Modular transcriptional fingerprint for complete KD in the training, test and incomplete KD sets.

<p>Colored dots represent the percentage of significantly overexpressed (red) or underexpressed (blue) transcripts within a module in patients with complete KD compared to controls (see [D] for module map key). Blank modules indicate no significant differences between patients and controls. (A) Patients with complete KD demonstrated significant overexpression of modules related to inflammation (M3.2, M4.2, M4.6, M4.13, M5.1, 5.7), platelets (M1.1), neutrophils (M5.15). Conversely, genes related to adaptive immunity: T cells (M4.1, M4.15), B cells (M4.10), lymphoid lineage (M6.19), and cytotoxicity/NK cells (M3.6), were significantly underexpressed. (D) Key to the functional interpretation of each transcriptional module (M): module sets 1 to 6 are indicated on the y-axis, and module numbers within each set are indicated on the x-axis. Scatter plot showing modular correlations (Spearman's r) between training (x-axis) and the test (y-axis) sets (E) and incomplete KD set (F). The inflammation modules (M4.6, M4.2, 3.2) were the most highly correlated between the training and the test sets.</p

Modular fingerprints of patients with KD, GAS and adenovirus infections.

<p>Modular transcriptional fingerprints were derived and compared in children with cKD (n = 39), GAS (n = 17) and adenovirus (n = 19) infection. Colored spots represent the percentage of significantly overexpressed (red) or underexpressed (blue) transcripts within a module in patients with a disease state compared with age- and sex-matched healthy controls (16 HC for KD comparisons and 10 HC for GAS and AdV respectively).</p

Biosignature of KD.

<p><b>(</b>A) Statistical group comparisons between children with complete KD and healthy matched controls (HC) (Mann-Whitney test p<0.01, Benjamini-Hochberg multiple test correction and 1.25-fold change) yielded 8,799 significantly differentially expressed transcripts. Transcripts were organized by hierarchical clustering, where each row represents a single transcript and each column an individual participant. Normalized expression levels are indicated as overexpressed (red) or underexpressed (blue) compared to the median expression of healthy controls (yellow). (B) The 8,799 biosignature was applied to an independent test set of 37 children with complete KD and HC. Unsupervised hierarchical clustering of the KD signature grouped all complete KD patients in the test set together (blue bar) except for one patient who was on the ninth day of illness at the time of sample collection, and (C) all incomplete KD patients were grouped together. Black line indicates the cluster separation.</p

Demographic, clinical and laboratory data of study patients.

<p>Demographic, clinical and laboratory data of study patients.</p

Transcriptional profiles in Kawasaki disease versus adenovirus and Group A streptococcus infections.

<p>Supervised learning KNN algorithm with 6 neighbors and a p value ratio cutoff of 0.5 was used to identify the top genes that best discriminated cKD from adenovirus (AdV) disease from (A) and GAS (B). Predicted class is indicated by the corresponding lighter-colored rectangles. <b>(A)</b> The KNN algorithm identified 25 classifier genes that best discriminated AdV from KD. Leave one out cross validation of the training set (A) and test set (B) correctly classified 43 of 45 patients with 96% accuracy (complete KD [n = 26; blue); AdV [n = 19, red]. <b>(B)</b> Using the same strategy we identified 10 genes that best discriminated GAS from cKD. (C and D). Leave-one-out cross-validation of the 10 classifiers in the training set (C) and test set (D) correctly classified 28 of the 36 samples (complete KD [n = 19; blue]; GAS [n = 17, green] with 78% accuracy; one patient in the training set and test sets could not be predicted.</p

MDTH scores measured over time.

<p>MDTH scores were analyzed in 18 patients with available sequential samples: acute (pre-treatment), 24 hours after IVIG (24 post IVIG), and convalescent (5–8 weeks after treatment). The three patients that failed to respond to the first dose of IVIG (IVIG-NR) are shown in red color, and the patient with coronary artery dilation, is shown in blue color. One subject, marked with an asterisk (*), was a non-responder to IVIG, required a corticosteroid taper and had residual arthralgias on the follow-up visit during convalescence (in red).</p

Multivariable logistic regression for the outcome of non-response to treatment.

<p>Multivariable logistic regression for the outcome of non-response to treatment.</p

Area under the ROC curve (AUC) of MDTH scores to predict response to IVIG therapy.

<p>The optimal threshold for MDTH defined using Youden’s J statistic, which maximizes sensitivity and specificity is 10,428. When MDTH is dichotomized as “low” or “high” based on this threshold, low MDTH values have 88% sensitivity and 60% specificity for classifying patients as responders, and AUC remains good at 0.741.</p

Performance characteristics of 25 and 10 classifier genes to discriminate complete KD versus adenovirus (A) and GAS infections (B).

<p>Performance characteristics of 25 and 10 classifier genes to discriminate complete KD versus adenovirus (A) and GAS infections (B).</p

Molecular distance to health scores (MDTH) in children with KD.

<p>MDTH scores are a metric that converts the global transcriptional perturbation of each sample into an objective score indicating the degree of transcriptional perturbation of each individual patient compared with a healthy control baseline. (A) MDTH scores were significantly higher in patients who failed to respond to initial treatment with IVIG, (12,290 vs. 5,019; p = 0.009) (B) MDTH scores were correlated with pre-treatment, baseline C-reactive protein serum concentrations [n = 84], (C) and inversely correlated with days of fever at the time of sample acquisition, (Spearman’s correlation coefficient).</p