Prognostic pathways for high-risk sepsis patients

Abstract

The present invention relates to compounds useful in the prevention and treatment of sepsis and subjects suffering from or at risk of developing sepsis.The present invention particularly relates to compounds that inhibit AR cell signaling pathway activity or compounds that inhibit TGF beta cell signaling pathway activity.The present invention further relates to a method for measuring AR and / or TGF beta pathway activity in blood samples from subjects suffering from or at risk of developing sepsis, and a method for administering an AR and / or TGF beta pathway inhibitor when AR and / or TGF beta pathway activity exceeds a certain threshold.

Description

[Technical field]

[0001] The present invention relates to compounds useful in the prevention and treatment of sepsis and subjects suffering from or at risk of developing sepsis, or in preventing an immunosuppressed state of the immune system.The present invention particularly relates to compounds that inhibit AR cell signaling pathway activity, and / or compounds that inhibit TGF beta cell signaling pathway activity.The present invention further relates to a method for measuring AR and / or TGF beta pathway activity in a blood sample of a subject suffering from or at risk of developing sepsis, and a method for administering an AR and / or TGF beta pathway inhibitor when AR and / or TGF beta pathway activity exceeds a certain threshold. [Background technology]

[0002] Sepsis has been recently redefined as an infection accompanied by organ dysfunction (10). Sepsis is a dysregulated immune response to infection. Sepsis is generally a complication of severe bacterial infections in which bacteria amplify in the bloodstream. The condition is characterized by a systemic inflammatory response that may result in septic shock with hypotension and hyperlactemia refractory to fluid resuscitation, progressive organ failure, and, ultimately, death, by mechanisms that are not fully understood. Mortality rates from sepsis range from 25%-30% for severe sepsis and 40%-70% for septic shock. The clinical presentation of sepsis is highly variable depending on the etiology, i.e., the underlying disease or condition. The most common sources of bacterial infection are the respiratory, genitourinary, and digestive systems, as well as the skin and soft tissues. Although clinical presentation is variable, fever and tachycardia with extreme shivering are often the first clinical symptoms of sepsis, with pneumonia and genitourinary infection being the most common causes leading to sepsis. Specific treatment consists mainly of giving antibiotics to treat the infection, in addition to standard measures to maintain blood circulation and prevent hypoxia. However, the source of bacterial infection is often unknown, and therefore the appropriate antibiotic treatment is an educated guess until information about the causative bacteria and its sensitivity / resistance to antibiotics is available from blood cultures. This information allows for adjustment of treatment if the bacterial species becomes resistant to the administered antibiotic. However, blood cultures take a long time (24-48 hours) and become positive in about 30% of all patients with infection, but for patients with bacterial sepsis, each delay in administering effective antibiotics increases the relative risk of death.

[0003] For these reasons, many patients progress to septic shock, resulting in a very high mortality rate associated with this disease. Besides antibiotics, treatment of subjects with septic shock generally consists of intravenous fluid administration and vasopressor therapy. Many additional treatments, such as corticosteroids, have been tried to reverse the inflammatory septic shock state, but none have proven clinical benefit. However, it cannot be excluded that they may benefit a so far as yet unidentifiable subset of patients (10).

[0004] One reason is that it is not clear which cellular mechanisms are responsible for the transition to septic shock and organ damage. Knowledge of the pathophysiology of sepsis is expected to provide novel drug targets for preventing and treating sepsis in a rational manner.

[0005] Apart from antibiotics and supportive measures to maintain splanchnic blood circulation, no treatments have proven effective, although it cannot be excluded that some treatments may benefit a so far unidentifiable subset of patients. (10) One reason for the failure to develop effective treatments is the heterogeneity among septic patients, i.e., variations in underlying medical conditions and drug use, as well as genetic variations that affect the immune response in individual patients.

[0006] One of the reasons for the failure to develop an effective drug for everything is likely the heterogeneity among patients, which is a result of the many causes and conditions that can lead to sepsis and the even larger number of known and unknown factors that influence its progression but also determine response or resistance to the drug (e.g., bacterial species type, genetics, other coexisting diseases, etc.).

[0007] Detailed assessment of functional immune responses in patients with sepsis would enable novel general and personalized treatment approaches and improve the efficacy of treatment. Diagnostic assessment of immune function is currently limited to routine blood measurements such as immune cell counts and inflammatory markers (e.g., C-reactive protein), but does not provide information on the functional activity state of the various types of immune cells that are responsible for the abnormal immune response in patients with sepsis.

[0008] The continuum of inflammatory responses to microorganisms is now classified as infection, sepsis, and septic shock, replacing the more ancient terms sepsis, severe sepsis, and septic shock. Additionally, the systemic inflammatory response syndrome (SIRS) was a condition defined by the presence of two of four criteria: fever, tachycardia, tachypnea, and leukocytosis or leukopenia, in the absence of (proven) infection; more recently, this diagnosis has been abandoned due to lack of sensitivity and specificity.

[0009] The clinical criteria used to identify septic patients in hospitals who are at high risk for sepsis-related death are: (1) altered mental status; (2) systolic blood pressure less than 100 mmHg and respiratory rate greater than 22 breaths per minute. The use of these criteria allows for improved identification of high-risk patients, but does not identify low-risk patients.

[0010] The best rational means of reducing mortality from sepsis is to identify patients at risk of developing sepsis as early as possible. Such high-risk patients can then be stratified for more intensive monitoring (e.g., in an ICU), identification and elimination of the infectious focus (e.g., indwelling catheters), more frequent performance of bacterial cultures, administration of prophylactic antibiotics, and removal of immunosuppressants.

[0011] Urinary catheters, open wounds or wounds with drains, intravascular lines, etc. are all almost always associated with some degree of bacterial colonization and therefore present a risk of sepsis in susceptible patients. These conditions are present in the majority of hospitalized patients and therefore represent a patient population with improved clinical outcomes upon early assessment of risk for sepsis and septic shock. Patient genetics and the functional status of the immune system are likely relevant factors determining risk.

[0012] The susceptibility of a patient with an infection to developing sepsis and progressing to septic shock is determined, in part, by the immune response that the patient mounts against the infectious agent.

[0013] The functionality of the immune response (immunostimulatory versus immunosuppressive, inflammatory versus adaptive T cell-mediated responses) is determined by multiple intrinsic factors such as age, genetic alterations, comorbidities (e.g., chronic diseases such as diabetes), previous treatments (such as chemotherapy, bone marrow transplantation, corticosteroids), and extrinsic factors such as current immunosuppressive treatments.

[0014] The consequences of all these factors on the function of the immune system in an individual patient cannot yet be assessed. This currently makes it very difficult to predict (1) which patients with an infection are at risk of developing sepsis, (2) how large that risk is, and (3) what the risk of progression to septic shock and, ultimately, death is. Realizing that the state of the immune response is an important determinant of the risk of developing sepsis also allows the development of novel treatments for patients with infection or sepsis based on improving a weakened immune function (i.e., immunotherapy).

[0015] However, due to the large heterogeneity between patients, including genetic variants that affect both the immune response and the susceptibility to developing sepsis, the molecular causes of immune response dysfunction in sepsis differ between individuals. This means that personalized medicine approaches based on the characterization of the cellular mechanisms that determine the functional immune status in individual patients are needed to select the most effective immune-targeted drugs with minimal side effects (e.g., cytokine storm). Personalized medicine based on the genetic profile of patients has not proven to be effective.

[0016] Clearly, the immune dysfunction phenotype involved in susceptibility to sepsis is determined not only by the genetic profile but also, to an important extent, by many environmental cues of the patient (e.g., bacterial species, bacterial load, comorbidities, drugs, etc.). This means that an individual's functional immune status should be characterized at the phenotypic level, that is, the activity of cellular mechanisms that control immune function.

[0017] Immune cell function is regulated by highly regulated interactions between signaling pathways, such as the TGF-beta, PI3K, MAPK, JAK-STAT, and AR pathways. Recently, novel assays have been developed to measure the activity of signaling pathways in cell or tissue samples, including blood samples, based on target gene mRNA measurements that are interpreted by Bayesian computer models and translated into pathway activity scores (2), (3), (4). Measurement of the activity of these pathways allows characterization of the functional state of any type of immune cell (5).

[0018] The diagnosis of sepsis is based in part on subjective clinical criteria and is therefore not very sensitive or specific, mainly due to the wide variability among patients. (1) When sepsis is suspected based on clinical criteria, rapid confirmation of the diagnosis remains important to accomplish patient management and treatment planning. For example, confirmation by blood culture is a time-consuming process that can take several days and cannot wait.

[0019] There is a need for faster confirmatory diagnostic tests, as well as tests to predict which patients with infection (mainly in-hospital) are at risk of developing sepsis, to predict the risk of progressing to septic shock, and to predict the risk of dying. Clinical actions that can be taken based on such tests may be, for example, stratifying subjects for admission to an intensive care unit (ICU) versus remaining on a general ward, for surgical exploration and removal of the source of infection in high-risk patients, and for specific treatments, such as targeted immunotherapy. Summary of the Invention [Problem to be solved by the invention]

[0020] In summary, there is a high need for additional tests that can be rapidly performed and that can be used to (1) predict the risk of sepsis in infected hospitalized (or home) patients, (2) diagnose sepsis early, (3) predict the risk of progression to septic shock and death, and (4) predict response to treatment, such as (targeted) therapy or immunotherapy, on an individual patient basis (personalized medicine). Furthermore, (5) additional methods of treating or preventing sepsis are urgently needed. [Means for solving the problem]

[0021] According to a first aspect of the present invention, the above problem is solved by an AR cell signaling pathway inhibitor for use in the prevention or treatment of sepsis in a subject.

[0022] According to a second aspect of the present invention, the above problem is solved by a TGF-beta pathway inhibitor for use in the prevention or treatment of sepsis in a subject.

[0023] Detailed Description of the Embodiments The functional state of immune cells is determined by a small number of so-called cell signaling pathways (STPs) (

[20] ,

[21] ,

[22] ,

[23] ,

[24] ; each of which is incorporated herein by reference in its entirety). Recently, novel assays have been developed to quantitatively measure the activity of STPs in cell and tissue samples, including blood samples (

[25] ,

[26] ,

[27] ,

[28] ; each of which is incorporated herein by reference in its entirety). Measurement of the combined activity of these STPs in blood cells is expected to enable quantitative assessment of the innate and adaptive immune responses in an individual.

[0024] STP analysis was performed on publicly available gene expression data from multiple clinical sepsis studies (

[30] , incorporated by reference in its entirety). The studies described herein show that AR and TGF-beta activity was increased in septic patients compared to healthy controls, indicating that these pathways are novel drug targets for treating or preventing sepsis.

[0025] Treatment with pathway inhibitors, particularly AR or TGF beta pathway inhibitors, or a combination thereof, is beneficial for subjects with sepsis or at risk of developing sepsis. Treatment of sepsis with AR inhibitors has been tried before in mouse models, but without success. This is due, in part, to gender-based differences in androgen and / or testosterone levels and the resulting activity of the AR pathway. From this study, it was concluded that treatment of sepsis with AR inhibitors is not feasible. The present disclosure now shows that, first of all, patients can be stratified by AR pathway activity, allowing treatment of only those patients with strongly increased AR pathway activity. More importantly, patients with infections who are at risk of developing sepsis and have high AR pathway activity in their associated blood cells can benefit from preventive treatment with AR inhibitors. Such preventive treatment with AR inhibitors has not been proposed to the best of our knowledge. The rationale behind this is that an active AR pathway leads to immunosuppression. See, for example, Gubbels Bupp and Jorgensen 2018[5], which is incorporated by reference in its entirety.

[0026] AR protein was found to be expressed in various innate and adaptive immune cells, including neutrophils, macrophages, mast cells, monocytes, megakaryocytes, B cells, and T cells, suggesting that the AR pathway is indeed ligand-induced, and we confirm here that monocytic cells are the main cell type responsible for sepsis symptoms.Interestingly, AR protein is also expressed in hematopoietic stem cells, lymphocytes, and myeloid progenitor cells.Evidence from different studies points to the immunosuppressive role of androgens in different immune cell types, mainly by changing the expression of inflammatory and anti-inflammatory mediators that are important for proper immune response.

[0027] Consistent with the inflammatory response in sepsis, there is evidence that several immunosuppressive events occur in which immunosuppressive cytokines such as IL-10, TGF-β, and IL6 play a role. According to a clinical observational cohort study, septic patients develop chronic critical illness, have a 6-month survival rate of 63%, and continue to display a cytokine profile of chronic inflammation, as well as a biomarker profile characteristic of persistent immunosuppression

[31] . A study from Hiraki et al.

[32] used a mouse abdominal sepsis model in which a TGF-β depleting antibody was administered, which resulted in improved survival of the mice. This suggests a causal role of the TGF-beta pathway in sepsis. So far, no treatment or prevention with TGF-beta inhibitors has been suggested.

[0028] The present invention is further based on the inventors' innovation that the analysis of signaling pathway activity can be used to characterize blood samples, for example blood samples consisting of at least one immune cell type or a mixed population of immune cell types, for example, by determining the activity of the AR signaling pathway and, if necessary, determining the activity or activities of additional or alternative pathways, such as the TGF beta pathway, the MAPK-AP1 pathway and the JAK-STAT3 pathway. The inventors demonstrate that, when determined on a blood sample, such as a whole blood sample, based on AR pathway activity alone, it can serve as a test to establish or confirm the diagnosis of sepsis in a patient, using clinical criteria suggestive of sepsis. If low AR pathway activity is found, sepsis is still present, but the patient is more likely to be a survivor. Furthermore, the level of AR pathway activity can be used to distinguish between low and high risk of death. This means that, based on blood samples obtained from subjects with sepsis, a distinction can be made based on AR pathway activity between samples obtained from subjects with high risk of death and samples obtained from subjects with low risk of death. Finally, correlations between pathway activity and sepsis versus controls or between low and high risk of mortality were found to be specific to these four pathways; other signaling pathways were not correlated with the presence or absence of sepsis in whole blood samples or with the severity of sepsis (including risk of mortality) (data not shown).

[0029] Two important pathways that are elevated in sepsis and highly indicate poor prognosis are AR pathway and TGF beta pathway.These pathways also show increased activity levels in pre-sepsis state.Therefore, we next investigated whether these pathways are causative and thus whether they can be used to prevent or treat sepsis in patients by inhibiting AR and / or TGF beta pathway activity.

[0030] As already shown before, personalized medicine brings further advantages, considering the clinical heterogeneity between sepsis patients. Until now, it has not been possible to stratify patients who will benefit from AR or TGF beta pathway inhibitors to prevent or treat sepsis. The method described herein now allows accurate assessment of AR or TGF beta pathway activity in blood samples. This allows differentiation between patients with low and high AR and / or TGF beta pathway activity, and allows those with high AR and / or TGF beta pathway activity to be administered AR and / or TGF beta inhibitors to normalize pathway activity.

[0031] Based on the findings described below, the inventors therefore theorize that treatment with pathway inhibitors, particularly AR and / or TGF beta pathway inhibitors, will be beneficial for subjects with sepsis or at risk of developing sepsis. Treatment of mice with sepsis with AR inhibitors has been described previously, partially successful but partially unsuccessful

[16] . It has been found that male mice with sepsis benefit from treatment with AR inhibitors, but female mice with sepsis do not. However, it should be noted that there are large gender-based differences in response to sepsis (mortality is much higher in male mice), and therefore it is not very clear to what extent these results can be extrapolated to humans.

[0032] Based on our results, the difference in response to treatment with AR and / or TGF beta inhibitors can be explained by the fact that not all sepsis patients show high AR and / or TGF beta pathway activity, and therefore only those with a large increase in AR and / or TGF beta pathway activity will benefit from treatment with AR and / or TGF beta pathway inhibitors.More importantly, patients with infections who are at risk of developing infection and show high AR and / or TGF beta pathway activity will benefit most from preventive treatment with AR and / or TGF beta inhibitors.The rationale behind this assumption is that active AR and / or TGF beta pathways lead to immunosuppression.See, for example, Gubbels Bupp and Jorgensen, Androgen-Induced Immunosuppression, Front Immunol.2018;9:794

[11] , which is incorporated by reference in its entirety.

[0033] AR has been found to be expressed in various innate and adaptive immune cells, including neutrophils, macrophages, mast cells, monocytes, megakaryocytes, B cells, and T cells. Interestingly, AR is also expressed in hematopoietic stem cells, lymphocytes, and myeloid progenitor cells

[13] . Evidence from different studies points to a significant immunosuppressive role of androgens and TGF-beta in different immune cell types, mainly by decreasing and / or promoting the expression of pro- and anti-inflammatory mediators.

[0034] Until now, it has not been possible to stratify patients who will benefit from AR and / or TGF beta pathway inhibitors for the prevention or treatment of sepsis, but the method described herein allows accurate assessment of AR and TGF beta pathway activity in blood samples.This allows easy discrimination between patients with low and high AR and / or TGF beta pathway activity, and allows administration of AR and / or TGF beta inhibitors to those with high AR and / or TGF beta pathway activity.

[0035] To test this hypothesis, we designed an in vitro experiment in which monocytes were stimulated with LPS and then treated with various AR and / or TGF beta pathway inhibitors. Monocytes were chosen because they have been described as playing a major role in sepsis. See, for example, Sukhacheva, The role of monocytes in the progression of sepsis, Clinical Laboratory Int. 26 August 2020

[14] or 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

[15] , both of which are incorporated by reference in their entirety. We were able to demonstrate that (bacterial) LPS can stimulate the AR and TGF beta signaling pathways in monocytes and that this effect can be strongly attenuated by incubating monocytes with AR and / or TGF beta pathway inhibitors after stimulation.

[0036] Monocytes are one of the key cell types that play a role in sepsis, where AR signaling activity is increased, and this activity can be measured in monocytes, and since AR and TGF beta signaling are known to have immunosuppressive and inflammatory roles, it seems entirely plausible that treating patients with high AR and / or TGF beta pathway activity will result in successful prevention or treatment of sepsis, or at least reduce symptoms.Example 10 provides the first proof of concept that AR pathway inhibitors can be used to treat or prevent sepsis by using a THP-1 cell model.The data show that LPS induces both AR and TGF beta pathway activity in THP-1 monocytic cells, and AR pathway activity levels can be restored to nearly baseline levels by adding AR pathway inhibitors.

[0037] Example 11 provides data that further supports that AR inhibitor and TGF beta inhibitor can be used for the prevention and treatment of sepsis.To support this, prevention and treatment models are designed to test AR and TGF beta inhibitor.In these models, monocyte cell line (THP-1) is used, because monocyte is regarded as one of the relevant cells in septic response, and has been shown to cause the upregulation of both AR and TGF beta pathway activity in septic patients (see previous examples).

[0038] In the prevention model, cells were pre-incubated with pathway inhibitors or negative control (DMSO) for 24 hours, followed by incubation with LPS (or dihydrotestosterone (DHT) as a positive control) for 24 hours. RNA was then extracted from the cells and pathway activity was determined by methods described herein. These data are presented in Tables 7, 8 and 10.

[0039] In the treatment model, pathway inhibitors and LPS were added simultaneously and THP-1 cells were incubated for 24 hours before RNA isolation and pathway activity determination. These data are shown in Tables 9A and 11.

[0040] To prove that the effect of LPS-induced increase in AR and TGF-beta pathway activity is cell type specific, a comparison was made between monocytic cells (THP-1) and lymphoid cell lines (MOLT-4). These data are shown in Tables 9A and 9B and demonstrate that LPS causes activation of both AR and TGF-beta pathway activity in THP-1 cells, while MOLT-4 does not show any change in pathway activation. It should be noted that in THP-1 cells, the activity is slightly reduced by adding bicalutamide or enzalutamide, but the concentration of LPS used is very high, explaining the negligible effect of these pathway inhibitors in this particular experiment.

[0041] We next reconsidered whether the effect would be observed on PBMCs derived from healthy subjects as well. Since PBMCs contain only 5-10% monocytes, the effect was expected to be negligible. Indeed, the data in Tables 12A and 12B show that LPS did not significantly upregulate AR or TGFbeta pathway activity in PBMCs obtained from healthy volunteers, likely due to the low percentage of monocytes where an effect would be expected. Nevertheless, a slight decrease in both AR and TGFbeta pathway activity was observed when LPS was combined with (R)-bicalutamide, and this effect could be dramatically increased by also including bactosertib.

[0042] Nevertheless, PBMCs from patients with sepsis were obtained and analyzed, and were not and were treated with different pathway inhibitors.AR and TGF beta pathway activity was generally found to be elevated in patients with sepsis, and was surprisingly reduced by incubating PBMCs with AR and / or TGF beta pathway inhibitors for 24 hours, providing further evidence that AR and TGF beta cell signaling pathway inhibitors can be used in the treatment of sepsis.

[0043] Taken together, Examples 1-9 provide evidence that several pathways are upregulated in patients with sepsis or at risk of developing sepsis, and further that pathway analysis makes it possible to stratify patients, for example to select those at highest risk of a fatal outcome.

[0044] Thus, in a first aspect, the present invention relates to an AR cell signaling pathway inhibitor for use in preventing or treating sepsis in a subject. Alternatively, the present invention relates to a method of preventing or treating sepsis in a subject in need thereof, comprising administering to the subject an AR cell signaling pathway inhibitor.

[0045] As used herein, preventing or treating sepsis with a pathway inhibitor refers to administering a pathway inhibitor to a subject in need thereof, meaning a subject with sepsis or at risk of developing sepsis. The route of administration is generally known to those skilled in the art and depends on the compound. Thus, the methods and uses described herein are not limited to a particular route of administration, and refer to, for example, enteral, parenteral or topical administration. Examples of enteral administration routes that can be used include oral, rectal, sublingual, sublabial or buccal administration, with oral administration being preferred. Parenteral administration may include, for example, epidural, intraventricular, transdermal, sublingual or buccal, sublingual, intranasal, intraarterial, intraarticular, intracardiac, intracavernosal, intralesional, intramuscular, intraocular, intraosseous, intraperitoneal, intrathecal, intravaginal, intravenous, intravesical, intravitreal, subcutaneous, transdermal, perivascular or transmucosal administration, by needle. A preferred parenteral route is subcutaneous or intramuscular administration.

[0046] As used herein, prevention refers to a situation where a subject is considered not to have sepsis, but is at high risk of developing sepsis, where prevention is considered to mean reducing the subject's chance of developing sepsis.As used herein, treatment refers to a situation where a subject has sepsis, where treatment refers to reducing symptoms, shortening the time the subject is septic, reducing the chance of death, or curing the subject, compared to a situation where the subject does not receive the respective treatment.Treatment also refers to reducing the subject's immunosuppressive state.

[0047] The subject is a human or a non-human mammal. Preferably, the subject is a human. In some embodiments, the subject has elevated AR cell signaling pathway activity or has AR cell signaling pathway activity above a threshold. Alternatively, the present invention relates to a method for preventing or treating sepsis, comprising administering an AR cell signaling pathway inhibitor to a subject with elevated AR cell signaling pathway activity or a subject with AR cell signaling pathway activity above a threshold.

[0048] As used herein, elevated when referring to pathway activity should be interpreted as being higher or increased when compared to baseline or control levels. For example, pathway activity, such as AR or TGF-beta pathway activity, can be determined on blood samples of a cohort of healthy individuals to establish an average pathway activity value as a baseline or control level. The pathway activity measured in the subject's blood sample is then compared to this average value. Because there is natural variation in observed pathway activity in a healthy cohort, a threshold can be set above what is said to be increased or elevated pathway activity. Similarly, a threshold can be set below what is said to be decreased or reduced pathway activity. For example, when using pathway activity obtained from a cohort of healthy subjects, a threshold can be set using the average pathway activity plus or minus one standard deviation. A more stringent threshold can be set by making the threshold two, three, or even four times the average plus or minus the standard deviation of pathway activity obtained from blood samples of a healthy cohort.

[0049] In some embodiments, the AR cell signaling pathway is determined in a blood sample obtained from a subject. The pathway activity is determined on whole blood or isolated blood cells, such as, but not limited to, PBMCs, monocytes, or neutrophils. Alternatively, the present invention relates to a method for preventing or treating sepsis, comprising administering an AR cell signaling pathway inhibitor to a subject with elevated AR cell signaling pathway activity or a subject with AR cell signaling pathway activity above a threshold, and the AR cell signaling pathway is determined in a blood sample obtained from the subject.

[0050] In some embodiments, the AR cell signaling pathway inhibitor is administered when the AR cell signaling pathway activity determined in the blood sample of the subject is found to be elevated or exceeds a certain threshold.The threshold can be predetermined.Those skilled in the art know that the numerical value obtained and the expression of pathway activity depend on the method used.Therefore, the same method is ideally used to determine pathway activity, such as AR pathway activity, in the blood sample of the subject and in the blood sample of a reference, such as healthy subject.

[0051] Thus, in one embodiment, the present invention provides an AR cell signaling pathway inhibitor for use in preventing or treating sepsis, the use comprising: - determining AR cell signaling pathway activity in a blood sample from the subject; and - administering an AR cell signaling pathway inhibitor to the patient if AR cell signaling pathway activity in the subject's blood sample is found to be elevated or above a certain threshold. The present invention relates to an AR cell signaling pathway inhibitor, comprising:

[0052] Those skilled in the art know that there are multiple methods available for determining AR cell signaling pathway activity.For example, in the uses and methods described herein, AR pathway activity can be determined by reporter assay, nuclear staining of receptor transcription complex, phosphorylation-based assay, ELISA, or based on the marker or target gene described herein.However, those skilled in the art know that other methods can be used, and therefore this list should not be interpreted as limiting.Thus, the methods and uses described herein are not limited to a specific method for determining pathway activity.For example, the methods described herein below can be used. Thus, in an embodiment, determining AR cell signaling pathway comprises: determining or receiving the expression level of three or more target genes of AR cell signaling pathway in a blood sample of a subject; determining the activity level of AR cell signaling pathway-associated transcription factor (TF) element in the sample, the TF element controlling the transcription of three or more target genes, and determining is based on evaluating a calibrated mathematical pathway model that relates the expression level of three or more target genes to the activity level of AR cell signaling pathway, and based on the determined activity level of AR cell signaling pathway-associated TF element, predicting the activity of AR cell signaling pathway in the blood sample from the subject.Details about the method of predicting pathway activity based on the determination of the activity level of TF element are provided below.

[0053] As used herein, the term AR cell signaling pathway inhibitor is used interchangeably with the term AR pathway inhibitor, and is used to describe compounds or biologics that inhibit, reduce or lower the transcription of target genes by activated androgen receptor complex.AR pathway inhibitors are direct inhibitors or indirect inhibitors, meaning that they directly interfere with the signal transduction cascade of AR cell signaling, or indirectly interfere with the pathway that regulates the downstream AR cell signaling pathway.For example, inhibitors function by making the ligand for AR pathway unavailable, such as testosterone, inhibiting the metabolic step of testosterone to DHT reduction, inhibiting the migration of activated AR to the nucleus, or inhibiting the initiation of transcription by AR transcription complex, or reducing AR expression, for example, by AR degradation. For example, etanercept (also known as Enbrel) is primarily known as a compound for inhibiting tumor necrosis factor (TNF), but our data show that when this compound is used in a relevant model system, a significant reduction in both AR and TGF beta pathway signaling is observed, and therefore, for the purposes of the present invention, etanercept is considered to be an AR pathway inhibitor as well as a TGF beta pathway inhibitor.For example, resatorbid (also known as TAK-242) is primarily known as a Toll-like receptor 4 (TLR4) antagonist, but our data show that when this compound is used in a relevant model system, a significant reduction in both AR and TGF beta pathway signaling is observed, and therefore, for the purposes of the present invention, resatorbid is considered to be an AR pathway inhibitor as well as a TGF beta pathway inhibitor. For example, filgotinib (also known as Jyseleca) is primarily known as a JAK1 inhibitor, however, our data indicates that when this compound is used in relevant model systems, significant reductions in both AR and TGF-beta pathway signaling are observed and therefore, for purposes of the present invention, filgotinib is considered to be an AR pathway inhibitor as well as a TGF-beta pathway inhibitor.Therefore, as used herein, AR inhibitor is any compound or biological agent that can inhibit AR pathway activity in a relevant model system, such as, but not limited to, THP-1 cells, and AR pathway activity is predicted by the model described herein.The second effect of AR pathway inhibitor can be the change in the activity of other signaling pathways, for example, by the production of intermediate proteins or the interaction between the transcription factors of various signaling pathways.

[0054] Thus, in one embodiment, the AR cell signaling pathway inhibitor is selected from the group consisting of steroidal antiandrogens, nonsteroidal antiandrogens, androgen synthesis inhibitors, CYP17A1 inhibitors, CYP11A1 (P450scc) inhibitors, 5α-reductase inhibitors, and antigonadotropic hormones, or combinations thereof. Alternatively, the AR cell signaling pathway inhibitor is selected from the group consisting of bicalutamide, (R)-bicalutamide (HY-14249), enzalutamide (MDV3100), N-desmethylenzalutamide, proxalutamide (GT0918), apalutamide (ARN-509), N-desmethyl-apalutamide, darolutamide (ODM-201; BAY-1841788), ketodarolutamide (ORM-15341), galeterone ( TOK-001), D4-abiraterone, A-485, dexamethasone, mifepristone (RU486), cyproterone acetate, chlormadinone acetate, cyproterone acetate, megestrol acetate, osaterone acetate, nomegestrol acetate, dienogest, oxendolone, drospirenone, spironolactone, medrogestone, bicalutamide, flutamide, nilutamide, apalutamide, cimetidine, topirutamide , abiraterone acetate, ketoconazole, ceviteronel, aminoglutethimide, dutasteride, alphatradiol, dutasteride, episteride, finasteride, A-485, ARCC-4, ARD-266, saw palmetto extract, leuprorelin, estrogens (e.g., estradiol (and its esters), ethinyl estradiol, conjugated estrogens, diethylstilbestrol), GnRH analogs, GnRH agonists (e.g., goserelin, leuprorelin), GnRH antagonists (e.g., cetrorelix), and progestogens (e.g., chlormadinone acetate, cyproterone acetate, gestorone caproate, medroxyprogesterone acetate, megestrol acetate), etanercept, resatorbid, filgotinib, or combinations thereof.In a more preferred embodiment, the AR cell signaling pathway inhibitor is selected from the group consisting of bicalutamide, (R)-bicalutamide (HY-14249), enzalutamide (MDV3100), N-desmethylenzalutamide, proxalutamide (GT0918), apalutamide (ARN-509), N-desmethyl-apalutamide, darolutamide (ODM-201; BAY-1841788), ketodarolutamide (ORM-15341), galeterone (TOK-001), D4-abiraterone, A-485, dexamethasone, mifepristone (RU486), etanercept, resatorbid, filgotinib, or a combination thereof.

[0055] In one embodiment, the invention relates to an AR cell signaling pathway inhibitor for use in preventing or treating sepsis in a subject as described herein, wherein the use is for preventing sepsis in a subject at risk of developing sepsis.

[0056] In one embodiment, the invention relates to an AR cell signaling pathway inhibitor for use in preventing or treating sepsis in a subject as described herein, wherein the use is for the treatment of sepsis in a subject suffering from sepsis.

[0057] It is particularly advantageous to combine AR inhibitor with TGF beta inhibitor to prevent or treat sepsis in subject.Therefore, in some embodiments, AR cell signaling pathway inhibitor is administered together with TGF beta cell signaling pathway inhibitor, where AR cell signaling pathway inhibitor and TGF beta cell signaling pathway inhibitor are the same compound or different compounds.

[0058] AR and TGF beta inhibitors are combined in a single therapeutic agent or administered separately.Thus, in some embodiments, AR cell signaling pathway inhibitor is administered before TGF beta cell signaling pathway inhibitor, or AR cell signaling pathway inhibitor is administered simultaneously with TGF beta cell signaling pathway inhibitor, or AR cell signaling pathway inhibitor is administered after TGF beta cell signaling pathway inhibitor.

[0059] The activation of AR and TGF beta cell signaling pathway in sepsis and subjects at risk of developing sepsis is altered. Therefore, some subjects benefit more from AR and / or TGF beta cell signaling pathway inhibitors. Therefore, it is beneficial to determine AR and / or TGF beta pathway activity in blood samples from subjects before treatment. Thus, in one embodiment, the present invention provides an AR cell signaling pathway inhibitor for use in preventing or treating sepsis in a subject, the use being: - determining AR cell signaling pathway activity and TGF beta cell signaling pathway activity in a blood sample of the subject; - administering an AR pathway inhibitor to the patient if AR cell signaling pathway activity is found to be elevated or above a certain threshold; and - administering a TGF-beta pathway inhibitor to the patient if TGF-beta cell signaling pathway activity is found to be elevated or above a certain threshold. The present invention relates to an AR cell signaling pathway inhibitor, comprising:

[0060] For example, when high AR pathway and low TGF beta pathway activity are measured in the blood sample of the subject, administer AR pathway inhibitor, or when high AR pathway and high TGF beta pathway activity are measured in the blood sample of the subject, administer both AR and TGF beta pathway inhibitor.Alternatively, when low AR pathway and high TGF beta pathway activity are measured in the blood sample of the subject, administer TGF beta pathway inhibitor.

[0061] Thus, in certain embodiments, the TGF beta cell signaling pathway inhibitor is selected from the group consisting of a small molecule kinase inhibitor, an anti-TGF-beta ligand antibody, an anti-TβR receptor antibody, or an antisense oligonucleotide, or a combination thereof, or the TGF beta cell signaling pathway inhibitor is selected from the group consisting of bactosertib (EW-7197), galunisertib (LY2157299), LY3200882, (E)-SIS3, fresolimumab, XPA681, XPA08 9, LY2382770, LY3022859, ISTH0036, ISTH0047, pyrrole-imidazole polyamide, etanercept, resatorbidi, filgotinib, or a combination thereof; preferably selected from the group consisting of bactosertib (EW-7197), galunisertib (LY2157299), LY3200882, (E)-SIS3, etanercept, resatorbidi, filgotinib, or a combination thereof.

[0062] In one embodiment, the use is for the prevention of sepsis, and the AR inhibitor is bicalutamide and / or the TGF-beta inhibitor is bactosertib.

[0063] In one embodiment, the use is for the treatment of sepsis, and the AR inhibitor is bicalutamide, (R)-bicalutamide, ketodarolutamide (ORM-15341), D4-abiraterone, etanercept, resatorbid, N-desmethylenzalutamide, N-desmethyl-apultamide, or filgotinib, or a combination thereof, and / or the TGF beta inhibitor is bactosertib, etanercept, resatorbid, or filgotinib, or a combination thereof.

[0064] In one embodiment, the AR inhibitor is (R)-bicalutamide and the TGF beta inhibitor is etanercept, or the AR inhibitor is (R)-bicalutamide and the TGF beta inhibitor is filgotinib.Preferably, the use is for the treatment of sepsis, the AR inhibitor is (R)-bicalutamide and the TGF beta inhibitor is etanercept, or the AR inhibitor is (R)-bicalutamide and the TGF beta inhibitor is filgotinib.Alternatively, the use is for the treatment of sepsis, the AR inhibitor is combined with etanercept or filgotinib, and preferably, the AR inhibitor is (R)-bicalutamide.

[0065] In one embodiment, the present invention relates to a compound for use in preventing or treating sepsis. Alternatively, the present invention relates to a method for preventing sepsis in a subject or treating a subject with sepsis, comprising administering to the subject a compound. The compound is preferably bicalutamide, (R)-bicalutamide (HY-14249), enzalutamide (MDV3100), N-desmethylenzalutamide, proxalutamide (GT0918), apalutamide (ARN-509), N-desmethyl-apalutamide, darolutamide (ODM-201; BAY-1841788), ketodarolutamide (ORM-15341), galeterone (TOK-001), D4-abiraterone, A-485, dexamethasone, mifepris ton (RU486), cyproterone acetate, chlormadinone acetate, cyproterone acetate, megestrol acetate, osaterone acetate, nomegestrol acetate, dienogest, oxendolone, drospirenone, spironolactone, medrogestone, bicalutamide, flutamide, nilutamide, apalutamide, cimetidine, topirutamide, abiraterone acetate, ketoconazole, ceviteronel, aminoglutethimide, dutasteride, alphatradiol, dutasteride, epristeril finasteride, A-485, ARCC-4, ARD-266, saw palmetto extract, leuprorelin, estrogens (e.g., estradiol (and its esters), ethinyl estradiol, conjugated estrogens, diethylstilbestrol), GnRH analogs, GnRH agonists (e.g., goserelin, leuprorelin), GnRH antagonists (e.g., cetrorelix), progestogens (e.g., chlormadinone acetate, cypro acetate, The compound is selected from the group consisting of terone, gestorone caproate, medroxyprogesterone acetate, megestrol acetate), etanercept, resatorbidi, filgotinib, bactosertib (EW-7197), galunisertib (LY2157299), LY3200882, (E)-SIS3, fresolimumab, XPA681, XPA089, LY2382770, LY3022859, ISTH0036, ISTH0047, pyrrole-imidazole polyamides, or combinations thereof.

[0066] In one embodiment, the invention relates to an AR cell signaling pathway inhibitor for use in preventing or treating sepsis in a subject as described herein, wherein the use is for preventing sepsis in a subject at risk of developing sepsis.

[0067] In one embodiment, the invention relates to an AR cell signaling pathway inhibitor for use in preventing or treating sepsis in a subject as described herein, wherein the use is for the treatment of sepsis in a subject suffering from sepsis.

[0068] In a second aspect, the present invention relates to a TGF beta pathway inhibitor for use in preventing or treating sepsis in a subject. Alternatively, the present invention relates to a method of preventing or treating sepsis in a subject in need thereof, comprising administering a TGF beta cell signaling pathway inhibitor to the subject. In an embodiment, the subject has elevated TGF beta cell signaling pathway activity or has TGF beta cell signaling pathway activity above a threshold. Alternatively, the present invention relates to a method of preventing or treating sepsis, comprising administering a TGF beta cell signaling pathway inhibitor to a subject with elevated TGF beta cell signaling pathway activity or a subject with TGF beta cell signaling pathway activity above a threshold.

[0069] In certain embodiments, the TGF beta cell signaling pathway is determined in a blood sample obtained from the subject. Pathway activity is determined on whole blood or isolated blood cells, such as, but not limited to, PBMCs, monocytes, or neutrophils. Alternatively, the present invention relates to a method of preventing or treating sepsis, comprising administering a TGF beta cell signaling pathway inhibitor to a subject with elevated TGF beta cell signaling pathway activity or a subject with above a threshold TGF beta cell signaling pathway activity, wherein the TGF beta cell signaling pathway is determined in a blood sample obtained from the subject.

[0070] In one embodiment, the TGF beta cell signaling pathway inhibitor is administered when the TGF beta cell signaling pathway activity determined in the subject's blood sample is found to be elevated or exceeds a certain threshold. The threshold may be predefined. Those skilled in the art know that the numerical value obtained and the representation of pathway activity depend on the method used. Therefore, the same method is ideally used to determine pathway activity, such as TGF beta pathway activity, in the subject's blood sample and in a reference blood sample, such as a healthy subject's blood sample.

[0071] Thus, in one embodiment, the present invention provides a TGF beta cell signaling pathway inhibitor for use in preventing or treating sepsis, the use comprising: - determining TGF beta cell signaling pathway activity in a blood sample of the subject; and - administering a TGF beta cell signaling pathway inhibitor to the patient when TGF beta cell signaling pathway activity in the subject's blood sample is found to be elevated or above a certain threshold. The present invention relates to a TGF beta cell signaling pathway inhibitor, comprising:

[0072] Those skilled in the art know that there are multiple methods available for determining TGF beta cell signaling pathway activity.For example, in the uses and methods described herein, TGF beta pathway activity is determined by reporter assay, nuclear staining of receptor transcription complex, phosphorylation-based assay, ELISA.However, those skilled in the art know that other methods may be used, and therefore this list should not be interpreted as limiting.Thus, the methods and uses described herein are not limited to a particular method of determining pathway activity.For example, the methods described herein below may be used. Thus, in an embodiment, the determination of the TGF beta cell signaling pathway comprises determining or receiving the expression levels of three or more target genes of the TGF beta cell signaling pathway in a blood sample of the subject; determining the activity levels of TGF beta cell signaling pathway-associated transcription factor (TF) elements in the sample, the TF elements controlling the transcription of the three or more target genes, the determination being based on evaluating a calibrated mathematical pathway model relating the expression levels of the three or more target genes to the activity levels of the TGF beta cell signaling pathway, and inferring the activity of the TGF beta cell signaling pathway in the blood sample from the subject based on the determined activity levels of the TGF beta cell signaling pathway-associated TF elements. More details on the method of inferring pathway activity based on the determination of the activity levels of TF elements are provided below.

[0073] As used herein, the term TGF beta cell signaling pathway inhibitor is used interchangeably with the term TGF beta pathway inhibitor to describe a compound or biologic that inhibits, reduces, or decreases the transcription of target genes by activated SMAD protein complexes. A TGF beta pathway inhibitor is a direct inhibitor or an indirect inhibitor, meaning that it is directly in the signaling cascade of TGF beta cell signaling or indirectly interferes with a pathway that regulates a downstream TGF beta cell signaling pathway. For example, an inhibitor functions by making unavailable or preventing a ligand for the TGF beta pathway, such as TGF beta, from binding to the TGF beta receptor, inhibiting the downstream signaling cascade that leads to phosphorylation of SMAD proteins, inhibiting the translocation of activated SMAD protein complexes to the nucleus, or inhibiting the initiation of transcription by SMAD transcription complexes. For example, etanercept (also known as Enbrel) is primarily known as a compound for inhibiting tumor necrosis factor (TNF), but our data show that when this compound is used in a relevant model system, a significant reduction in both AR and TGF beta pathway signaling is observed, and therefore, for the purposes of the present invention, etanercept is considered to be an AR pathway inhibitor as well as a TGF beta pathway inhibitor.For example, resatorbid (also known as TAK-242) is primarily known as a Toll-like receptor 4 (TLR4) antagonist, but our data show that when this compound is used in a relevant model system, a significant reduction in both AR and TGF beta pathway signaling is observed, and therefore, for the purposes of the present invention, resatorbid is considered to be an AR pathway inhibitor as well as a TGF beta pathway inhibitor. For example, filgotinib (also known as Jyseleca) is primarily known as a JAK1 inhibitor, however, our data show that when this compound is combined with (R)-bicalutamide in relevant model systems, a significant reduction in both AR and TGF-beta pathway signaling is observed and therefore, for purposes of the present invention, filgotinib is considered to be an AR pathway inhibitor as well as a TGF-beta pathway inhibitor.Thus, as used herein, a TGF-beta inhibitor is any compound or biological agent capable of inhibiting TGF-beta pathway activity in a relevant model system, such as, but not limited to, THP-1 cells, where TGF-beta pathway activity is predicted by the models described herein.

[0074] Thus, in one embodiment, the TGF beta cell signaling pathway inhibitor is selected from the group consisting of a small molecule kinase inhibitor, an anti-TGF-beta ligand antibody, an anti-TβR receptor antibody, or an antisense oligonucleotide, or a combination thereof. Alternatively, the TGF beta cell signaling pathway inhibitor is selected from the group consisting of bactosertib (EW-7197), galunisertib (LY2157299), LY3200882, (E)-SIS3, fresolimumab, XPA681, XPA089, LY2382770, LY3022859, ISTH0036, ISTH0047, pyrrole-imidazole polyamides, etanercept, resatorbid, filgotinib, or a combination thereof. In a preferred embodiment, the TGF beta cell signaling pathway inhibitor is selected from the group consisting of bactosertib (EW-7197), galunisertib (LY2157299), LY3200882, (E)-SIS3, etanercept, resatorbidi, filgotinib or a combination thereof.

[0075] In one embodiment, the invention relates to a TGF beta cell signaling pathway inhibitor for use in the prevention or treatment of sepsis in a subject as described herein, wherein the use is for the prevention of sepsis in a subject at risk of developing sepsis.

[0076] In one embodiment, the invention relates to a TGF beta cell signaling pathway inhibitor for use in the prevention or treatment of sepsis in a subject as described herein, wherein the use is for the treatment of sepsis in a subject suffering from sepsis.

[0077] It is particularly advantageous to combine an AR inhibitor with a TGF beta inhibitor to prevent or treat sepsis in a subject.Thus, in an embodiment, a TGF beta pathway inhibitor is administered together with an AR pathway inhibitor, and the TGF beta pathway inhibitor and the AR pathway inhibitor are the same compound or different compounds.The AR and TGF beta inhibitor are combined in a single therapeutic agent or administered separately.Thus, in an embodiment, an AR cell signaling pathway inhibitor is administered before a TGF beta cell signaling pathway inhibitor, or an AR cell signaling pathway inhibitor is administered simultaneously with a TGF beta cell signaling pathway inhibitor, or an AR cell signaling pathway inhibitor is administered after a TGF beta cell signaling pathway inhibitor.In an embodiment, the present invention provides an AR cell signaling pathway inhibitor for use in preventing or treating sepsis in a subject, the use being: - determining AR cell signaling pathway activity and TGF beta cell signaling pathway activity in a blood sample of the subject; - administering an AR pathway inhibitor to the patient if AR cell signaling pathway activity is found to be elevated or above a certain threshold; and - administering a TGF-beta pathway inhibitor to the patient if TGF-beta cell signaling pathway activity is found to be elevated or above a certain threshold. The present invention relates to an AR cell signaling pathway inhibitor, comprising:

[0078] In one embodiment, the AR cell signaling pathway inhibitor is selected from the group consisting of steroidal antiandrogens, nonsteroidal antiandrogens, androgen synthesis inhibitors, CYP17A1 inhibitors, CYP11A1 (P450scc) inhibitors, 5α-reductase inhibitors, and antigonadotropic hormones, or combinations thereof. Alternatively, the AR cell signaling pathway inhibitor is selected from the group consisting of bicalutamide, (R)-bicalutamide (HY-14249), enzalutamide (MDV3100), N-desmethylenzalutamide, proxalutamide (GT0918), apalutamide (ARN-509), N-desmethyl-apalutamide, darolutamide (ODM-201; BAY-1841788), ketodarolutamide (ORM-15341), galeterone ( TOK-001), D4-abiraterone, A-485, dexamethasone, mifepristone (RU486), cyproterone acetate, chlormadinone acetate, cyproterone acetate, megestrol acetate, osaterone acetate, nomegestrol acetate, dienogest, oxendolone, drospirenone, spironolactone, medrogestone, bicalutamide, flutamide, nilutamide, apalutamide, cimetidine, topirutamide In some embodiments, the agonist is selected from the group consisting of acetaminophen, abiraterone acetate, ketoconazole, ceviteronel, aminoglutethimide, dutasteride, alphatradiol, dutasteride, episteride, finasteride, A-485, ARCC-4, ARD-266, saw palmetto extract, leuprorelin, estrogens (e.g., estradiol (and its esters), ethinyl estradiol, conjugated estrogens, diethylstilbestrol), GnRH analogs, GnRH agonists (e.g., goserelin, leuprorelin), GnRH antagonists (e.g., cetrorelix), progestogens (e.g., chlormadinone acetate, cyproterone acetate, gestorone caproate, medroxyprogesterone acetate, megestrol acetate), etanercept, resatorbid, filgotinib, or combinations thereof.In a more preferred embodiment, the AR cell signaling pathway inhibitor is selected from the group consisting of bicalutamide, (R)-bicalutamide (HY-14249), enzalutamide (MDV3100), N-desmethylenzalutamide, proxalutamide (GT0918), apalutamide (ARN-509), N-desmethyl-apalutamide, darolutamide (ODM-201; BAY-1841788), ketodarolutamide (ORM-15341), galeterone (TOK-001), D4-abiraterone, A-485, dexamethasone, mifepristone (RU486), etanercept, resatorbid, filgotinib, or a combination thereof.

[0079] In one embodiment, the present invention thus relates to an AR and / or TGF beta pathway inhibitor for use in preventing sepsis in a subject suffering from an infection, preferably wherein the subject has elevated AR and / or TGF beta cell signaling pathway activity as determined in a blood sample obtained from the subject.

[0080] Optionally, AR and / or TGF beta cell signaling pathway activity is determined on a blood sample obtained from the subject, and an AR and / or TGF beta pathway inhibitor is administered if AR and / or TGF beta cell signaling pathway activity is found to be elevated or above a certain threshold. The AR and / or TGF beta cell signaling pathway is determined using the methods described herein, in particular the methods according to the first aspect of the invention.

[0081] Currently, methods for preventing sepsis aim to reduce the underlying infection, such as administering antibiotics. Now, for the first time, it is possible to identify and treat patients with infections at risk of developing sepsis using the methods described herein. By determining the subject's AR and / or TGF beta activity in blood samples, candidates for AR and / or TGF beta pathway inhibitor treatment can now be easily identified. A strong correlation was found between increased AR and / or TGF beta pathway activity and the development of sepsis due to infection. This is probably caused by the immunosuppressive effect of AR and / or TGF beta pathway activity, for example, in monocytes. Monocytes have a strong inflammatory role in the innate immune response, and systemic inflammation is a hallmark of sepsis. Therefore, it seems plausible that AR and / or TGF beta pathway activity is the cause, and thus inhibition of AR and / or TGF beta activity increases the chances of sepsis being prevented or alleviated. Instead of determining AR and / or TGF beta pathway activity, the decision to administer an AR and / or TGF beta pathway inhibitor may also be determined by determining the activity 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, preferably AR, CREB3L4, DHCR24, EAF2, ELL2, FKBP5, GUCY1A3, IGF1, KLK3, LCP1, LRIG1, NDRG1, NKX3_1, NTS, PLAU, PMEPA1, PPAP2A, PRKACB, PTPN1, SGK1, TACC2, TMPRSS2, and UGT2B15. and / or the genes ANGPTL4, CDC42EP3, CDC42EP4, CDC51, CDC51, CDC61, CDC71, CDC81, CDC91, CDC101, CDC111, CDC121, CDC131, CDC141, CDC151, CDC161, CDC171, CDC181, CDC191, CDC201, CDC211, CDC221, CDC231, CDC241, CDC251, CDC261, CDC271, CDC281, CDC291, CDC301, CDC311, CDC321, CDC331, CDC341, CDC351, CDC361, CDC371, CDC381, CDC391, CDC391, CDC421, CDC431, CDC441, CDC451, CDC461, CDC471, CDC481, CDC491, CDC51, CDC52 ...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, preferably CDC42EP3, GADD45A, GADD45B, HMGA2, ID1, IL11, INPP5D, JUNB, MMP2, MMP9, NKX2_5, OVOL1, PDGFB, PTHLH, SGK1, SKIL, SMAD4, The method is based on determining the expression levels of three or more, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more TGF beta target genes selected from group 2 consisting of SMAD5, SMAD6, TIMP1, VEGFA, more preferably, CDC42EP3, GADD45A, GADD45B, ID1, JUNB, MMP9, PDGFB, SGK1, SKIL, SMAD5, SMAD6, TIMP1, VEGFA, even more preferably, CDC42EP3, GADD45A, GADD45B, ID1, JUNB, MMP9, PDGFB, SGK1, SMAD5, TIMP1, VEGFA.

[0082] The present invention further relates to an AR and / or TGF beta pathway inhibitor for use in the treatment or alleviation of a subject suffering from sepsis, wherein the subject has elevated AR and / or TGF beta cell signaling pathway activity or AR and / or TGF beta cell signaling pathway activity above a certain threshold as determined in a blood sample obtained from the subject.

[0083] Optionally, AR and / or TGF beta cell signaling pathway activity is determined on a blood sample obtained from the subject, and an AR and / or TGF beta pathway inhibitor is administered if AR and / or TGF beta cell signaling pathway activity is found to be elevated or above a certain threshold. In an embodiment, AR and / or TGF beta pathway activity is determined by the method described herein. Since TGF beta pathway activity is also found to be elevated in patients with sepsis, it is speculated that it would be beneficial to inhibit TGF beta pathway activity together with AR pathway activity. This can be achieved by administering an AR inhibitor and a TGF beta inhibitor as two different compounds, or a single compound that inhibits both AR and TGF beta can be used. For example, compound A-458 has been found to specifically inhibit both AR and TGF beta pathways and could be beneficially used for this purpose. Thus, in an embodiment of the use according to the present invention, TGF beta pathway activity is further determined. In an embodiment, TGF beta pathway activity is determined by the method described herein.

[0084] Instead of determining TGF beta pathway activity, the decision to administer a TGF beta pathway inhibitor may also be determined by determining the activity 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, preferably CDC42EP3, 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. The present invention is based on determining the expression levels of three or more genes, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more genes selected from group 2 consisting of 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, and even more preferably, CDC42EP3, GADD45A, GADD45B, ID1, JUNB, MMP9, PDGFB, SGK1, SMAD5, TIMP1, VEGFA.

[0085] In a preferred embodiment, the AR pathway inhibitor is administered together with a TGF-beta pathway inhibitor, and the AR pathway inhibitor and the TGF-beta pathway inhibitor are the same compound or are different compounds.

[0086] AR pathway inhibitors can be steroidal antiandrogens, nonsteroidal antiandrogens, androgen synthesis inhibitors, CYP17A1 inhibitors, CYP11A1 (P450scc) inhibitors, 5α-reductase inhibitors, and antigonadotropins. Non-limiting examples include cyproterone acetate, chlormadinone acetate, cyproterone acetate, megestrol acetate, osaterone acetate, nomegestrol acetate, dienogest, oxendolone, drospirenone, spironolactone, medrogestone, bicalutamide, flutamide, nilutamide, apalutamide, darolutamide, enzalutamide, proxalutamide, cimetidine, topirutamide, abiraterone acetate, ketoconazole, ceviteronel, aminoglutethimide, dutasteride, alphatradiol, dutasteride, epristeride, finasteride, A-485, ARCC-4, ARD-2 66, saw palmetto extract, leuprorelin, estrogens (e.g., estradiol (and its esters), ethinyl estradiol, conjugated estrogens, diethylstilbestrol), GnRH analogs, GnRH agonists (e.g., goserelin, leuprorelin), GnRH antagonists (e.g., cetrorelix), progestogens (e.g., chlormadinone acetate, cyproterone acetate, gestorone caproate, medroxyprogesterone acetate, megestrol acetate), etanercept, resatorbid, filgotinib, or combinations thereof.

[0087] Thus, in one embodiment, the AR inhibitor is selected from the group consisting of cyproterone acetate, chlormadinone acetate, cyproterone acetate, megestrol acetate, osaterone acetate, nomegestrol acetate, dienogest, oxendolone, drospirenone, spironolactone, medrogestone, bicalutamide, flutamide, nilutamide, apalutamide, darolutamide, enzalutamide, proxalutamide, cimetidine, topirutamide, abiraterone acetate, ketoconazole, ceviteronel, aminoglutethimide, dutasteride, alphatradiol, dutasteride, epristeride, finasteride, A-485, ARCC-4, ARD-266, saw palmetto extract, leuprorelin, estrogens (e.g., estradiol (and its esters) , ethinyl estradiol, conjugated estrogens, diethylstilbestrol), GnRH analogs, GnRH agonists (e.g., goserelin, leuprorein), GnRH antagonists (e.g., cetrorelix), progestogens (e.g., chlormadinone acetate, cyproterone acetate, gestorone caproate, medroxyprogesterone acetate, megestrol acetate), etanercept, resatorbid, filgotinib or a combination thereof, preferably, the AR inhibitor is selected from the group consisting of A-485, ARCC-4, ARD-266, bicalutamide, etanercept, resatorbid, filgotinib or a combination thereof, more preferably, the AR inhibitor is A-485.

[0088] The TGF beta pathway inhibitor may be a small molecule kinase inhibitor, an anti-TGF-beta ligand antibody, an anti-TβR receptor antibody, or an antisense oligonucleotide. Non-limiting examples are TEW-7197, galunisertib, LY2157299, fresolimumab, XPA681, XPA089, LY2382770, LY3022859, ISTH0036, ISTH0047, pyrrole-imidazole polyamides, etanercept, resatorbid, filgotinib, or combinations thereof.

[0089] Thus, in one embodiment, the TGF beta inhibitor is selected from the list including TEW-7197, galunisertib, LY2157299, fresolimumab, XPA681, XPA089, LY2382770, LY3022859, ISTH0036, ISTH0047, pyrrole-imidazole polyamides, etanercept, resatorbid, filgotinib or combinations thereof.

[0090] Importantly for the present invention, the activity of AR, TGF beta, MAPK-AP1, and JAK-STAT3 pathways is associated with immune suppression. The finding that these pathways, especially the AR pathway, are abnormally activated on average in patients with sepsis is important because it indicates that normal activity of this pathway is probably associated with normal immune function. This would be demonstrated by our finding that a small number of patients with normal AR pathway activity are sepsis survivors, as are control subjects without infection. This would suggest that normal immune function is necessary for sepsis survival. Overall, it is clear that a good immune response is important for preventing infection, for developing sepsis, and for preventing sepsis-related death. It is emphasized again that it is surprising that this can be determined on the measured signaling pathway activity in whole blood.

[0091] Measuring pathway activity on blood samples or on a specific subset of cells derived from a whole blood sample provides information about the immune status of patients with infection. Patients with infections, such as urogenital infections associated with bladder catheters, can be assumed to be at higher risk of developing sepsis if their immune response is suppressed. Measuring the activity of the AR pathway, as well as the TGF beta, MAPK-AP1, and JAK-STAT3 pathways, in blood samples from patients with infection provides information about the risk of developing sepsis, which is higher when the activity of these pathways, especially the AR pathway, is increased. This allows timely prediction of the risk of sepsis in patients with infection, especially bacterial infection.

[0092] The present inventors demonstrate for the first time that determination of pathway activity can be used to diagnose sepsis in patients and to stratify blood samples obtained from subjects, for example, to distinguish between sepsis and septic shock. More surprising, however, is the finding that this analysis can be performed on blood samples, such as whole blood samples.

[0093] The present invention was achieved by thoroughly testing the activity of several signaling pathways, such as the AR signaling pathway, the TGF beta pathway, the MAPK-AP1 pathway and the JAK-STAT3 signaling pathway, in blood samples obtained from healthy control subjects, recovered septic subjects and septic subjects who died as a result of septic shock.

[0094] We then evaluated whether the target genes of the identified related pathways can be used as the basis for making predictions (for example, whether the subject has sepsis or not).Using a simple model based on gene expression levels and corrected only for the correlation of gene expression levels (up- or down-regulated), we found that the selection of three genes from either AR cell signaling pathway target genes, TGF beta cell signaling pathway target genes, or pooled AR and TGF beta cell signaling pathway target genes is sufficient to make predictions with very high specificity and good sensitivity.The sensitivity of prediction can be increased by increasing the amount of genes used in the assay, or by selecting genes more selectively (as shown in Example 9), or by using three (or more) genes in the pathway activity model described herein.

[0095] The data presented in Example 9 show that the predictions described herein (e.g., diagnosing a subject with sepsis, predicting high or low chance of death, predicting the risk of developing sepsis for a subject with bacterial infection) can be made based on the expression levels of three or more genes from the lists described herein. Thus, the pathway models described in more detail below can be used, but are not required to do so.

[0096] The three or more genes envisaged in the present invention can be used as follows: the expression levels of the three or more genes are determined based on the mRNA levels in a sample (e.g., a blood sample obtained from a subject). The expression levels of the three or more genes are normalized using one or more references, e.g., housekeeping genes. The normalized expression levels of the three or more genes are multiplied by "1" or "-1" depending on their correlation with pathway activity (+1 if gene expression increases with higher pathway activity and -1 if gene expression decreases with higher pathway activity, the correlation of each gene is also shown in Example 9). Then, the normalized expression levels corrected for expression correlation are summed or multiplied. Here, the obtained value for the three or more expression levels can be compared to the value obtained from the expression levels of the three or more genes from a reference sample (e.g., a subject with sepsis or a healthy subject), or it can be compared to multiple references. Alternatively, the obtained value for the three or more expression levels can be compared to one or more set values. For example, based on reference samples obtained from healthy subjects and septic subjects, cut-off values ​​can be determined that define the upper limit of non-septic subjects and the lower limit of septic subjects.As a non-limiting example, based on the expression levels of three genes, the following values ​​are calculated in three healthy subjects: 5, 8, 4; in three septic subjects, the following values ​​are calculated: 43, 30, 24. Based on these results, a threshold value of 19 is calculated. Here, for a subject, the same three genes are used to calculate a score, and if the score is below 19, the subject is considered to be non-septic, and if the score is above 19, the subject is considered to be septic.

[0097] Therefore, when the AR and / or TGF beta pathway activity or functional status of a blood sample is used in the methods, predictions or diagnoses described herein, the AR and / or TGF beta pathway activity or functional status of a blood sample is replaced by simply using three or more genes selected from the AR and / or TGF beta pathway target genes as criteria in the method, prediction or diagnosis.

[0098] The same principle can be applied to patients with infection, particularly bacterial infection, where increased AR (and TGF beta) signaling pathway activity correlates with increased chance of developing sepsis.Thus, three or more genes selected from the target genes of AR and TGF beta cell signaling pathway described herein can be used to predict or calculate the risk of developing sepsis in patients with infection (e.g., bacterial infection).

[0099] In Example 9, the correlation between different gene expression levels and pathway activity was further used to provide cut-off values ​​for a more selective list of genes. These are shown using T values, where T=0 means no selection (all target genes are used) and T=0.3 refers to a cut-off value of correlation of 0.3. Each list of genes for each T value (T=0, T=0.3, T=0.4, T=0.5) was determined for AR and TGF beta cell signaling pathways and is listed in Example 9. The genes listed in the embodiments and the preferred embodiments for group 1 and group 2 correspond to selected target genes for AR and TGF beta pathways, respectively, and represent different lists of T values.

[0100] The ability to reliably determine whether a blood sample obtained from a subject or individual is obtained from a subject or individual with sepsis is desirable for several reasons. Currently, the diagnosis of sepsis is initially based on clinical parameters such as respiratory rate, heart rate and blood pressure, which can be complemented by simple and non-specific clinical chemistry test values ​​such as lactate, CRP, electrolytes, urea, creatinine, etc. This is not a very accurate diagnosis (not sensitive and specific enough), and therefore, when sepsis is suspected in a subject, the diagnosis must be confirmed by blood pathogen culture to detect the causative pathogen and profile its antibiotic resistance, which is a time-consuming process that takes several days to complete. In contrast, the determination of the functional status of a blood sample by determining one or more pathway activities based on extracted mRNA can be achieved in just 2-3 hours.

[0101] Once sepsis is diagnosed by the method described herein or other means, it is advantageous to determine whether the subject has high or low risk of progressing to septic shock and whether the patient has high or low risk of death.Currently, subjects with sepsis, particularly septic shock patients, are generally treated in intensive care units or critical care units, which are expensive.This is not always necessary, considering that the functional status of blood samples defined herein can be used to determine the risk of progression to septic shock and the risk of death of subjects.Therefore, once sepsis is diagnosed, risk assessment can be carried out based on the functional status of blood samples, for example, based on the determined AR pathway activity, to determine whether the subject has high or low risk of progression to septic shock or high or low risk of death.If the subject has low risk of septic shock or low risk of death, subsequent treatment does not necessarily need to be performed in ICU, thus saving substantial costs. Furthermore, if a subject is determined to be at high risk, treatment in an ICU may be beneficial and additional monitoring or treatment may be warranted to further reduce risk, e.g., intensify the search for the causative pathogen and eradicate its source.

[0102] For the reasons described, the functional status of a blood sample is a useful tool. As used herein, the "functional status of a blood sample" is defined as the combined information of the determined activity or activities of a pathway or pathways whose activity or activities have been determined. In general, the activity of a pathway can be determined as active or not active, or the activity can be determined with reference to a control sample. The control sample may be a blood sample obtained from a healthy subject, but it may also refer to a sample or data used to calibrate the model used to determine the pathway activity. Thus, the control sample is not necessarily a blood sample, but may be a different sample with a known functional status of the pathway (i.e., active or not active). By comparing the activity to a reference, such as a control sample, the activity can be expressed as a binary value (i.e., the pathway is active or not active), or it can be expressed as a relative value expressed as a number. Thus, for example, if only the AR pathway is determined on a blood sample, the functional status of the blood sample can be qualified as active AR activity or inactive AR activity with reference to the control sample. Alternatively, if the relative value of pathway activity is expressed as a number, with an inactive control sample defined as having a value of 0 and an active control sample defined as having a value of 1, the pathway activity determined in a blood sample obtained from a subject may be, for example, 0.81, indicating that the pathway activity is closer to active than to inactive.

[0103] Thus, the pathway activity based on the determined gene expression levels is preferably expressed as a numerical value. Using the model described below, the gene expression levels of the pathway can be used to quantify pathway activity with reference to calibrated expression levels of the pathway genes and / or with reference to a control sample (e.g., a blood sample obtained from a healthy subject). This quantification can be a simple binary model (e.g., a value of 0 for an inactive pathway and a value of 1 for an active pathway), or it can be more complex by quantifying the contribution of each gene whose expression level is determined, multiplied by a weighting factor, if necessary. Thus, the "state of the blood sample" described herein is the numerical value of the determined pathway activity, or, if multiple pathway activities are determined, it is the combined numerical value attributed to the determined pathways.

[0104] Thus, preferably, the status of the blood sample obtained from the subject comprises one or more activities of a signaling pathway, said signaling pathway activity being preferably AR signaling pathway activity, AR and TGFbeta signaling pathway activity, AR and MAPK-AP1 signaling pathway activity, AR and JAK-STAT3 signaling pathway activity, AR, TGFbeta and MAPK-AP1 signaling pathway activity, AR, TGFbeta and JAK-STAT3 signaling activity and / or AR, TGFbeta, MAPK-AP1 and JAK-STAT3 signaling activity, said signaling activity being based on determined expression levels of three or more target genes of the respective pathways.

[0105] According to this, it is an embodiment of the present invention that the determination of the functional status of the blood sample is further based on the respective reference signaling pathway activity or a combination of reference activities of the signaling pathways. Similarly, the determination of the diagnosis or the risk of death is further based on the reference activity of the respective signaling pathway. The reference activity reflects the activity of the respective signaling pathway found in blood samples obtained from healthy subjects and blood samples obtained from septic patients with known clinical outcomes (e.g., recovery from septic shock, death from septic shock).

[0106] For the purposes of the present invention, the determination of the expression level of a gene or target gene based on extracted RNA is part of a method, which includes a step of determining the expression level of a gene or target gene on RNA extracted from a blood sample obtained from a patient using methods known to those skilled in the art or described herein. The method further includes a step of obtaining a blood sample from the patient to extract RNA. Alternatively, the expression level is determined separately and the removal step (of the expression level of the target gene) is not an active step in the method of the present invention. In such a case, the expression level is provided as an input value, e.g., a relative expression level with reference to one or more control gene expression levels.

[0107] By comparing each of the three or more gene expression levels in the subject to be diagnosed with the three or more reference expression levels, a prediction can be made regarding the subject's status (e.g., septic or non-septic, likelihood of developing sepsis, likelihood of dying from sepsis). Alternatively, by comparing each of the reference pathway activities with each of the respective pathway activities in the subject to be diagnosed, the status of the blood sample containing each of the respective pathway activities can be determined.

[0108] As used herein, "expression level" refers to quantifying the number of mRNA copies transcribed from a gene. Generally, this number is a relative value, not an absolute value, and therefore is preferably normalized, for example, with reference to the expression of one or more housekeeping genes. Housekeeping genes are genes that are assumed to have a constant expression level regardless of the cell type and / or the functional state of the cell (i.e., from a diseased or healthy subject), and therefore can be used to normalize experimentally determined relative expression levels. Housekeeping genes are generally known to those skilled in the art, and non-limiting examples of housekeeping genes that can be used for normalization are beta-actin, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and transcription factor IID TATA binding protein (TBP).

[0109] A set of cell signaling pathway target genes whose expression levels are preferably analyzed have been identified, or methods for identifying suitable target genes are described herein. For example, three or more, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes from each evaluated cell signaling pathway can be analyzed to determine pathway activity for use in determining pathway activity by mathematical models.

[0110] The blood sample obtained from the subject may be any type of blood sample, i.e. blood drawn, for example, using a cannula, whole blood or a defined fraction of blood, such as isolated PBMCs, isolated CD4+ cells, mixed CD8+ and T cells, isolated neutrophils or isolated monocytes. Preferably, the sample is whole blood. The provided invention is based on the surprising finding that whole blood, despite the versatility of the cells contained therein, can be used for pathway analysis to determine the functional status of said blood sample, and that said functional status of said blood sample can be used, for example, for the diagnosis and prognosis of a subject having or suspected of having sepsis, from which the blood sample was obtained.

[0111] Thus, the activity of one or more signaling pathways can be used as biomarkers to characterize the functional status of a blood sample, which is useful for early prediction of the onset of sepsis in patients with infection, diagnosing sepsis in a subject, predicting the progression to septic shock and risk of death in subjects with sepsis, and selecting a therapy for a subject with sepsis.

[0112] In some embodiments, signaling pathway measurements are performed using qPCR, multiplex qPCR, multiplexed qPCR, ddPCR, RNAseq, RNA expression arrays, or mass spectrometry. For example, gene expression microarray data, e.g., Affymetrix microarrays, or RNA sequencing methods such as Illumina sequencers can be used.

[0113] As used herein, the term "subject" refers to any living organism. In some embodiments, the subject is an animal, preferably a mammal. In certain embodiments, the subject is a human being, such as a medical subject. Although the present invention is not necessarily limited to a particular group of subjects, it is clear that subjects with sepsis or suspected to have sepsis or at risk of developing sepsis will benefit most from the invention described herein. Thus, the subject from whom the blood sample was obtained is preferably a subject with sepsis or a subject suspected to have sepsis or at risk of developing sepsis, if sepsis has already been confirmed by another means (e.g., blood culture or by the claimed method). A subject suspected of having sepsis is a subject who meets one or more criteria that define SIRS or sepsis, such as the presence of fever, tachycardia, and / or leukocytosis or leukocytopenia. Alternatively, a subject suspected of having sepsis is a subject who has or is at risk of developing sepsis, such as a subject who has an infection that leads to sepsis or a subject who has cancer, diabetes, immunosuppression, a subject who has spent a long time in an intensive care unit, a subject who was born prematurely, a subject who has a low APGAR score, etc. A subject may also be a subject at risk of developing sepsis, and as used herein, "a subject at risk of developing sepsis" refers to a subject who does not currently have sepsis but has one or more increased risk factors that cause the subject to develop sepsis, such as the presence of a urinary catheter, an open wound or wound with a drain, an intravascular line, etc.

[0114] As used herein, the term "clinical parameters" refers to respiratory rate, heart rate, blood pressure. The term "clinical parameters" may further refer to symptoms selected from fever, chills, very low body temperature, less than normal urination, nausea, vomiting, diarrhea, fatigue, weakness, rashy or discolored skin, sweating, clammy skin, or severe pain.

[0115] The blood sample used according to the present invention may be an extracted sample, i.e., a sample extracted from a subject. Examples of samples include, but are not limited to, a whole blood sample, isolated PBMCs, isolated CD4+ cells, mixed CD8+ and T cells, isolated neutrophils, or isolated monocytes. Isolated PBMCs, isolated CD4+ cells, mixed CD8+ and T cells, isolated neutrophils, or isolated monocytes are generally obtained from a whole blood sample by methods known to those skilled in the art. Furthermore, those skilled in the art are familiar with how to obtain a whole blood sample from a subject using conventional methods for drawing blood. The term "sample" as used herein also encompasses, for example, when cells, tissues, and / or bodily fluids are taken from a subject, for example, placed on a microscope slide or fixative, and when a portion of this sample is extracted, for example, by laser capture microdissection (LCM), or by punching, or by scraping the cells of interest from the slide, or by fluorescence activated cell sorting techniques, to carry out the claimed method. Additionally, the term "sample" as used herein also encompasses, for example, cells, tissues and / or bodily fluids obtained from a subject and placed on a microscope slide, and the claimed methods are performed on the slide. Preferably, the sample is a bodily fluid, in particular whole blood, or one or more cell types isolated from a whole blood sample.

[0116] The terms "pathway," "signal transduction pathway," "signaling pathway," and "cell signaling pathway" are used interchangeably herein.

[0117] "Signaling pathway activity" refers to the activity of signaling pathway-associated transcription factor (TF) elements in a sample, which TF elements control the transcription of target genes in driving the expression of the target genes, i.e., the rate at which the target genes are transcribed, e.g., in terms of high activity (i.e., fast) or low activity (i.e., slow), or other dimensions such as levels, values, or the like associated with such activity (e.g., rate). Thus, for the purposes of the present invention, the term "activity" as used herein is also meant to refer to activity levels obtained as intermediate results during the "pathway analysis" described herein.

[0118] As used herein, the term "transcription factor element" (TF element) preferably refers to an intermediate or precursor protein or protein complex of an active transcription factor, or an active transcription factor protein or protein complex that controls a specific target gene expression. For example, a protein complex contains at least the intracellular domain of one of the respective signaling pathway proteins together with one or more cofactors, thereby controlling the transcription of the target gene. Preferably, the term refers to a protein or protein complex transcription factor that is induced by the cleavage of one of the respective signaling pathway proteins that results in the intracellular domain.

[0119] As used herein, the term "target gene" refers to a gene whose transcription is directly or indirectly controlled by a respective transcription factor element. A "target gene" may be a "direct target gene" and / or an "indirect target gene" (as used herein).

[0120] Pathway analysis allows for quantitative measurement of signaling pathway activity in blood cells based on inferring signaling pathway activity from measurements of mRNA levels of well-validated direct target genes of the transcription factors associated with the respective signaling pathway (see, e.g., W Verhaegh et al., 2014, supra; W Verhaegh, A van de Stolpe, Oncotarget, 2014, 5(14):5196).

[0121] According to this, it is an embodiment of the present invention that the determination of the functional status of the blood sample and / or the subsequent use, such as the diagnosis or prediction of the risk of death of a patient, is further based on the respective combinations of the reference activities of the signaling pathways.Similarly, the determination of the signaling pathway abnormality factor is further based on the reference activities of each signaling pathway.The reference activities reflect the activities of each signaling pathway found in the blood sample of a healthy subject.

[0122] By comparing each of the reference pathway activities with each of the pathway activities in the subject to be diagnosed, the functional status of the blood sample containing each of the pathway activities can be determined. The functional status of the blood sample indicates whether the activity of each pathway deviates (abnormally) from the reference activity of each pathway. The functional status of the blood sample is then translated into a diagnosis or risk of death. The functional status of the blood sample can also be calculated directly from the combination of pathway activities. The functional status of the blood sample is considered as a multi-pathway score, MPS, which indicates the possibility that the subject has sepsis or the risk of death as a result of sepsis. Thus, "functional status of the blood sample" refers to a dimension, such as a level or value, that relates the combination of pathway activities to the possibility that the subject has sepsis or the risk of death as a result of sepsis.

[0123] As used herein, the term "sepsis" refers to a condition that occurs when the body's response to an infection causes injury to its tissues and organs. Sepsis is an inflammatory immune response triggered by an infection. Bacterial infection is the most common cause, but fungal, viral, and protozoan infections can also result in sepsis. Common locations of primary infection include the lungs, brain, urinary tract, skin, and abdominal organs.

[0124] The functional status of the blood sample is based on a single cell signaling pathway activity or a "combination of cell signaling pathway activities". This means that the functional status of the blood sample is influenced by the activity of one or more cell signaling pathways. The activity of one or more cell signaling pathways can be predicted and / or combined by the mathematical model described herein. In a preferred embodiment, the functional status of the blood sample is based on a combination of signaling pathway activities including the activity of more than two cell signaling pathways. Such a combination of signaling pathway activities includes the activity of more than four, or even more, different signaling pathways, such as three or four, or even five, six, seven or eight.

[0125] Many different formulas can be devised to determine the functional status of a blood sample, which is generally based on a combination of the activity of two or more cell signaling pathways in a subject, i.e. MPS = F(Pi) + X, i = 1...N, Here, MPS denotes the functional status and / or risk score of the blood sample (the term "MPS" is used herein as an abbreviation for "multi-pathway score" to indicate that the functional status of the blood sample may be influenced by the activity of more than one cell signaling pathway), Pi denotes the activity of cell signaling pathway i, N denotes the total number of cell signaling pathways used to calculate the functional status of the blood sample, and X is a proxy for possible additional factors and / or parameters that enter into the formula. Such a formula is more specifically a polynomial of a certain degree in given variables or linear combinations of variables. The weighting coefficients and powers in such a polynomial are set based on expert knowledge, but typically a training data set including known ground truth data, e.g. survival data, is used to obtain estimates of the weighting coefficients and powers of the above formula. The activities are then combined using the above formula to generate the MPS. The weighting coefficients and powers of the scoring function are then optimized such that a high MPS correlates with a higher probability that the patient has sepsis and / or has a high risk of death, and vice versa. Optimization of the correlation of the scoring function with known data can be performed using a number of analytical techniques, for example the Cox proportional hazards test (preferably as used herein), the log-rank test, Kaplan-Meier estimates, combined with standard optimization techniques such as gradient descent or manual fitting.

[0126] As used herein, the term "risk score" or "risk factor" generally refers to a prediction, risk assessment or diagnosis for a subject based on the functional status of a blood sample. For example, a risk score is a diagnosis of a subject with sepsis (the risk that the subject is septic), a risk of death for a septic subject, and / or a risk of developing sepsis for a non-septic subject, a risk of recurrence for a subject previously diagnosed with sepsis.

[0127] Preferably, the determination of the activity or activities of a signaling pathway, the combination of pathway activities and the application thereof is carried out, for example, as described in the following documents: No. 6,393,962, each of which is incorporated herein in its entirety for purposes of determining activity of the respective signaling pathway: International Patent Application Publication No. WO2013011479 (entitled "ASSESSMENT OF CELLULAR SIGNALING PATHWAY ACTIVITY USING PROBABILISTIC MODELING OF TARGET GENE EXPRESSION"), WO2014102668 (entitled "ASSESSMENT OF CELLULAR SIGNALING PATHWAY ACTIVITY USING LINEAR COMBINATION(S) OF TARGET GENE EXPRESSIONS"), WO2015101635 (entitled "ASSESSMENT OF THE PI3K CELLULAR SIGNALING PATHWAY ACTIVITY USING MATHEMATICAL MODELLING OF TARGET GENE EXPRESSION"), WO2016062891 (entitled "ASSESSMENT OF TGF-β CELLULAR SIGNALING PATHWAY ACTIVITY USING MATHEMATICAL MODELLING OF TARGET GENE EXPRESSION”), WO2017029215 (title “ASSESSMENT OF NFKB CELLULAR SIGNALING PATHWAY ACTIVITY USING MATHEMATICAL MODELLING OF TARGET GENE EXPRESSION”), WO2014174003 (“MEDICAL PROGNOSIS AND PREDICTION OF TREATMENT RESPONSE USING MULTIPLE CELLULAR SIGNALLING PATHWAY ACTIVITIES"), WO2016062892 ("MEDICAL PROGNOSIS AND PREDICTION OF TREATMENT RESPONSE USING MULTIPLE CELLULAR SIGNALINGWO2016062893 (entitled "MEDICAL PROGNOSIS AND PREDICTION OF TREATMENT RESPONSE USING MULTIPLE CELLULAR SIGNALING PATHWAY ACTIVITIES"), WO2018096076 (entitled "Method to distinguish tumor suppressive FOXO activity from oxidative stress"), and patent applications WO2018096076 (entitled "Method to distinguish tumor suppressive FOXO activity from oxidative stress"), WO2019068585 (entitled "Assessment of Notch cellular signaling pathway activity using mathematical modelling of target gene expression"), WO2019120658 (entitled "Assessment of MAPK-MAPK-AP1 cellular signaling pathway activity using mathematical modelling of target gene expression"), WO2019068543 (entitled "Assessment of JAK-JAK-STAT3 cellular signaling pathway activity using mathematical modelling of target gene expression"), WO2019068562 (entitled "Assessment of JAK-STAT1 / 2 cellular signaling pathway activity using mathematical modelling of target gene expression"), and WO2019068623 (entitled "Determining functional status of immune cells types and immune response").

[0128] These models have been biologically validated for the ER, AR, PI3K-FOXO, HH, Notch, TGHG-β, Wnt, NFκB, JAK-STAT1 / 2, JAK-JAK-STAT3 and MAPK-MAPK-AP1 pathways on several cell types.

[0129] A unique set of cell signaling pathway target genes is identified whose expression levels are preferably analyzed. For use in the mathematical model, three or more, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes from each evaluated cell signaling pathway can be analyzed to determine pathway activity.

[0130] Common to the pathway analysis methods for determining the activity of different signaling pathways disclosed herein, a concept preferably applied herein for the purposes of the present invention, the activity of a signaling pathway in a cell, such as a cell present in a blood sample, can be determined by receiving the expression level of one or more, preferably three or more, target genes of the signaling pathway, determining the activity level of a signaling pathway-associated transcription factor (TF) element in the sample, the TF element controlling the transcription of the three or more target genes, the determination being based on evaluating a calibrated mathematical pathway model relating the expression level of one or more, preferably three or more, target genes to the activity level of the signaling pathway, and optionally inferring the activity of the signaling pathway in the cell present in the blood sample based on the determined activity level of the signaling pathway-associated TF element. As described herein, the activity level can be used directly as an input for determining the functional status and / or diagnosis and / or risk score of the blood sample, which is also contemplated by the present invention.

[0131] As used herein, the term "activity level" of a TF element refers to the level of activity of the TF element with respect to the transcription of its target gene.

[0132] The calibrated mathematical pathway model is a probabilistic model, preferably a Bayesian network model, based on conditional probabilities relating the activity levels of the signaling pathway-associated TF elements with the expression levels of three or more target genes, or the calibrated mathematical pathway model is based on one or more linear combinations of the expression levels of three or more target genes. For the purposes of the present invention, the calibrated mathematical pathway model is preferably a centroid or linear model, or a Bayesian network model based on conditional probabilities.

[0133] In particular, the determination of the expression level in the subject and, optionally, the prediction of the activity of the signaling pathway is performed, for example, by, inter alia, (i) evaluation of a portion of a calibrated probabilistic pathway model, preferably a Bayesian network, representing the cell signaling pathway for a set of inputs including the expression levels of three or more target genes of the cell signaling pathway measured in the subject's sample, (ii) prediction of the activity level in the subject of a signaling pathway-associated transcription factor (TF) element that controls the transcription of three or more target genes of the cell signaling pathway, the prediction being based on a conditional probability relating the activity level of the signaling pathway-associated TF element to the expression level of the three or more target genes of the cell signaling pathway measured in the subject's sample, and, optionally, (iii) prediction of the activity of the cell signaling pathway based on the estimated activity level of the signaling pathway-associated TF element in the subject's sample. This is described in detail in International Patent Application Publication WO2013 / 011479A2 ("Assessment of cellular signaling pathway activity using probabilistic modeling of target gene expression"), the contents of which are incorporated herein in their entirety.

[0134] In an exemplary alternative, the determination of expression level in the subject and, if necessary, the prediction of the activity of the cell signaling pathway are carried out by, in particular, (i) determining the activity level of a signaling pathway-associated TF element that controls the transcription of three or more target genes of the cell signaling pathway in the sample of the subject, based on the evaluation of a calibrated mathematical pathway model based on one or more linear combinations of the expression levels of three or more target genes of the cell signaling pathway, which relate the expression levels of the three or more target genes of the cell signaling pathway and the activity levels of the signaling pathway-associated TF element, and, if necessary, (ii) predicting the activity of the cell signaling pathway in the subject based on the determined activity levels of the signaling pathway-associated TF element in the sample of the subject. This is described in detail in International Patent Application Publication WO2014 / 102668A2 ("Assessment of cellular signaling pathway activity using linear combination(s) of target gene expressions").

[0135] Further details regarding predicting cell signaling pathway activity using mathematical modeling of target gene expression can be found in W Verhaegh et al., 2014, supra.

[0136] To facilitate rapid identification of the references, the references are assigned to each signaling pathway of interest herein, and exemplary corresponding target genes suitable for determining the activity of the signaling pathway are indicated. In this regard, particular reference is also made to the sequence listing of the target genes provided together with the references.

[0137] AR: KLK2, PMEPA1, TMPRSS2, NKX3 1, ABCC4, KLK3, FKBP5, ELL2, UGT2B15, DHCR24, PPAP2A, NDRG1, LRIG1, CREB3L4, LCP1, GUCY1A3, AR, and EAF2 (WO2013 / 011479, WO2014 / 102668); KLK2, PMEPA1, TMPRSS2, NKX3 1, ABCC4, KLK3, FKBP5, ELL2, UGT2B15, DHCR24, PPAP2A, NDRG1, LRIG1, CREB3L4, LCP1, GUCY1A3, AR, and EAF2 (WO2014 / 174003); TGF-β: ANGPTL4, CDC42EP3, CDKNIA, CDKN2B, CTGF, GADD45A, GADD45B, HMGA2, ID1, IL11, SERPINE1, INPP5D, JUNB, MMP2, MMP9, NKX2-5, OVOL1, PDGFB, PTHLH, SGK1, SKIL, SMAD4, SMAD5, SMAD6, SMAD7, SNAI1, SNAI2, TIMP1, and VEGFA (WO2016 / 062891, WO2016 / 062893); MAPK-AP-1: 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 (WO2019 / 120658); and JAK-JAK-STAT3: 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 (WO2019 / 068543).

[0138] In some embodiments, signaling pathway measurements are performed using qPCR, multiplex qPCR, multiplexed qPCR, ddPCR, RNAseq, RNA expression arrays, or mass spectrometry. For example, gene expression microarray data, e.g., Affymetrix microarrays, or RNA sequencing methods such as Illumina sequencers can be used.

[0139] The calibrated mathematical pathway model is preferably a cenotoid or linear model, or a Bayesian network model based on conditional probabilities. For example, the calibrated mathematical pathway model is a probabilistic model, preferably a Bayesian network model, based on conditional probabilities relating the functional status and / or risk score of the blood sample to the activity of the signaling pathway, or the calibrated mathematical pathway model is based on one or more linear combinations of the activity of the signaling pathway.

[0140] A unique set of cell signaling pathway target genes is identified whose expression levels are preferably analyzed. For use in the mathematical model, three or more, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes from each evaluated cell signaling pathway can be analyzed to determine pathway activity.

[0141] As a non-limiting example, the following method can be used to generate a model for determining signaling pathway activity: In a plurality of data sets, the expression RNA levels of different genes are determined in samples in which the pathway is assumed to be active and in samples in which the pathway is assumed to be inactive. The expression levels are normalized, for example, based on the expression levels of housekeeping genes. A threshold can be determined based on the normalized expression levels of samples in which the pathway is assumed to be active or inactive, and if the normalized expression level of a gene in a sample is below the threshold, the pathway is more likely to be inactive, and if the expression level is above the threshold, the pathway is more likely to be active. Based on this threshold, a simple model can be constructed in which the expression levels are assigned values ​​determined in blood samples of subjects with or suspected of having sepsis, and pathway activity is determined as the sum of these values ​​for each gene whose expression level is determined. Alternatively, the values ​​obtained for each gene of each pathway can be compared to the values ​​obtained for said genes in reference blood samples from healthy subjects (i.e. subjects without sepsis).

[0142] According to an embodiment of the invention, said determining the expression levels of three or more target genes of the AR signaling pathway, the TGF beta signaling pathway, the MAPK-AP1 signaling pathway and / or the JAK-STAT3 signaling pathway comprises: Preferably, the set of target genes of the AR pathway comprises three or more target genes, such as 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 target genes selected from the group consisting of KLK2, PMEPA1, TMPRSS2, NKX31, ABCC4, KLK3, FKBP5, ELL2, UGT2B15, DHCR24, PPAP2A, NDRG1, LRIG1, CREB3L4, LCP1, GUCY1A3, AR and EAF2, and / or; Preferably, the set of target genes of the TGF beta pathway comprises three or more target genes, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 target genes selected from the group consisting of CDC42EP3, GADD45A, ID1, MMP9, SGK1, SMAD5, SMAD7, VEGFA, JUNB, TIMP1, SKIL and CCKN1A, ANGPTL4, CDC42EP3, CDKN1A, CTGF, GADD45A, GADD45B, determining the expression level of three or more target genes of the TGF beta signaling pathway, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes, comprising determining the expression level of three or more target genes selected from the list consisting of HMGA2, ID1, IL11, JUNB, PDGFB, PTHLH, SERPINE1, SGK1, SKIL, SMAD4, SMAD5, SMAD6, SMAD7, SNAI2, VEGFA, and / or; Preferably, the set of target genes of the MAPK-AP1 pathway includes three or more target genes, for example, 3, 4, 5, 6, 7, 8, 9, 10, or 11 target genes selected from the group consisting of DNMT1, EGFR, ENPP2, GLRX, MMP9, PLAUR, TIMP1, LOR, EZR, DDIT3, and TP53. determining the expression level of three or more target genes of the MAPK-AP1 signaling pathway, comprising determining the expression level of three or more target genes, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes selected from the list consisting of LRX, IL2, IVL, LOR, MMP1, MMP3, MMP9, SERPINE1, PLAU, PLAUR, PTGS2, SNCG, TIMP1, TP53, and VIM; and / or; Preferably, the set of target genes of the JAK-STAT3 pathway includes three or more target genes selected from the group consisting of BCL2, BIRC5, CD274, FOS, HSPA1A, JUNB, MMP9, STAT1, TIMP1, BCL2L1, HSPA1B, HSP90AB1, HSP90B1, POU2F1, and ICAM1, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes. determining the expression level of three or more target genes of the JAK-STAT3 signaling pathway, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes, comprising determining the expression level of three or more target genes selected from the list consisting of P90AA1, 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. Includes.

[0143] Thus, in an embodiment, the method according to the invention comprises the steps of diagnosing a subject from whom a blood sample has been obtained, comprising: - clinical parameters, and - Functional status of blood samples the subject is diagnosed as having sepsis or the subject is diagnosed as not having sepsis based on The method further comprises comparing the functional status of the subject's blood sample with at least one functional status of a blood sample obtained from a healthy or non-septic control subject. In a preferred embodiment, the functional status of the blood sample comprises AR signaling pathway activity determined to be higher than the AR signaling pathway activity determined in a control blood sample obtained from a healthy or non-septic control, and the subject from whom the blood sample is obtained is diagnosed as having sepsis if the subject has at least one clinical parameter associated with sepsis. The inventors have found that the pathway activity determined on the blood sample obtained from a subject can be used to diagnose whether the subject has or does not have sepsis. As detailed in the experimental data, the determination of the AR signaling pathway is sufficient to distinguish between a blood sample obtained from a subject with sepsis and a blood sample obtained from a healthy individual. If desired, other pathway activities, such as TGF-beta signaling pathway activity, MAPK-AP1 signaling pathway activity, and / or JAK-STAT3 signaling pathway activity, may be included in the diagnosis. Thus, the functional status of the blood sample determined herein can be used as a diagnostic tool to rapidly diagnose a subject.

[0144] Based on the expression level in blood sample of three or more target genes from AR pathway, AR signaling pathway activity, and thus the functional status of blood sample, can be determined. This AR signaling pathway can be expressed as a quantitative value and compared with the AR signaling pathway activity determined on blood sample obtained from healthy subject or known septic subject. Thus, the step of diagnosing the subject preferably further comprises comparing the functional status of the blood sample of the subject with the functional status of blood sample obtained from known septic patient. Since the diagnostic step is based on the functional status of the blood sample, this step further uses the TGF beta, MAPK-AP1, and / or JAK-STAT3 signaling pathway activity determined, if any. By including additional pathways, the certainty of diagnosis is further improved.

[0145] The functional state of the blood sample comprises at least the AR signaling pathway activity, which is expressed as a numerical value.Thus, by comparing the functional state of the blood sample from the subject with the functional state of the control blood sample obtained from a healthy subject, diagnosis can be made based on the difference or similarity of the numerical value represented by at least the AR signaling pathway activity.Therefore, the comparison is preferably performed using multiple functional states of reference blood samples to increase accuracy.More preferably, the comparison further comprises comparing with one or more, preferably multiple, additional reference functional states of blood samples obtained from known septic subjects.

[0146] For example, when using multiple reference blood samples of healthy individuals, AR signaling pathway activity can be calculated for each sample and average value can be determined.The AR signaling pathway activity of the subject to be diagnosed can be similar to the average reference AR signaling pathway activity (e.g., within 1 or 2 standard deviations of the calculated average activity), and then the subject is diagnosed as not having sepsis.Alternatively, the AR signaling pathway can be higher compared to the calculated average AR signaling pathway activity (e.g., at least 1 or 2 standard deviations higher than the average), and then the subject is diagnosed as having sepsis.Optionally, other pathway activity can be included in this comparison.The above method can be used regardless of the method used to calculate signaling pathway activity, provided that the same method is used for the sample from the subject to be diagnosed and the reference sample.

[0147] Preferably, for diagnosing a subject with sepsis, determining the expression levels of three or more target genes of the AR signaling pathway, the TGF beta signaling pathway, the MAPK-AP1 signaling pathway and / or the JAK-STAT3 signaling pathway comprises: determining the expression levels of three or more target genes of the AR signaling pathway, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes, wherein the three or more, e.g., 3, 4, or 5 target genes are selected from the group consisting of FKBP5, LRIG1, PMEPA1, DHCR24, and LCP1; and / or; determining the expression levels of three or more target genes of the TGF beta signaling pathway, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes, wherein the three or more, e.g., 3, 4, or 5 target genes are selected from the group consisting of MMP9, GADD45A, CDC42EP3, TIMP1 and SMAD5; and / or; determining the expression level of three or more target genes of the MAPK-AP1 signaling pathway, comprising determining the expression level of three or more target genes of the MAPK-AP1 pathway, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes, wherein the three or more, e.g., 3, 4, or 5 target genes are selected from the group consisting of MMP9, TIMP1, DNMT1, FASLG and PLAUR; and / or; Determining the expression levels of three or more target genes, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes of the JAK-STAT3 signaling pathway, wherein the three or more, e.g., 3, 4, or 5 target genes are selected from the group consisting of MMP9, BCL2, TIMP2, HSPA1A, and HSPA1AB. Includes.

[0148] Thus, the present invention further provides a method for diagnosing a subject based on RNA extracted from a blood sample obtained from the subject, comprising: - determining, or receiving a result of, the expression level of three or more target genes of the AR pathway; - determining an AR signaling pathway activity based on the determined expression levels of said three or more target genes of the AR signaling pathway; and, where appropriate, - determining, or receiving a result of said determining, the expression level of three or more target genes of the TGF-beta pathway; - determining a TGF beta signaling pathway activity based on the determined expression levels of said three or more target genes of the TGF beta signaling pathway; - determining, or receiving a result of said determining, the expression levels of three or more target genes of the MAPK-AP1 pathway; - determining the MAPK-AP1 signaling pathway activity based on the determined expression levels of the three or more target genes of the MAPK-AP1 signaling pathway; - determining, or receiving a result of said determining, the expression levels of three or more target genes of the JAK-STAT3 pathway; - determining a JAK-STAT3 signaling pathway activity based on the determined expression levels of the three or more target genes of the JAK-STAT3 signaling pathway; and further comprising the step of diagnosing the subject from whom the blood sample was obtained; - clinical parameters, and - Determined signaling pathway activity in blood samples the subject is diagnosed as having sepsis, or the subject is diagnosed as not having sepsis based on comparing the determined signaling pathway activity in the subject's blood sample with the determined signaling pathway activity in at least one blood sample obtained from a healthy subject or a non-septic control subject; The blood sample is obtained from a subject with sepsis, or from a subject suspected of having sepsis or at risk of developing sepsis.In a preferred embodiment, the AR signaling pathway activity is determined to be higher than the AR signaling pathway activity determined in a control blood sample obtained from a healthy or non-septic control, and the subject from whom the blood sample is obtained has at least one clinical parameter associated with sepsis, the subject is diagnosed with sepsis.

[0149] Thus, in an embodiment of the present invention, if the functional status of the blood sample comprises AR signaling pathway activity, the AR signaling pathway activity is determined to be higher than the AR signaling pathway activity determined in a reference blood sample obtained from a healthy subject, the subject is diagnosed as having sepsis. Alternatively, the diagnosis may be based directly on the expression level of three or more genes. Alternatively, the comparison may further comprise a reference blood sample obtained from a subject with known sepsis. In such a case, the expression level of three or more genes or the functional status of the blood sample of the subject can be compared with both the expression level of three or more genes or the functional status of the blood sample of a healthy individual and the expression level of three or more genes or the functional status of the blood sample of a subject with known sepsis. In such a case, the numerical values ​​assigned to the AR signaling pathway activity (and other pathway activity, if determined) can be compared. For example, if the subject has an AR signaling pathway close to, for example, within 1 or 2 standard deviations of the mean value for known septic subjects, the subject is diagnosed as having sepsis, or if the value is close to, for example, within 1 or 2 standard deviations of the mean value for healthy subjects, the subject is diagnosed as non-septic. Alternatively, statistical methods can be used to determine whether the subject is more likely to be in the non-septic or septic group (meaning that the value assigned to the AR signaling pathway is closer to the normal (healthy) mean or closer to the septic mean). Other signaling pathway activities can be included in this calculation as necessary. When using multiple signaling pathway activities for the diagnosis step, for example, both AR signaling pathway activity and TGF-beta signaling pathway activity, clustering methods are used to cluster the healthy control subjects, the known septic subjects, and the subjects to be diagnosed to establish whether the subjects to be diagnosed are in the healthy group or the septic group. As used herein, a reference blood sample from a subject with known sepsis is a blood sample obtained from a subject in whom a diagnosis of sepsis was later confirmed, for example by blood culture.

[0150] In an embodiment of the method according to the invention, said expression levels of three or more genes are used to predict the risk of death of the subject from which the blood sample was obtained, wherein the prediction is based on a comparison of the expression level of the three or more genes in the subject to a plurality of reference expression levels of the three or more genes obtained from a reference subject, the plurality of reference expression levels of the three or more genes obtained from a reference subject comprising expression levels of the three or more genes obtained from a non-survivor subject with sepsis and expression levels of the three or more genes obtained from a survivor subject with sepsis, and optionally further comprising expression levels of the three or more genes obtained from a healthy or non-septic control subject; The subject from whom the blood sample was obtained is determined to have sepsis; a low risk of death is predicted if 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 a reference subject with sepsis who is a survivor, or if 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 at least one healthy or non-septic control subject; An increased risk of death is predicted if the expression levels of three or more genes obtained from a subject with sepsis are similar to the expression levels of three or more genes obtained from a reference subject with sepsis who is a non-survivor.

[0151] In an alternative embodiment of the invention, the functional status of the blood sample is used to predict the risk of mortality of the subject from whom the blood sample was obtained; said prediction being based on a comparison of the functional status of the blood sample of the subject with a plurality of reference functional statuses of blood samples obtained from reference subjects, said plurality of reference functional statuses of blood samples obtained from reference subjects comprising at least one functional status of blood samples obtained from subjects with sepsis who are non-survivors and at least one functional status of blood samples obtained from subjects with sepsis who are survivors, and optionally further comprising at least one functional status of blood samples obtained from healthy or non-septic control subjects; The subject from whom the blood sample was obtained is determined to have sepsis; a low risk of death is predicted if the functional status of the blood sample obtained from the subject with sepsis is similar to the functional status of at least one blood sample obtained from a reference subject with sepsis who is a survivor, or if the functional status of the blood sample obtained from the subject with sepsis is similar to the functional status of at least one blood sample obtained from at least one healthy or non-septic control subject; An increased risk of death is predicted if the functional status of the blood sample obtained from the subject with sepsis is similar to at least one functional status of a blood sample obtained from a non-survivor reference subject with sepsis.

[0152] In one embodiment, said comparison of three or more genes or functional states of a blood sample obtained from a subject with a plurality of functional states of a blood sample obtained from a control subject is performed using clustering of the determined pathway activities, preferably by hierarchical clustering.

[0153] The inventors have found that predictions regarding risk of death can be made based on three or more target genes or functional status of blood samples obtained from a subject. By comparing the expression levels or functional status of three or more target genes of blood samples from a subject with reference samples of dead and surviving septic subjects, the probability that a subject will die from sepsis can be calculated. This prediction can be made by using numerical values ​​that represent different determined signaling pathway activities, such as AR signaling activity or the combination of AR and TGF beta signaling activity, and comparing these values ​​with the values ​​obtained for the reference group. Alternatively, this prediction can be directly based on the expression levels of three or more genes. This comparison can be made using statistical methods or, for example, by using clustering. When using clustering, a subject is predicted to have a high risk of death if the subject is clustered with the reference subjects that died, or is predicted to have a low risk of death if the subject is clustered with the reference subjects that survived.

[0154] Preferably, the prediction is made in a method according to the invention performed on a blood sample obtained from a patient with sepsis. Preferably, the prediction is made on a subject where it has already been established, for example by a method as described herein, that the subject has sepsis.

[0155] Preferably, the prediction is performed in the method according to the present invention, wherein said comparison of the expression levels or functional status of three or more genes of a blood sample obtained from a subject with the expression levels or functional status of a plurality of three or more genes of a blood sample obtained from a control subject is performed using clustering of the determined pathway activities, preferably by hierarchical clustering. Clustering based on the determined signaling activities allows identification of samples obtained from septic patients who will eventually die from sepsis. Thus, by comparing the signaling activities determined in blood samples obtained from a subject with known reference patients, if the respective signaling activities cluster with patients who died, the risk of death can be predicted to be high, or if the respective signaling activities cluster with patients who survived, the risk can be predicted to be low.

[0156] Preferably, for predicting the survival probability of a subject with sepsis, determining the expression levels of three or more target genes of the AR signaling pathway, the TGF beta signaling pathway, the MAPK-AP1 signaling pathway and / or the JAK-STAT3 signaling pathway comprises: determining the expression levels of three or more target genes of the AR signaling pathway, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes, wherein the three or more, e.g., 3, 4, or 5 target genes are selected from the group consisting of ELL2, FKBP5, EAF2, NDRG1 and DHCR24; and / or; determining the expression levels of three or more target genes of the TGF beta signaling pathway, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes, wherein the three or more, e.g., 3, 4, or 5 target genes are selected from the group consisting of ID1, SKIL, GADD45A, HMGA2, and SMAD4; and / or; determining the expression level of three or more target genes of the MAPK-AP1 signaling pathway, comprising determining the expression level of three or more target genes of the MAPK-AP1 pathway, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes, wherein the three or more, e.g., 3, 4, or 5 target genes are selected from the group consisting of BCL2L11, EZR, ENPP2, MMP3 and PLAUR; and / or; Determining the expression levels of three or more target genes, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes of the JAK-STAT3 signaling pathway, wherein the three or more, e.g., 3, 4, or 5 target genes are selected from the group consisting of BIRC5, HSP90B1, MMP3, IL10, and HIF1A. Includes.

[0157] Thus, in one embodiment, the present invention relates to a method for predicting risk of death of a subject from whom a blood sample was obtained, wherein said expression levels of three or more genes are used to predict risk of death of the subject from whom a blood sample was obtained, wherein the prediction is based on a comparison of the expression level of the three or more genes in the subject to a plurality of reference expression levels of the three or more genes obtained from a reference subject, the plurality of reference expression levels of the three or more genes obtained from a reference subject comprising expression levels of the three or more genes obtained from a non-survivor subject with sepsis and expression levels of the three or more genes obtained from a survivor subject with sepsis, and optionally further comprising expression levels of the three or more genes obtained from a healthy or non-septic control subject; The subject from whom the blood sample was obtained is determined to have sepsis; a low risk of death is predicted if 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 a reference subject with sepsis who is a survivor, or if 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 at least one healthy or non-septic control subject; wherein an increased risk of death is predicted if the expression levels of the three or more genes obtained from a subject with sepsis are similar to the expression levels of the three or more genes obtained from a reference subject with sepsis who is a non-survivor.

[0158] In an alternative embodiment, the present invention provides a method for determining a subject's risk of mortality based on RNA extracted from a blood sample from the subject, comprising: - determining, or receiving a result of, the expression level of three or more target genes of the AR pathway; - determining an AR signaling pathway activity based on the determined expression levels of said three or more target genes of the AR signaling pathway; and, where appropriate, - determining, or receiving a result of said determining, the expression level of three or more target genes of the TGF-beta pathway; - determining a TGF beta signaling pathway activity based on the determined expression levels of said three or more target genes of the TGF beta signaling pathway; - determining, or receiving a result of said determining, the expression levels of three or more target genes of the MAPK-AP1 pathway; - determining the MAPK-AP1 signaling pathway activity based on the determined expression levels of the three or more target genes of the MAPK-AP1 signaling pathway; - determining, or receiving a result of said determining, the expression levels of three or more target genes of the JAK-STAT3 pathway; - determining JAK-STAT3 signaling pathway activity based on the determined expression levels of the three or more target genes of the JAK-STAT3 signaling pathway. Including, signaling pathway activity in the blood sample is used to predict risk of mortality of the subject from which the blood sample was obtained; the prediction is based on a comparison of signaling pathway activity in the subject's blood sample to signaling pathway activity in a plurality of blood samples obtained from reference subjects, the plurality of blood samples obtained from reference subjects including at least one blood sample obtained from a non-survivor subject with sepsis and at least one blood sample obtained from a survivor subject with sepsis, and optionally further including at least one blood sample obtained from a healthy or non-septic control subject; The subject from whom the blood sample was obtained is determined to have sepsis; a low risk of death is predicted if the signaling pathway activity in the blood sample obtained from the subject with sepsis is similar to the signaling pathway activity in at least one blood sample obtained from a reference subject with sepsis who is a survivor, or if the signaling pathway activity in the blood sample obtained from the subject with sepsis is similar to the signaling pathway activity in at least one blood sample obtained from a healthy or non-septic control subject; The present invention relates to a method in which an increased risk of death is predicted if the signaling pathway activity in a blood sample obtained from a subject with sepsis is similar to the signaling pathway activity in at least one blood sample obtained from a reference subject with sepsis who is a non-survivor.

[0159] In one embodiment, the comparison of the expression levels or signaling pathway activities of three or more genes in a blood sample obtained from a subject with the expression levels or signaling pathway activities of three or more genes in multiple blood samples obtained from control subjects is performed using clustering of the determined pathway activities, preferably by hierarchical clustering.

[0160] In a further embodiment, the subject from whom the blood sample was obtained does not have sepsis, and the expression levels of the three or more genes are used to determine the risk of the subject to develop sepsis, and the method further comprises comparing the expression levels of the three or more genes from the subject from whom the blood sample was obtained with the expression levels of the three or more genes obtained from a healthy or non-septic control subject.

[0161] In an alternative embodiment, the subject from whom the blood sample was obtained does not have sepsis, and the functional status of the blood sample is used to determine the risk of the subject developing sepsis; The method further comprises comparing the functional status of the blood sample from the subject from which the blood sample was obtained with at least one functional status of a blood sample obtained from a healthy or non-septic control subject; Preferably, the subject from whom the blood sample is obtained is predicted to be at risk of developing sepsis if the functional status of the blood sample comprises AR signaling pathway activity, which 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.In a preferred embodiment, the prediction of the risk of developing sepsis for the subject without sepsis is further based on TGF-beta signaling pathway activity, and the subject is at risk of developing sepsis if the TGF-beta signaling pathway activity is determined to be higher than the TGF-beta signaling pathway activity determined in the control blood sample obtained from a healthy or non-septic control subject.In a more preferred embodiment, the subject from whom the blood sample is obtained is determined to be at risk of developing sepsis without sepsis if the signaling pathway activity of both AR and TGF-beta is determined to be higher than the AR and TGF-beta signaling pathway activity determined in the control blood sample obtained from a healthy or non-septic control subject. Preferably, the subject without sepsis is a subject with a bacterial infection.

[0162] The inventors have found that in patients who do not have sepsis but are at risk of developing sepsis, e.g., patients with bacterial infection, patients at high risk of developing sepsis can be identified as high risk, which correlates with increased AR and, optionally, TGF-beta signaling pathway activity.

[0163] Thus, the present invention further provides a method for determining the risk of a non-septic subject to develop sepsis based on RNA extracted from a blood sample from the subject, comprising: - determining, or receiving a result of, the expression level of three or more target genes of the AR pathway; - determining an AR signaling pathway activity based on the determined expression levels of said three or more target genes of the AR signaling pathway; and, where appropriate, - determining, or receiving a result of said determining, the expression level of three or more target genes of the TGF-beta pathway; - determining a TGF beta signaling pathway activity based on the determined expression levels of said three or more target genes of the TGF beta signaling pathway; and / or - determining, or receiving a result of said determining, the expression levels of three or more target genes of the MAPK-AP1 pathway; - determining the MAPK-AP1 signaling pathway activity based on the determined expression levels of said three or more target genes of the MAPK-AP1 signaling pathway; and / or - determining, or receiving a result of said determining, the expression levels of three or more target genes of the JAK-STAT3 pathway; - determining JAK-STAT3 signaling pathway activity based on the determined expression levels of the three or more target genes of the JAK-STAT3 signaling pathway. Including, signaling pathway activity in the blood sample is used to determine a risk of the subject developing sepsis; The subject from whom the blood sample was obtained does not have sepsis, The method further comprises comparing signaling pathway activity in the blood sample from the subject from which the blood sample was obtained with signaling pathway activity in at least one blood sample obtained from a healthy or non-septic control subject.

[0164] Preferably, the subject without sepsis is a subject with a bacterial infection.

[0165] In one embodiment, the subject from whom the blood sample was obtained is predicted to be at risk for developing sepsis if the AR signaling pathway activity is determined to be higher than the AR signaling pathway activity determined in a control blood sample obtained from a healthy or non-septic control subject.

[0166] In a further embodiment of the invention, the subject from whom the blood sample was obtained has recovered from sepsis, and the expression levels of the three or more genes in the blood sample are used to monitor the risk of the subject developing a recurrence of sepsis; The method further includes comparing the expression levels of the three or more genes from the subject from which the blood sample was obtained with the expression levels of the three or more genes from a healthy or non-septic control subject.

[0167] In an alternative embodiment, the subject from whom the blood sample was obtained has recovered from sepsis, and the functional status of the blood sample is used to monitor the risk of the subject developing a recurrence of sepsis; The method further comprises comparing the functional status of the blood sample from the subject from which the blood sample was obtained with at least one functional status of a blood sample obtained from a healthy or non-septic control subject; Preferably, the subject from whom the blood sample is obtained is predicted to be at risk of developing sepsis recurrence if the functional status of the blood sample comprises AR signaling pathway activity, which 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.In a preferred embodiment, the prediction of the risk of developing sepsis recurrence for the subject who has recovered from sepsis is further based on TGF-beta signaling pathway activity, and the subject is at risk of developing sepsis recurrence if the TGF-beta signaling pathway activity is determined to be higher than the TGF-beta signaling pathway activity determined in the control blood sample obtained from a healthy or non-septic control subject.In a more preferred embodiment, the subject who has recovered from sepsis is determined to be at risk of developing sepsis recurrence if the signaling pathway activity of both AR and TGF-beta is determined to be higher than the AR and TGF-beta signaling pathway activity determined in the control blood sample obtained from a healthy or non-septic control subject.

[0168] It has been found that after recovery from sepsis, patients remain susceptible to sepsis recurrence for a long period of time.The present inventors demonstrate that higher recurrence risk is correlated with increased AR and TGF beta signaling pathway activity, and that based on AR signaling pathway activity alone or in combination with TGF beta signaling pathway activity, subjects at risk of sepsis recurrence can be identified.

[0169] Thus, in a preferred embodiment, the present invention provides a method for determining the risk of relapse in a subject recovered from sepsis based on RNA extracted from a blood sample derived from the subject, comprising: - determining, or receiving a result of, the expression level of three or more target genes of the AR pathway; - determining an AR signaling pathway activity based on the determined expression levels of said three or more target genes of the AR signaling pathway; and, where appropriate, - determining, or receiving a result of said determining, the expression level of three or more target genes of the TGF-beta pathway; - determining a TGF beta signaling pathway activity based on the determined expression levels of said three or more target genes of the TGF beta signaling pathway; and / or - determining, or receiving a result of said determining, the expression levels of three or more target genes of the MAPK-AP1 pathway; - determining the MAPK-AP1 signaling pathway activity based on the determined expression levels of said three or more target genes of the MAPK-AP1 signaling pathway; and / or - determining, or receiving a result of said determining, the expression levels of three or more target genes of the JAK-STAT3 pathway; - determining JAK-STAT3 signaling pathway activity based on the determined expression levels of the three or more target genes of the JAK-STAT3 signaling pathway. Including, the subject from whom the blood sample was obtained has recovered from sepsis, and signaling pathway activity in the blood sample is used to monitor the risk of the subject developing a recurrence of sepsis; The method further comprises comparing signaling pathway activity in the blood sample from the subject from which the blood sample was obtained with signaling pathway activity in at least one blood sample obtained from a healthy or non-septic control subject.

[0170] In one embodiment, the subject from whom the blood sample was obtained is predicted to be at risk of developing a recurrence of sepsis if the AR signaling pathway activity is determined to be higher than the AR signaling pathway activity determined in a control blood sample obtained from a healthy or non-septic control subject.

[0171] According to a preferred embodiment of the invention, 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 (MDSCs), dendritic cells, isolated neutrophils, isolated lymphocytes or isolated monocytes.

[0172] In an embodiment of the invention, the signaling pathway activity or activities are determined based on evaluating a calibrated mathematical model relating three or more expression levels determined for the pathway or pathways based on RNA extracted from a blood sample to the activity or activities of the signaling pathway or pathways.

[0173] According to a preferred embodiment of the present invention, the functional status of the blood sample is determined based on evaluating a calibrated mathematical model relating the activity of the signaling pathways in the blood sample to a numerical value. The model is programmed to interpret the combination of pathway activities to determine the functional status of the blood sample of the subject to be diagnosed and, if necessary, to further use this functional status to provide a diagnosis or risk of death. In particular, determining the functional status of the blood sample includes (i) receiving the activity of each signaling pathway in the blood sample of the subject to be diagnosed, (ii) determining the functional status of the blood sample of said subject, wherein the determination is based on evaluating a calibrated mathematical model relating the activity of each signaling pathway to the functional status of the blood sample.

[0174] The calibrated mathematical pathway model is preferably a symmetric or linear model, or a Bayesian network model based on conditional probabilities. For example, the calibrated mathematical pathway model is a probabilistic model, preferably a Bayesian network model, based on conditional probabilities relating the functional state of the blood sample and the activity of the signaling pathways, or the calibrated mathematical pathway model is based on one or more linear combinations of the activity of the signaling pathways.

[0175] According to the mathematical model, the activity of the signaling pathways is interpreted to provide a functional status of the blood sample, which is further translated into a diagnosis or interpreted to provide a diagnosis directly. The functional status of the blood sample predicts or provides the probability that a subject has sepsis and / or the probability that a subject with sepsis will die as a result of septic shock.

[0176] Thus, determining a diagnosis or determining a risk of mortality comprises determining a functional status of the blood sample based on a combination of activity of cell signaling pathways in the blood sample and translating the functional status into a diagnosis or risk of mortality. According to a preferred embodiment of the invention, the activity of each signal pathway is determined or determinable by pathway analysis as described herein.

[0177] Thus, in a preferred embodiment of the invention, the method comprises the steps of providing a blood sample obtained from a subject and extracting RNA from said blood sample.

[0178] In certain embodiments of the invention, the subject is a pediatric subject.

[0179] In a second aspect, the present invention relates to a computer-implemented method for carrying out the method of the first aspect of the invention and its various embodiments.

[0180] According to a third aspect, the present invention relates to a device for determining the functional status of a blood sample and / or for diagnosing a subject with sepsis and / or for predicting the risk of death of a control, the device comprising a digital processor for carrying out the method of the first aspect of the present invention and its various embodiments.In a preferred embodiment, the present invention relates to a device for determining the functional status of a blood sample, the device comprising a digital processor for carrying out said method, the device comprising an input device for receiving data indicative of a target gene expression profile for three or more target genes of the AR signaling pathway, optionally data indicative of a target gene expression profile for three or more target genes of the TGF beta signaling pathway and / or the MAPK-AP1 signaling pathway and the JAK-STAT3 signaling pathway.

[0181] According to a fourth aspect, the present invention relates to a non-transitory storage medium for determining the functional status of a blood sample and / or for diagnosing a subject with sepsis and / or for predicting the risk of mortality of a control, the non-transitory storage medium storing instructions executable by a digital processing device to carry out the method of the first aspect of the invention and its various embodiments. In a preferred embodiment, the present invention relates to a ..., the non-transitory storage medium storing instructions executable by a digital processing device to carry out the method of the first aspect of the invention and its various embodiments. In a preferred embodiment, the present invention relates to a non-transitory storage medium storing - receiving data indicative of a target gene expression profile for three or more target genes of the AR signaling pathway, and optionally further receiving data indicative of target gene expression levels of three or more target genes of the TGF beta signaling pathway and / or the MAPK-AP1 signaling pathway and / or the JAK-STAT3 signaling pathway; - determining an AR signaling pathway activity, and optionally a TGF beta signaling pathway activity and / or a MAPK-AP1 signaling pathway activity and / or a 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 TGF beta signaling pathway and / or the MAPK-AP1 signaling pathway and / or the JAK-STAT3 signaling pathway; - determining a functional status of a blood sample based on the determined AR signaling pathway activity and, optionally, TGF beta signaling pathway activity and / or MAPK-AP1 signaling pathway activity and / or JAK-STAT3 signaling pathway activity, the functional status of the blood sample being determined to have the determined AR signaling pathway activity and, optionally, TGF beta signaling pathway activity and / or MAPK-AP1 signaling pathway activity and / or JAK-STAT3 signaling pathway activity; and - Where appropriate, provide a diagnosis or prognosis based on the functional status of a blood sample The present invention relates to a computer program product comprising instructions for carrying out a method comprising:

[0182] The non-transitory recording medium may be a computer-readable recording medium, such as a hard drive or other magnetic recording medium, an optical disk or other optical recording medium, a random access memory (RAM), a read-only memory (ROM), a flash memory, or other electronic recording medium, a network server, etc. The digital processing device may be a portable device (e.g., a personal digital assistant or smartphone), a notebook computer, a desktop computer, a tablet computer or device, a remote network server, etc.

[0183] According to a fifth aspect, the present invention relates to a computer program for determining the functional status of a blood sample and / or for diagnosing a subject with sepsis and / or for predicting a subject's risk of mortality, the computer program comprising program code means for causing the digital processing device to carry out the method according to the first aspect of the invention and its various embodiments when the computer program runs on the digital processing device. The computer program can be stored / distributed on a suitable medium, such as an optical or solid-state medium, supplied together with or as part of other hardware, but can also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0184] In a sixth aspect, the present invention provides a kit-of-parts comprising primers and optionally probes for determining the expression level of three or more, such as 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more, genes, comprising: Three or more genes, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more genes, are selected from group 1 and group 2, and 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 UGT2B1 5; Group 2 relates to a kit of parts consisting 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.

[0185] Primer and probe design is a routine technique in the field of gene detection and quantification. Primers can be designed by online programs such as Primer3 (https: / / primer3.ut.ee / ). Probes for qPCR are typically designed to have a fluorescent moiety and a quencher that bind to the amplification product and allow for the distinction between bound and unbound states. The genomic sequences of the above genes can be easily found in genome databases such as UCSC Genome Browser, Ensembl Genome Browser or NCBI Genome Data Viewer.

[0186] In one embodiment, 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, G ADD45A, 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. In an embodiment, the three or more genes are selected from group 1.

[0187] In an alternative embodiment, the invention further provides a kit-of-parts comprising primers for inferring activity of one or more cell signaling pathways by determining the expression level of one or more sets of target genes of each cell signaling pathway, wherein the cell signaling pathway comprises the AR pathway, and optionally further comprises one or more of the TGF beta pathway, the MAPK-AP1 pathway, and the JAK-STAT3 pathway; The set of target genes of the AR pathway comprises three or more target genes, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes, selected from the group including KLK2, PMEPA1, TMPRSS2, NKX3_1, ABCC4, KLK3, FKBP5, ELL2, UGT2B15, DHCR24, PPAP2A, NDRG1, LRIG1, CREB3L4, LCP1, GUCY1A3, AR and EAF2, preferably, the set of target genes of the AR pathway comprises three or more target genes, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 target genes, selected from the group consisting of ELL2, FKBP5, GUCY1A3, LRIG1, PLAU, PMEPA1, PRKACB, SGK1, NDRG1, CREB3L4, DHCR24 or PTPN1; The set of target genes of the TGF beta pathway comprises three or more target genes, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes, selected from the group including ANGPTL4, CDC42EP3, CDKN1A, CTGF, GADD45A, GADD45B, HMGA2, ID1, IL11, JUNB, PDGFB, PTHLH, SERPINE1, SGK1, SKIL, SMAD4, SMAD5, SMAD6, SMAD7, SNAI2, VEGFA, preferably, the set of target genes of the TGF beta pathway comprises three or more target genes, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 target genes, selected from the group consisting of CDC42EP3, GADD45A, ID1, MMP9, SGK1, SMAD5, SMAD7, VEGFA, JUNB, TIMP1, SKIL and CCKN1A; the set of target genes of the MAPK-AP1 pathway comprises three or more target genes, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 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; preferably, the set of target genes of the MAPK-AP1 pathway comprises three or more target genes, e.g., 3, 4, 5, 6, 7, 8, 9, 10, or 11 target genes, selected from the group consisting of DNMT1, EGFR, ENPP2, GLRX, MMP9, PLAUR, TIMP1, LOR, EZR, DDIT3, and TP53; The set of target genes of the JAK-STAT3 pathway includes 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. The present invention relates to a kit-of-parts comprising three or more target genes selected from the group, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes, preferably wherein the set of target genes of the JAK-STAT3 pathway comprises three or more target genes, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more target genes, selected from the group consisting of BCL2, BIRC5, CD274, FOS, HSPA1A, JUNB, MMP9, STAT1, TIMP1, BCL2L1, HSPA1B, HSP90AB1, HSP90B1, POU2F1 and ICAM1.

[0188] In a preferred embodiment of the invention, the kit-of-parts further comprises an apparatus according to the third aspect of the invention, and / or a non-transitory recording product according to the fourth aspect of the invention, and / or a computer program according to the fifth aspect of the invention.

[0189] In an embodiment, the present invention relates to the use of a kit according to the sixth aspect in a method according to the first aspect.

[0190] In a seventh aspect, the present invention relates to a method for diagnosing or prognosing in vitro or ex vivo whether a subject has sepsis, has septic shock or has an increased risk of death as a result of sepsis using a kit according to the sixth aspect of the invention, preferably 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 expression; or 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 correlate with sepsis.

[0191] In an alternative embodiment, the invention provides a method for in vitro or ex vivo diagnosis or prognosis of whether a subject has sepsis, has septic shock, or has an increased risk of mortality as a result of sepsis using a kit, the kit comprising primers for inferring activity of one or more cell signaling pathways by determining the expression level of one or more sets of target genes of the respective cell signaling pathways, the cell signaling pathways comprising the AR pathway, and optionally further comprising one or more of the TGF beta pathway, the MAPK-AP1 pathway, and the JAK-STAT3 pathway; The set of target genes of the AR pathway comprises three or more target genes selected from the group including KLK2, PMEPA1, TMPRSS2, NKX3_1, ABCC4, KLK3, FKBP5, ELL2, UGT2B15, DHCR24, PPAP2A, NDRG1, LRIG1, CREB3L4, LCP1, GUCY1A3, AR, and EAF2; the set of target genes of the TGF beta 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; 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; The present invention relates to a method in which the set of target genes of the JAK-STAT3 pathway comprises three or more target genes selected from the group including 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.

[0192] This application describes certain preferred embodiments. Modifications and alterations will occur to others upon reading and understanding the above detailed description. It is intended that this application be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

[0193] Those skilled in the art can understand and effect other variations to the disclosed embodiments in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0194] It is to be understood that the method of the first aspect, the computer-implemented invention of the second aspect, the apparatus of the third aspect, the non-transitory recording medium of the fourth aspect, the computer program of the fifth aspect and the kit of the sixth aspect have similar and / or identical preferred embodiments, in particular as defined in the dependent claims.

[0195] In the claims, the word "comprise" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality.

[0196] A single unit or device fulfills the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0197] Calculations such as the determination of the risk of mortality performed by one or several units or devices can be performed by any other number of units or devices.

[0198] The computer program may be stored / distributed on a suitable medium, such as an optical recording medium or a solid-state medium, supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems.

[0199] It shall be understood that a preferred embodiment of the invention may also be any combination of the dependent claims or the above embodiments with the respective independent claim.

[0200] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter.

[0201] General: In all figures where signaling pathway analysis scores are depicted, these are given as the log2odds of pathway activity, derived from probability scores of pathway activity provided by Bayesian pathway model analysis. Log2odds scores indicate the level of signaling pathway activity on a linear scale.

[0202] The public datasets that were analysed are shown with their GSE numbers (usually at the bottom of each figure) and the individual samples are shown with their GSM numbers (usually in the right-most column of the cluster figures).

[0203] All validation samples for signaling pathway models or immune response / immune system models were independent samples and were not used for calibration of the respective models being validated. [Brief description of the drawings]

[0204] [Figure 1]Figure 1 shows the AR signaling pathway activity (top) and TGF-beta signaling pathway activity (bottom) in septic shock patients and healthy control subjects derived from the dataset GSE26440. The data were obtained from whole blood samples from septic shock patients (survivors), septic shock patients (non-survivors), control subjects (healthy subjects), and control subjects (non-sepsis survivors). The graph shows the log2 odds of the respective signaling pathway activities; statistical differences are shown above the bars, where "ns" (not significant) depicts a p-value of 5.00e^-02 < p <= 1.00e+00, * depicts a p-value of 1.00e^-02 < p <= 5.00e-0.2, ** depicts a p-value of 1.00e^-03 < p <= 1.00e-02, *** depicts a p-value of 1.00e^-04 < p <= 1.00e-03, and **** depicts a p-value of p <= 1.00e-04. [Diagram 2] Figure 2 shows the MPK-AP1 signaling pathway activity (top) and JAK-STAT3 signaling pathway activity (bottom) in septic shock patients and healthy control subjects derived from the dataset GSE26440. The data were obtained from whole blood samples from septic shock patients (survivors), septic shock patients (non-survivors), control subjects (healthy subjects), and control subjects (non-sepsis survivors). The graph shows the log2 odds of the respective signaling pathway activities; statistical differences are shown above the bars, where "ns" (not significant) depicts a p-value of 5.00e^-02 < p <= 1.00e+00, * depicts a p-value of 1.00e^-02 < p <= 5.00e-0.2, ** depicts a p-value of 1.00e^-03 < p <= 1.00e-02, *** depicts a p-value of 1.00e^-04 < p <= 1.00e-03, and **** depicts a p-value of p <= 1.00e-04. [Diagram 3]Figure 3 shows the AR signaling pathway activity (top) and TGF-beta signaling pathway activity (bottom) of septic shock patients and healthy control subjects derived from the dataset GSE4607. The data were obtained from whole blood samples from control subjects, septic shock patients (non-survivors), and septic shock patients (survivors). The graphs show the log2 odds of each signaling pathway activity; statistical differences are shown above the bars, where "ns" (not significant) depicts a p-value of 5.00e^-02 < p <= 1.00e+00, * depicts a p-value of 1.00e^-02 < p <= 5.00e-02, ** depicts a p-value of 1.00e^-03 < p <= 1.00e-02, *** depicts a p-value of 1.00e^-04 < p <= 1.00e-03, and **** depicts a p-value of p <= 1.00e-04. [Figure 4] Figure 4 shows the MPK-AP1 signaling pathway activity (top) and JAK-STAT3 signaling pathway activity (bottom) of septic shock patients and healthy control subjects derived from the dataset GSE4607. The data were obtained from whole blood samples from control subjects, septic shock patients (non-survivors), and septic shock patients (survivors). The graphs show the log2 odds of each signaling pathway activity; statistical differences are shown above the bars, where "ns" (not significant) depicts a p-value of 5.00e^-02 < p <= 1.00e+00, * depicts a p-value of 1.00e^-02 < p <= 5.00e-02, ** depicts a p-value of 1.00e^-03 < p <= 1.00e-02, *** depicts a p-value of 1.00e^-04 < p <= 1.00e-03, and **** depicts a p-value of p <= 1.00e-04. [Diagram 5]Figure 5 shows the AR signaling pathway activity (top) and TGF-beta signaling pathway activity (bottom) in septic shock patients and healthy control subjects derived from the dataset GSE66099. The data were obtained from whole blood samples from control subjects, septic shock patients (non-survivors), and septic shock patients (survivors). The graphs show the log2 odds of each signaling pathway activity; statistical differences are shown above the bars, where "ns" (not significant) depicts a p-value of 5.00e^-02 < p <= 1.00e+00, * depicts a p-value of 1.00e^-02 < p <= 5.00e-0.2, ** depicts a p-value of 1.00e^-03 < p <= 1.00e-02, *** depicts a p-value of 1.00e^-04 < p <= 1.00e-03, and **** depicts a p-value of p <= 1.00e-04. [Figure 6] Figure 6 shows the MPK-AP1 signaling pathway activity (top) and JAK-STAT3 signaling pathway activity (bottom) in septic shock patients and healthy control subjects derived from the dataset GSE66099. The data were obtained from whole blood samples from control subjects, septic shock patients (non-survivors), and septic shock patients (survivors). The graphs show the log2 odds of each signaling pathway activity; statistical differences are shown above the bars, where "ns" (not significant) depicts a p-value of 5.00e^-02 < p <= 1.00e+00, * depicts a p-value of 1.00e^-02 < p <= 5.00e-0.2, ** depicts a p-value of 1.00e^-03 < p <= 1.00e-02, *** depicts a p-value of 1.00e^-04 < p <= 1.00e-03, and **** depicts a p-value of p <= 1.00e-04. [Figure 7]Figure 7 shows the AR signaling pathway activity (top) and TGF-beta signaling pathway activity (bottom) of septic shock patients and healthy control subjects derived from the dataset GSE95233. The data were obtained from whole blood samples from control subjects (CS = healthy control subjects; PC = non-septic patient controls), septic shock patients (NS = non-survivors), and septic shock patients (SV = survivors). The graph shows the log2 odds of the respective signaling pathway activities; statistical differences are shown above the bars, where "ns" (not significant) depicts a p-value of 5.00e^-02 < p <= 1.00e+00, * depicts a p-value of 1.00e^-02 < p <= 5.00e-0.2, ** depicts a p-value of 1.00e^-03 < p <= 1.00e-02, *** depicts a p-value of 1.00e^-04 < p <= 1.00e-03, and **** depicts a p-value of p <= 1.00e-04. [Figure 8] Figure 8 shows the MPK-AP1 signaling pathway activity (top) and JAK-STAT3 signaling pathway activity (bottom) of septic shock patients and healthy control subjects derived from the dataset GSE95233. The data were obtained from whole blood samples from control subjects (CS = healthy control subjects; PC = non-septic patient controls), septic shock patients (NS = non-survivors), and septic shock patients (SV = survivors). The graph shows the log2 odds of the respective signaling pathway activities; statistical differences are shown above the bars, where "ns" (not significant) depicts a p-value of 5.00e^-02 < p <= 1.00e+00, * depicts a p-value of 1.00e^-02 < p <= 5.00e-0.2, ** depicts a p-value of 1.00e^-03 < p <= 1.00e-02, *** depicts a p-value of 1.00e^-04 < p <= 1.00e-03, and **** depicts a p-value of p <= 1.00e-04. [Figure 9]Figure 9 shows the cluster diagram of individual samples in the dataset GSE26440 based on AR and TGF beta signaling pathways. Grayscale coding represents the logarithmic scale of individual pathway scores. Hierarchical clustering was used. The color coding on the left depicts black = septic shock patients (survivors); light gray = septic shock patients (non-survivors); medium gray = normal controls; dark gray = control survivors. [Figure 10] Figure 10 shows the cluster diagram of individual samples in the dataset GSE26440 based on AR, TGF-beta and MAPK-AP1 signaling pathways. Grayscale coding represents the logarithmic scale of individual pathway scores. Hierarchical clustering was used. The color coding on the left depicts black = septic shock patients (survivors); light gray = septic shock patients (non-survivors); medium gray = normal controls; dark gray = control survivors. [Figure 11] Figure 11 shows the cluster diagram of individual samples in the dataset GSE26440 based on AR, TGF beta, MAPK-AP1 and JAK-STAT3 signaling pathways. Grayscale coding represents the logarithmic scale of individual pathway scores. Hierarchical clustering was used. The color coding on the left depicts black = septic shock patients (survivors); light gray = septic shock patients (non-survivors); medium gray = normal controls; dark gray = control survivors. [Figure 12] Figure 12 shows the cluster diagram of individual samples in the dataset GSE4607 based on AR and TGF beta signaling pathways. Grayscale coding represents the logarithmic scale of individual pathway scores. Hierarchical clustering was used. The color coding on the left depicts: black = control; light gray = septic shock patients (non-survivors); dark gray = septic shock patients (survivors). [Figure 13]Figure 13 shows the cluster diagram of individual samples in the dataset GSE4607 based on AR, TGF beta and MAPK-AP1 signaling pathways. Grayscale coding represents the logarithmic scale of individual pathway scores. Hierarchical clustering was used. The color coding on the left depicts black=control; light gray=septic shock patients (non-survivors); dark gray=septic shock patients (survivors). [Figure 14] Figure 14 shows the cluster diagram of individual samples in the dataset GSE4607 based on AR, TGF beta, MAPK-AP1 and JAK-STAT3 signaling pathways. Grayscale coding represents the logarithmic scale of individual pathway scores. Hierarchical clustering was used. The color coding on the left depicts black=control; light gray=septic shock patients (non-survivors); dark gray=septic shock patients (survivors). [Figure 15] Figure 15 shows the cluster diagram of individual samples in the dataset GSE66099 based on AR and TGF beta signaling pathway. Grayscale coding represents the logarithmic scale of individual pathway scores. Hierarchical clustering was used. The color coding on the left depicts black = septic shock patients; light gray = sepsis patients; dark gray = control subjects. [Figure 16] Figure 16 shows the cluster diagram of individual samples in the dataset GSE66099 based on AR, TGF beta and MAPK-AP1 signaling pathways. Greyscale coding represents the logarithmic scale of individual pathway scores. Hierarchical clustering was used. The color coding on the left depicts black = septic shock patients; light grey = sepsis patients; dark grey = control subjects. [Figure 17] Figure 17 shows cluster diagrams of individual samples in the dataset GSE66099 based on AR, TGFbeta, MAPK-AP1 and JAK-STAT3 signaling pathways. Greyscale coding represents the logarithmic scale of individual pathway scores. Hierarchical clustering was used. The color coding on the left depicts black = septic shock patients; light grey = sepsis patients; dark grey = control subjects. [Figure 18] Figure 18 shows the cluster diagram of individual samples in the dataset GSE95233 based on AR and TGF beta signaling pathway. Grayscale coding represents the logarithmic scale of individual pathway scores. Hierarchical clustering was used. The color coding on the left depicts black = septic shock patients; light gray = sepsis patients; dark gray = control subjects. [Figure 19] Figure 19 shows the cluster diagram of individual samples in the dataset GSE95233 based on AR, TGF beta and MAPK-AP1 signaling pathways. Grayscale coding represents the logarithmic scale of individual pathway scores. Hierarchical clustering was used. The color coding on the left depicts black = septic shock patients; light gray = sepsis patients; dark gray = control subjects. [Figure 20] Figure 20 shows the cluster diagram of individual samples in the dataset GSE95233 based on AR, TGF beta, MAPK-AP1 and JAK-STAT3 signaling pathways. Grayscale coding represents the logarithmic scale of individual pathway scores. Hierarchical clustering was used. The color coding on the left depicts: black = blood control; light gray = control survivors; medium gray = day 1 non-survivors; dark gray = day 1 survivors. [Figure 21] Figure 21 depicts pathway activity obtained from isolated THP-1 cells. THP-1 cells were incubated directly with H. pylori bacteria, incubated with H. pylori bacterial supernatant, and compared to control THP-1 cells. AR, ER, FOXO, Hedgehog, and TGF-beta pathway activity was determined and relative values ​​are plotted. [Figure 22] Figure 22 depicts pathway activity obtained from isolated THP-1 cells. THP-1 cells were incubated with different concentrations of a bacterial product, lipopolysaccharide (LPS), and compared to control THP-1 cells. The activity of AR, ER, FOXO, hedgehog, and TGF-beta pathways was determined, and the relative values ​​are plotted. [Figure 23-34]Box plots showing the predictive ability of subsets of genes. Each figure depicts a random selection of N genes with N=1, 2, 3, 4, 5 or 6 for different subsets of target genes of AR cell signaling pathway (Figures 23-26), TGF beta cell signaling pathway (Figures 27-30) or AR and TGF beta cell signaling pathway combined (Figures 31-34). Either the entire set of target genes was used (T=0, Figures 23, 27 and 31) or a cutoff was used to select a subset of target genes based on their contribution to the pathway activity score (T=0.3, 0.4 or 0.5, Figures 24-26, Figures 28-30, Figures 32-34). From each selected set of genes, 1000 random selections of N genes were performed to determine whether each gene selection could be used to distinguish sepsis patients from healthy subjects (at least 2 SD difference). The results are plotted in the form of box plots, where set 1 represents the combined dataset of GSE26440, GSE4607 and GSE66099, set 2 represents dataset GSE95233 and set 3 represents dataset GSE57065. The median is indicated by a bold line in the box plots, the 25th percentile is indicated by the lower boundary of the box plots and the 10th percentile is indicated by a dotted line. [Diagram 35] Figure 35 is a schematic diagram of AR inhibitor experiment. It depicts the experimental setup to determine whether AR inhibitors can be used to alleviate the LPS drop on monocytes (THP-1 cells). Briefly, monocyte cells (THP-1) are cultured with or without LPS for 24 hours, then the medium is replaced, and then both conditions are cultured with or without DHT. Both LPS and DHT are expected to activate AR cell signaling pathway. In parallel experiments, THP-1 cells are first cultured with LPS for 24 hours, then the medium is replaced, and cells are cultured with one of ARCC-4, ARD-266, A-458 and bicalutamide. [Diagram 36]Figure 36 shows the pathway activity determined in AR inhibitor experiments. Figure 36 describes the experimental results of different conditions outlined in Figure 35 in terms of measured cell signaling pathway activity. The figures depict the AR, ER, HH (hedgehog) and TGF beta cell signaling pathway activity determined in different experimental groups, respectively. The experiment was performed in triplicate. The standard deviation of measured activity is shown on the graph. [Figure 37] Figure 37 shows the pathway activity determined in AR inhibitor experiments. Figure 37 describes the experimental results of different conditions outlined in Figure 35 in terms of measured cell signaling pathway activity. The figures depict the AR, ER, HH (hedgehog) and TGF beta cell signaling pathway activity determined in different experimental groups, respectively. The experiments were performed in triplicate. The standard deviation of measured activity is shown on the graph. [Figure 38] Figure 38 shows the pathway activity determined in AR inhibitor experiments. Figure 38 describes the experimental results of different conditions outlined in Figure 35 in terms of measured cell signaling pathway activity. The figures depict the AR, ER, HH (hedgehog) and TGF beta cell signaling pathway activity determined in different experimental groups, respectively. The experiments were performed in triplicate. The standard deviation of measured activity is shown on the graph. [Figure 39] Figure 39 shows the pathway activity determined in AR inhibitor experiments. Figure 39 describes the experimental results of different conditions outlined in Figure 35 in terms of measured cell signaling pathway activity. The figures depict the AR, ER, HH (hedgehog) and TGF beta cell signaling pathway activity determined in different experimental groups, respectively. The experiments were performed in triplicate. The standard deviation of measured activity is shown on the graph. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0205] The following examples are merely illustrative of particularly preferred methods and selected aspects associated therewith. The teachings provided herein can be used to construct several tests and / or kits to, for example, detect, predict, and / or diagnose the functional status of one or more blood samples. Furthermore, when using the methods described herein, it is possible to advantageously guide the prescription of drugs, predict drug response, and monitor the effectiveness (and / or adverse effects) of drugs. The following examples should not be construed as limiting the scope of the present invention. EXAMPLES

[0206] Method and sample description Affymetrix HG-U133Plus2.0 data derived from samples from clinical and preclinical studies using whole blood samples (GSE26440, GSE4607, GSE66099, GSE95233, for more information on sample type and preparation, see Table 1) using the Gene Expression Omnibus (GEO) database (https: / / www.ncbi.nlm.gov / gds / ). Pathway analysis was used to determine signaling pathway activity (AR, ER, PR, GR, HH, Notch, TGF beta, WNT, JAK-STAT1 / 2, JAK-STAT3, NFκB, PI3K, MAPK). For hierarchical clustering, the clustering tool Seaborn Cluster Map was used.

[0207] For the analysis, public Affymetrix U133P2.0 data was used from the GEO database (GSE26440, GSE4607, GSE66099, GSE95233, see Table 1 for more information on sample type and preparation). Pathway analysis of datasets GSE26440, GSE4607, GSE66099 and GSE95233 showed significant differences in multiple pathways, including AR and TGF-beta pathway activity, between normal (healthy) control subjects and septic shock subjects using a two-tailed Mann-Whitney-Wilcoxon test (see Figures 1-8 for pathways and p-values).

[0208] Combinations of significant pathways could be used to identify / diagnose subjects with sepsis from controls.Furthermore, computational models were created to calculate risk scores for risk of dying from sepsis and risk of surviving sepsis based on AR and / or TGF-beta pathway activity.Hierarchical clustering was used to identify samples that clustered closer to controls / healthy people who were more likely to survive.

[0209] [Table 1] EXAMPLES

[0210] Computational model for calculating risk scores Furthermore, we were able to classify septic subjects into low, medium and high risk with respect to their risk of dying from sepsis. We used a computational model-based interpretation of multiple signaling pathway activity scores to classify septic subjects into low, medium and high risk.

[0211] To construct a linear model for the interpretation of pathway activity scores, pathway activity in healthy people was assessed by calculating the mean pathway activity with 1 and 2 standard deviations (SD). If pathway activity is outside the 2 SD boundary of normal health, it is considered an abnormally active pathway, which means 1 point in the model. If necessary, another threshold such as 3 SD of the mean can be used. The points are summed to generate a cumulative abnormal pathway activity score that directly determines the likelihood of risk.

[0212] Other computational models for calculating the risk score may be Bayesian models, centroid-based models, etc. EXAMPLES

[0213] Linear Models Using Calibration and Validation Sets For this purpose, the dataset GSE26440 was used as the training set model, and the dataset GSE4607 was used to validate the model. For both the AR and TGF-beta pathways, 2SD above the mean pathway activity score measured in the healthy control population was used for the classification model, and then this same value was applied to the independent validation dataset GSE4607. If both AR and TGF-beta were 2SD higher than the control sample, the sepsis subject was classified as high risk (2 points). If either AR or TGF-beta was 2SD higher than the control, the subject was classified as medium risk (1 point), and a difference of less than 2SD was classified as low risk (0 points). Please refer to Table 2 for the determined mean, standard deviation, and 2SD upper boundary for the pathways.

[0214] For the prognostic model, low, intermediate and high risk groups are identified for stratification of subjects. In the intermediate risk group, one of the pathways is upregulated, whereas in the high risk group, both pathways are upregulated.

[0215] The performance of the prognostic model is shown in Table 3. For the GSE26440 dataset (n=76, 10% non-survivors (n=8), 68% survivors (n=51), 22% controls (n=17)), the non-survivors group could be classified as 3 for high risk, 5 for medium risk and 0 for low risk. For the validation set GSE4607 (n=83, 17% non-survivors (n=14), 65% survivors (n=54) and 18% controls (n=15)), the above model could be used to classify the non-survivors group as 10 for high risk, 2 for medium risk and 2 for low risk.

[0216] Furthermore, because the combined AR and TGF beta pathway score was 2SD higher than the control sample, the combined AR and TGF beta pathway total score can also be used as a prognostic marker with high risk classification (1 point). If the combined AR and TGF beta score was less than 2SD different compared to the control, the sample was classified as low risk (0 point) (data not shown).

[0217] For other datasets (GSE66099, GSE95233 and GSE57064), we also see samples with low AR and / or TGF-beta pathway activity. However, we do not have survival data to prove that these subjects have a higher chance of surviving sepsis.

[0218] [Table 2]

[0219] [Table 3] EXAMPLES

[0220] Linear Model 2 - Using upper bound per dataset Due to the difference between tests and sample collection, it is probably more specific to determine the upper boundary per data set. Table 4 lists the mean, standard deviation and SD upper boundary of AR activity of GSE26440 and GSE4607. In Table 5, the above linear model is used, but in this embodiment, 2SD upper boundary is used based on each separate data set. All non-survivors are placed into medium risk group and high risk group. Control samples are only located in low risk group and can be used as diagnostic markers.

[0221] [Table 4]

[0222] [Table 5] EXAMPLES

[0223] Linear model 3 - using only AR paths and upper bounds per dataset The same principle as above is used in this example, only the 2SD upper boundary is used and the model is based only on 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 seen in Table 6. The control samples are only located in the low risk group and can be used as diagnostic markers.

[0224] [Table 6] EXAMPLES

[0225] Clustering Methods Hierarchical clustering (seaborn cluster map) was used to determine whether they could be grouped based on pathway activity of interest. Significant models between control and sepsis groups were selected for the AR, TGF-beta, JAK-STAT3, and MAPK-AP1 pathways.

[0226] For the data set GSE4607, some sepsis samples are clustered near the control group (orange). These subjects probably have a higher chance of survival. These samples are also clustered in the low-risk group in the above model that was based on AR and TGF beta. However, no clear distinction is shown between septic shock survivors and non-survivors. EXAMPLES

[0227] Cell stimulation with H. Pylori To investigate whether bacteria or bacterial products can induce the same pathway activity observed in patients with sepsis, in vitro experiments were performed in which bacteria or the bacterial product LPS were added to monocytic cells derived from a cell line as a model system for blood monocytes.

[0228] Porphyromonas gingivalis (ATCC, 33277) bacteria were cultured in an anaerobic culture hood using dehydrated HBI (Oxoid, CM1032) using the manufacturer's instructions.

[0229] THP-1 cells, 4x10 5Cells were cultured in 6-well plates at a density of 1:100 cells / well for 48 hours. 48 hours after cell seeding, cells were washed with PBS and treated with bacteria at 1:100 MOI directly or with 20% "supernatant" of 4-hour bacterial culture at 37°C, 5% CO2. 20% "supernatant" was prepared by exposing cells to bacteria at MOI of 1:100 and diluting overnight bacterial culture to 20% concentration in cell culture medium after filtering using a 0.2 μm filter to remove all bacteria. 48 hours after cell seeding, cells were washed with PBS and treated with bacteria at 1:100 MOI directly or with 20% "supernatant" of 4-hour bacterial culture at 37°C, 5% CO2. Cells were then washed with PBS, lysed in RNeasy mini kit lysis buffer (Qiagen, Cat. No. 74104) and stored at -80°C until further processing. RNA was extracted using RNeasy mini kit (Qiagen, 74104). qPCR was performed using the Philips Research OncoSignal platform.

[0230] To determine whether Helicobacter bacteria have a bacteria-specific effect on pathway activity in THP-1 cells, qRT-PCR was performed. Cells were treated with bacteria directly or with growth medium of bacterial cultures. As shown in Figure 21, pathway activity was increased for AR, FOXO, TGF-beta, and WNT pathways. However, no significant differences in the FOXO and WNT pathways were detected in sepsis samples. This is due to the fact that monocytes only comprise 4-8% of the blood composition, and other blood cell types also play important roles. EXAMPLES

[0231] Cell stimulation with LPS To study the inflammatory process, monocytic THP-1 cells (ATCC® TIB-202™) were stimulated with three different concentrations of LPS from E. coli (0 ng / ml, 10 ng / ml, 50 ng / ml and 100 ng / ml) in THP-1 culture medium (DMEM supplemented with 10% FBS, 1% Glutamax and 1% penicillin streptomycin, 37° C., 5% CO2). 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, pathway activity was increased for AR, FOXO, TGF beta and WNT pathways in LPS-stimulated cells. Activation of AR and TGF beta pathways was also observed in septic samples, confirming the role of these pathways in inflammation. However, no significant differences in FOXO and WNT pathways were detected in septic samples. This is due to the fact that monocytes only comprise 4-8% of the blood composition, while other blood cell types also play important roles. EXAMPLES

[0232] Validation of a subset of targeted genes To verify whether a subset of pathway target genes (e.g., 3 target genes selected from the whole) is still predictive, a random selection of N genes was performed for AR and TGF beta cell signaling pathway target genes to assess the chances that a random selection of N genes from the whole list is predictive. To do this, individual target genes of the AR and TGF beta cell signaling pathways 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 whole gene set, and subsequent higher values ​​for T correspond to more stringent selection), N genes were randomly selected 1000 times and a very simple linear model was used to calculate the score as shown below. Calculations were performed for N values ​​ranging from 1 to 6 on the combined datasets GSE26440, GSE4607 and GSE66099 (set 1), or GSE95233 (set 2), or GSE57065 (set 3). Furthermore, calculations were performed on AR target genes, TGF beta target genes and pooled AR and TGF beta target genes.

[0233] The protocol was carried out as follows: 1. Obtain a list of genes corresponding to the pathway of interest and obtain their probe sets. 2. Per gene, obtain the probe set that shows the highest 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 the candidate gene list by taking all genes that show their absolute probeset-pathway correlation above a threshold T. 4. Iteratively (1000 times), select a random sublist of N genes from the candidate gene list. 5. Create a simple linear classifier containing these N genes by assigning them a weight of +1 or -1 depending on the sign of the probeset-pathway correlation. 6. Apply the linear classifier to all samples and calculate a score. 7. For each test set, GSE26440, GSE4607 and GSE66099 combined, or GSE95233, or GSE57065, a. Determine the mean and standard deviation of the scores on the normal samples. b. Calculate the threshold by taking the mean plus twice 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, as a check, also the fraction of normal samples above the threshold. 8. A box plot distribution of the fractions determined over the 1000 random draws is generated.

[0234] For an example considering the AR and TGFB pathways, manually selected genes (Figure 33) and a correlation threshold of T=0.4 augmented with a random set of N=3 genes, the combined test set GSE26440, GSE4607 and GSE66099: - The median fraction of sepsis samples detected (thick line in box) is approximately 0.60, meaning that half of the random list would give a sensitivity of 60% or greater. - The 25th percentile of sepsis samples detected is approximately 0.32, meaning that three-quarters of a random list would give a sensitivity of 32% or greater. - The 10th percentile of sepsis samples detected is approximately 0.12, meaning that 90% of random listings would give a sensitivity of 12% or greater. - The specificity with the choice of threshold (mean+2stdev) is approximately 97.5%, which is confirmed by the low fraction observed for normal samples.

[0235] Box plots obtained from these subsets are depicted in Figures 23-34. From these data sets, it can be concluded that, depending on the data set used and the selection criteria for target genes, as few as one target gene is sufficient to distinguish between blood samples obtained from septic subjects and blood samples obtained from non-septic subjects, but in all cases, random selection with at least three genes results in a set of genes that shows high specificity and desirable sensitivity. Therefore, it can be concluded that a minimum of three target genes of the AR cell signaling pathway, the TGF beta cell signaling pathway, or pooled target genes from the AR and TGF beta cell signaling pathways (as defined herein) are sufficient to diagnose subjects with sepsis.

[0236] From these data, a diagnosis of sepsis can be reliably made based on the expression levels of three genes selected from the various sets of genes provided herein. Although the genes in each set were successfully identified using pathway models, this example shows that the use of pathway models is not required for diagnosis, and a diagnosis can be made purely based on expression levels.

[0237] The gene sets used in the analysis are as follows (the symbol in front of the gene name indicates a positive or negative correlation): AR-T=0 AR:+ABCC4, +APP, -AR, -CDKN1A, -CREB3L4, +DHCR24, +EAF2, +ELL2, +FGF8, +FKBP5, -GUCY1A3, +IGF1, -KLK2, -KLK3, +L CP1, -LRIG1, +NDRG1, -NKX3_1, -NTS, -PLAU, -PMEPA1, -PPAP2A, -PRKACB, +PTPN1, +SGK1, -TACC2, -TMPRSS2, -UGT2B15 AR--T=0.3 <h2 style=";text-align:left;direction:ltr">AR:-AR、-CREB3L4、+DHCR24、+EAF2、+ELL2、+FKBP5、-GUCY1A3、+IGF1、-KLK3、+LCP1、-LRIG1、+NDRG1、-NKX3_1、-PMEPA1、-PRKACB、-TMPRSS2<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> AR--T=0.4<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> AR:-AR、-CREB3L4、+DHCR24、+EAF2、+ELL2、+FKBP5、+LCP1、-LRIG1、+NDRG1、-PMEPA1、-PRKACB、-TMPRSS2<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> AR--T=0.5<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> AR:+DHCR24、+EAF2、+ELL2、+FKBP5、+LCP1、-PMEPA1、-PRKACB<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> TGFB-T=0<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> 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および+VEGFA<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> TGFB--T=0.3<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> TGFB:+CDC42EP3、+GADD45A、+GADD45B、+HMGA2、+ID1、+IL11、+INPP5D、+JUNB、-MMP2、+MMP9 、-NKX2_5、-OVOL1、-PDGFB、+PTHLH、+SGK1、+SKIL、+SMAD4、-SMAD5、+SMAD6、+TIMP1、+VEGFA<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> TGFB--T=0.4<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> TGFB:+CDC42EP3、+GADD45A、+GADD45B、+ID1、+JUNB、+MMP9、-PDGFB、+SGK1、+SKIL、-SMAD5、+SMAD6、+TIMP1、+VEGFA<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> TGFB--T=0.5<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">TGFB:+CDC42EP3、+GADD45A、+GADD45B、+ID1、+JUNB、+MMP9、-PDGFB、-+SGK1、-SMAD5、+TIMP1、+VEGFA<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> AR;TGFB--T=0<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> 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<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> 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および+VEGFA<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> AR;TGFB--T=0.3<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> AR:-AR、-CREB3L4、+DHCR24、+EAF2、+ELL2、+FKBP5、-GUCY1A3、+IGF1、-KLK3、+LCP1、-LRIG1、+NDRG1、-NKX3_1、-PMEPA1、-PRKACB、+SGK1、-TMPRSS2<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> TGFB:+CDC42EP3、+GADD45A、+GADD45B、+HMGA2、+ID1、+IL11、+INPP5D、+JUNB、-MMP2、+MMP9 、-NKX2_5、-OVOL1、-PDGFB、+PTHLH、+SGK1、+SKIL、+SMAD4、-SMAD5、+SMAD6、+TIMP1、+VEGFA<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> AR;TGFB--T=0.4<h2 style=";text-align:left;direction:ltr"> 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 EXAMPLES

[0238] Validation of AR inhibitors as a treatment option for sepsis Based on the above data, it is theorized that AR cell signaling pathway inhibitors can be administered to treat sepsis or at least alleviate its symptoms.As can be deduced from Example 7 and Example 8, and Figure 21 and Figure 22, AR and TGF beta cell signaling pathway activity increases upon stimulation with H pylori supernatant or LPS in monocytes (THP-1 cells).To confirm this hypothesis, this model system is used to predict the medical outcome of AR pathway inhibitors for treating sepsis.

[0239] Figure 35 describes the experimental setup used. Briefly, monocytic cells (THP-1) are cultured with or without LPS for 24 hours, then the medium is changed, and then both conditions are cultured with or without DHT. Both LPS and DHT are expected to activate the AR cell signaling pathway. In parallel experiments, THP-1 cells are first cultured with LPS for 24 hours, then the medium is changed, and cells are cultured with one of ARCC-4, ARD-266, A-458 and bicalutamide.

[0240] All experimental conditions are subjected to cell signaling pathway analysis. The measured ER, AR, HH, and TGF beta cell signaling pathway activities are shown in Figure 36-Figure 39, respectively. Figure 36 shows that LPS or DHT increases AR cell signaling pathway activity in monocytes, which appears to be a small additive effect. Furthermore, Figure 36 shows that the AR activity induced by LPS can be at least partially restored to baseline levels by the addition of AR pathway inhibitors.

[0241] Figures 37 and 38 show that ER and HH cell signaling pathway activity is not substantially affected by either LPS, DHT or AR pathway inhibitors, thus demonstrating that the effects shown in Figure 36 are specific.

[0242] Figure 39 shows that TGF beta signaling is also increased by LPS, which is consistent with other data shown herein that sepsis affects both AR and TGF beta pathways.As expected, DHT does not increase TGF beta activity.Interestingly, A-458 shows a reduction in LPS-induced TGF beta activity as well as a reduction in AR activity, suggesting that it functions as a dual AR / TGF beta inhibitor.As expected, the remaining AR inhibitors cannot alleviate the effect of LPS on TGF beta cell signaling pathway activity.

[0243] From these data, it is concluded that sepsis increases AR and TGF beta cell signaling pathway activity in blood cells, which can be detected in patient blood samples and used for rapid diagnosis of sepsis or prediction of patients at risk of developing sepsis. Furthermore, these data show that the elevated AR and TGF beta can be at least partially attributed to monocytes, and that the effect can be reproduced by adding LPS to cultured monocytes. Furthermore, these data show that the increase in AR signaling pathway activity induced by LPS can be reduced by AR pathway inhibitors, as shown by in vitro experiments using monocytes. This indicates that it is likely that AR inhibitors can be used to treat subjects with sepsis, or at least reduce (alleviate) their symptoms, provided that the patient has increased AR pathway activity or abnormal expression of sepsis-related genes. It further highlights the need for comparative studies to identify patients at risk of developing sepsis or patients with sepsis who would benefit from treatment with AR inhibitors. EXAMPLES

[0244] Screening for pathway inhibitors to prevent or treat sepsis in a THP-1 model for sepsis Introduction Sepsis is a life-threatening infection in which the immune response is dysregulated, leading to multiple organ dysfunction or failure.

[17] Sepsis is generally a complication of severe bacterial infection and is characterized by a systemic immune response, resulting in septic shock. Mortality rates range from 25-30% for sepsis and 40%-70% for septic shock.[4],

[18] ,

[19]

[0245] Apart from antibiotics and supportive measures to maintain splanchnic blood circulation, no treatments have proven effective, although it cannot be excluded that some treatments may benefit a so far unidentifiable subset of patients.

[17] One reason for the failure to develop effective treatments is the heterogeneity among septic patients, i.e., variations in underlying medical conditions and drug use, as well as genetic variations that affect the immune response in individual patients.

[0246] Detailed assessment of functional immune responses in patients with sepsis would enable personalized medicine approaches and improve the efficacy of treatment. Diagnostic assessment of immune function is currently limited to routine blood measurements such as immune cell counts and inflammatory markers (e.g., C-reactive protein), but does not provide information on the functional activity state of the various types of immune cells that are responsible for the abnormal immune response in patients with sepsis.

[0247] The functional state of immune cells is determined by a small number of so-called cell signaling pathways (STPs)

[20] ,

[21] ,

[22] ,

[23] ,

[24] . In recent years, novel assays have been developed to quantitatively measure the activity of STPs in cell and tissue samples, including blood samples

[25] ,

[26] ,

[27] ,

[28] . Measurement of the combined activity of these STPs in blood cells is expected to enable a quantitative assessment of the innate and adaptive immune responses in individual patients

[23] ,

[29] .

[0248] STP analysis was performed on publicly available gene expression data from multiple clinical sepsis trials.

[30] The studies showed that AR and TGF-beta activity was increased in septic patients compared to healthy controls, indicating that these pathways are novel drug targets for treating or preventing sepsis.

[0249] Treatment with pathway inhibitors, particularly AR and TGF beta pathway inhibitors, is beneficial for subjects with sepsis or at risk of developing sepsis. Treatment of patients with sepsis with AR inhibitors has been tried before, but without success. Based on our results, this can be explained by the fact that not all patients show high AR pathway activity, and therefore only patients with strongly increased AR pathway activity will benefit from treatment with AR pathway inhibitors. More importantly, patients with infections who are at risk of developing sepsis and have high AR pathway activity in their associated blood cells will benefit from preventive treatment with AR inhibitors. The rationale behind this assumption is that active AR pathway leads to immunosuppression. See, for example, Gubbels Bupp and Jorgensen [5], which is incorporated by reference in its entirety.

[0250] AR protein is expressed in various innate and adaptive immune cells, including neutrophils, macrophages, mast cells, monocytes, megakaryocytes, B cells, and T cells, suggesting that the AR pathway is indeed ligand-induced.Interestingly, AR protein is also expressed in hematopoietic stem cells, lymphocytes, and myeloid progenitor cells.Evidence from different studies points to the immunosuppressive role of androgens in different immune cell types, mainly by altering the expression of inflammatory and anti-inflammatory mediators that are important for proper immune response.

[0251] Consistent with the inflammatory response in sepsis, there is evidence that several immunosuppressive events occur in which immunosuppressive cytokines such as IL-10, TGF-β, and IL6 play a role. According to a clinical observational cohort study, septic patients develop chronic critical illness, have a 6-month survival rate of 63%, and continue to display a cytokine profile of chronic inflammation, as well as a biomarker profile characteristic of persistent immunosuppression

[31] . A study from Hiraki et al.

[32] used a mouse abdominal sepsis model in which a TGF-β depleting antibody was administered, which resulted in improved survival of the mice. This suggests a causal role of the TGF-β pathway in sepsis. Taken together, the AR and TGF-β pathways play a causal role in sepsis by modifying the immune response toward an immunosuppressive and proinflammatory state.

[0252] As already shown before, considering the clinical heterogeneity between sepsis patients, individualized treatment is likely to be necessary to obtain clinical benefit.Until now, it has not been possible to stratify patients who will benefit from AR or TGF beta pathway inhibitors to prevent or treat sepsis, but the method described herein allows accurate assessment of AR and TGF beta pathway activity in blood samples.This allows differentiation between patients with low and high AR and / or TGF beta pathway activity, and administration of inhibitors that inhibit AR and / or TGF beta to patients with high AR and / or TGF beta pathway activity to normalize pathway activity.

[0253] Screening of existing drugs for redevelopment in sepsis In order to develop novel rational therapies to prevent and treat sepsis in an individualized manner, several laboratory models of abnormal immune cell (monocyte) function in sepsis were investigated: Monocytic cell line (THP-1) stimulated with lipopolysaccharide (LPS) LPS-stimulated primary blood-derived monocytes LPS-stimulated PBMCs from healthy volunteers PBMCs from sepsis patients.

[0254] The effect of LPS on STP activity in a laboratory sepsis model was compared with previously described STP activity in blood cells (whole blood samples) of patients with sepsis. This analysis confirmed that LPS activates AR and TGFbeta STPs in a dose-dependent manner in THP-1 and primary monocytic cell models of sepsis (Tables 7-10). AR and TGFbeta pathway activity was induced by LPS only in monocytic cells and not in lymphoid cell lines (MOLT-4, see Table 10). As expected, LPS had no effect on the activity of these STPs in PBMCs, since monocytes constitute only 5-10% of the cells in the PBMC samples, the other cells being lymphocytes (70-90%), resulting in an over-dilution of the effect on monocytes

[33] . The difference in the results between monocytes and PBMCs also highlights the specificity of the measured differences in STP activity.

[0255] Addition of the original ligands for AR and TGF beta STP to the monocytic cell model also confirmed that the AR and TGF beta pathways can indeed be properly activated in monocytic cells, and thus are functional STPs in this cell type. Based on these results, AR and TGF beta STP are identified as potential drug targets for sepsis in monocytic cells.

[0256] We then proceeded to screen for drugs that could independently or in combination correct the abnormal AR and TGFbeta STP activity in these laboratory sepsis models and explored their potential use for two clinical applications: 1.Prevention of sepsis; 2. Treatment of established sepsis.

[0257] For the first application, THP-1 cells were pre-incubated with selected drugs for 24 hours, and then LPS was added for 24 hours. For treatment application, drugs and LPS were added to cells simultaneously for 24 hours. Importantly, some drugs need to be processed in the body to be effective, which is the case for some androgen receptor (AR) pathway inhibitors, such as bicalutamide. In this case, active variants of drugs were also tested (in the case of bicalutamide, (R)-bicalutamide).

[0258] The following drugs (or drug combinations) were tested in the THP-1 monocyte sepsis model by reducing LPS-induced activity of AR and / or TGFbeta STP: Bicalutamide and bactosertib were tested in the anti-sepsis model (Table 10). In sepsis treatment models, (R)-bicalutamide, ketodarolutamide (ORM-15341), D4-abiraterone, etanercept, resatorbid, bactosertib, N-desmethylenzalutamide, N-desmethyl-apultamide, R-bicalutamide + etanercept and R-bicalutamide + filgotinib (Table 11).

[0259] Effect of drugs on PBMC samples from septic patients The following drugs (or drug combinations) were tested in PBMCs and showed a reduction in AR and / or TGFbeta STP. For the TGFbeta pathway, the following compounds showed a significant reduction: bactosertib (p=0.0079), bactosertib+R-bicalutamide (p=0.0004), D4-abiraterone (p=0.0527), galunisertib (p=0.0013), D4-abiraterone+bactosertib (p=0.0040), bactosertib+etanercept (10 μg / ml) (p=0.0270) and D4-abiraterone+galunisertib (p=0.0075) using a paired one-tailed t-test (Table 13).

[0260] Both the AR and TGF-beta pathways were significantly lower for galunisertib (AR p=0.0302).

[0261] The following compounds showed a reduction in the AR pathway, but not significantly; bactosertib in 2 / 3 patients, bactosertib + R-bicalutamide in 3 / 3 patients and bactosertib + etanercept (10 μg / ml) in 4 / 5 patients (Table 13).

[0262] In septic patients, other factors besides LPS also play a role in the symptomatology, such as a reduced immunosuppressive state reflected by a reduced function of the adaptive immune system (lymphocytes) and an increased activity of the innate, inflammatory immune (monocyte lineage) system.

[0263] TGF-beta is known to induce an inflammatory state in monocytes and an immunosuppressive state in lymphocytes

[41] ,

[23] . The drugs (and combinations) described herein can be used to reduce the inflammatory phenotype of monocytes and the immunosuppressive phenotype of lymphocytes. Envisaged uses of these drugs to prevent and treat sepsis: It is important to consider that in critically ill septic patients, oral administration of drugs is not possible or preferred, given the inability to administer drugs orally and poor gastrointestinal drug absorption. In patients at risk for sepsis, oral administration is possible. Thus, potentially, orally active drugs can be used to prevent sepsis in infected patients. For the treatment of patients with sepsis or septic shock, the formulation should preferably be changed to an intravenous drug, or alternatively, it may be a subcutaneous or intramuscular formulation.

[0264] Thus, although all of these drugs are available for oral administration and can be repurposed for the prevention of sepsis and treatment of early sepsis, the development of alternative formulations (intravenous, subcutaneous, intramuscular) would be preferred for the treatment of severely ill septic patients. The most effective doses for both oral and intravenous / subcutaneous / intramuscular administration can be determined in routine dose-effect studies.

[0265] method: THP-1 sepsis model LPS is considered the main mediator of sepsis symptoms. We designed an in vitro experiment in which monocytes were stimulated with LPS and then treated with various inhibitors. Monocytes were chosen because they have been described as playing a major role in sepsis. See, for example, Sukhacheva,

[34] , Clinical Laboratory Int. 26 August 2020 or Haverman et al.

[15] , 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, pp. 132-141. To study inflammatory processes, monocytic THP-1 cells (ATCC® TIB-202™) were stimulated with DMSO or LPS (0.5 or 5 ng / ml, from E. coli) or LPS in combination with drugs in culture medium (DMEM supplemented with 10% FBS, 1% Glutamax and 1% Penicillin-Streptomycin, 37° C., 5% CO2).

[0266] Anti-sepsis model: THP-1 cells were pre-incubated with selected drugs or DMSO for 24 hours, then cultured with LPS or PBS for 24 hours. Compounds and concentrations are listed in Table 9.

[0267] Sepsis treatment model: For treatment application, drugs and LPS were added simultaneously to the cells for 24 hours. Compounds and concentrations are listed in Tables 11 and 12 for PBMCs of healthy volunteers.

[0268] MOLT-4 cell line: MOLT-4 (ATCC CRL-1582) was cultured in RPMI1640 medium supplemented with 10% FBS, 1% Glutamax, and 1% penicillin-streptomycin at 37° C. and 5% CO 2 .

[0269] RNA extraction and STP activity score After stimulation, cells were harvested and RNA was extracted using the RNeasy mini kit (Qiagen, 74104). qPCR-based STP activity scores were calculated using the Philips Research OncoSignal platform (www.philips.com / oncosignal) as described herein.

[0270] Measuring signaling pathway activity on Affymetrix microarray data derived from cell samples Pathway activity scores (PAS) were calculated from Affymetrix expression microarray data derived from the GEO database (https: / / www.ncbi.nlm.nih.gov / gds / )

[35] . For each signaling pathway, normalized PAS is presented on the log2odds scale. The log2odds scores for pathway activity are derived from probability scores for the activity of pathway-associated transcription factors calculated by computational models such as those described in

[36] ,

[37] ,

[38] .

[0271] Microarray data quality control Quality control (QC) was performed on the Affymetrix data of each individual sample as previously described

[38] . In summary, QC parameters included the average of all probe intensities, the presence of negative or extremely high (>16 bits) intensity values, polyA RNA (sample preparation spike-in) and labeled cRNA (hybridization spike-in) controls, GAPDH and ACTB 3' / 5' ratios, center of intensity, positive and negative boundary control values ​​determined by the affyQCReport package in R, and RNA degradation values ​​determined by the AffyRNAdeg function from the Affymetrix package in R

[39] ,

[40] . Sample data that failed QC were removed prior to data analysis.

[0272] PBMCs from healthy subjects and sepsis patients PBMC samples from healthy volunteers and septic patients were obtained by tissue lysis. Briefly, cryopreserved PBMCs were first washed with washing buffer (PBS / 1% BSA / 2mM EDTA), centrifuged, and the pellet was resuspended in culture medium (RPMI1640 with phenol red + 8.5% charcoal-stripped FBS + 1% Glx + 1% p / s). 0.5x10 per well were cultured. 5 ~1x10 6 Cells were seeded in 24-well plates and rested for 1 hour before treatment experiments. Ten PBMC samples from healthy donors were used to (1) measure baseline pathway levels and (2) activate with LPS.

[0273] Sepsis treatment model: PBMCs (n=20) from septic patients were treated with compound, DMSO or a combination of compounds for 24 hours (PBMCs from healthy subjects and septic patients were from the same hospital to minimize bias in sample preparation). After stimulation, cells were harvested and RNA was extracted using RNeasy mini kit (Qiagen, 74104). qPCR was performed using the Philips Research OncoSignal platform.

[0274] result: Functional AR and TGF beta STP in monocytic cells AR and TGF-beta pathways are functional in monocytic cells (Table 7). Testosterone induced the activity of AR pathway in THP-1 cells, which could be inhibited by bicalutamide. TGF-beta induced the activity of TGF-beta pathway activity, which was inhibited by a specific and known TGF-beta pathway inhibitor. In conclusion, both pathways were shown to be functional in monocytic cells.

[0275] [Table 7]

[0276] Monocytic / monocyte-based sepsis model LPS increases AR and TGF-beta STP activity in a monocytic / monocytic sepsis model In THP-1 monocytic cells, STP activity was increased in a dose-dependent manner for AR and TGF beta pathways in LPS-stimulated cells using 5 and 0.5 ng / ml LPS (Table 8A). The increase in AR, TGF beta STP activity by LPS was also observed in primary monocytes from human volunteers stimulated with 10 ng / ml LPS for 6 hours (dataset GSE84161 in GEO database) (Table 8B). AR and TGF beta pathway activity was only induced by LPS in monocytic cells, but not in lymphoid cell line (MOLT-4) (Table 9).

[0277] [Table 8]

[0278] [Table 9]

[0279] THP1 (A) and MOLT-4 (B) cell lines were stimulated with LPS, DHT or DMSO for 5 hours and then inhibited with bicalutamide or enzalutamide. Normalized. AR and TGFbeta STP activities are shown as normalized scores.

[0280] Drug Discovery: Drugs that inhibit LPS-induced AR and TGF-beta STP in the THP-1 sepsis model (prevention and treatment) Based on the above data, it was hypothesized that sepsis could be prevented and treated by administering drugs that inhibit AR and / or TGF beta cell signaling pathway activity.

[0281] Preventing Sepsis Monocyte cells were first cultured with 25 μM bicalutamide or DMSO for 24 h, followed by incubation with LPS, DHT or PBS. Experiments were performed in triplicate and repeated twice with robust results.

[0282] As shown in Table 10, pretreatment with bicalutamide alone did not result in an increase in either pathway compared to DMSO. Both LPS and DHT increase AR cell signaling pathway activity in monocytes, and LPS also activates TGFbeta. Pretreatment with bicalutamide followed by stimulation with LPS or DHT resulted in significantly lower activation of AR pathway activity by LPS compared to LPS without pretreatment (bicalutamide AR; p=0.04, DHT AR; p=0.0003, n=9).Interestingly, bicalutamide showed a reduction in the TGFbeta activity induced by LPS as well as a reduction in AR activity, suggesting that it functions as a dual AR / TGFbeta inhibitor.

[0283] [Table 10]

[0284] TGF-beta inhibitors for the prevention of sepsis Using a similar approach with AR inhibitors (first 24h of inhibitor, then 24h of stimulation with LPS) As shown in Table 10B, pretreatment with bactosertib alone did not result in an increase in either pathway compared to DMSO. LPS increased both AR and TGF-beta. Pretreatment with bactosertib followed by LPS stimulation resulted in significantly lower activation of TGF-beta pathway activity compared to no pretreatment (p=1.3E-05 one-tailed t-test). No effect of bactosertib was seen on the AR signaling pathway.

[0285] These results show that the increase in AR and TGF beta signaling pathway activity induced by LPS can be attenuated by AR pathway inhibitors, as shown by in vitro experiments using monocytes.TGF beta signaling pathway activity can be attenuated using TGF beta pathway inhibitors.This indicates that drugs / compounds that inhibit AR and TGF beta STP can be used to prevent sepsis in patients who are at high risk of developing sepsis.

[0286] Sepsis Treatment Two concentrations of LPS, 0.5 and 5 ng / ml, were used in the experiments, as well as higher (px-53-55) and lower (px-13-15) passage numbers for the THP-1 cell lines (Table 11). THP-1 cell lines with lower passage numbers were more responsive towards LPS activation compared to higher passage numbers (data not shown).

[0287] THP-1 monocyte sepsis model: For the AR signaling pathway, the following compounds; (R)-bicalutamide, ketodarolutamide, D4-abiraterone, etanercept, resatorbid, bactosertib, N-desmethyl-apalutamide and N-desmethylenzalutamide separately could reduce AR PAS, and the combinations of (R)-bicalutamide + etanercept and (R)-bicalutamide + filgotinib could reduce pathway activity scores in THP-1 monocytes (Table 11).

[0288] Regarding the TGF-beta signaling pathway, (R)-bicalutamide, D4-abiraterone, etanercept, filgotinib, resatorbidiol and bactosertib were able to reduce PAS (Table 11).

[0289] Both the AR and TGF-beta pathways could be reduced by etanercept, resatorbid and (R)-bicalutamide plus etanercept (Table 11).

[0290] [Table 11] TIFF2024514404000013.tif167170

[0291] PBMCs from healthy volunteers: PBMCs of healthy volunteers were stimulated with 5ng / ml LPS in combination with (R)-bicalutamide and (R)-bicalutamide + bactosertib for 24 hours (Experiment 1). In Experiment 2, (R)-bicalutamide and etanercept were tested with 0.5ng / ml LPS (Table 12A for Experiment 1 and Table 12B for Experiment 2). 0.5 and 5ng / ml LPS did not significantly increase AR and TGFbeta PAS, which was shown in the THP-1 sepsis model.

[0292] Therefore, the LPS stimulated PBMC model did not appear to be a good model for sepsis. For completeness, we added results regarding treatment with drugs.

[0293] In PBMC samples, monocytes are only 5-10% of the cells, whereas the majority of cells in PBMC are T cells, 40-60%. Perhaps monocytes are too diluted in the PBMC samples to see a clear overall effect of PAS increased by LPS. The effects of the following compounds may be indicative of monocytes or a combined effect on T cells.

[0294] (R)-bicalutamide + bactosertib showed significantly lower (paired t-test) AR (p=1E-05) and TGF-beta (p=5E-04) PAS compared to PBMC stimulated with LPS. (R)-bicalutamide showed a reduction in AR and TGF-beta in 3 of 6 healthy volunteers in experiment 1 and 3 of 4 in experiment 2 (experiment 2: bicalutamide + LPS: AR p=5E-02). Etanercept reduced AR PAS in 2 of 4 healthy volunteers and TGF-beta PAS in 3 of 4 (not significant).

[0295] [Table 12]

[0296] PBMC sepsis patients: To investigate the effects of selected drugs that were effective in the THP-1 sepsis model in neutralizing the stimulatory effects of LPS on AR and TGF-beta pathway activity, PBMCs from actual sepsis patients were subsequently treated in vitro with these drugs and drug combinations.

[0297] Although LPS-stimulated PBMCs from healthy volunteers were not considered to be a good laboratory model system for sepsis, we decided to investigate the effects on PBMCs derived from actual patients with sepsis for the following reasons: LPS is not the only abnormal factor present in the blood of septic patients; other bacteria-derived or LPS-induced secondary molecules, such as peptidoglycan (PepG) and lipoteichoic acid (LTA), are likely to affect STP activity in lymphoid immune cells in addition to monocytic cells. It was also previously hypothesized that at least a subset of patients with bacterial infections who develop sepsis are likely to have an immunosuppressed immune system

[41] ,

[23] . At least some lymphocytes show increased TGF-beta pathway activity and potentially AR pathway activity upon immunosuppression

[23] ,

[24] .

[0298] In total, 18 PBMC sepsis samples were treated with selected inhibitors (and combinations) for 24 hours, with 6 patient samples per experiment.

[0299] Experiment 1 (Table 13: A, B): bactosertib, bactosertib + R-bicalutamide, etanercept (10 μg / ml) and R-bicalutamide.

[0300] Experiment 2 (Table 13: C, D): resatorbidi, D4-abiraterone, A-485 and galunisertib.

[0301] Experiment 3 (Table 13: E, F): D4-abiraterone + bactosertib, R-bicalutamide + etanercept (10 μg / ml), bactosertib + etanercept (10 μg / ml) and D4-abiraterone + galnicertib.

[0302] Unfortunately, pathway activity could not be measured in all patients due to poor sample quality in septic patients, which also limited the statistical value due to the low sample size per experiment.

[0303] For the TGF-beta pathway, the following compounds showed a significant decrease in pathway activity: bactosertib (p=0.0079), bactosertib + R-bicalutamide (p=0.0004), D4-abiraterone (p=0.0527), galunisertib (p=0.0013), D4-abiraterone + bactosertib (p=0.0040), bactosertib + etanercept (10 μg / ml) (p=0.0270) and D4-abiraterone + galunisertib (p=0.0075).

[0304] Pathway activity scores for both the AR and TGF-beta pathways were significantly lower for galunisertib (AR p=0.0302).

[0305] The following compounds showed a reduction in the AR pathway, but the reduction was not significant; bactosertib in 2 / 3 patients, bactosertib + R-bicalutamide in 3 / 3 patients and bactosertib + etanercept (10 μg / ml) in 4 / 5 patients.

[0306] As mentioned above, in septic patients, other factors besides LPS also play a role in the symptomatology, such as a reduced immunosuppressive state reflected in a reduced function of the adaptive immune system (lymphocytes) and an increased activity of the innate, inflammatory immune (monocyte lineage) system.

[0307] TGF-beta is known to induce an inflammatory state in monocytes and an immunosuppressive state in lymphocytes

[23] . Using the drugs (and combinations) described herein, the inflammatory phenotype of monocytes and the immunosuppressive phenotype of lymphocytes can be reduced. Because an immunosuppressive state is believed to predispose to the development of sepsis and to the worsening clinical outcome of sepsis, effective drugs and drug combinations are likely to be effective both to prevent the development of sepsis in patients with bacterial infections and to improve the clinical outcome of sepsis by restoring the function of adaptive and innate immune responses [4]. Prevention of this state can prevent both sepsis as a long-term immunosuppressive effect.

[0308] [Table 13] TIFF2024514404000016.tif255152TIFF2024514404000017.tif83170

Claims

1. 1. An AR cell signaling pathway inhibitor for use in preventing or treating sepsis in a subject, comprising administering an AR cell signaling pathway inhibitor when AR cell signaling pathway activity determined in a blood sample of the subject is found to be elevated or above a certain threshold; AR cell signaling pathway inhibitors include bicalutamide, (R)-bicalutamide (HY-14249), enzalutamide (MDV3100), N-desmethylenzalutamide, proxalutamide (GT0918), apalutamide (ARN-509), N-desmethyl-apalutamide, darolutamide (ODM-201; BAY-1841788), ketodarolutamide (ORM-15341), galeterone (TOK-001), D4-abiraterone, A-485, mifepristone (RU486), cyproterone acetate, chlormadinone acetate, osaterone acetate, oxendolone, spironolactone, medrogestone, bicalutamide, flutamide, nilutamide , apalutamide, cimetidine, topirutamide, abiraterone acetate, ketoconazole, ceviteronel, dutasteride, alphatradiol, dutasteride, episteride, finasteride, A-485, ARCC-4, ARD-266, saw palmetto extract, leuprorelin, GnRH analogs, GnRH agonists (e.g., goserelin), GnRH antagonists, and progestogens (e.g., chlormadinone acetate, cyproterone acetate, gestorone caproate, medroxyprogesterone acetate, megestrol acetate), etanercept, resatorbidi, or combinations thereof.

2. Use, Determining AR cell signaling pathway activity in a blood sample from the subject; and administering an AR cell signaling pathway inhibitor to the patient if AR cell signaling pathway activity in the blood sample of the subject is found to be elevated or above a certain threshold. The AR cell signaling pathway inhibitor of claim 1 , comprising:

3. Determining the AR cell signaling pathway Determining or receiving the expression levels of three or more target genes of the AR signaling pathway in a blood sample from the subject; determining an activity level of an AR cell signaling pathway-associated transcription factor (TF) element in the sample, where the TF element controls the transcription of three or more target genes, and where the determining is based on evaluating a calibrated mathematical pathway model relating the expression levels of the three or more target genes to the activity level of the AR cell signaling pathway; and Inferring the activity of the AR cell signaling pathway in a blood sample from the subject based on the determined activity level of the AR cell signaling pathway-associated TF element. Including, The AR cell signaling pathway inhibitor of claim 2, wherein the three or more target genes are selected from 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.

4. The AR cell signaling pathway inhibitor according to any one of claims 1 to 3, wherein the AR cell signaling pathway inhibitor is selected from the group consisting of bicalutamide, (R)-bicalutamide (HY-14249), enzalutamide (MDV3100), N-desmethylenzalutamide, proxalutamide (GT0918), apalutamide (ARN-509), N-desmethyl-apalutamide, darolutamide (ODM-201; BAY-1841788), ketodarolutamide (ORM-15341), galeterone (TOK-001), D4-abiraterone, A-485, and mifepristone (RU486), etanercept, resatorbido, or a combination thereof.

5. 5. The AR cell signaling pathway inhibitor according to any one of claims 1 to 4, wherein the use is for the prevention of sepsis in a subject at risk of developing sepsis.

6. 5. The AR cell signaling pathway inhibitor according to any one of claims 1 to 4, wherein the use is for the treatment of sepsis in a subject suffering from sepsis.

7. 7. The AR cell signaling pathway inhibitor of any one of claims 1 to 6, wherein the AR cell signaling pathway inhibitor is administered together with a TGF beta cell signaling pathway inhibitor, and the AR cell signaling pathway inhibitor and the TGF beta cell signaling pathway inhibitor are the same compound or different compounds.

8. 8. The AR cell signaling pathway inhibitor of claim 7, wherein the AR cell signaling pathway inhibitor is administered prior to the TGF beta cell signaling pathway inhibitor, or the AR cell signaling pathway inhibitor is administered simultaneously with the TGF beta cell signaling pathway inhibitor, or the AR cell signaling pathway inhibitor is administered after the TGF beta cell signaling pathway inhibitor.

9. Use, Determining AR cell signaling pathway activity and TGF beta cell signaling pathway activity in a blood sample of the subject; administering an AR pathway inhibitor to the patient if AR cell signaling pathway activity is found to be elevated or above a certain threshold; and If TGF-beta cell signaling pathway activity is found to be elevated or above a certain threshold, administering a TGF-beta pathway inhibitor to the patient. Including, Determination of the TGF beta cell signaling pathway Determining or receiving an expression level of three or more target genes of the TGF beta signaling pathway in a blood sample of the subject; determining an activity level of a TGF beta cell signaling pathway-associated transcription factor (TF) element in the sample, the TF element controlling the transcription of three or more target genes, the determining being based on evaluating a calibrated mathematical pathway model relating the expression levels of the three or more target genes to the activity level of the TGF beta cell signaling pathway; and Inferring the activity of the TGF beta cell signaling pathway in a blood sample from the subject based on the determined activity level of the TGF beta cell signaling pathway-associated TF element. Including, The three or more target genes of TGF beta are selected from 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; 9. The AR cell signaling pathway inhibitor of any one of claims 1 to 8, wherein the TGF beta cell signaling pathway inhibitor is selected from the group consisting of bactosertib (EW-7197), galunisertib (LY2157299), LY3200882, (E)-SIS3, fresolimumab, XPA681, XPA089, LY2382770, LY3022859, ISTH0036, ISTH0047, pyrrole-imidazole polyamides or combinations thereof.

10. 10. The AR cell signaling pathway inhibitor of any one of claims 7 to 9, wherein the TGF beta cell signaling pathway inhibitor is selected from the group consisting of bactosertib (EW-7197), galunisertib (LY2157299), LY3200882, (E)-SIS3, or a combination thereof.

11. 1. A TGF beta cell signaling pathway inhibitor for use in preventing or treating sepsis in a subject, the TGF beta cell signaling pathway inhibitor being administered when TGF beta cell signaling pathway activity determined in a blood sample of the subject is found to be elevated or above a certain threshold, A TGF beta cell signaling pathway inhibitor selected from the group consisting of bactosertib (EW-7197), galunisertib (LY2157299), LY3200882, (E)-SIS3, fresolimumab, XPA681, XPA089, LY2382770, LY3022859, ISTH0036, ISTH0047, pyrrole-imidazole polyamides, or combinations thereof.

12. Use, Determining TGF beta cell signaling pathway activity in a blood sample of the subject; and administering a TGF beta cell signaling pathway inhibitor to the patient if TGF beta cell signaling pathway activity in the subject's blood sample is found to be elevated or above a certain threshold. The TGF beta cell signaling pathway inhibitor of claim 11, comprising:

13. Determination of the TGF beta cell signaling pathway Determining or receiving an expression level of three or more target genes of the TGF beta signaling pathway in a blood sample of the subject; determining an activity level of a TGF beta cell signaling pathway-associated transcription factor (TF) element in the sample, the TF element controlling the transcription of three or more target genes, the determining being based on evaluating a calibrated mathematical pathway model relating the expression levels of the three or more target genes to the activity level of the TGF beta cell signaling pathway; and Inferring the activity of the TGF beta cell signaling pathway in a blood sample from the subject based on the determined activity level of the TGF beta cell signaling pathway-associated TF element. Including, The TGF beta cell signaling pathway inhibitor of claim 12, wherein the three or more target genes of TGF beta are selected from 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.

14. 14. The TGF beta cell signaling pathway inhibitor of any one of claims 11 to 13, wherein the TGF beta cell signaling pathway inhibitor is selected from the group consisting of bactosertib (EW-7197), galunisertib (LY2157299), LY3200882, (E)-SIS3 or a combination thereof.

15. 15. The TGF beta cell signaling pathway inhibitor according to any one of claims 11 to 14, wherein the TGF beta cell signaling pathway inhibitor is administered together with an AR cell signaling pathway inhibitor, and the TGF beta cell signaling pathway inhibitor and the AR cell signaling pathway inhibitor are the same compound or different compounds.

16. 16. The TGF beta cell signaling pathway inhibitor of claim 15, wherein the TGF beta cell signaling pathway inhibitor is administered prior to the AR cell signaling pathway inhibitor, or the TGF beta cell signaling pathway inhibitor is administered simultaneously with the AR cell signaling pathway inhibitor, or the TGF beta cell signaling pathway inhibitor is administered after the AR cell signaling pathway inhibitor.

17. An AR cell signaling pathway inhibitor and a TGF beta cell signaling pathway inhibitor for use in preventing or treating sepsis in a subject, comprising: the TGF beta cell signaling pathway inhibitor and the AR cell signaling pathway inhibitor are different compounds; AR cell signaling pathway inhibitors include bicalutamide, (R)-bicalutamide (HY-14249), enzalutamide (MDV3100), N-desmethylenzalutamide, proxalutamide (GT0918), apalutamide (ARN-509), N-desmethyl-apalutamide, darolutamide (ODM-201; BAY-1841788), ketodarolutamide (ORM-15341), galeterone (TOK-001), D4-abiraterone, A-485, mifepristone (RU486), cyproterone acetate, chlormadinone acetate, osaterone acetate, oxendolone, spironolactone, medrogestone, bicalutamide, furuta amide, nilutamide, apalutamide, cimetidine, topirutamide, abiraterone acetate, ketoconazole, ceviteronel, dutasteride, alphatradiol, dutasteride, episteride, finasteride, A-485, ARCC-4, ARD-266, saw palmetto extract, leuprorelin, GnRH analogs, GnRH agonists (e.g., goserelin), GnRH antagonists, and progestogens (e.g., chlormadinone acetate, cyproterone acetate, gestorone caproate, medroxyprogesterone acetate, megestrol acetate), etanercept, resatorbid, or combinations thereof; AR cell signaling pathway inhibitors and TGF beta cell signaling pathway inhibitors, wherein the TGF beta cell signaling pathway inhibitor is selected from the group consisting of bactosertib (EW-7197), galunisertib (LY2157299), LY3200882, (E)-SIS3, fresolimumab, XPA681, XPA089, LY2382770, LY3022859, ISTH0036, ISTH0047, pyrrole-imidazole polyamides or combinations thereof.

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