from the measured blood biomarker values by geometric mean, multivariate Linear Discriminant Analysis (LDA) or distributed gradient enhanced decision tree (GBDT) machine learning; or (b)

c. Is a multiple of the level of each biomarker.

The present invention provides a diagnostic system comprising a kit, reagents and an instrument for generating a kawasaki disease score from a sample from a subject, the sample being blood, serum or plasma, comprising: a detection reagent for measuring the amount of one or more kawasaki disease biomarkers selected from the group of biomarkers consisting of one or more of IFI27, C19ORF59, CACNA1E, CASP, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100a12, S100A9, TLR7, and ZHF 185.

In some embodiments, further comprising:

a. A platform system for measuring kawasaki disease biomarkers;

b. calculation table for calculating Kawasaki disease score

c. An indication of whether the patient has kawasaki disease is determined.

In some embodiments, the panel includes C19ORF59 and IFI27.

In some embodiments, the panel includes C19ORF59, IFI27, S100a12, and S100A9.

The invention provides a kawasaki disease biomarker panel, which is characterized by comprising one or more biomarkers selected from IFI27, C19ORF59, CACNA1E, CASP, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100a12, S100A9, TLR7 and ZHF 185.

In some embodiments, C19ORF59 and IFI27 are included.

In some embodiments, C19ORF59, IFI27, S100a12, and S100A9 are included.

In some embodiments, C19ORF59, IFI27, S100A12, S100A9, CLIC3, and SLC11A1 are included.

In some embodiments, C19ORF59, IFI27, S100A12, S100A9, CLIC3, SLC11A1, CACNA1E, and LGALS2 are included.

Within the art of the disclosure described herein, it will be readily apparent to the skilled artisan for these and other embodiments.

Drawings

The best mode for carrying out the invention is to make the detailed description in connection with the accompanying tables and drawings. At least one drawing is included in the patent or application document. It is emphasized that, according to common practice, the various features of the drawing are not to scale. Rather, the dimensions of the various features are arbitrarily expanded or reduced for clarity of presentation.

FIG. 1. Discovery and validation process of KD disease biomarkers. Potential molecular biomarkers were initially determined by GEO analysis, literature discovery and multiplex protein biomarker studies. In the discovery cohort, 61 gene targets were narrowed down to 18 genes. The final group was validated in a validation queue of KD (n=80) and a heat-generating control group (n=80).

FIG. 2 is a model construction based on IFI27 and C19ORF59, where a is the delta C (t) construction of the set based on IFI27 and C19ORF 59. Serum copy numbers of each biomarker were measured using qPCR instrument quantsudio 6, using the corresponding primers. b is the model, and the AUC is 0.930 and the optimal cut-off value is 3.274 according to ROC analysis. c is the behavior of the model at the optimal cut-off.

Fig. 3 is a cut-off-based risk prediction, where a is two thresholds and three risk level score models divide the cohort population into high-risk, middle and low-risk populations, the solid line is FC (Febrile control) group, the dotted line is KD group. b is a classification of the population based on a risk scoring system. c is that at best cut-off 4.958, the high-risk Positive Predictive Value (PPV) reaches 91.8% and the low-risk patient population with risk score below 3.558 has 89.5% Negative Predictive Value (NPV).

Fig. 4. Classification of coronary Z scores according to KD risk classification. Our group captured most cases of aneurysms. The panel may also identify KD patients with normal or dilated coronary arteries.

Detailed Description

Biomarkers for Kawasaki Disease (KD), KD biomarker sets, and methods of obtaining a KD biomarker level representation of a sample are provided. These compositions and methods find use in a number of applications, including, for example, diagnosing KD, assessing if KD is present, monitoring subjects with KD, and determining methods of treating KD. Furthermore, systems, devices and kits for carrying out the methods are provided. These and other objects, advantages and features of the present invention will become apparent to those skilled in the art when the details of these compositions and methods are described in greater detail below.

Before the present methods and compositions are described, it is to be understood that this invention is not limited to 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 present invention, as the scope of the present invention will be limited only by the appended claims.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described can be used in the practice or testing of the present invention, some of the possible and preferred methods and materials are now described. All publications mentioned are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. It is to be understood that the disclosure herein covers any conflict with the disclosure of the incorporated publication.

As will be apparent to those of skill in the art, each of the specific embodiments described has discrete components and features that can be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the invention. Any of the described methods may be performed in the order of the events, or in any other order that is logically possible.

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

As described above, the present invention relates to methods, compositions, systems (e.g., qPCR systems such as AB quantsudio 6) and kits for providing an assessment of Kawasaki Disease (KD), including diagnosis, risk assessment, monitoring and/or KD in a therapeutic theme. By "Kawasaki disease" or "KD" is meant a complication of multiple system inflammation and fever, possibly accompanied by rash, swelling of hands and feet (oedema), redness and swelling of the eyes and inflammation, swelling of the lymph nodes in the neck, irritation and inflammation of the mouth, lips and throat. KD occurs mainly in children under 5 years of age, but older children, adolescents and adults still may be infected with KD. KD can lead to cardiovascular disease and aneurysms if left untreated within 10 days after onset of fever. By "diagnosing" KD or "providing a diagnosis of KD" is generally meant providing a diagnosis of KD, e.g., determining whether or not affected by KD (e.g., a subject with complete or incomplete symptoms of KD); classifying the KD of a subject to determine its subtype of disease or disorder; determining the severity of KD, etc. By "risk assessment" KD or "providing a KD risk assessment as one of the clinical signs" is generally meant providing a KD risk as another clinical sign, e.g., diagnosing a risk or susceptibility to a subject in the presence of other clinical symptoms; assessing the risk of disease progression and/or disease outcome; the response of the subject to KD therapy is predicted, e.g., positive, negative, no response, etc. By "monitoring" KD is generally meant monitoring the condition of a subject, e.g., providing information for diagnosis of KD, providing information for prognosis/risk of KD, along with other clinical signs, providing information about the efficacy or efficacy of KD therapy, etc. By "treating" KD is meant any therapeutic method that prescribes or provides KD in a mammal, including: (a) Preventing KD and related cardiovascular events in a subject that may be susceptible to KD but has not yet been diagnosed; (b) Inhibiting KD and cardiac events, i.e., arresting the development of symptoms thereof and cardiac aneurysms; or (c) alleviate KD, even if KD degenerates and reduces cardiac arteries.

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

Kawasaki disease biomarkers and panel

In certain aspects of the invention, kawasaki disease biomarkers and sets of kawasaki disease biomarkers are provided. By "kawasaki disease biomarker" is meant a representation of a molecular entity associated with a kawasaki disease phenotype in a sample. For example, kawasaki disease biomarkers may have a different representation (i.e., representation at a different level) in a sample of an individual compared to a healthy individual. In certain instances, elevated levels of the biomarker, such as C10ORF59, are associated with a kawasaki disease phenotype. For example, the RNA copy number of the biomarker may be 1.5-fold, 2-fold, 2.5-fold, 3-fold, 4-fold, 5-fold, 7.5-fold, 10-fold or more higher in a sample associated with a kawasaki disease phenotype than in a sample not associated with a kawasaki disease phenotype. In other cases, the reduced level of the biomarker is associated with a kawasaki disease phenotype, such as IFI27. For example, the RNA copy number of the biomarker may be 10%, 20%, 30%, 40%, 50% or more lower in a sample associated with the kawasaki disease phenotype than in a sample not associated with the kawasaki disease phenotype.

Kawasaki disease biomarkers may include proteins and peptides related to kawasaki disease and their corresponding genetic sequences, i.e., RNA, DNA, etc. By "gene" or "recombinant gene" is meant a nucleic acid comprising an open reading frame for encoding a protein.

The boundaries of the coding sequence are defined by a start codon at the 5 'end (amino) and a translation stop codon at the 3' end (carboxyl). The transcription termination sequence may be located 3' to the coding sequence. Furthermore, the gene may optionally include its natural promoter (i.e., the promoter to which the exons and introns of the gene are operably linked in non-recombinant cells, i.e., naturally occurring cells), and associated regulatory sequences, may have sequences upstream of the AUG start site and/or sequences that do not include untranslated leader sequences, signal sequences, downstream untranslated sequences, transcription initiation and termination sequences, polyadenylation signals, translation initiation and termination sequences, ribosome binding sites, and the like.

As shown in the examples of the present 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 assessment, e.g., diagnosis of KD, assessment of risk of developing KD, monitoring of subjects with KD, determination of treatment of subjects with KD, and the like. Including but not limited to IFI27, C19ORF59, CACNA1E, CASP, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100a12, S100A9, TLR7, ZHF185, and the like.

In addition, KD sets are provided herein. By "KD biomarker panel" is meant two or more KD biomarkers, e.g., 2, 4, 6, 8, 10, 12, 14, 16, and 18 biomarkers, which when taken together in their levels (i.e., copy numbers) can be used to provide KD assessment, e.g., to perform KD diagnosis, risk assessment, monitoring, and/or treatment. Of particular importance are the groups comprising IFI27, C19ORF59, S100a12, S100A9, CACNHA1E and CLIC 3. For example, in certain embodiments, the KD set can include Δc (t) for C19ORF59 and IFI 27.

Other KD biomarker sets for use as KD sets in the present methods can be readily determined by one of ordinary skill using any convenient statistical method, e.g., as known herein or described in the working examples herein. For example, KD classification analysis may be performed by combining Genetic Algorithm (GA) and full-pair (AP) Support Vector Machine (SVM) methods, with predictive features determined automatically, e.g., by iterative GA/SVM, resulting in a very compact set of non-redundant KD-related analytes for optimal classification performance. While different sets of classifiers will typically possess only a small number of overlapping genetic features, they will have similar levels of accuracy as described above and in the working examples herein that provide KD assessment.

Method

The invention provides in certain aspects methods of obtaining a KD biomarker level representation of a subject. By KD biomarker level representation is meant a representation of one or more KD biomarker levels in a biological sample of a subject, e.g., a KD biomarker panel. The term "biological sample" includes various sample types obtained from an organism, which can be used in diagnostic, prognostic or monitoring assays. The term includes blood and other liquid samples from biological sources or derivatives and progeny thereof. The term includes samples that have been treated in any way after collection, such as by treatment with reagents, solubilization or enrichment of certain components, and the like. The term includes clinical samples, as well as cell supernatants, cell lysates, serum, plasma, biological fluids, and tissue samples. Clinical samples for use in the methods of the invention may be obtained from a variety of sources, particularly blood samples.

In particular, the sample source includes a blood sample or preparation thereof, such as whole blood, serum or plasma. In many embodiments, a suitable initial source for a human sample is a blood sample. Thus, the sample for the subject's assay is typically a sample from blood. The blood sample may be derived from whole blood or a portion thereof, such as serum, plasma, etc., and in certain embodiments, the sample is derived from blood, allowed to coagulate, and the serum is separated and collected for measurement.

In certain embodiments, the sample is serum or a serum-derived sample. Any convenient method may be used to prepare the fluid serum sample. In many embodiments, the method employs skin puncturing (e.g., finger prick, venipuncture) to draw venous blood into a clotting or serum separation tube, allowing the blood to clot, and centrifuging the serum away from the coagulated blood. Serum was then collected and stored until assayed. Once a sample from the patient is obtained, the sample can be assayed to determine the level of KD biomarkers.

The subject sample is typically obtained from an individual during a clinical visit in which the patient is continuously repeatedly febrile. Kawasaki disease is most likely to occur in children under five years of age, but it may also occur in any age group, including adolescents and adults.

Once the sample is obtained, it can be used directly, frozen or maintained in an appropriate medium for a short period of time. Typically, the sample will be from a human patient, although animal models may also be used, for example: horses, cattle, pigs, dogs, cats, rodents (e.g., mice, rats, hamsters), primates, and the like. Any convenient tissue sample may be evaluated in the subject methods so long as it exhibits differential expression of one or more of the kawasaki disease biomarkers disclosed herein in a kawasaki patient. Typically, a suitable sample source will be derived from a body fluid that is capable of analyzing that molecular entities of interest (e.g., proteins, peptides, and RNAs) have been released.

The subject sample may be treated in a number of ways to enhance detection of one or more kawasaki disease biomarkers. For example, if the sample is blood, red blood cells may be removed from the sample (e.g., by centrifugation) prior to performing the test. Such treatment may reduce the non-specific background level of detection of kawasaki disease biomarker levels using affinity reagents. The sample may be concentrated using procedures well known in the art (e.g., acid precipitation, alcohol precipitation, salt precipitation, hydrophilic precipitation, filtration) to enhance detection of kawasaki disease biomarkers. In some embodiments, the pH of the test and control samples will be adjusted and maintained at a pH near neutral. Such pH adjustment will prevent complex formation, thereby 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 practicing the present methods, KD biomarker levels in a biological sample of an individual 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," "detect," "measure," "evaluate," and "determine" are used interchangeably to refer to any form of measurement, including determining whether an element is present and including quantitative and qualitative measurements. The evaluation may 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 herein. "oligonucleotide" refers to a polymer of nucleic acids (RNA or DNA sequences) rather than a specific length of a molecule. Thus, RNA, DNA and fragments thereof are included within the definition of oligonucleotide. The term also encompasses modified oligonucleotides, such as methylated DNA, DNA with fluorescent dye/quencher attached, modified base RNA/DNA, DNA primers, and the like. Included within the definition are oligonucleotides comprising one or more base analogs, oligonucleotides with alternative linkages, and other modifications known in nature and non-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 the desired gene in a patient sample to derive expression of the nucleic acid biomarker. Any convenient protocol may be used to detect the level of nucleic acid in a sample. While many different methods of 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 an array-based gene expression analysis protocol. These applications are hybridization assays, in which nucleic acids that show "probe" nucleic acids of the genes to be measured/analyzed are used to generate biomarker expression. In these assays, a target nucleic acid sample is first prepared from the initial nucleic acid sample from which the assay is performed, where the preparation may include labeling the target nucleic acid with a label, such as a member in a signal generating system. After preparation of the target nucleic acid sample, the sample is contacted with the array under hybridization conditions to form a target nucleic acid complex complementary to the probe sequence attached to the surface of the array. The presence of hybridized complex is then detected qualitatively or quantitatively.

Specific hybridization techniques for generating biomarker profiles employed in the methods include those described in U.S. Pat. nos. 5,143,854;5,288,644;5,324,633;5,432,049;5,470,710;5,492,806;5,503,980;5,510,270;5,525,464;5,547,839;5,580,732;5,661,028; the techniques described in U.S. Pat. No. 5,800,992, the disclosure of which is incorporated herein by reference, and includes 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 comprising probes for each phenotype determining gene to be assayed is contacted with a target nucleic acid as described above. The contacting is performed under hybridization conditions, e.g., under stringent hybridization conditions, and unbound nucleic acids are then removed. The term "stringent detection conditions" as used herein refers to conditions compatible with conditions that produce binding pairs of sufficient complementarity, e.g., surface binding and solution phase nucleic acids, to provide the desired detection specificity, while incompatible with the formation of binding pairs between binding members that are not sufficiently complementary to provide the desired specificity. Stringent detection conditions are the sum or combination of hybridization and wash conditions.

The pattern generated by the hybridized nucleic acids provides expression information about each of the probed genes, wherein the expression information is based on whether the gene is expressed and on the usual expression level, expression data, e.g. biomarker representations in transcriptome form, may be qualitative and quantitative.

Alternatively, non-array-based methods may be employed to quantify the level of one or more nucleic acids in a sample, including amplification protocol-based methods, such as Polymerase Chain Reaction (PCR) -based detection, including quantitative PCR, reverse transcription PCR (RT-PCR), real-time PCR, and the like.

Where it is desired to detect protein levels, any convenient protocol may be used to assess protein levels, wherein the level of one or more proteins in the sample to be tested is determined. For example, one representative and convenient protocol type for determining protein levels is enzyme-linked immunosorbent assay (ELISA). In ELISA and ELISA-based assays, one or more antibodies specific for the protein of interest may be immobilized on a selected solid surface, preferably a surface exhibiting protein affinity, such as the wells of a polystyrene microwell plate. After removal of the incompletely adsorbed material, the wells of the assay plate are coated with a non-specific "blocking" protein known to antigenically neutralize the test sample, such as Bovine Serum Albumin (BSA), casein or milk powder solution. This prevents non-specific adsorption sites on the immobilized surface, thereby reducing the background caused by non-specific binding of antigen to the surface. After removal of unbound blocking protein, the sample to be tested is contacted with the immobilized surface under conditions conducive to the formation of immune complexes (antigen/antibody). Such conditions include dilution of the sample with a diluent, such as Bovine Serum Albumin (BSA) or Bovine Gamma Globulin (BGG) in PBS/Tween or pbsa triton-X100, which also helps reduce non-specific background and allows the sample to be incubated at a temperature of about 25 ° -27 ℃ for about 2-4 hours (although other temperatures may be used). After incubation, the surface contacted with the antisera is washed to remove non-immune complex forming materials. One exemplary washing procedure includes washing with a solution such as PBS/Tween, PBS/Triton-X100, or borate buffer. The presence and amount of immune complexes is then determined by subjecting the bound immune complexes to a second antibody having a different target specificity than the first antibody and detecting binding of the second antibody. In certain embodiments, the secondary antibody will have an associated enzyme, such as urease, peroxidase or alkaline phosphatase, which will produce a color precipitate upon incubation with the appropriate chromogenic substrate. For example, an incubation with urease or peroxidase-conjugated anti-human IgG may be performed for a period of time under conditions conducive to immune complex formation (e.g., incubation in PBS solution containing PBS/Tween for 2 hours at room temperature). After incubation with the secondary antibody and washing to remove unbound material, the amount of label can be quantified by incubation with chromogenic substrates (such as urea and bromocresol purple) or hydrogen peroxide and 2,2' -diaminobenzothiazole-6-sulfonic Acid (ABTS) with peroxidase labeling, and then measuring the extent of color formation (e.g., using a visible spectrophotometer).

The previous method may be modified by first combining the sample with a detection plate. The primary antibody is then incubated with a detection plate, followed by detection of the bound primary antibody using a labeled secondary antibody having specificity for the primary antibody.

The antibody or antibody-immobilized solid matrix may be made of various materials and various shapes, such as microwell plates, microbeads, impregnated rods, resin particles, and the like. The matrix can be selected to maximize signal-to-noise ratio, minimize background binding, and be easy and inexpensive to isolate. The washing may be performed in a manner most suitable for the matrix used, for example by removing the beads or impregnated rods from the reservoir, evacuating or diluting the wells of the microwell or washing the beads, particles, chromatographic columns or filters with a washing solution or solvent.

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 techniques, flow cytometry, western blot, and immunohistochemistry.

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

Once the level of one or more KD biomarkers is determined, the measurement can be analyzed by a variety of methods to obtain a representation of KD biomarker levels.

For example, measurements of one or more KD biomarkers can be analyzed separately to develop KD scores. As used herein, a "KD score" is a normalized level of one or more KD biomarkers in a patient sample, e.g., 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 to the selected gene expression or the signal of the whole panel. Other methods of calculating the KD profile are also common knowledge to the skilled person.

As another example, measurements of a set of KD biomarkers can be collectively analyzed to yield a single KD score. By "KD score" is meant a single metric representing the weighted level of each KD biomarker in a KD set. Thus, in some embodiments, the method comprises detecting the biomarker levels of the KD set in the sample, and calculating a KD score based on the weighted levels of the KD biomarkers. KD scores for patient samples can be calculated using a variety of methods and algorithms known. For example, weighted biomarker levels (e.g., each normalized biomarker level multiplied by a weighting factor) may 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 set, its weighting factor or simply "weight" may reflect a change in the level of the analyte in the sample. For example, the analyte level of each KD biomarker may be logarithmically converted and weighted to 1 (for those biomarkers that increase in level in KD) or-1 (for those biomarkers that decrease in level in KD), respectively, and then the ratio of the increased biomarker sum to the decreased biomarker sum is determined to derive the KD signature. In other cases, the weights 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 Machine (SVM), random forest, and the like, and may be calculated using a dataset obtained from the dataset of the resulting sample. In some cases, the weight of each biomarker is defined by a dataset obtained from a patient sample. In other cases, the weight of each biomarker may be defined based on a reference dataset or "training dataset".

These analysis methods can be readily performed by one of ordinary skill using a computer system, for example, using any known hardware, software, and data storage media, and employing any convenient algorithm for such analysis. For example, the data mining algorithm may be applied through "cloud computing," smartphone-based or client server platform-based, or the like.

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

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

Application of

The resulting KD biomarker level characterization has many uses. For example, biomarker level characterization can be used to diagnose KD, i.e., determine whether a subject is affected by KD, the type of KD (full KD and incomplete KD), the severity of KD (normal heart phenotype, distension or aneurysms, etc.). In some cases, the subject may develop clinical symptoms of KD, such as fever, rash, swelling of hands and feet, irritation and redness of the white area of the eye, cervical lymphadenectasis, and irritation and inflammation of the mouth, lips, and throat.

As another example, when a patient develops incomplete KD, KD biomarker level characterization can be used to assess KD risk, i.e., KD clinical characterization as a risk indicator. For example, KD biomarker level characterization can be utilized as a diagnosis of KD in a subject in lieu of additional clinical characterization. By "add biomarker profile if an individual has KD" is meant that the likelihood of whether an individual has KD can be determined even in the presence of less than four clinical profiles according to the AHA guidelines. The KD biomarker level characterization and KD score can be used as clinical characterization of disease progression and/or disease outcome, e.g., diagnosis of expected confirmation of KD, expected KD duration, whether KD will develop cardiac phenotype, etc. KD biomarker level characterization can be used to predict a subject's response to KD therapy, e.g., positive, negative, or no response.

As another example, KD can be monitored using KD biomarker level characterization. By "monitoring" KD, it is generally meant monitoring a condition of a subject, such as informing a diagnosis of KD, informing a prognosis of KD, providing information about the efficacy or efficacy of KD treatment, and the like.

As another example, KD biomarker level characterization can be used to determine a subject's treatment regimen. In this context, "treatment" and similar terms generally refer to obtaining a desired pharmacological and/or physiological effect. The effect may be as a prophylactic measure, completely or partially preventing a disease or a symptom thereof, or may be as a therapeutic measure, partially or completely curing a disease and/or an adverse reaction associated with a disease. Herein, "treatment" encompasses any treatment of a mammal, including: (a) Preventing a disease, i.e., preventing an individual from suffering from the disease from not yet being diagnosed as suffering from the disease; (b) inhibiting the disease, i.e., arresting its development; or (c) alleviating the disease, i.e., causing regression of the disease. The therapeutic agent may be administered before, during or after the onset of the disease or injury. In particular, the treatment of ongoing diseases, wherein the treatment stabilizes or alleviates adverse clinical symptoms in the patient. The subject treatment may be performed prior to and in some cases after the disease symptom stage. The terms "individual," "subject," "host," and "patient" are used interchangeably herein and refer to any mammalian subject, particularly a human, in need of diagnosis, treatment, or therapy. KD therapy is well known and may include bed rest, drinking more water, low salt diet, blood pressure controlling drugs, corticosteroids, inducing pregnancy, etc.

In certain embodiments, a subject method of providing a KD risk assessment, e.g., diagnosing KD, a KD risk assessment, monitoring KD therapy, etc., can include comparing the obtained KD biomarker level representation to a KD phenotyping element to identify similarity or variability to the phenotyping element, and then utilizing the identified similarity or variability to provide a KD assessment, e.g., diagnosing KD, a KD risk assessment, monitoring KD therapy, determining KD therapy necessity, etc. By "phenotyping element" is meant an element representing 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 the phenotype of a subject, such as whether the subject is healthy or has KD, whether the subject has a KD that is likely to progress to complete/confirmed with incomplete KD, whether the subject has a KD that is responsive to therapy, etc.

For example, the KD phenotype identification element may be a sample from an individual with or without KD that can be used as an experimentally determined reference/control for the biomarker level representation of a given individual. As another example, KD phenotype identification elements can be a biomarker level representation, such as a biomarker profile or score, that represents KD status, which can be used as a reference/control to interpret the biomarker level representation for a given individual. The phenotyping element may be a positive reference/control, e.g. a sample or a biomarker level representation thereof from a child with KD or a child with KD that has progressed from incomplete to complete KD or is controllable by known treatment methods, or a sample or a biomarker level representation thereof for KD that has been determined to be responsive to IVIG. Alternatively, the phenotyping element may be a negative reference/control, e.g. a sample from a child not suffering from KD or from other febrile diseases or a biomarker level representation thereof. Preferably, the phenotyping elements are samples of the same type or if biomarker level representations are obtained from samples in which the individual being monitored generates biomarker level representations, they should be samples of the same type. For example, if the serum of an individual is being evaluated, the phenotyping element is preferably plasma.

In certain embodiments, the resulting biomarker level representation will be compared to a single phenotyping element to obtain information about whether the individual tested has KD. In other embodiments, the resulting biomarker level representation will be compared to two or more phenotyping elements. For example, the resulting biomarker level representation may be compared to negative and positive references to obtain confirmation as to whether the individual would develop KD. As another example, the resulting biomarker level representation may be compared to a reference representing KD responsive to treatment and representing KD non-responsive to treatment to obtain information as to whether the patient is responsive to treatment.

In certain embodiments, the obtained biomarker level characterization is compared to a single phenotyping element to obtain information about whether the individual being tested has KD. In other embodiments, the obtained biomarker level characterization will be compared to two or more phenotyping elements. For example, the obtained biomarker level characterization may be compared to negative and positive references to obtain confirmation as to whether the individual would develop KD. As another example, the obtained biomarker level characterization may be compared to a reference representing a KD that is sensitive to the treatment and a reference representing a KD that is insensitive to the treatment to obtain information about whether the patient is responsive to the treatment. Comparing the obtained biomarker level characterization to one or more phenotyping elements may use any convenient method, wherein various methods are known to the skilled person. For example, to those skilled in the art of qPCR, they will know that qPCR data can be compared by, for example, normalizing the cycle threshold C (t) of a known amount of RNA, comparing the normalized values, and the like. The results of the comparison step indicate how similar or dissimilar the obtained biomarker level profile is to the control/reference profile, and this similarity/dissimilarity information can be used, for example, to predict onset of KD, diagnose KD, monitor KD patients, etc. Also, to those skilled in the array art, they will appreciate that the array profiles may be compared by, for example, comparing digital images of the profiles, comparing databases of the profiles, etc. Patents describing methods of comparing expression profiles include, but are not limited to, U.S. Pat. nos. 6,308,170 and 6,228,575, the contents of which are incorporated herein by reference. Methods of comparing biomarker horizontal profiles are also described above. Similarity may be based on relative biomarker levels, absolute biomarker levels, or a combination of both. In certain embodiments, a computer storing a program for receiving input from a biomarker level characterization of an individual being monitored is used to make a similarity determination, e.g., from a user, determine similarity to one or more reference profiles or reference scores, and return KD clinical characterization predictions, e.g., to a user (e.g., laboratory technician, doctor, pregnant woman, etc.). Further description of computer-implemented aspects is provided below. In certain embodiments, the similarity determination can be based on visual comparison of biomarker levels, e.g., comparing the KD score to a series of phenotyping elements (e.g., a series of KD scores) to determine a reference KD score that is most similar to the subject. The above-described comparison step yields various different types of information about the detected cells/body fluids, depending on the type and nature of the phenotyping element that compares the obtained biomarker level profile. Thus, the above comparison step can yield positive/negative predictions of onset of KD, positive/negative diagnosis of KD, characterization of KD, response information of KD to treatment, and the like.

In other embodiments, the biomarker level representation is directly used to make KD diagnosis, KD risk assessment, or monitor KD therapy without comparison to a phenotyping element.

The method can be applied to different types of objects to be tested. In many embodiments, the subject belongs to a mammalian species, including carnivora (e.g., dogs and cats), rodentia (e.g., mice, guinea pigs, and rats), lagomorpha (e.g., rabbits), and primates (e.g., humans, chimpanzees, and monkeys). In certain embodiments, the animal or host, i.e., the subject (also referred to herein as a patient), is a human.

In certain embodiments, the method of providing a KD assessment comprises providing a diagnostic, prognostic, or monitoring result. In certain embodiments, KD assessment of the present disclosure is accomplished by providing an assessment by one of skill in the art, e.g., one of skill in determining whether a patient is currently suffering from KD, the patient's KD type, stage, or severity, etc. (KD diagnosis); prediction of patient risk, disease progression, response to treatment, etc., by those skilled in the art (i.e., those skilled in the art "KD prognosis"); or monitoring of KD by one skilled in the art. Thus, the method may further comprise the step of generating or outputting a report providing the results of the assessment by a person skilled in the art, which report may be in the form of an electronic medium (e.g. an electronic display on a computer display screen), or in the form of a tangible medium (e.g. a report printed on paper or other tangible medium). Any form of reporting may be employed, for example, as known in the art or as described in more detail below.

Reporting

As described herein, a "report" is an electronic or tangible file that includes reporting elements that provide information of interest related to subject 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 more detail above. In some embodiments, the subject report includes at least a KD assessment by a technician, such as a KD diagnosis, a KD prognosis as a KD clinical sign, a KD monitoring analysis, a treatment recommendation, and the like. The subject report may be generated electronically, in whole or in part. The subject report may further include one or more of the following: 1) Information about the test facility; 2) Service provider information; 3) Patient data; 4) Sample data; 5) An assessment report, which may include various information, including a) the reference values employed and b) test data, which may include, for example, protein level determinations; 6) Other functions.

The report may include information about the detection facility and which information is relevant to the hospital, clinic, or laboratory in which the sample and/or data was collected and generated. Sample collection may include obtaining a fluid sample, such as blood, saliva, urine, and the like. Tissue samples from subjects, such as tissue biopsies and the like. Data generation may include measuring biomarker concentrations for KD patients versus healthy individuals, i.e., individuals who do not and/or do not develop KD. The information may include one or more details such as the identity, date and time of the laboratory technician performing the analysis and/or inputting the input data, the location where the sample and/or resulting data was stored, lot numbers of reagents (e.g., kits, etc.) used in the assay, etc., in relation to the name and location of the test facility. The report field containing this information may typically be populated with user-provided information.

The report may include information about the service provider, which may be located outside or within the medical facility where the user is located. Examples of such information may include the name and location of the service provider, the name of the censoring person, and the name of the individual performing the sample collection and/or data generation, as necessary or desired. The report field with this information may typically be populated with user entered data that may be selected from a specified selection (e.g., 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 explanatory report.

The report may include a patient data portion including patient history (e.g., age, ethnicity, serotype, past KD onset, and any other characteristics of the patient), as well as managing patient data such as information identifying the patient (e.g., name, patient date of birth (DOB), gender, mailing and/or residence address, medical Record Number (MRN), room and/or bed number of medical facility, insurance information, etc.), the name of the patient's doctor or other health professional requiring monitoring evaluation, if different from the doctor requiring patient care (e.g., primary care doctor).

The report may include a sample data portion that may provide information about the biological sample being analyzed in the monitoring assessment, such as the source of the biological sample obtained from the patient (e.g., blood, saliva, or tissue type, etc.), the manner of sample processing (e.g., storage temperature, preparation protocol), and the date and time of collection. The report field with this information may typically be populated with user-entered data, some of which may be provided as a selection of pre-scripted scripts (e.g., using a drop down menu). The report may include a results portion.

The report may include an evaluation report portion that may include information generated after processing the data described herein. The interpretive report may include a prediction of the likelihood of the subject developing KD. The explanatory report includes diagnosis of KD. The interpretive report may include features of KD. The evaluation portion of the report may also optionally include suggestions. For example, where the results indicate that KD may occur, the advice may include advice to change diet, take antihypertensives, 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 elements of a more detailed information report about the selected database. For example, the reported patient data element may include a hyperlink to an electronic patient record or a site for accessing such patient record, which is stored in a confidential database. The latter embodiment may be of interest to a hospitalization system or clinic environment. When in electronic format, the report is recorded on a suitable physical medium, such as a computer readable medium, e.g., computer memory, flash drive, CD, DVD, etc.

It will be readily appreciated that the report may include all or part of the above, provided that the report generally includes at least enough of these to provide analysis (e.g., a calculated KD biomarker level representation; prediction, diagnosis, or characterization of KD) as desired by the user.

Reagents, systems and kits

The invention also provides reagents, systems, and kits thereof for practicing one or more of the above methods. The reagents, systems and kits involved can vary widely. Reagents include the above biomarker level representations specifically designed to produce KD biomarkers from a sample, such as one or more detection elements, oligonucleotides for detecting nucleic acids, antibodies or peptides for detecting proteins, and the like. In some cases, the detection element comprises a reagent for detecting the abundance of a single KD biomarker; for example, the detection element can be a dipstick, 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, and the like.

Another type of such agent is a probe nucleic acid array, in particular, wherein genes (biomarkers) are represented. Various array formats are known in the art, with various probe structures, substrate compositions and ligation techniques (e.g., dot blot arrays, microarrays, etc.). In particular, representative array structures include those described in U.S. patent 5143854: 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 disclosure of which is incorporated herein by reference, and WO 95/21265; WO 96/31622; WO 97/10365; WO 97/27317; EP373 203; and EP 785 280.

Another reagent specifically tailored for the production of biomarker level expression of genes (e.g., KD genes) is a collection of gene-specific primers designed for the selective amplification of such genes (e.g., using PCR-based techniques, real-time RT-PCR). Gene-specific primers and methods of use thereof are described in U.S. Pat. No. 5994076, the disclosure of which is incorporated herein by reference.

One reagent specifically tailored for the generation of biomarker level profiles (e.g., KD biomarker level profiles) is a collection of antibodies that specifically bind to protein biomarkers, e.g., in ELISA format, in xMAP ™ microsphere format, on a proteomic array, in suspension, analyzed by flow cytometry, western blotting, dot blotting, or immunohistochemistry. Methods using the same antibodies are well known in the art. These antibodies may be provided in solution. Alternatively, they may be pre-bound to a solid substrate, for example, the wells of a multi-well culture dish or the surface of xMAP microspheres.

In particular, the probe array, primer set or antibody set comprises a specific biochemical substrate selected from IFI27, C19ORF59, CACNA1E, CASP, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100a12, S100A9, TLR7 and ZHF185, or a combination thereof. The subject probe, primer or antibody collection or reagents may include reagents specific for only the genes/proteins/lipids/cofactors listed above, or they may include reagents specific for other genes/proteins/lipids/cofactors not listed above, such as probes, primers or antibodies specific for genes/proteins/lipids/cofactors expressed thereby, which are known in the art to be associated with KD, such as IFI27 and C19ORF59.

In some cases, such qPCR instruments may be provided, such as AB quantstus 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, for example, reagents sold together. For example, nucleic acid or antibody based detection of sample nucleic acids or proteins, respectively, can be coupled to an electrochemical biosensor platform that will allow for multiplexed assay personalized KD care for these biomarkers.

The systems and kits of the invention may include an array, a collection of gene-specific primers, or a collection of protein-specific antibodies as described above. The systems and kits may further include one or more additional reagents used in various methods, such as primers for producing target nucleic acids, dNTPs, and/or rtps, which may be pre-mixed or isolated, one or more uniquely labeled dNTPs and/or rtps, e.g., biotinylated or Cy3 or Cy 5-labeled dNTPs, gold or silver particles with different scattering spectra, or other post-synthesis labeling reagents such as chemically active derivatives of fluorescent dyes, enzymes such as reverse transcriptase, DNA polymerase, RNA polymerase, and the like, various buffer media such as hybridization and wash buffers, pre-prepared probe arrays, labeled probe purification reagents and components such as spin columns, and the like, signal generation and detection reagents such as labeled secondary antibodies, streptavidin alkaline phosphatase conjugates, chemiluminescent or chemiluminescent matrices, and the like.

The subject systems and kits may also include one or more KD phenotyping elements, which in many embodiments are reference or control samples or biomarker representations, which may be used, for example, by suitable experimental or computational means to make KD prognosis based on "input" biomarker level profiles, e.g., which have been determined with the biomarker determining elements described above. Representative KD phenotyping 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 be presented in a kit in a variety of forms in the subject, one or more of which may be present in the kit. The instructions may be in the form of printed information on a suitable medium or substrate, such as in the packaging of a kit, packaging inserts on one or more sheets of paper on which the information is printed, and the like. Another way is a computer readable medium, such as a floppy disk, CD, etc. on which the information has been recorded. Another means that may exist is a website address that can access the deleted website information over the internet. Any convenient method can be used in the kit.

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

Examples

The following examples are put forth so as to provide those of ordinary skill in the art with a description of how to make and use the invention, and are not intended to limit the scope of what the inventors regard as their invention nor are they intended to represent that the experiments below are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g., amounts, temperature, etc.), but some should be accounted for by experimental errors and deviations. Unless otherwise indicated, parts are parts by weight, molecular weight is weight average molecular weight, temperature is in degrees celsius, and pressure is at or near atmospheric.

Materials and methods

KD patient validation cohorts, demographics, and clinical criteria. Blood samples were taken from the diagnosed KD and heat-generating control samples were taken from the same cohort, but later determinations were not KD. KD patients met the american heart association complete and incomplete KD clinical criteria and received IVIG treatment within 10 days after the first fever. Patient blood samples were collected and analyzed at baseline prior to IVIG or drug treatment administration.

Meta analysis of vasculitis and identified KD trace cases. Differentially expressed genes were extracted from seven datasets of PBMC microarray experiments. These seven data sets included PBMC microarray experiments for analysis of primary vasculitis, including KD, subjects from NCBI gene expression complex (GEO) 20:4KD data set, GSE15297 (KD versus FC), GSE18606 (KD versus normal control), GSE9864 (KD versus normal control); GSE9863 (KD versus normal); 3 other vasculitis datasets, GSE33910 (aortic arteritis vs normal control), GSE17114 (behcet disease vs normal control) and GSE16945 (aortic arteritis vs normal control). After DEG is found in each dataset, an Innovative Pathway Analysis (IPA) is performed to determine the pathway associated with DEG in each of the seven datasets. The genetic biomarker, which is present in the DEG list of at least one dataset and which participates in a common enrichment pathway shared by at least one to seven datasets, constitutes a collective meta-feature of vasculitis. To identify potential biomarker candidates, the gene markers were further filtered using a human biological fluid proteome database with known serum and urine detectable proteins, including data from the HUPO plasma proteome program, the plasma proteome institute, the MAPU proteome database, and the urinary exome database.

For document retrieval, ge nie is used to mine the summary of documents in the pubmed database. Currently, we have 20396 genes and 1183931 gene summary links (551555 unique PMIDs) for mining analysis. Using G nie, genes were ranked according to different subjects, their pubMed primers were classified using machine learning, and subject keywords such as "kawasaki disease", "myocardial dysfunction", "vascular inflammation", "vascular leakage", "coronary neoplasm", "ischemia" and "multisystem inflammation syndrome" were tested. For each topic keyword search, the significant cutoff for the summary was set to p <0.05, and the false discovery rate of the genes was set to <0.05. All significantly enriched genes were ranked using Fisher statistics. To compare the differences in gene ranking under different topic keywords, the gene ranking for 7 topics was normalized. The relative rank of the first ranked gene is 1 and the relative rank of the last gene in the active gene list is 0. As a result, 12 candidate genes were selected for further testing according to their ranking in each keyword. The heart found nine antigen phases of the panel. A total of 61 genes were tested for differential expression in KD and other febrile patients.

Biomarker detection was performed by qPCR platform. Blood was collected from KD and fever control groups. Blood is stored and prepared quickly, either fresh or frozen at-80 degrees celsius, for later preparation. RNA was directly extracted from blood using RNA extraction kit. qPCR assays were performed on the quantsudio 6 IVD platform, and RNA from 61 protein targets was extracted using a commercially available one-step or two-step qPCR kit. The assays were performed according to the reagent manufacturer recommended procedure and dilutions, and the linearity, LOQ/LOD and minimum C (t) values for each primer were determined.

And (5) carrying out statistical analysis. The entire study population included 200 patients, including 100 diagnosed KD and 100 febrile control cases. The RNA copy number of the targeted gene is measured for each gene of interest. Between KD patients and febrile control groups, patient characteristics were tested using a fisher accurate test of gender and a rank sum test of age. Single variable analysis was performed on single analytes from KD patients versus analytes from the febrile control group. Subject operating characteristics (ROC) analysis was performed and the specificity, sensitivity, positive Predictive Value (PPV), negative Predictive Value (NPV) and area under the curve (AUC) values for each analyte were determined. Wilcoxon rank sum test and fold change analysis was also used to compare analyte concentration values for all KD patients with a febrile control. Age differences of the confirmed KD diagnosis and the febrile control were compared using a similar rank sum test. To aid in statistical analysis, any measured value below the limit of quantitation is extrapolated to half the limit of quantitation and any analyte value above the Upper Limit of Quantitation (ULQ) is extrapolated to twice the ULQ value. This ensures a greater tolerance for data below LOQ or above ULQ. Neither of the values set to LOQ nor ULQ affects the analysis. Using standard rank sum test p-values less than 0.05, fold change greater than 1.5 or less than 0.67, and AUC greater than 0.6, 19 significant genes were selected for differential analysis.

By iterative search of all ten analyte combinations, analyte concentrations were analyzed by analytes that were linearly significantly different to find the maximum ROC AUC value. The geometric mean of each different combination of analytes was calculated and applied to the geometric mean by logarithmic transformation, then scaled to the range of 0 and 10, and ROC analyzed. For the first iteration, the best analyte with AUC >0.5 highest was selected once according to univariate analysis. The analyte was added to the previous optimal AUC combination set in the next iteration. If a new analyte improves the performance of the model by maximizing AUC, it will remain in the combination and vice versa. This process is repeated until a maximum AUC is reached and the remaining features constitute the final combination.

KD biomarker panels and diagnostic scores. The model was tested using in-sample validation to evaluate the performance of the area under the receiver operating characteristics. The geometric mean from the final set is used to generate the KD score. In a bigram, an optimal euonym index with a single cutoff value is used to determine the optimal cutoff value. Other operating characteristics, such as sensitivity, specificity, positive Predictive Value (PPV) and Negative Predictive Value (NPV), calculate the confidence interval for all indicators to be 95% based on the optimal cut-off value. All statistics were performed by R software version 4.1 (R statistics calculation basis). Double sided p-values were calculated, p < 0.05 being considered significant.

Results

Patient demographics and characteristics. The focus of this study was on the pediatric population with persistent high fever, with a total of 100 later diagnosed KD cases and 100 febrile children (table 1 a). 30 patients had 5 clinical symptoms, 52 KD patients had four clinical symptoms, meeting the complete KD diagnostic criteria of the AHA guidelines at the time of blood collection (table 1 b), while the remaining 18 were diagnosed as incomplete at the time of blood collection. The age of the diagnosed KD (1.4 years, 0.8-2.4) is younger than the febrile control group (3.0 years, 1.7-4.0), p <0.001.IRB approval limited clinical information in the control group, but none of the febrile control patients met KD diagnostic criteria.

Table 1a patient demographics information: KD patients were slightly younger than the febrile control group.

Table 1b. Summary of KD symptoms in study cohorts.

Meta analysis determined the qPCR discovery panel for KD. 13 pathways overlap by meta analysis of 7 vasculitis and KD microarray data. 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 gene markers (meta markers) that may be differentially expressed in blood (fig. 1). VEGF, MMP8 and HGF are among our vasculitides and are reported to be differentially expressed in serum between KD and FC (p-value < 0.0001) and between KD and normal control (p-value p < 0.001) subjects. This observation provides direct evidence supporting the effectiveness of our overall biomarker discovery method, and our hypothesis that meta-analysis of vasculitis PBMC microarray datasets can lead to specific KD diagnostic biomarkers. This observation also corresponds to our successful experience in identifying novel preeclampsia biomarkers using the same method. Furthermore, literature thinking methods are used on the basis of the methods of G nie et al. Currently, we have 20396 genes and 1183931 gene summary links (551555 unique PMIDs) for mining analysis. The method of using G nie uses machine learning to classify pubMed references to genes and uses topic keywords (such as "kawasaki disease", "myocardial dysfunction", "vascular inflammation", "vascular leakage", "coronary artery", "aneurysms", "ischemia" and "multisystem inflammation syndrome". Each topic keyword search was tested in our study, the cutoff value was set to p <0.05, the false discovery rate of genes was set to <0.05. All significantly enriched genes were ranked using Fisher statistics.

Two-step discovery and validation processes were performed to narrow the final gene expression range of the validation study. The 19 genes were selected from the initial 5x5 cohort and then an additional 15x15 cohort was used to determine the best cohort for the KD score cohort. The two genes, C19ORF59 and IFI27, and their δc (t) were further determined as the best combination for KD identification. And finally verifying the queue.

Binary and risk classification model performance. The KD of each gene biomarker in the exploratory discovery panel and qPCR results of the febrile control cohort were analyzed separately by univariate analysis (table 2).

Table 2. 19 gene biomarkers for qPCR detection using the QuanStudio 6 qPCR instrument. Biomarkers were ranked according to area under the receiver operating characteristic curve (AUC) under univariate analysis, ranked top to bottom.

Subsequently, the best combination with the maximum AUC was constructed using a linear method. The first two pairs of δC (t) values, C19ORF59 and IFI27, produced a panel by the difference in δC (t) values between the two gene expression after the initial discovery and validation cohort study (FIG. 1). The two genomes were further validated in an 80KD validation queue and an 80 fever control queue. Both genomes achieved an overall ROC ACU of 0.930 at an optimal cut-off of 4.558.

The diagnostic model has a robust AUC of 0.930 for diagnosing KD (b in fig. 2). AUC (area under ROC curve) quantifies the ability of a diagnostic test to distinguish between individuals with and without disease. AUC for a perfect test without false positive or false negative is 1.00; tests that are not better than random chance in identifying true positives have AUC of 0.5. Based on the optimal cut-off 4.558 of the kawasaki disease score (KD score) determined using the Youden index, a diagnostic cut-off was determined to optimize the sensitivity and specificity of the effective KD diagnosis. The overall binary classification model performance sensitivity for the total population was 84% (77% -91%), the specificity was 91% (85% -96%), the Positive Predictive Value (PPV) was 90.3%, and the Negative Predictive Value (NPV) was 85% (c in fig. 2).

In addition, a two-threshold-based scale was created to divide patients into three levels of risk scoring systems for effective KD risk stratification to aid in clinical diagnosis of KD: low risk (KD score < 3.558), medium risk (3.558-4.958) and high risk (KD score > 4.958). As shown in fig. 3 a. For the high risk KD group, PPV was 91.8% and the NPV for the low risk KD (febrile disease) group was 89.5% (b in fig. 3). This result indicated 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 (c in FIG. 3). The KD score of a diagnostic test is intended to be an in vitro diagnostic test to assist a physician in making decisions, especially for incomplete KD.

We also examined the correlation of coronary abnormalities with KD scoring system in a study cohort diagnosed with KD. The Z-score of individual KD patients was calculated and divided into three categories: no coronary artery involvement (Z score < 2), only dilation (Z score 2 to < 2.5) and aneurysms (Z score > 2.5). Our test captures 24 (92.3%) of the most KD patients with aneurysms in the high-risk group, and can identify normal KD patients in the coronary arteries (FIG. 4). The data also indicate that our panel can be positively identified as KD without the need for a significant cardiac stress signal or phenotype.

We developed a diagnostic marker panel to aid in diagnosing kawasaki disease with ROC AUC of 0.930. Serological tests 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 statistical model based on linear geometric means with an AUC of 0.930. A simple model based on the linear geometric mean of the combined biomarkers avoids training bias, resulting in overfitting in the validation set and poor prediction results. The model is also more likely to shift different patient populations from other regions.

Several studies have previously aimed at the discovery of a specific biomarker or group of multiple biomarkers containing serological protein analytes, cytokines or gene expression profiles by LC-MS based techniques or gene microarray methods to identify potential KD biomarkers. Recently, the research of three serological biomarkers, namely myeloid-related protein 8/14 (MRP 8/14), human Neutrophil Elastase (HNE) and C-reactive protein, as a prospective biomarker group, has been conducted with ROC AUC values as high as 0.82, but negative predictive values are relatively poor. Another study uses a random forest model to determine cut-off values using geometric mean of analyte concentrations and the best eudragit index to achieve AUC values similar to our simple four analyte set. A complex statistical model like a random forest is more difficult to interpret to achieve than a simple equal weight linear model. Random forest models also have difficulty transferring models from an original queue to another queue from a different region, which may limit their utility as actual clinical analyses.

Wright et al also developed a blood gene expression profile of 13 transcripts using microarray data that could distinguish between KD cases and febrile patients. The study uses parallel regularized regression model searches to distinguish KD cases. The panel obtained an AUC of 0.946 in the final validation set. However, both training and validation queues used only microarray data generated from the study, and further validation of gene expression was performed with more quantitative determinations of data, such as qPCR of 13 genes. The panel needs to be rechecked and validated in a separate queue using quantitative qPCR analysis of the focused genome. Gene expression profiling has recently shown that KD has similar COVID-19 infection in childhood multisystem inflammatory syndrome caused by novel coronavirus infection, but no common associated cardiac phenotype. This suggests that the genetic features capture the host immune response to KD primarily rather than cardiac events. It would be an interesting thing to observe from whole blood gene expression analysis whether the gene expression of our serum biomarkers is also up-regulated by quantitative PCR analysis.

In this study we used quantitative PCR detection and IVD-qPCR instrument (Quantum studio 6) in combination with available clinical detection to rapidly adapt to clinical diagnosis of KD. The combined model is also based on simple differences (RNA copy number) of the two molecular targets, significantly improving the transferability of the model between the queues without overfitting the data. Overfitting of data typically occurs if the data is processed through complex statistical models or artificial intelligence machine learning algorithms. The limitation of this study is that there is no additional larger validation queue to determine whether the panel can be easily transferred to a different patient population. KD is considered by the U.S. food and drug government to be a rare orphan. Thus, timely registration may be difficult. The most important medical need for KD is patients with incomplete KD and often misdiagnosed as other febrile diseases. IVIG treatment was not received within ten days after fever, and the likelihood of a later cardiac event was much higher. The KD diagnostic test panel, which can be deployed with currently available clinical tests and standard statistical algorithms, can greatly improve the speed and accuracy of KD diagnosis.

The invention includes, but is not limited to, the following technical scheme:

a method of determining the presence of kawasaki disease biomarker levels in a subject, the method comprising:

a. assessing a set of kawasaki disease biomarkers in a sample, e.g., blood, serum, or plasma, from a subject to determine the expression level of each kawasaki disease biomarker in the sample;

b. obtaining a level representation of kawasaki disease biomarkers based on the level of each kawasaki disease biomarker in the group;

c. wherein the set of kawasaki disease biomarkers comprises one or more biomarkers selected from IFI27, C19ORF59, CACNA1E, CASP, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100a12, S100A9, TLR7, and ZHF 185.

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

Further, wherein the biomarker expression level is represented by a circulation threshold C (t).

Further, wherein the set comprises C19ORF59 and IFI27.

Further, wherein the set comprises C19ORF59, IFI27, S100a12 and S100A9.

Further, wherein the group comprises C19ORF59, IFI27, S100a12, S100A9, CLIC3 and SLC11A1.

Further, wherein the group comprises C19ORF59, IFI27, S100a12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

Further, wherein the group comprises C19ORF59, IFI27, S100a12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS, ABCC1 and CAMK4.

Further, a report providing a representation of kawasaki disease biomarker levels, such as absolute concentration or medium fold (MoM) of the circulation threshold C (t), is also included.

Further, wherein the kawasaki disease biomarker level appears to derive a kawasaki disease score, wherein the kawasaki disease score:

a. can be derived from the cycle threshold C (t) difference between two different genes, such as C19ORF59 and IFI 27;

b. derived from the measured blood biomarker values by geometric mean, multivariate Linear Discriminant Analysis (LDA) or distributed gradient enhanced decision tree (GBDT) machine learning (e.g., XGBoost); or (b)

c. May 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, for example, may be derived from the following equation: [ C (t) ₁ –C(t) ₂ ]X[C(t) ₃ – C(t) ₄ ]X[C(t) ₅ – C(t) ₆ ]/(normalization factor) x10=kawasaki disease score.

A method for diagnosing kawasaki disease in a subject, comprising obtaining a level representation of a kawasaki disease biomarker in a sample from the subject, the subject being diagnosed as kawasaki disease when the kawasaki disease score is greater than 4.558.

Further, wherein the set comprises C19ORF59 and IFI27.

Further, wherein the set comprises C19ORF59, IFI27, S100a12 and S100A9.

a. can be derived from the cycle threshold differences between gene #1 and gene #2, e.g., C19ORF59 and IFI 27;

c. May 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 from the following equation: [ C (t) ₁ –C(t) ₂ ]X[C(t) ₃ – C(t) ₄ ]X[C(t) ₅ – C(t) ₆ ]/(normalization factor) x10=kawasaki disease score.

A method for kawasaki disease risk assessment of a subject, comprising obtaining a representation of the level of kawasaki disease biomarker of a sample from the subject:

a. for kawasaki disease risk assessment, the kawasaki disease score evaluates the risk of developing kawasaki disease over three different ranges to determine the risk of developing kawasaki disease;

b. taking a low kawasaki disease risk (adjusted according to the population) below the low score cutoff of 3.558 as an example, it is shown that the patient has a low risk of kawasaki disease;

c. taking high Kawasaki disease risk (adjusted according to the population) above the high score cutoff of 4.958 as an example, the patient is shown to have a higher risk of Kawasaki disease;

d. the fraction between the low score cutoff and the high score cutoff indicates that the patient is at moderate risk of kawasaki disease.

Further, wherein the set comprises C19ORF59 and IFI27.

Further, wherein the set comprises C19ORF59, IFI27, S100a12 and S100A9.

Further, a report providing a representation of kawasaki disease biomarker levels, such as absolute concentration or medium fold of cycle threshold (MoM), is also included.

Further, wherein the kawasaki disease biomarker presents deriving a kawasaki disease score, wherein the kawasaki disease score:

c. May be a multiple of each biomarker level, such as concentration or MoM, normalized to fit a scale of 0-10, for example, may be derived from the following equation: [ C (t) ₁ –C(t) ₂ ]X[C(t) ₃ – C(t) ₄ ]X[C(t) ₅ – C(t) ₆ ]/(normalization factor) x10=kawasaki disease score.

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

Further, wherein the set comprises C19ORF59 and IFI27.

Further, wherein the group includes S100a12 and S100A9.

Further, wherein the group comprises S100a12, S100A9, CLIC3 and SLC11A1.

Further, wherein the group comprises S100a12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

Further, wherein the group comprises S100a12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS, ABCC1 and CAMK4.

Further, a report providing a representation of kawasaki disease biomarker levels, such as absolute concentration or fold of mediator (MoM), is also included.

b. derived from the measured blood biomarker values by geometric mean, multivariate Linear Discriminant Analysis (LDA), or distributed gradient enhanced decision tree (GBDT) machine learning (e.g., XGBoost); or (b)

A diagnostic system comprising a kit, reagents and an instrument for generating a kawasaki disease score from a sample, such as blood, serum or plasma, from a subject, comprising: detection reagents for measuring the amount of one or more kawasaki disease biomarkers selected from the group of biomarkers consisting of IFI27, C19ORF59, CACNA1E, CASP, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100a12, S100A9, TLR7 and ZHF185.

The diagnostic system further comprises:

a. a platform system for measuring kawasaki disease biomarkers, such as the quantsudio 6 system;

b. calculation table for calculating Kawasaki disease score

c. An indication of whether the patient has kawasaki disease is determined.

Further, wherein the set comprises C19ORF59 and IFI27.

Further, wherein the set comprises C19ORF59, IFI27, S100a12 and S100A9.

Further, comprising selecting a patient suspected of having kawasaki disease for intravenous immunoglobulin (IVIG) treatment, the method comprising:

a. determining a kawasaki disease score for the patient;

b. diagnosing the patient according to the method and selecting the patient for IVIG administration if the patient is diagnosed with kawasaki disease;

c. if the Kawasaki disease score of the patient is in the high risk range and the medium risk range, the patient is selected for IVIG administration.

Further, monitoring the effect of kawasaki disease treatment on a patient suffering from kawasaki disease, comprising:

a. determining a kawasaki disease score for the patient;

c. selecting the patient for IVIG administration if the kawasaki disease score of the patient is in the high risk range and the medium risk range; and

d. effective treatment will result in a decrease in kawasaki disease score.

A method comprising a process of kawasaki disease sample assay to retain necessary data, comprising:

a. measuring each biomarker concentration of a biomarker panel in a blood, plasma, or serum sample taken from a patient suspected of having kawasaki disease, the kawasaki disease biomarker panel comprising 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 for a control subject, wherein differential expression indicates that the patient has kawasaki disease; the assay further includes determining kawasaki disease scores from the biomarker concentrations of the patient.

A method of measuring at least a pair of kawasaki disease biomarkers of a biomarker panel comprising IFI27, C19ORF59, CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100a12, S100A9, TLR7, and ZHF185 by oligonucleotide primers targeting the biomarkers, the primers specifically binding to the biomarkers comprising RNA/DNA sequence determinants of the biomarkers or fragments thereof to calculate a kawasaki disease score:

a. the primer is selected from a sequence consisting of a portion of a complementary DNA sequence of the biomarker target;

b. a reporter dye or dye/quencher reporter label that accurately meters the circulation threshold of the amount of targeted biomarker, e.g., RNA or DNA in a sample;

qpcr instruments, such as quantsudio 6, are used to accurately determine reporter/dye signals based on amplification of biomarker templates.

Kawasaki disease biomarker panel comprising one or more biomarkers selected from IFI27, C19ORF59, CACNA1E, CASP, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100a12, S100A9, TLR7, and ZHF 185.

Further, C19ORF59 and IFI27 are included.

Further, the kawasaki disease biomarker panel comprises C19ORF59, IFI27, S100a12 and S100A9.

Further, the biomarker panel comprises C19ORF59, IFI27, S100a12, S100A9, CLIC3, and SLC11A1.

Further, the kawasaki disease biomarker panel includes C19ORF59, IFI27, S100a12, S100A9, CLIC3, SLC11A1, CACNA1E, and LGALS2.

Further, the kawasaki disease biomarker group comprises C19ORF59, IFI27, S100a12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS2, ABCC1 and CAMK4.

Claims

1. Use of a detection reagent for measuring the expression level of kawasaki disease biomarkers of a biomarker panel comprising IFI27 and C19ORF59 in the preparation of a kit for determining the presence of kawasaki disease biomarker levels in a subject, the method of determining the presence of kawasaki disease biomarker levels in a subject comprising:

b. A level representation of the kawasaki disease biomarker is obtained based on the expression level of each kawasaki disease biomarker in the group.

2. The use according to claim 1, wherein the expression level of the oligonucleotide of each kawasaki disease biomarker is measured.

3. The use according to claim 2, wherein the RNA or DNA expression level of each kawasaki disease biomarker is measured.

4. The use according to claim 1, characterized in that the expression level of the biomarker is expressed as a circulation threshold C (t).

5. The use according to claim 1, wherein the panel further comprises one or more biomarkers selected from the group consisting of CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100a12, S100A9, TLR7 and ZHF 185.

6. The use according to claim 1, wherein the group comprises C19ORF59, IFI27, S100a12 and S100A9.

7. The use according to claim 1, wherein said group comprises C19ORF59, IFI27, S100a12, S100A9, CLIC3 and SLC11A1.

8. The use according to claim 1, wherein said group comprises C19ORF59, IFI27, S100a12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

9. The use according to claim 1, wherein said group comprises C19ORF59, IFI27, S100a12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS, ABCC1 and CAMK4.

10. The use of claim 1, wherein the method further comprises providing a report of kawasaki disease biomarker level representations.

11. The use of claim 1, wherein the method further comprises deriving a kawasaki disease score from the kawasaki disease biomarker level presentation, wherein the kawasaki disease score:

b. deriving from the measured expression level of the blood biomarker by geometric mean, multivariate Linear Discriminant Analysis (LDA) or distributed gradient enhanced decision tree (GBDT) machine learning; or (b)

c. Is a multiple of the expression level of each biomarker.

12. Use of a detection reagent for measuring the expression level of kawasaki disease biomarkers of a biomarker panel comprising IFI27 and C19ORF59 for the preparation of a diagnostic system for diagnosis of kawasaki disease in a subject.

13. The use according to claim 12, wherein the panel further comprises one or more biomarkers selected from CACNA1E, CASP5, CR1, CLIC3, CRTAM, FKBP5, HGF, IL1RL1, KLHL2, MAPK14, NKTR, SLC11A1, S100a12, S100A9, TLR7 and ZHF 185.

14. The use according to claim 12, wherein the group comprises C19ORF59, IFI27, S100a12 and S100A9.

15. The use according to claim 12, wherein said group comprises C19ORF59, IFI27, S100a12, S100A9, CLIC3 and SLC11A1.

16. The use according to claim 12, wherein said group comprises C19ORF59, IFI27, S100a12, S100A9, CLIC3, SLC11A1, CACNA1E and LGALS2.

17. The use according to claim 12, wherein said group comprises C19ORF59, IFI27, S100a12, S100A9, CLIC3, SLC11A1, CACNA1E, LGALS, ABCC1 and CAMK4.