PROGNOSTIC PATHWAYS FOR HIGH RISK SEPSIS PATIENTS

Abstract

The present invention relates to means and methods that can be used—based on a blood sample of a subject having sepsis, a subject suspected to have sepsis or a subject at risk to develop sepsis, to diagnose the subject with sepsis. The methods can further be used for making a prediction, e.g. whether the subject is likely to develop sepsis, or whether the subject has a high mortality risk as a result of sepsis. The invention further provides for compounds for use in the treatment or prevention of sepsis.

Claims

1. A method for diagnosing a subject with sepsis based on a blood sample obtained form the subject, wherein said diagnosis is based on RNA extracted from the blood sample, the method comprising the steps of: determining the expression level of three or more genes, wherein said three or more genes are selected from group 1 and 2, wherein group 1 consist of the genes ABCC4, APP, AR, CDKN1A, CREB3L4, DHCR24, EAF2, ELL2, FGF8, FKBP5, GUCY1A3, IGF1, KLK2, KLK3, LCP1, LRIG1, NDRG1, NKX3_1, NTS, PLAU, PMEPA1, PPAP2A, PRKACB, PTPN1, SGK1, TACC2, TMPRSS2, and UGT2B15, and wherein group 2 consist of the genes ANGPTL4, CDC42EP3, CDKN1A, CDKN2B, CTGF, GADD45A, GADD45B, HMGA2, ID1, IL11, INPP5D, JUNB, MMP2, MMP9, NKX2_5, OVOL1, PDGFB, PTHLH, SERPINE1, SGK1, SKIL, SMAD4, SMAD5, SMAD6, SMAD7, SNAI1, SNAI2, TIMP1 and VEGFA, and wherein: an increased expression of ABCC4, APP, FGF8, FKBP5, ELL2, DHCR24, NDRG1, LCP1, EAF2, PTPN1, CDC42EP3, CDKN2B, CTGF, GADD45A, GADD45B, HMGA2, ID1, IGF1, IL11, INPP5D, JUNB, MMP9, PTHLH, SERPINE1, SGK1, SKIL, SMAD4, SMAD6, SNAI2, TIMP1 and VEGFA or a decreased expression of CDKN1A, KLK2, KLK3, PMEPA1, TMPRSS2, NKX2_5, NKX3_1, NTS, PLAU, UGT2B15, PPAP2A, LRIG1, TACC2, CREB3L4, GUCY1A3, AR, ANGPTL4, MMP2, OVOL1, PDGFB, PRKACB, SMAD5, SMAD7 and SNAI1 correlates with sepsis, and wherein said subject is diagnosed with sepsis based on the expression levels of the three or more genes and if the subject from which the blood sample has been obtained further has at least one clinical parameter associated with sepsis.

2. Method according to claim 1, wherein group 1 consists of the genes AR, CREB3L4, DHCR24, EAF2, ELL2, FKBP5, GUCY1A3, IGF1, KLK3, LCP1, LRIG1, NDRG1, NKX3_1, PMEPA1, PRKACB, TMPRSS2, preferably AR, CREB3L4, DHCR24, EAF2, ELL2, FKBP5, LCP1, LRIG1, NDRG1, PMEPA1, PRKACB, TMPRSS2 more preferably DHCR24, EAF2, ELL2, FKBP5, LCP1, LRIG1, PMEPA1, PRKACB, and/or group 2 consists of the genes CDC42EP3, GADD45A, GADD45B, HMGA2, ID1, IL11, INPP5D, JUNB, MMP2, MMP9, NKX2_5, OVOL1, PDGFB, PTHLH, SGK1, SKIL, SMAD4, SMAD5, SMAD6, TIMP1, VEGFA, preferably CDC42EP3, GADD45A, GADD45B, ID1, JUNB, MMP9, PDGFB, SGK1, SKIL, SMAD5, SMAD6, TIMP1, VEGFA, more preferably CDC42EP3, GADD45A, GADD45B, ID1, JUNB, MMP9, PDGFB, SGK1, SMAD5, TIMP1, VEGFA.

3. Method according to claim 1 or 2 wherein the three or more genes are selected from group 1.

4. Method according to any one of the preceding claims, wherein the three or more expression levels are compared to a reference value or reference expression level obtained from a reference sample, preferably wherein said reference sample comprises a sample from a subject with sepsis and/or a sample from a healthy subject.

5. Method according to any one of the preceding claims, wherein the genes of group 1 are AR target genes and are used to determine the AR cellular signaling pathway activity, and wherein the genes of group 2 are TGFbeta target genes and are used to determine the TGFbeta cellular signaling pathway activity, the method further comprising: determining the AR and/or TGFbeta cellular signaling pathway activity, based on the determined expression levels of said three or more target genes of the AR and/or TGFbeta cellular signaling pathway, wherein an increased AR and an increased TGFbeta cellular signaling pathway activity correlates with sepsis, and wherein said subject is diagnosed with sepsis based on the AR and/or TGFbeta cellular signaling pathway and if the subject from which the blood sample has been obtained further has at least one clinical parameter associated with sepsis, wherein said cellular signaling pathway activity or signaling pathway activities is determined based on evaluating a calibrated mathematical model relating the three or more expression levels determined for the pathway or pathways based on the RNA extracted from a blood sample to the activity or activities of the signaling pathway or signaling pathways.

6. Method according to any one of the preceding claims, wherein said blood sample is obtained from a subject with sepsis or obtained from a subject suspected to have sepsis or an subject at risk of developing sepsis or a subject recovering from sepsis.

7. Method according to any one of the preceding claims, wherein said expression levels of the three or more genes are used in predicting the mortality risk for the subject from which the blood sample has been obtained, wherein said prediction is based on a comparison of the expression levels of the three or more genes of the subject with a plurality of reference expression levels of three or more genes obtained from reference subjects, wherein said plurality of reference expression levels of the three or more genes obtained from reference subjects comprises expression levels of the three or more genes obtained from subject with sepsis which is a non-survivor and expression levels of the three or more genes obtained from subject with sepsis which is a survivor, and optionally further comprises expression levels of the three or more genes obtained from a healthy or non-septic control subject, wherein the subject from which the blood sample is obtained is confirmed to have sepsis, and wherein a low mortality risk is predicted when the expression levels of the three or more genes obtained from the subject with sepsis are similar to expression levels of the three or more genes obtained from reference subject with sepsis which is a survivor or when the expression levels of the three or more genes obtained from the subject with sepsis are similar to the expression levels of the three or more genes obtained from the at least one healthy or non-septic control subject, and wherein a high mortality risk is predicted when the expression levels of the three or more genes obtained from the subject with sepsis are similar to the expression levels of the three or more genes obtained from the reference subject with sepsis which is a non-survivor.

8. Method according to any one of claims 1 to 6, wherein the subject from which the blood sample has been obtained does not have sepsis, and wherein the expression levels of the the three or more genes are used to determine the risk that the subject will develop sepsis, the method further comprising comparing the expression levels of the the three or more genes of the subject from which the blood sample has been obtained to expression levels of the three or more genes obtained from a healthy or non-septic control subject.

9. Method according to any one of claims 1 to 4, wherein the subject from which the blood sample has been obtained has recovered from sepsis, and wherein the expression levels of the the three or more genes of the blood sample are used to monitor the risk that the subject will develop a recurrence of sepsis, the method further comprising comparing the expression levels of the three or more genes of the subject from which the blood sample has been obtained to expression levels of the three or more genes obtained from a healthy or non-septic control subject.

10. Method according to any one of the preceding claims, wherein the blood sample is a whole blood sample, isolated peripheral blood mononuclear cells (PBMCs), isolated CD4+ cells, isolated CD8+ cells, Regulatory T-cells, mixed CD8+ and T cells, myeloid derived suppressor cells (MDSC), dendritic cells, isolated neutrophils, isolated lymphocytes or isolated monocytes.

11. Kit of parts, comprising primers and optionally probes for determining the expression levels of three or more genes, wherein the three or more genes are selected from group 1 and group 2, wherein group 1 consists of: ABCC4, APP, AR, CDKN1A, CREB3L4, DHCR24, EAF2, ELL2, FGF8, FKBP5, GUCY1A3, IGF1, KLK2, KLK3, LCP1, LRIG1, NDRG1, NKX3_1, NTS, PLAU, PMEPA1, PPAP2A, PRKACB, PTPN1, SGK1, TACC2, TMPRSS2, and UGT2B15, and wherein group 2 consists of: ANGPTL4, CDC42EP3, CDKN1A, CDKN2B, CTGF, GADD45A, GADD45B, HMGA2, ID1, IL11, INPP5D, JUNB, MMP2, MMP9, NKX2_5, OVOL1, PDGFB, PTHLH, SERPINE1, SGK1, SKIL, SMAD4, SMAD5, SMAD6, SMAD7, SNAI1, SNAI2, TIMP1 and VEGFA.

12. A method for in vitro or ex vivo diagnosing or prognosticating whether a subject has sepsis, has septic shock or has a high mortality risk or has a low mortality risk as a result of sepsis using the kit as defined in claim 11.

13. An AR pathway inhibitor for use in the prevention of sepsis in a subject suffering from an infection, preferably wherein the subject has an elevated AR cellular signaling pathway activity as determined in a blood sample obtained from the subject, optionally, wherein the AR cellular signaling pathway activity is determined on a blood sample obtained from the subject and the AR pathway inhibitor is administered if the AR cellular signaling pathway activity is found to be elevated or to exceed a certain threshold.

14. An AR pathway inhibitor for use in the treatment or alleviation of a subject suffering from sepsis wherein the subject has an elevated AR cellular signaling pathway activity or an AR cellular signaling pathway activity exceeding a certain threshold as determined in a blood sample obtained from the subject, optionally, wherein the AR cellular signaling pathway activity is determined on a blood sample obtained from the subject and the AR pathway inhibitor is administered if the AR cellular signaling pathway activity is found to be elevated or to exceed a certain threshold.

15. AR pathway inhibitor for use according to claim 13 or 14, wherein the AR pathway inhibitor is administered together with a TGFbeta pathway inhibitor, wherein the AR pathway inhibitor and the TGFbeta pathway inhibitor are the same compound or a different compound.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0281] General: In all the figures where signal transduction pathway analysis scores are depicted, these are given as log2odds scores for pathway activity, derived from the probability scores for pathway activity provided by the Bayesian pathway model analysis. Log2odds scores indicate the level of activity of a signaling pathway on a linear scale.

[0282] Analyzed public datasets are indicated with their GSE number (in principle at the bottom of each figure), and individual samples with their GSM number (in principle most right column for clustering diagrams).

[0283] All validation samples for a signaling pathway model or an immune response/system model are independent samples and have not been used for calibration of the respective model to be validated.

[0284] FIG. 1 shows AR signaling pathway activity (top) and TGFbeta signaling pathway activity (bottom) for septic shock patients and healthy control subjects from dataset GSE26440. The data are obtained from whole blood samples from septic shock patients (survivors), septic shock patients (non-survivors), control subject (healthy subjects) and control subjects (non-septic survivors). The graphs show the log2odds for the respective signaling pathway activity; statistical differences are indicated above the bars, where “ns” (not significant) depicts a p-value of 5.00e{circumflex over ( )}-02<p<=1.00e+00, * depicts a p value of 1.00e{circumflex over ( )}-02<p<=5.00e−02, ** depicts a p value of 1.00e{circumflex over ( )}-03<p<=1.00e−02, *** depicts a p value of 1.00e{circumflex over ( )}-04<p<=1.00e−03, and **** depicts a p value of p<=1.00e−04.

[0285] FIG. 2 shows MPK-AP1 signaling pathway activity (top) and JAK-STAT3 signaling pathway activity (bottom) for septic shock patients and healthy control subjects from dataset GSE26440. The data are obtained from whole blood samples from septic shock patients (survivors), septic shock patients (non-survivors), control subject (healthy subjects) and control subjects (non-septic survivors). The graphs show the log2odds for the respective signaling pathway activity; statistical differences are indicated above the bars, where “ns” (not significant) depicts a p-value of 5.00e{circumflex over ( )}-02<p<=1.00e+00, * depicts a p value of 1.00e{circumflex over ( )}-02<p<=5.00e−02, ** depicts a p value of 1.00e{circumflex over ( )}-03<p<=1.00e−02, *** depicts a p value of 1.00e{circumflex over ( )}-04<p<=1.00e−03, and **** depicts a p value of p<=1.00e−04.

[0286] FIG. 3 shows AR signaling pathway activity (top) and TGFbeta signaling pathway activity (bottom) for septic shock patients and healthy control subjects from dataset GSE4607. The data are obtained from whole blood samples from control subjects, septic shock patients (Non-survivors) and septic shock patients (Survivors). The graphs show the log2odds for the respective signaling pathway activity; statistical differences are indicated above the bars, where “ns” (not significant) depicts a p-value of 5.00e{circumflex over ( )}-02<p<=1.00e+00, * depicts a p value of 1.00e{circumflex over ( )}-02<p<=5.00e−02, ** depicts a p value of 1.00e{circumflex over ( )}-03<p<=1.00e−02, *** depicts a p value of 1.00e{circumflex over ( )}-04<p<=1.00e−03, and **** depicts a p value of p<=1.00e−04.

[0287] FIG. 4 shows MPK-AP1 signaling pathway activity (top) and JAK-STAT3 signaling pathway activity (bottom) for septic shock patients and healthy control subjects from dataset GSE4607. The data are obtained from whole blood samples from control subjects, septic shock patients (Non-survivors) and septic shock patients (Survivors). The graphs show the log2odds for the respective signaling pathway activity; statistical differences are indicated above the bars, where “ns” (not significant) depicts a p-value of 5.00e{circumflex over ( )}-02<p<=1.00e+00, * depicts a p value of 1.00e{circumflex over ( )}-02<p<=5.00e−02, ** depicts a p value of 1.00e{circumflex over ( )}-03<p<=1.00e−02, *** depicts a p value of 1.00e{circumflex over ( )}-04<p<=1.00e−03, and **** depicts a p value of p<=1.00e−04.

[0288] FIG. 5 shows AR signaling pathway activity (top) and TGFbeta signaling pathway activity (bottom) for septic shock patients and healthy control subjects from dataset GSE66099. The data are obtained from whole blood samples from control subjects, septic shock patients (Non-survivors) and septic shock patients (Survivors). The graphs show the log2odds for the respective signaling pathway activity; statistical differences are indicated above the bars, where “ns” (not significant) depicts a p-value of 5.00e{circumflex over ( )}-02<p<=1.00e+00, * depicts a p value of 1.00e{circumflex over ( )}-02<p<=5.00e−02, ** depicts a p value of 1.00e{circumflex over ( )}-03<p<=1.00e−02, *** depicts a p value of 1.00e{circumflex over ( )}-04<p<=1.00e−03, and **** depicts a p value of p<=1.00e−04.

[0289] FIG. 6 shows MPK-AP1 signaling pathway activity (top) and JAK-STAT3 signaling pathway activity (bottom) for septic shock patients and healthy control subjects from dataset GSE66099. The data are obtained from whole blood samples from control subjects, septic shock patients (Non-survivors) and septic shock patients (Survivors). The graphs show the log2odds for the respective signaling pathway activity; statistical differences are indicated above the bars, where “ns” (not significant) depicts a p-value of 5.00e{circumflex over ( )}-02<p<=1.00e+00, * depicts a p value of 1.00e{circumflex over ( )}-02<p<=5.00e−02, ** depicts a p value of 1.00e{circumflex over ( )}-03<p<=1.00e−02, *** depicts a p value of 1.00e{circumflex over ( )}-04<p<=1.00e−03, and **** depicts a p value of p<=1.00e−04.

[0290] FIG. 7 shows AR signaling pathway activity (top) and TGFbeta signaling pathway activity (bottom) for septic shock patients and healthy control subjects from dataset GSE95233. The data are obtained from whole blood samples from control subjects (CS=healthy control subject; PC=non-septic patient control), septic shock patients (NS=Non-survivors) and septic shock patients (SV=Survivors). The graphs show the log2odds for the respective signaling pathway activity; statistical differences are indicated above the bars, where “ns” (not significant) depicts a p-value of 5.00e{circumflex over ( )}-02<p<=1.00e+00, * depicts a p value of 1.00e{circumflex over ( )}-02<p<=5.00e−02, ** depicts a p value of 1.00e{circumflex over ( )}-03<p<=1.00e−02, *** depicts a p value of 1.00e{circumflex over ( )}-04<p<=1.00e−03, and **** depicts a p value of p<=1.00e−04.

[0291] FIG. 8 shows MPK-AP1 signaling pathway activity (top) and JAK-STAT3 signaling pathway activity (bottom) for septic shock patients and healthy control subjects from dataset GSE95233. The data are obtained from whole blood samples from control subjects (CS=healthy control subject; PC=non-septic patient control), septic shock patients (NS=Non-survivors) and septic shock patients (SV=Survivors). The graphs show the log2odds for the respective signaling pathway activity; statistical differences are indicated above the bars, where “ns” (not significant) depicts a p-value of 5.00e{circumflex over ( )}-02<p<=1.00e+00, * depicts a p value of 1.00e{circumflex over ( )}-02<p<=5.00e−02, ** depicts a p value of 1.00e{circumflex over ( )}-03<p<=1.00e−02, *** depicts a p value of 1.00e{circumflex over ( )}-04<p<=1.00e−03, and **** depicts a p value of p<=1.00e−04.

[0292] FIG. 9 shows a clustering diagram for the individual samples in dataset GSE26440 based on the AR and TGFbeta signaling pathways. The greyscale coding represents a logarithmic scale for the individual pathway scores. Hierarchical clustering was used. The color coding on the left side depicts: black=septic shock patient (survivor); light grey=septic shock patient (non-survivor); medium grey=normal control; dark grey=control survivor.

[0293] FIG. 10 shows a clustering diagram for the individual samples in dataset GSE26440 based on the AR, TGFbeta and MAPK-AP1 signaling pathways. The greyscale coding represents a logarithmic scale for the individual pathway scores. Hierarchical clustering was used. The color coding on the left side depicts: black=septic shock patient (survivor); light grey=septic shock patient (non-survivor); medium grey=normal control; dark grey=control survivor.

[0294] FIG. 11 shows a clustering diagram for the individual samples in dataset GSE26440 based on the AR, TGFbeta, MAPK-AP1 and JAK-STAT3 signaling pathways. The greyscale coding represents a logarithmic scale for the individual pathway scores. Hierarchical clustering was used. The color coding on the left side depicts: black=septic shock patient (survivor); light grey=septic shock patient (non-survivor); medium grey=normal control; dark grey=control survivor.

[0295] FIG. 12 shows a clustering diagram for the individual samples in dataset GSE4607 based on the AR and TGFbeta signaling pathways. The greyscale coding represents a logarithmic scale for the individual pathway scores. Hierarchical clustering was used. The color coding on the left side depicts: black=control; light grey=septic shock patient (non-survivor); dark grey=septic shock patient (survivor).

[0296] FIG. 13 shows a clustering diagram for the individual samples in dataset GSE4607 based on the AR, TGFbeta and MAPK-AP1 signaling pathways. The greyscale coding represents a logarithmic scale for the individual pathway scores. Hierarchical clustering was used. The color coding on the left side depicts: black=control; light grey=septic shock patient (non-survivor); dark grey=septic shock patient (survivor).

[0297] FIG. 14 shows a clustering diagram for the individual samples in dataset GSE4607 based on the AR, TGFbeta, MAPK-AP1 and JAK-STAT3 signaling pathways. The greyscale coding represents a logarithmic scale for the individual pathway scores. Hierarchical clustering was used. The color coding on the left side depicts: black=control; light grey=septic shock patient (non-survivor); dark grey=septic shock patient (survivor).

[0298] FIG. 15 shows a clustering diagram for the individual samples in dataset GSE66099 based on the AR and TGFbeta signaling pathways. The greyscale coding represents a logarithmic scale for the individual pathway scores. Hierarchical clustering was used. The color coding on the left side depicts: black=septic shock patient; light grey=septic patient; dark grey=control subject.

[0299] FIG. 16 shows a clustering diagram for the individual samples in dataset GSE66099 based on the AR, TGFbeta and MAPK-AP1 signaling pathways. The greyscale coding represents a logarithmic scale for the individual pathway scores. Hierarchical clustering was used. The color coding on the left side depicts: black=septic shock patient; light grey=septic patient; dark grey=control subject.

[0300] FIG. 17 shows a clustering diagram for the individual samples in dataset GSE66099 based on the AR, TGFbeta, MAPK-AP1 and JAK-STAT3 signaling pathways. The greyscale coding represents a logarithmic scale for the individual pathway scores. Hierarchical clustering was used. The color coding on the left side depicts: black=septic shock patient; light grey=septic patient; dark grey=control subject.

[0301] FIG. 18 shows a clustering diagram for the individual samples in dataset GSE95233 based on the AR and TGFbeta signaling pathways. The greyscale coding represents a logarithmic scale for the individual pathway scores. Hierarchical clustering was used. The color coding on the left side depicts: black=septic shock patient; light grey=septic patient; dark grey=control subject.

[0302] FIG. 19 shows a clustering diagram for the individual samples in dataset GSE95233 based on the AR, TGFbeta and MAPK-AP1 signaling pathways. The greyscale coding represents a logarithmic scale for the individual pathway scores. Hierarchical clustering was used. The color coding on the left side depicts: black=septic shock patient; light grey=septic patient; dark grey=control subject.

[0303] FIG. 20 shows a clustering diagram for the individual samples in dataset GSE95233 based on the AR, TGFbeta, MAPK-AP1 and JAK-STAT3 signaling pathways. The greyscale coding represents a logarithmic scale for the individual pathway scores. Hierarchical clustering was used. The color coding on the left side depicts: black=blood control; light grey=control survivor; medium grey=non-survivor day 1; dark grey=survivor day 1.

[0304] FIG. 21 depicts the pathway activities obtained from isolated THP-1 cells. THP-1 cells were incubated with H. pylori bacteria supernatant, directly incubated with H. pylori bacteria and compared with control THP-1 cells. Activities of the AR, ER, FOXO Hedgehog and TGFbeta pathways were determined and relative values are plotted.

[0305] FIG. 22 depicts the pathway activities obtained from isolated THP-1 cells. THP-1 cells were incubated with different concentrations of the bacterial product lipopolysaccharide (LPS) and compared with control THP-1 cells. Activities of the AR, ER, FOXO Hedgehog and TGFbeta pathways were determined and relative values are plotted.

[0306] FIG. 23 to FIG. 34. Boxplots shown predictive capacity of subsets of genes.

[0307] Each Figure depicts the random selection of N genes for N=1, 2, 3, 4, 5, or 6 for different subsets of the AR cellular signaling pathway (FIGS. 23 to 26), the TGFbeta cellular signaling pathway (FIGS. 27 to 30) or the combined target genes of the AR and the TGFbeta cellular signaling pathways (FIGS. 31 to 34). Either the entire set of target genes was used (T=0, FIGS. 23, 27 and 31) or a cutoff was used to select a subset of the target genes based on their contribution to the pathway activity score (T=0.3, 0.4 or 0.5, FIGS. 24-26, 28-30, 32-34). From each selected set of genes a random selection of N genes was made 1000 times, and the respective gene selections were used to determine whether sepsis patients can be distinguished from healthy subjects (at least 2 SD difference). The results are plotted in the form of box plots, where set 1 represents combined datasets GSE26440, GSE4607 and GSE66099, set 2 represents dataset GSE95233 and set 3 represents dataset GSE57065. The median is indicated by the thick line in the box, the 25.sup.th percentile by the lower boundary of the box and the 10.sup.th percentile by the dotted line.

[0308] FIG. 35 Schematic overview of AR inhibitor experiment. Depicts the experimental setup to determine if AR inhibitors can be used to mitigate the effect of LPS on monocytes (THP1 cells). In brief, monocyte cells (THP1) are cultured for 24 hours with or without LPS, after which the medium is changed and both conditions are subsequently cultured with or without DHT. Both LPS and DHT are anticipated to activate the AR cellular signaling pathway. In a parallel experiment, THP1 cells are first cultured for 24 hours with LPS, after which the medium is changed and the cells are cultured with one of ARCC-4, ARD-266, A-458 and bicalutamide.

[0309] FIGS. 36 to 39 pathway activities as determined in the AR inhibitor experiment. FIGS. 36 to 39 describe the experimental outcome of the different conditions outlined in FIG. 35 in terms of measured cellular signaling pathway activities. The figures depict the AR, ER, HH (Hedgehog) and TGFbeta cellular signaling pathway activities as determined in the different experimental groups respectively. Experiments were performed in triplicate, standard deviations of the measured activities are indicated in the graphs.

DETAILED DESCRIPTION OF EMBODIMENTS

[0310] The following examples merely illustrate particularly preferred methods and selected aspects in connection therewith. The teaching provided herein may be used for constructing several tests and/or kits, e.g., to detect, predict and/or diagnose the functional status of one or more blood samples. Furthermore, upon using methods as described herein drug prescription can advantageously be guided, drug response prediction and monitoring of drug efficacy (and/or adverse effects) can be made. The following examples are not to be construed as limiting the scope of the present invention.

Example 1—Methods and Sample Description

[0311] Using the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/gds/) Affymetrix HG-U133Plus2.0 data from samples from clinical and preclinical studies in which whole blood samples (GSE26440, GSE4607, GSE66099, GSE95233, for more information about sample type and preparation see Table 1) were used. We used the pathway analysis to determine the signal transduction pathway activities (AR, ER, PR, GR, HH, Notch, TGFbeta, WNT, JAK-STAT1/2, JAK-STAT3, NFkB, PI3K, MAPK). For the hierarchical clustering we used the clustering tool Seaborn clustermap.

[0312] For analysis public Affymetrix U133P2.0 data were used from the GEO database (GSE26440, GSE4607, GSE66099, GSE95233, for more information about sample type and preparation see Table 1). Pathway analysis of datasets GSE26440, GSE4607, GSE66099 and GSE95233 showed significant differences in multiple pathways including AR and TGFBeta pathway activity using Mann-Whitney-Wilcoxon two-sided test between normal (healthy) control subjects and septic shock subjects (for pathways and p values see FIGS. 1-8).

[0313] Using a combination of significant pathways, we could identify/diagnose the sepsis subjects from the controls. Furthermore, based on the AR and or TGFBeta pathway activity a computational model was made to calculate a risk score with respect to the risk to die and survive from sepsis. With hierarchical clustering we identified samples which clustering near control/healthy people, which are more likely to survive.

TABLE-US-00001 TABLE 1 Sample type and preparation information per dataset. Patients GEO original in Dataset dataset Patients Method GSE26440 76 Children <=10 blood samples were obtained within 24 hours (duplicates years of initial presentation tothe paediatric removed for intensive care unitwith septic shock. analysis) Total RNA was isolated from whole blood samples using the PaxGene ™ blood RNA system. GSE4607  83 Children <10 blood samples (for RNA and serum isolation) years were obtained within 24 hours of admission to the PICU, heretofore referred to as “Day 1” of septic shock. Total RNA was isolated from whole blood samples using the PaxGene ™ Blood RNA System (PreAnalytiX, Qiagen/Becton Dickson, Valencia, CA Patients meeting criteria for “sepsis” or “severe sepsis” were placed in the categories of SIRS and septic shock, respectively, for study purposes. GSE66099 (unique 128 Patients Consist of dataset; GSE4607, GSE8121, patients from (duplicates from 5 days GSE9692, GSE13904, GSE26378, and GSE4607, removed for to 18 years GSE26440, data was renormalized again. All GSE8121, analysis) old. datasets use the PaxGene ™ Blood RNA GSE9692, System and whole blood as input GSE13904, GSE26378, and GSE26440) GSE95233 125 53-80 years The first whole blood sample (EDTA tubes) was collected at the onset of shock (i.e., within 30 min after the beginning of vasoactive treatment, D 0) Total RNA was extracted with PAXgene ™ Blood RNA kit.

Example 2: Computational Models to Calculate a Risk Score

[0314] Furthermore, we were able to classify low, medium and high-risk sepsis subjects, with respect to the risk to die from sepsis. We used the computational model-based interpretation of multiple signaling pathway activity scores to classify the low, medium and high risk sepsis subjects.

[0315] To construct a linear model for interpretation of pathway activity scores we assessed the pathway activities in healthy people, by calculating the average pathway activity with 1 and 2 Standard Deviation (SD). When a pathway activity falls outside the boundaries of 2SD of the normal healthy, we consider this an abnormally active pathway, which means in the model 1 point. Optionally another threshold, such as 3SD of the mean, can be used. Adding up the points generates a cumulative abnormal pathway activity score, which directly determines the likelihood of the risk.

[0316] Other computational models to calculate a risk score can be Bayesian models, centroid-based models etc.

Example 3: Linear Model Using Calibration and Validation Set

[0317] For this, we used dataset GSE26440 as training set model and validated the model with dataset GSE4607. For both the AR and TGFBeta pathway 2SD above the mean pathway activity scores measured in the healthy control population was used for the classification model, this same value was then applied to the independent validation dataset GSE4607. When both AR and TGFBeta were 2SD higher than the control samples, the sepsis subjects were classified as high-risk (2 points). When either AR or TGFBeta were 2SD higher than the control, subjects were classified as medium-risk (1 point) and less than 2 SD difference was classified as low-risk (0 points). See table 2 for the determined means, standard deviations and 2SD top boundary for the pathways.

[0318] For the prognostic model, low, medium and high risk groups are identified for subject stratification. In the medium group one of the pathways is upregulated whereas in the high group both pathways are upregulated.

[0319] In table 3 the performance of the prognostic model is shown. For the GSE 26440 dataset (n=76, non survivor 10% (n=8), survivor 68% (n=51), Control 22% (n=17)) we could classify of the non survivors group, 3 as high risk, 5 as medium risk and 0 as low risk. For the validation set GSE 4607 (n=83, non survivor 17% (n=14), survivor 65% (n=54) and control 18% (n=15)) we could classify of the non survivors group 10 as high, 2 as medium and 2 as low using the model described above.

[0320] In addition, the combined pathway sum score of AR and TGFBeta can also be used for the prognostic marker, in which high risk is classified (1 point) as the combined AR and TGFBeta pathway score were 2SD higher than the control samples. When the combined AR and TGFBeta score were less than 2 SD difference compared to control, the samples are classified as low-risk (0 points) (data not shown).

[0321] For the other datasets (GSE66099, GSE95233 and GSE57064) we also see samples with a low AR and/or TGFBeta pathway activity. However, we lack survival data to prove that these subjects have a higher change to survive from sepsis.

TABLE-US-00002 TABLE 2 Mean values, standard deviations and 2SD top boundary of the activity of AR, TGFbeta and combined based on GSE26440. Standard 2SD Top Pathway Mean deviation boundary AR 18.0 2.6 23.3 TGFBeta 13.1 2.6 18.4 AR + TGFBeta 31.2 4.4 40.0 combined

TABLE-US-00003 TABLE 3 Model for the classification of low, medium and high-risk sepsis subjects that are likely to die. (n = number of samples). SD based on top boundary using control group. Scoring <2SD of AR or TGFBeta −> Low, >2 SD of only AR or TGFBeta −> Medium, >2 SD of AR and TGFBeta −> High. Training Validation GSE 26440 (n = 76, GSE 4607 (n = 83, non survivor (8) non survivor 17% (14) survivor (51) survivor 65% (54) Model control (17)) control 18% (15)) (2SD of Prediction Prediction AR, TGFBeta) NS and S NS and S High risk 38% (3/8) NS non survivor (3) 71% (10/14) NS non survivor (10) (AR + 43% (22/51) S survivor (22) 57% (31/54) S survivor (31) TGFBeta control (0) control (0) high) Medium risk 62% (5/8) NS non survivor (5) 14% (2/14) NS non survivor (2) (AR or 35% (18/51) S survivor (18) 28% (15/54) S survivor (15) TGFBeta control (2) control (0) high) Low risk 0% (0/8) NS non survivor (0) 14% (2/14) NS non survivor (2) (AR or 22% (11/51) S survivor (11) 64% (9/14) S survivor (9) TGFBeta control (15) control (15) low)

Example 4: Linear Model 2—Using Top Boundaries Per Dataset

[0322] Due to the differences between tests and sample taking it is probably more specific to determine top boundaries per dataset. In table 4 the Mean values, standard deviations and SD top boundaries of the activity of AR of GSE26440 and GSE4607 are listed. In table 5, the above described linear model is used but in this example a 2 SD top boundary is used based on each separate dataset. All non-survivals are placed in the medium and high-risk group. The control samples are only located in the low group and could be used as a diagnostic marker.

TABLE-US-00004 TABLE 4 Mean values, standard deviations and SD top boundaries of the activity of AR of GSE26440 and GSE4607 Standard 1SD Top 2SD Top 3SD Top Dataset Mean deviation boundary boundary boundary GSE26440 18.0 2.6 20.6 23.3 25.9 GSE4607 17.8 1.8 19.6 21.4 23.3

TABLE-US-00005 TABLE 5 Model for the classification of Low risk, medium risk and high risk sepsis subjects that are likely to die based on AR + TGFBeta. SD based on top boundary using control group. Scoring <2SD of AR or TGFBeta −> Low, >2 SD of only AR or TGFBeta −> Medium, >2 SD of AR and TGFBeta −> High. n = number of samples). GSE 26440 (n = 76, GSE 4607 (n = 83, non survivor (8) non survivor 17% (14) survivor (51) survivor 65% (54) control (17)) control 18% (15)) Model Prediction Prediction 2xSD NS and S NS and S High risk 38% (3/8) NS non survivor (3) 79% (11/14) NS non survivor (11) (AR + 43% (22/51) S survivor (22) 69% (37/54) S survivor (37) TGFBeta control (0) control (0) high) Medium risk 62% (5/8) NS non survivor (5) 21% (3/14) NS non survivor (3) (AR or 35% (18/51) S survivor (18) 15% (8/54) S survivor (8) TGFBeta control (2) control (0) high) Low risk 0% (0/8) NS non survivor (0) 0% (0/14) NS non survivor (0) (AR or 22% (11/51) S survivor (11) 17% (9/54) S survivor (9) TGFBeta control (15) control (15) low)

Example 5: Linear Model 3—Using Only AR Pathway and Top Boundaries Per Dataset

[0323] The same principle as described above is used in this example, only a 2DS top boundary is used and the model is based on only the AR pathway. The risk groups are in this case low or high risk of dying from sepsis. The performance of the model can be found in Table 6. The control samples are only located in the low group and could be used as a diagnostic marker.

TABLE-US-00006 TABLE 6 Prognostic model for the classification of Low and high-risk sepsis subjects that are likely to die and survive. (n = number of samples). SD based on top boundary using control group. Scoring <2SD of AR −> Low, >2 SD of AR −> High. GSE 26440 (n = 76, GSE 4607 (n = 83, non survivor (8) non survivor 17% (14) survivor (51) survivor 65% (54) control (17)) control 18% (15)) Model Prediction Prediction (2SD of AR) NS and S NS and S High 100% (8/8) NS non survivor (8) 93% (13/14) NS non survivor (13) (AR) 61% (31/51) S survivor (31) 72% (39/54) S survivor (39) control (1) control (0) Low 0% (0/8) NS non survivor (0) 7% (1/14) NS non survivor (1) (AR) 39% (20/51) S survivor (20) 28% (15/54) S survivor (15) control (16) control (15)

Example 6: Clustering Methods

[0324] We used hierarchical clustering (seaborn clustermap) to determine if it was possible to classify subjects based on their pathway activity. We selected the significant models between the control group and sepsis groups for the pathways AR, TGFBeta, JAK-STAT3 and MAPK-AP1.

[0325] For dataset GSE4607, several sepsis samples are clustered near the control group (orange). These subjects probably have a higher chance on survival. These samples are also clustered in the low risk group in the model described above which was based on the AR and TGFBeta. However, a clear distinction between the Septic Shock survivor and non survivors is not shown.

Example 7: Cell Stimulation with H. pylori

[0326] To investigate whether bacteria or bacterial products could induce the same pathway activities as observed in patients with sepsis, in vitro experiments were performed in which either bacteria or the bacterial product LPS was added to monocytic cells from a cell line, as a model system for monocytes in blood.

[0327] Porphyromonas gingivalis (ATCC, 33277) bacteria were cultured in an anaerobic culture hood using dehydrated HBI (Oxoid, CM1032) using manufactures instructions.

[0328] THP-1 cells were cultured in 6-wells plates with a density of 4×105 cells/well for 48 hours. After 48-hour cell seeding, cells were washed with PBS and treated with either direct bacteria of 1:100 MOI or using the 20% ‘supernatant’ of the bacterial culture at 37° C. 5% CO2 for 4 hours. Cells were exposed to bacteria with a MOI of 1:100, the 20% ‘supernatant’ was prepared by filtering the overnight bacteria culture was fusing 0.2 uM filter to remove whole bacteria and hereafter diluted in the cell culture media to a 20% concentration. After the 48-hour cell seeding, cells were washed with PBS and treated with either direct bacteria of 1:100 MOI or using the 20% ‘supernatant’ of the bacterial culture at 37° C. 5% CO2 for 4 hours. Hereafter, cells were washed with PBS and lysed in RNeasy mini kit lysis buffer (Qiagen, Cat No./ID: 74104) and stored at −80° C. until further processing. RNA was extracted using the RNeasy mini kit (Qiagen, 74104). qPCR was performed using the Philips Research OncoSignal platform.

[0329] To determine whether the Helicobacter bacteria have a bacteria-specific effect on the pathways activities of THP-1 cells, qRT-PCR was performed. Cells were either treated with direct bacteria or the growth media of the bacteria culture. As shown in FIG. 21, the pathway activity was increased for the AR, FOXO, TGFbeta and the WNT pathway. In the sepsis samples however, we did not detected a significant difference in the FOXO and WNT pathways, which could be due to the fact that monocytes only consists of 4-8% of the blood composition, and other blood cell types also play an important role.

Example 8: Cell Stimulation with LPS

[0330] To study the inflammation process we stimulated the monocytic THP-1 cells (ATCC® TIB-202™) using 3 different concentrations of LPS originated from E. coli (Ong/ml, 10 ng/ml, 50 ng/ml and 100 ng/ml) into culture medium (DMEM, supplemented with 10% FBS, 1% glutamax and 1% pen strep at 37° C. 5% CO2.) of THP-1 for 24 hours. After stimulation cells were harvested and RNA was extracted using the RNeasy mini kit (Qiagen, 74104). qPCR was performed using the Philips Research OncoSignal platform. As shown in FIG. 22, the pathway activity was increased for the pathways AR, FOXO, TGFBeta and WNT in the LPS stimulated cells. The activation of the AR and TGFBeta pathways was also seen in sepsis samples confirms the role of these pathways in inflammation. In the sepsis samples however, we did not detected a significant difference in the FOXO and WNT pathways, which could be due to the fact that monocytes only consists of 4-8% of the blood composition, and other blood cell types also play an important role.

Example 9: Validation of Subsets of Target Genes

[0331] To validate whether subsets of pathway target genes (e.g. three target genes selected from the total) are still predictive, random selections of N genes were made for the AR and the TGFbeta cellular signaling pathway target genes to evaluate the chance that a random selection of N genes from the total list is predictive. In order to do this, the individual target genes of the AR and TGFbeta cellular signaling pathway were ranked based on their relative contribution to the pathway score (T) as described below. For different thresholds of T (where T=0 corresponds to the entire gene set, and subsequent higher values for T correspond to more stringent selection) N genes were selected randomly 1000 times and a score was calculated as indicated below using a very simple linear model. The computation was performed for N values ranging from 1 to 6 on datasets GSE26440, GSE4607 and GSE66099 combined (set 1), or GSE95233 (set 2), or GSE57065 (set 3). Further the calculations were performed on the AR target genes, the TGFbeta target genes and pooled AR and TGFbeta target genes.

[0332] The protocol was performed as follows:

1. Take the list of genes corresponding to the pathways of interest, and take their probesets.
2. Per gene, take the probeset with maximum absolute correlation with the pathway score it contributes to (based on all sepsis and control samples; a gene may be involved in multiple pathways).
3. Select a candidate gene list by taking all genes with their absolute probeset-pathway correlation above a threshold T.
4. Repeatedly (1000 times), choose a random sub-list of N genes from the candidate gene list.
5. Make a simple linear classifier with those N genes by assigning them a weight +1 or −1 depending on the sign of their probeset-pathway correlation.
6. Apply that linear classifier on all samples to calculate a score.
7. For each test set, either GSE26440, GSE4607 and GSE66099 combined, or GSE95233, or GSE57065:
a. determine a mean and standard deviation of the score on the normal samples
b. calculate a threshold by taking the mean plus two times the standard deviation
c. determine the fraction of sepsis samples above the threshold, the fraction of sepsis non-survivors (if given) above the threshold, and for a check also the fraction of normal samples above the threshold
8. Make a box-plot distribution of the determined fractions over the 1000 random draws.

[0333] For an example where we consider the AR and TGFB pathways, a correlation threshold T=0.4, augmented with manually selected genes (FIG. 33), and random sets of N=3 genes, the results on the combination test set GSE26440, GSE4607 and GSE66099 shows:

[0334] the median fraction (thick line in the box) of detected sepsis samples is about 0.60, meaning that half of the random lists give a sensitivity of 60% or higher,

[0335] the 25th percentile (lower boundary of the box) of detected sepsis samples is about 0.32, meaning that three quarters of the random lists give a sensitivity of 32% or higher,

[0336] the 10th percentile (small dotted horizontal line) of detected sepsis samples is about 0.12, meaning that 90% of the random lists give a sensitivity of 12% or higher,

[0337] the specificity is at about 97.5% by the choice of the threshold (mean+2 stdev), and this is confirmed by the low fractions observed for the normal samples.

[0338] The boxplots resulting from these subsets are depicted in FIGS. 23 to XXX. From these datasets it can be concluded that depending on the dataset used and the selection criteria for target genes, as few as 1 target gene may be sufficient to distinguish between blood samples obtained from septic and non-septic subjects, but in all case a random selection at least three genes results in a set of genes with high specificity and a desirable sensitivity. Therefore it is concluded that a minimum of three target genes of the AR cellular signaling pathway, the TGFbeta cellular signaling pathway, or the pooled target genes from the AR and the TGFbeta cellular signaling pathway (as defined herein) suffice to diagnose a subject with sepsis.

[0339] From these data it can be concluded that a sepsis diagnosis can reliably be made based on three gene expression levels selected from the various sets of genes presented here. Although the respective sets of genes were successfully identified using the pathway models, this example demonstrates it is not necessary to use the pathway models in the diagnosis, and that diagnosis can be done purely based on the expression levels alone.

[0340] The genesets used in the analysis are as follows (the symbol in front of the gene name indicating positive or negative correlation):

AR—T=0

[0341] AR: +ABCC4, +APP, −AR, −CDKN1A, −CREB3L4, +DHCR24, +EAF2, +ELL2, +FGF8, +FKBP5, −GUCY1A3, +IGF1, −KLK2, −KLK3, +LCP1, −LRIG1, +NDRG1, −NKX3_1, −NTS, −PLAU, −PMEPA1, −PPAP2A, −PRKACB, +PTPN1, +SGK1, −TACC2, −TMPRSS2, −UGT2B15

AR—T=0.3

AR: −AR, −CREB3L4, +DHCR24, +EAF2, +ELL2, +FKBP5, −GUCY1A3, +IGF1, −KLK3, +LCP1, −LRIG1, +NDRG1, −NKX3_1, −PMEPA1, −PRKACB, −TMPRSS2

AR—T=0.4

AR: −AR, −CREB3L4, +DHCR24, +EAF2, +ELL2, +FKBP5, +LCP1, −LRIG1, +NDRG1, −PMEPA1, −PRKACB, −TMPRSS2

AR—T=0.5

AR: +DHCR24, +EAF2, +ELL2, +FKBP5, +LCP1, −PMEPA1, −PRKACB

TGFB—T=0

[0342] TGFB: −ANGPTL4, +CDC42EP3, −CDKN1A, +CDKN2B, +CTGF, +GADD45A, +GADD45B, +HMGA2, +ID1, +IL11, +INPP5D, +JUNB, −MMP2, +MMP9, −NKX2_5, −OVOL1, −PDGFB, +PTHLH, +SERPINE1, +SGK1, +SKIL, +SMAD4, −SMAD5, +SMAD6, −SMAD7, −SNAI1, +SNAI2, +TIMP1 and +VEGFA

TGFB—T=0.3

[0343] TGFB: +CDC42EP3, +GADD45A, +GADD45B, +HMGA2, +ID1, +IL11, +INPP5D, +JUNB, −MMP2, +MMP9, −NKX2_5, −OVOL1, −PDGFB, +PTHLH, +SGK1, +SKIL, +SMAD4, −SMAD5, +SMAD6, +TIMP1, +VEGFA

TGFB—T=0.4

TGFB: +CDC42EP3, +GADD45A, +GADD45B, +ID1, +JUNB, +MMP9, −PDGFB, +SGK1, +SKIL, −SMAD5, +SMAD6, +TIMP1, +VEGFA

TGFB—T=0.5

TGFB: +CDC42EP3, +GADD45A, +GADD45B, +ID1, +JUNB, +MMP9, −PDGFB, −+SGK1, −SMAD5, +TIMP1, +VEGFA

AR; TGFB—T=0

[0344] AR: +ABCC4, +APP, −AR, −CDKN1A, −CREB3L4, +DHCR24, +EAF2, +ELL2, +FGF8, +FKBP5, −GUCY1A3, +IGF1, −KLK2, −KLK3, +LCP1, −LRIG1, +NDRG1, −NKX3_1, −NTS, −PLAU, −PMEPA1, −PPAP2A, −PRKACB, +PTPN1, +SGK1, −TACC2, −TMPRSS2, −UGT2B15
TGFB: −ANGPTL4, +CDC42EP3, −CDKN1A, +CDKN2B, +CTGF, +GADD45A, +GADD45B, +HMGA2, +ID1, +IL11, +INPP5D, +JUNB, −MMP2, +MMP9, −NKX2_5, −OVOL1, −PDGFB, +PTHLH, +SERPINE1, +SGK1, +SKIL, +SMAD4, −SMAD5, +SMAD6, −SMAD7, −SNAI1, +SNAI2, +TIMP1 and +VEGFA

AR; TGFB—T=0.3

AR: −AR, −CREB3L4, +DHCR24, +EAF2, +ELL2, +FKBP5, −GUCY1A3, +IGF1, −KLK3, +LCP1, −LRIG1, +NDRG1, −NKX3_1, −PMEPA1, −PRKACB, +SGK1, −TMPRSS2

[0345] TGFB: +CDC42EP3, +GADD45A, +GADD45B, +HMGA2, +ID1, +IL11, +INPP5D, +JUNB, −MMP2, +MMP9, −NKX2_5, −OVOL1, −PDGFB, +PTHLH, +SGK1, +SKIL, +SMAD4, −SMAD5, +SMAD6, +TIMP1, +VEGFA

AR; TGFB—T=0.4

AR: −AR, −CREB3L4, +DHCR24, +EAF2, +ELL2, +FKBP5, +LCP1, −LRIG1, +NDRG1, −PMEPA1, −PRKACB, +SGK1, −TMPRSS2

TGFB: +CDC42EP3, +GADD45A, +GADD45B, +ID1, +JUNB, +MMP9, −PDGFB, +SGK1, +SKIL, −SMAD5, +SMAD6, +TIMP1, +VEGFA

AR; TGFB—T=0.5

AR: +DHCR24, +EAF2, +ELL2, +FKBP5, +LCP1, −LRIG1, −PMEPA1, −PRKACB, +SGK1

TGFB: +CDC42EP3, +GADD45A, +GADD45B, +ID1, +JUNB, +MMP9, −PDGFB, +SGK1, −SMAD5, +TIMP1, +VEGFA

Example 10: Validation of AR Inhibitors as a Treatment Option for Sepsis

[0346] Based on the above described data it was theorized that sepsis may be treated, or at least its symptoms may be alleviated, by administering an AR cellular signaling pathway inhibitor. As can be deducted from Examples 7 and 8, and FIGS. 21 and 22, AR and TGFbeta cellular signaling pathway activities are increased upon stimulation with H pylori supernatant or LPS in monocytes (THP-1 cells). To confirm this hypothesis, the applicant used this model system to predict a medical outcome of an AR pathway inhibitor for treating sepsis.

[0347] FIG. 35 describes the experimental set-up used. In brief, monocyte cells (THP1) are cultured for 24 hours with or without LPS, after which the medium is changed and both conditions are subsequently cultured with or without DHT. Both LPS and DHT are anticipated to activate the AR cellular signaling pathway. In a parallel experiment, THP1 cells are first cultured for 24 hours with LPS, after which the medium is changed and the cells are cultured with one of ARCC-4, ARD-266, A-458 and bicalutamide.

[0348] All experimental conditions were subjected to cellular signaling pathways analysis. The measured ER, AR, HH, and TGFbeta cellular signaling pathway activities are shown in FIGS. 36 to 39 respectively. FIG. 36 demonstrates that LPS or DHT increase AR cellular signaling pathway activity in monocytes, and that appears to be a small additive effect. Further, FIG. 36 demonstrates that AR activity induced by LPS can be at least partially reverted to baseline levels by addition of an AR pathway inhibitor.

[0349] FIGS. 37 and 38 demonstrate that the ER and HH cellular signaling pathway activities are not substantially affected by either LPS, DHT or the AR pathway inhibitors, therefore demonstrating that the effect shown in FIG. 36 is specific.

[0350] FIG. 39 shows that also TGFbeta signaling is increased by LPS, which is in line with other data shown herein wherein it is demonstrated that sepsis affects both AR and TGFbeta pathways. As expected, DHT did not increase TGFbeta activity. Interestingly, A-458 shows a reduction of LPS induced TGFbeta activity as well as a reduction of AR activity, suggesting it functions as a dual AR/TGFbeta inhibitor. As expected the remaining AR inhibitors were not able to mitigate the effect of LPS on TGFbeta cellular signaling pathway activity.

[0351] From these data it can be concluded that sepsis elevates AR and TGFbeta cellular signaling pathway activities in blood cells, which can detected in a patient's blood sample and used for quick diagnosis of sepsis or prediction of patients at risk of developing sepsis. Further, these data demonstrate that the elevated AR and TGFbeta can at least partially be attributed to monocytes, and that the effect can be recreated by adding LPS to cultured monocytes. Further these data demonstrate that LPS induced increased AR signaling pathway activity can be mitigated by an AR pathway inhibitor, as demonstrated by in vitro experiments using monocytes. This demonstrates that AR inhibitors can likely be used to treat, or at least reduce the symptoms (alleviate), of a subject with sepsis, on the premise that the patient has an increased AR pathway activity or abnormal expression of the sepsis-associated genes. It further emphasizes the need for a companion test to identify patients at risk of developing sepsis or patients with sepsis who would benefit from treatment with an AR inhibitor.

REFERENCES

[0352] [1] N. L. Stanski and H. R. Wong, “Prognostic and predictive enrichment in sepsis,” Nature Reviews Nephrology. Nature Publishing Group, 1 Jan. 2019. [0353] [2] “Recommendations|Sepsis: recognition, diagnosis and early management|Guidance|NICE.” [0354] [3] N. K. Patil, J. K. Bohannon, and E. R. Sherwood, “Immunotherapy: A promising approach to reverse sepsis-induced immunosuppression.,” Pharmacol. Res., vol. 111, pp. 688-702, 2016. [0355] [4] R. S. Hotchkiss, G. Monneret, and D. Payen, “Sepsis-induced immunosuppression: from cellular dysfunctions to immunotherapy.,” Nat. Rev. Immunol., vol. 13, no. 12, pp. 862-74, December 2013. [0356] [5] M. R. Gubbels Bupp and T. N. Jorgensen, “Androgen-Induced Immunosuppression,” Front. Immunol., vol. 9, p. 794, April 2018. [0357] [6] A. Trigunaite and J. Dimo, “Suppressive effects of androgens on the immune system,” Cell. Immunol., vol. 294, no. 2, pp. 87-94, April 2015. [0358] [7] von Dipl Biochem Daniela Röll, “Androgen-regulation of sepsis response: Beneficial role of androgen receptor antagonists.” [0359] [8] F. Fattahi and P. A. Ward, “Understanding Immunosuppression after Sepsis,” Nat. Rev. Mol. Cell Biol, vol. 42, pp. 51-65, 2017. [0360] [9] A. Roquilly et al., “Local Modulation of Antigen-Presenting Cell Development after Resolution of Pneumonia Induces Long-Term Susceptibility to Secondary Infections,” Immunity, vol. 47, no. 1, pp. 135-147.e5, July 2017. [0361] [10] M. Cecconi et al., “Sepsis and septic shock”, Lancet 2018; 392: 75-87. [0362] [11] Gubbels Bupp and Jorgensen, Androgen-Induced Immunosuppression, Front Immunol. 2018; 9: 794. [0363] [12] Malinen et al., Crosstalk between androgen and pro-inflammatory signaling remodels androgen receptor and NF-κB cistrome to reprogram the prostate cancer cell transcriptome, Nucleic Acids Res. 2017 Jan. 25; 45(2): 619-630. [0364] [13] Ben-Batalla et al., Influence of Androgens on Immunity to Self and Foreign: Effects on Immunity and Cancer, Front. Immunol., 2 Jul. 2020| https://doi.org/10.3389/fimmu.2020.01184. [0365] [14] Sukhacheva, The role of monocytes in the progression of sepsis, Clinical Laboratory Int. 26 Aug. 2020. [0366] [15] Haverman et al., The central role of monocytes in the pathogenesis of sepsis: consequences for immunomonitoring and treatment, The Netherlands Journal of Medicine, Volume 55, Issue 3, September 1999, Pages 132-141. [0367] [16] Angele et al., Gender differences in sepsis, Virulence, 5:1, 12-19, DOI: 10.4161/viru.26982

Clauses

[0368] CLAUSE 1. A method for determining a functional status of a blood sample, based on RNA extracted from the blood sample, the method comprising the steps of:

[0369] determining or receiving the result of a determination of the expression level of three or more target genes of the AR pathway;

[0370] determining the AR signaling pathway activity, based on the determined expression levels of said three or more target genes of the AR signaling pathway;

[0371] determining the functional status of the blood sample based at least on the determined AR signaling pathway activity, wherein said functional status of said blood sample is being determined as having the determined AR signaling pathway activity, wherein said blood sample is obtained from a subject with sepsis or obtained from a subject suspected to have sepsis or an subject at risk of developing sepsis.

CLAUSE 2. Method according to clause 1, wherein said method further comprises, determining or receiving the result of a determination of the expression level of three or more target genes of the TGFbeta pathway,

[0372] determining the TGFbeta signaling pathway activity based on the determined expression levels of said three or more target genes of the TGFbeta signaling pathway,

[0373] and wherein said functional status of said blood sample is further based on the determined TGFbeta signaling pathway activity, wherein said functional status is further being determined as having the determined TGFbeta signaling pathway activity.

CLAUSE 3. Method according to any one of the preceding clauses, wherein said method further comprises:

[0374] determining or receiving the result of a determination of the expression level of three or more target genes of the MAPK-AP1 signaling pathway, and determining the MAPK-AP1 signaling pathway activity based on said expression levels of the three or more target genes of the MAPK-AP1 signaling pathway, and/or

[0375] determining or receiving the result of a determination of the expression level of three or more target genes of the JAK-STAT3 signaling pathway, and determining the JAK-STAT3 signaling pathway activity based on said expression levels of the three or more target genes of the JAK-STAT3 signaling pathway,

[0376] and wherein said functional status of said blood sample is further based on the determined MAPK-AP1 signaling pathway activity and/or JAK-STAT3 signaling pathway activity, wherein said functional status is further being determined as having the determined MAPK-AP1 signaling pathway activity, and/or wherein said functional status is further being determined as having the determined JAK-STAT3 signaling pathway activity.

CLAUSE 4. Method according to any one of the preceding clauses wherein said determining the expression level of three or more target genes of the AR signaling pathway, the TGFbeta signaling pathway, the MAPK-AP1 signaling pathway and/or the JAK-STAT3 signaling pathway comprises:

[0377] determining the expression level of three or more target genes of the AR signaling pathway selected from the list consisting of: KLK2, PMEPA1, TMPRSS2, NKX3_1, ABCC4, KLK3, FKBP5, ELL2, UGT2B15, DHCR24, PPAP2A, NDRG1, LRIG1, CREB3L4, LCP1, GUCY1A3, AR and EAF2, and/or;

[0378] determining the expression level of three or more target genes of the TGFbeta signaling pathway comprises determining the expression level of three or more target genes selected from the list consisting of ANGPTL4, CDC42EP3, CDKN1A, CTGF, GADD45A, GADD45B, HMGA2, ID1, IL11, JUNB, PDGFB, PTHLH, SERPINE1, SGK1, SKIL, SMAD4, SMAD5, SMAD6, SMAD7, SNAI2, VEGFA, and/or;

[0379] determining the expression level of three or more target genes of the MAPK-AP1 signaling pathway comprises determining the expression level of three or more target genes selected from the list consisting of BCL2L11, CCND1, DDIT3, DNMT1, EGFR, ENPP2, EZR, FASLG, FIGF, GLRX, IL2, IVL, LOR, MMP1, MMP3, MMP9, SERPINE1, PLAU, PLAUR, PTGS2, SNCG, TIMP1, TP53, and VIM, and/or;

[0380] determining the expression level of three or more target genes of the JAK-STAT3 signaling pathway comprises determining the expression level of three or more target genes selected from the list consisting of AKT1, BCL2, BCL2L1, BIRC5, CCND1, CD274, CDKN1A, CRP, FGF2, FOS, FSCN1, FSCN2, FSCN3, HIF1A, HSP90AA1, HSP90AB1, HSP90B1, HSPA1A, HSPA1B, ICAM1, IFNG, IL10, JunB, MCL1, MMP1, MMP3, MMP9, MUC1, MYC, NOS2, POU2F1, PTGS2, SAA1, STAT1, TIMP1, TNFRSF1B, TWIST1, VIM, and ZEB1.

CLAUSE 5. Method according to any one of the preceding clauses, wherein said method further comprises the step of diagnosing the subject from which the blood sample has been obtained, wherein said subject is diagnosed to have sepsis or wherein said subject is diagnosed to not have sepsis based on:

[0381] a clinical parameter, and

[0382] the functional status of the blood sample,

[0383] the method further comprising comparing the functional status of the blood sample of the subject to at least one functional status of a blood sample obtained from a healthy or non-septic control subject, preferably wherein the subject is diagnosed to have sepsis if the functional status of the blood sample comprises an AR signaling pathway activity which AR signaling pathway activity is determined to be higher than the AR signaling pathway activity determined in the control blood sample obtained from a healthy or non-septic control and the subject from which the blood sample has been obtained has at least one clinical parameter associated with sepsis.

CLAUSE 6. Method according to any one of the preceding clauses, wherein said functional status of the blood sample is used in predicting the mortality risk for the subject from which the blood sample has been obtained,

[0384] wherein said prediction is based on a comparison of the functional status of the blood sample of the subject with a plurality of reference functional statuses of the blood samples obtained from reference subjects, wherein said plurality of reference functional statuses of the blood samples obtained from reference subjects comprises at least one functional statuses of blood sample obtained from subject with sepsis which is a non-survivor and at least one functional statuses of blood samples obtained from subject with sepsis which is a survivor, and optionally further comprises at least one functional statuses of blood samples obtained from a healthy or non-septic control subject,

[0385] wherein the subject from which the blood sample is obtained is confirmed to have sepsis, and

[0386] wherein a low mortality risk is predicted when the functional status of the blood sample obtained from the subject with sepsis is similar to the at least one functional status of the blood sample obtained from reference subject with sepsis which is a survivor or when the functional status of the blood sample obtained from the subject with sepsis is similar to the at least one functional status of the blood sample obtained from the at least one healthy or non-septic control subject, and

[0387] wherein a high mortality risk is predicted when the functional status of the blood sample obtained from the subject with is similar to the at least one functional status of the blood sample obtained from the reference subject with sepsis which is a non-survivor.

CLAUSE 7. Method according to clause 6, wherein said comparing of the functional status of the blood sample obtained from the subject with a plurality of functional statuses of the blood samples obtained from control subjects is performed using clustering of the determined pathway activities, preferably by hierarchical clustering.
CLAUSE 8. Method according to any one of clauses 1 to 4, wherein the subject from which the blood sample has been obtained does not have sepsis, and wherein the functional status of the blood sample is used to determine the risk that the subject will develop sepsis,

[0388] the method further comprising comparing the functional status of the blood sample of the subject from which the blood sample has been obtained to at least one functional status of a blood sample obtained from a healthy or non-septic control subject,

[0389] preferably wherein the subject from which the blood sample has been obtained is predicted to be at risk to develop sepsis if the functional status of the blood sample comprises an AR signaling pathway activity which AR signaling pathway activity is determined to be higher than the AR signaling pathway activity determined in the control blood sample obtained from a healthy or non-septic control subject.

CLAUSE 9. Method according to any one of clauses 1 to 4, wherein the subject from which the blood sample has been obtained has recovered from sepsis, and wherein the functional status of the blood sample is used to monitor the risk that the subject will develop a recurrence of sepsis,

[0390] the method further comprising comparing the functional status of the blood sample of the subject from which the blood sample has been obtained to at least one functional status of a blood sample obtained from a healthy or non-septic control subject,

[0391] preferably wherein the subject from which the blood sample has been obtained is predicted to be at risk to develop a recurrence of sepsis if the functional status of the blood sample comprises an AR signaling pathway activity which AR signaling pathway activity is determined to be higher than the AR signaling pathway activity determined in the control blood sample obtained from a healthy or non-septic control subject.

CLAUSE 10. Method according to any one of the preceding clauses, wherein the blood sample is a whole blood sample, isolated peripheral blood mononuclear cells (PBMCs), isolated CD4+ cells, isolated CD8+ cells, Regulatory T-cells, mixed CD8+ and T cells, myeloid derived suppressor cells (MDSC), dendritic cells, isolated neutrophils, isolated lymphocytes or isolated monocytes.
CLAUSE 11. Method according to any one of the preceding clauses, wherein said signaling pathway activity or signaling pathway activities is determined based on evaluating a calibrated mathematical model relating the to the three or more expression levels determined for the pathway or pathways based on the RNA extracted from a blood sample to the activity or activities of the signaling pathway or signaling pathways.

[0392] CLAUSE 12. A apparatus for determining the functional status of a blood sample, the apparatus comprising a digital processor configured to perform the method according to any one of the preceding clauses, comprising an input adapted to receive data indicative of a target gene expression profile for the three or more target genes of the AR signaling pathway, optionally data indicative of a target gene expression profile for the three or more target genes of the TGFbeta signaling pathway and/or the MAPK-AP1 signaling pathway and/or the JAK-STAT3 signaling pathway.

CLAUSE 13. Computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out a method comprising:

[0393] receiving data indicative of a target gene expression profile for three or more target genes of the AR signaling pathway, optionally further receiving data indicative of the target gene expression levels of three or more target genes of the TGFbeta signaling pathway and/or the MAPK-AP1 signaling pathway and/or the JAK-STAT3 signaling pathway,

[0394] determining the AR signaling pathway activity, and optionally TGFbeta signaling pathway activity and/or the MAPK-AP1 signaling pathway activity and/or the JAK-STAT3 signaling pathway activity based on the determined expression levels of said three or more target genes of the AR signaling pathway and optionally the TGFbeta signaling pathway and/or the MAPK-AP1 signaling pathway and/or the JAK-STAT3 signaling pathway,

[0395] determining the functional status of the blood sample based on the determined AR signaling pathway activity and optionally TGFbeta signaling pathway activity and/or the MAPK-AP1 signaling pathway activity and/or the JAK-STAT3 signaling pathway activity, wherein said functional status of said blood sample is being determined as having the determined AR signaling pathway activity and optionally the TGFbeta signaling pathway activity and/or the MAPK-AP1 signaling pathway activity and/or the JAK-STAT3 signaling pathway activity, and

[0396] optionally providing a diagnosis or prediction based on the functional status of the blood sample.

CLAUSE 14. Kit of parts, comprising primers for inferring activity of one or more cellular signaling pathway(s) by determining the expression levels of one or more set(s) of target genes of the respective cellular signaling pathway(s), wherein the cellular signaling pathway(s) comprise(s) a AR pathway, and optionally further comprises one or more of an TGFbeta pathway, an MAPK-AP1 pathway and a JAK-STAT3 pathway,

[0397] wherein the set of target genes of the AR pathway comprises three or more target genes selected from the group comprising: KLK2, PMEPA1, TMPRSS2, NKX3_1, ABCC4, KLK3, FKBP5, ELL2, UGT2B15, DHCR24, PPAP2A, NDRG1, LRIG1, CREB3L4, LCP1, GUCY1A3, AR and EAF2, and

[0398] wherein the set of target genes of the TGFbeta pathway comprises three or more target genes selected from the group comprising: ANGPTL4, CDC42EP3, CDKN1A, CTGF, GADD45A, GADD45B, HMGA2, ID1, IL11, JUNB, PDGFB, PTHLH, SERPINE1, SGK1, SKIL, SMAD4, SMAD5, SMAD6, SMAD7, SNAI2, VEGFA, and

[0399] wherein the set of target genes of the MAPK-AP1 pathway comprises three or more target genes selected from the group comprising: BCL2L11, CCND1, DDIT3, DNMT1, EGFR, ENPP2, EZR, FASLG, FIGF, GLRX, IL2, IVL, LOR, MMP1, MMP3, MMP9, SERPINE1, PLAU, PLAUR, PTGS2, SNCG, TIMP1, TP53, and VIM, and

[0400] wherein the set of target genes of the JAK-STAT3 pathway comprises three or more target genes selected from the group comprising: AKT1, BCL2, BCL2L1, BIRC5, CCND1, CD274, CDKN1A, CRP, FGF2, FOS, FSCN1, FSCN2, FSCN3, HIF1A, HSP90AA1, HSP90AB1, HSP90B1, HSPA1A, HSPA1B, ICAM1, IFNG, IL10, JunB, MCL1, MMP1, MMP3, MMP9, MUC1, MYC, NOS2, POU2F1, PTGS2, SAA1, STAT1, TIMP1, TNFRSF1B, TWIST1, VIM, and ZEB1,

[0401] optionally the kit further comprising the apparatus according to clause 12 and/or the computer program product of clause 13.

CLAUSE 15. A method for in vitro or ex vivo diagnosing or prognosticating whether a subject has sepsis, has septic shock or has a high mortality risk as a result of sepsis using a kit, the kit comprising primers for inferring activity of one or more cellular signaling pathway(s) by determining the expression levels of one or more set(s) of target genes of the respective cellular signaling pathway(s), wherein the cellular signaling pathway(s) comprise(s) a AR pathway, and optionally further comprises one or more of an TGFbeta pathway, an MAPK-AP1 pathway and a JAK-STAT3 pathway,

[0402] wherein the set of target genes of the AR pathway comprises three or more target genes selected from the group comprising: KLK2, PMEPA1, TMPRSS2, NKX3_1, ABCC4, KLK3, FKBP5, ELL2, UGT2B15, DHCR24, PPAP2A, NDRG1, LRIG1, CREB3L4, LCP1, GUCY1A3, AR and EAF2, and

[0403] wherein the set of target genes of the TGFbeta pathway comprises three or more target genes selected from the group comprising: ANGPTL4, CDC42EP3, CDKN1A, CTGF, GADD45A, GADD45B, HMGA2, ID1, IL11, JUNB, PDGFB, PTHLH, SERPINE1, SGK1, SKIL, SMAD4, SMAD5, SMAD6, SMAD7, SNAI2, VEGFA, and

[0404] wherein the set of target genes of the MAPK-AP1 pathway comprises three or more target genes selected from the group comprising: BCL2L11, CCND1, DDIT3, DNMT1, EGFR, ENPP2, EZR, FASLG, FIGF, GLRX, IL2, IVL, LOR, MMP1, MMP3, MMP9, SERPINE1, PLAU, PLAUR, PTGS2, SNCG, TIMP1, TP53, and VIM, and

[0405] wherein the set of target genes of the JAK-STAT3 pathway comprises three or more target genes selected from the group comprising: AKT1, BCL2, BCL2L1, BIRC5, CCND1, CD274, CDKN1A, CRP, FGF2, FOS, FSCN1, FSCN2, FSCN3, HIF1A, HSP90AA1, HSP90AB1, HSP90B1, HSPA1A, HSPA1B, ICAM1, IFNG, IL10, JunB, MCL1, MMP1, MMP3, MMP9, MUC1, MYC, NOS2, POU2F1, PTGS2, SAA1, STAT1, TIMP1, TNFRSF1B, TWIST1, VIM, and ZEB1.