Biomarker panels for guiding dysregulated host response therapy

Abstract

To provide a method for identifying a therapy recommendation for a subject exhibiting dysregulated host response.SOLUTION: A classification of the subject of subtype A, subtype B, or subtype C is obtained. The therapy recommendation for the subject is identified based at least in part on the classification. Responsive to the classification of the subject comprising the subtype A, the therapy recommendation can be no immunosuppressive therapy. Responsive to the classification of the subject comprising the subtype B, the therapy recommendation can be no therapy recommendation, immune stimulation therapy, suppression of immune regulation therapy, blocking of immune suppression therapy, blocking of complement activity therapy, and / or anti-inflammatory therapy. Responsive to the classification of the subject comprising the subtype C, the therapy recommendation can be no therapy recommendation, immune stimulation therapy, suppression of immune regulation therapy, blocking of immune suppression therapy, modulators of coagulation therapy, and / or modulators of vascular permeability therapy.SELECTED DRAWING: Figure 1A

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Patent Application No. 62 / 909,530, filed October 2, 2019, and U.S. Provisional Patent Application No. 63 / 009,331, filed April 13, 2020, the entire disclosures of which are each incorporated herein by reference in their entirety for all purposes. [Background technology]

[0002] background The host response is a complex pathophysiological process resulting from injury, such as infection, trauma, burns, and other injuries. A variety of host responses may manifest clinically, including immune responses, inflammatory responses, coagulopathic responses, and any other type of response to physical injury. In some cases, the host response to physical injury may malfunction, resulting in an acute, life-threatening syndrome. The term "dysregulated host response" referred to herein refers to a case in which the host response to physical injury malfunctions, resulting in an acute, life-threatening syndrome. For example, a dysregulated immune response to infection may manifest clinically as sepsis. As another example, a dysregulated immune response to a non-infectious injury, such as a burn, may manifest clinically as systemic inflammatory response syndrome (SIRS). 52 .

[0003] Sepsis is an acute, life-threatening syndrome caused by a dysregulated immune response to infection. 1,2 Approximately 1.7 million patients are diagnosed with sepsis each year 15 A recent study based on electronic medical record data from over 7 million admissions across 409 U.S. hospitals estimated the hospitalization rate for sepsis at 6%. 15 The average length of hospital stay for patients with sepsis is 75% longer than for most other conditions, and their mortality rate accounts for over 50% of hospital deaths. 16Sepsis ranks as one of the most expensive hospitalizations in the United States, accounting for approximately 13% of total hospitalization costs, or more than $24 billion in hospitalization costs. 16 The cost of sepsis increases based on the severity level of sepsis and the timing of clinical presentation (e.g., on admission or during hospitalization). Cases of sepsis not present on admission spend nearly twice as long in the hospital, in the intensive care unit, and on a ventilator compared with patients who present with sepsis on admission. 17 .

[0004] Beyond the initial recognition, according to the Surviving Sepsis Campaign guidelines, the foundation of early sepsis management is now based on five key measures known as the "1-hour bundle." The "1-hour bundle" includes: (1) lactate level measurement; (2) blood culture collection; (3) administration of broad-spectrum antibiotics; (4) fluid resuscitation with 30 ml / kg of crystalloids if hypotension or lactate ≥ 4 mmol / L; and (5) vasopressors to maintain mean arterial pressure ≥ 65 mmHg in patients who remain hypotensive during or after resuscitation. 18 .

[0005] After this initial treatment is administered, patients are evaluated frequently over the next few hours according to their clinical response. For patients with poor clinical response, further adjustments can be made regarding the amount of fluid administered and / or the selection of antibiotic therapy and measures for source management (e.g., device removal, surgical intervention, or additional investigation).

[0006] Despite the appropriate application of these measures, nearly 30% of septic patients remain hypotensive and require vasopressors to maintain a mean arterial pressure of ≥ 65 mmHg, leading to septic shock. 19 , sepsis subtypes and conditions with predicted in-hospital mortality rates greater than 40% 1Among patients with septic shock, nearly 40% remain without clinical improvement (refractory septic shock), defined as a systolic blood pressure <90 mmHg for more than 1 hour after both adequate fluid resuscitation and vasopressor therapy. In this population of patients with refractory septic shock, glucocorticoid therapy may provide improvement. 1 .

[0007] Corticosteroids remain a controversial treatment for patients with sepsis. Specifically, current guidelines provide a weak recommendation for corticosteroids in patients with sepsis by stating that both steroids and no steroids are reasonable management options. 20 .

[0008] Despite often promising preclinical studies, over 100 interventional trials have failed to demonstrate significantly improved survival among patients with sepsis, leaving clinicians with limited interventions and patients with mortality rates as high as 40% among those who develop septic shock. 4-12 .

[0009] Similar trends have also been observed in other manifestations of dysregulated host responses that are not caused by infection, such as SIRS, which can be caused by severe burns. Summary of the Invention

[0010] overview

[0003] Embodiments disclosed herein relate to methods, non-transitory computer-readable media, systems, and kits for determining a patient subtype, determining a treatment recommendation for the patient, and generating a treatment hypothesis for the patient subtype. In various embodiments described herein, the method includes analyzing quantitative data of one or more biomarker sets derived from a sample obtained from the patient using a patient subtype classifier. The patient subtype classifier outputs a classification for the patient that guides the determination of a treatment recommendation.

[0011] Disclosed herein is a method for determining a patient subtype, comprising obtaining or obtaining quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets, wherein Group 1 comprises biomarker 1, biomarker 2, and biomarker 3, wherein biomarker 1 is one or more of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, or MMP8, wherein biomarker 2 is one or more of SERPINB1 or GSPT1, and wherein biomarker 3 is one or more of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, or TOMM70A, and wherein Group 2 comprises biomarker 4, biomarker 5, and biomarker 6, and wherein biomarker 6 is one or more of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, or MMP8, wherein biomarker 2 is one or more of SERPINB1 or GSPT1, and wherein biomarker 3 is one or more of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, or TOMM70A. Biomarker 4 is one or more of ZNF831, MME, CD3G or STOM; biomarker 5 is one or more of ECSIT, LAT or NCOA4; biomarker 6 is one or more of SLC1A5, IGF2BP2 or ANXA3; group 3 includes biomarker 7, biomarker 8 and biomarker 9; biomarker 7 is one or more of C14orf159 or PUM2; biomarker 8 is one or more of EPB42 or is one or more of RPS6KA5, and biomarker 9 is one or more of EPB42 or GBP2; group 4 includes biomarker 10, biomarker 11 and biomarker 12, where biomarker 10 is one or more of MSH2, DCTD or MMP8, biomarker 11 is one or more of HK3, UCP2 or NUP88, and biomarker 12 is one or more of GABARAPL2 or CASP4;wherein group 5 comprises biomarker 13, biomarker 14, and biomarker 15, wherein biomarker 13 is one or more of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G, biomarker 14 is one or more of EPB42, GSPT1, LAT, HK3, or SERPINB1, and biomarker 15 is one or more of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and determining a classification of the subject based on the quantitative data using a patient subtype classifier.

[0012] In various embodiments, at least one biomarker set is Group 5 and biomarker 13 is one or more of STOM, MME, BNT3A2, or HLA-DPA1. In various embodiments, at least one biomarker set is Group 5 and biomarker 14 is one or more of EPB42, GSPT1, LAT, HK3, or SERPINB1. In various embodiments, at least one biomarker set is Group 5 and biomarker 15 is one or more of SLC1A5, IGF2BP2, or ANXA3.

[0013] Further disclosed herein is a method for determining a treatment recommendation for a patient, the method comprising: determining a therapeutic target for a patient selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, TOMM70A, ZNF831, MME, CD3G, STO obtaining or having obtained quantitative data for two or more biomarkers selected from the group consisting of M, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2, ANXA3, C14orf159, PUM2, EPB42, RPS6KA5, GBP2, MSH2, DCTD, HK3, UCP2, NUP88, GABARAPL2 and CASP4; and determining a classification of the subject based on the quantitative data using a patient subtype classifier.

[0014] Further disclosed herein is a method for determining a treatment recommendation for a patient, comprising obtaining or obtaining quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets, wherein Group 1 comprises two or more biomarkers selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, and TOMM70A; and Group 2 comprises ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2, and and ANXA3, Group 1 comprising two or more biomarkers selected from the group consisting of C14orf159, PUM2, EPB42, RPS6KA5, EPB42 and GBP2, Group 2 comprising two or more biomarkers selected from the group consisting of MSH2, DCTD, MMP8, HK3, UCP2, NUP88, GABARAPL2 and CASP4; and Group 3 comprising two or more biomarkers selected from the group consisting of MSH2, DCTD, MMP8, HK3, UCP2, NUP88, GABARAPL2 and CASP4; and Group 4 comprising two or more biomarkers selected from the group consisting of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, CD3G, EPB42, GSPT1, LAT, HK3, SERPINB1, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2 and TNFRSF1A; and determining classification of the subject based on the quantitative data using a patient subtype classifier. In various embodiments, the methods described herein further include identifying a treatment recommendation for the subject based at least in part on the classification.

[0015] Also disclosed herein is a method for determining a treatment recommendation for a patient, comprising obtaining a classification of a subject exhibiting host response dysregulation, wherein the classification comprises obtaining, or having obtained, quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets (wherein Group 1 comprises biomarker 1, biomarker 2, and biomarker 3, and wherein biomarker 1 is selected from the group consisting of EVL, BTN3A2, HL Biomarker 1 is one or more of A-DPA1, IDH3A, ACBD3, EXOSC10, SNRK or MMP8, biomarker 2 is one or more of SERPINB1 or GSPT1, biomarker 3 is one or more of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3 or TOMM70A, and group 2 is biomarker 4, biomarker 5 and biomarker 6, wherein biomarker 4 is one or more of ZNF831, MME, CD3G, or STOM, biomarker 5 is one or more of ECSIT, LAT, or NCOA4, and biomarker 6 is one or more of SLC1A5, IGF2BP2, or ANXA3; Group 3 includes biomarker 7, biomarker 8, and biomarker 9, wherein biomarker 7 is one or more of C14orf159 or PUM2, and biomarker Group 8 is one or more of EPB42 or RPS6KA5, and biomarker 9 is one or more of EPB42 or GBP2; Group 4 includes biomarker 10, biomarker 11, and biomarker 12, where biomarker 10 is one or more of MSH2, DCTD, or MMP8, biomarker 11 is one or more of HK3, UCP2, or NUP88, and biomarker 12 is one or more of GABARAPL2 or CASP4;Group 5 includes biomarker 13, biomarker 14, and biomarker 15, wherein biomarker 13 is one or more of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G; biomarker 14 is one or more of EPB42, GSPT1, LAT, HK3, or SERPINB1; and biomarker 15 is one or more of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and wherein the classification is determined using a patient subtype classifier based on the quantitative data; and identifying a treatment recommendation for the subject based at least in part on the classification.

[0016] In various embodiments, the subject's host response dysregulation comprises one of sepsis and a host response dysregulation not caused by infection. In various embodiments, the subject's classification comprises one of subtype A or subtype B. In various embodiments, the subject's classification comprises one of subtype A, subtype B, or subtype C. In various embodiments, in response to a subject's classification comprising subtype A, the treatment recommendation identified for the subject comprises at least no immunosuppressive therapy. In various embodiments, in response to a subject's classification comprising subtype A, the treatment recommendation identified for the subject further comprises at least no corticosteroid therapy. In various embodiments, the treatment recommendation identified for the subject further comprises no hydrocortisone.

[0017] In various embodiments, the treatment recommendation identified for the subject, depending on the classification of the subject as including subtype B, includes at least one of no treatment recommendation, immunostimulatory therapy, suppression of immunomodulatory therapy, blockade of immunosuppressive therapy, blockade of complement activation therapy, and anti-inflammatory therapy. In various embodiments, the treatment recommendation identified for the subject, depending on the classification of the subject as including subtype B, further includes at least one of a checkpoint inhibitor, a blocker of a complement component, a blocker of a complement component receptor, and a blocker of an inflammatory cytokine. In various embodiments, the treatment recommendation identified for the subject further comprises at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulator, IL-22 agonist, IFN-alpha regulator, IFN-lambda regulator, IFN-alpha 2b stimulator, anti-C5a, anti-C3a, anti-C5aR, anti-C3aR, anti-TNF-alpha and anti-IL-6, anti-HMGB1, ST2 antibody, IL-33 antibody.

[0018] In various embodiments, depending on the subject's classification as including subtype C, the therapy recommendation identified for the subject includes at least one of no therapy recommendation, immune stimulatory therapy, suppression of immunomodulatory therapy, blockade of immunosuppressive therapy, a modulator of coagulation therapy, and a modulator of vascular permeability therapy. In various embodiments, depending on the subject's classification as including subtype C, the therapy recommendation identified for the subject further includes at least one of a checkpoint inhibitor and an anticoagulant. In various embodiments, the therapy recommendation identified for the subject further includes at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a modulator, IL-22 agonist, IFN-alpha modulator, IFN-lambda modulator, IFN-alpha 2b stimulator, activated protein C, antithrombin, and thrombomodulin. In various embodiments, the method further includes administering or having administered a therapy to the subject based on the therapy recommendation.

[0019] In various embodiments, obtaining or obtaining quantitative data comprises obtaining a sample comprising a plurality of biomarkers from a subject exhibiting host response dysregulation; and determining quantitative data from the obtained sample. In various embodiments, the obtained sample comprises a blood sample from the subject. In various embodiments, the subject exhibiting host response dysregulation does not exhibit shock, and at least one set of biomarkers is one of Group 1, Group 3, or Group 4. In various embodiments, the subject exhibiting host response dysregulation further exhibits shock, and at least one set of biomarkers is one of Group 1, Group 2, Group 4, Group 5, Group 6, Group 7, or Group 8. In various embodiments, the subject exhibiting host response dysregulation is an adult subject, and at least one set of biomarkers is one of Group 1, Group 2, Group 3, Group 5, Group 6, Group 7, or Group 8. In various embodiments, the subject exhibiting host response dysregulation is a pediatric subject, and at least one set of biomarkers is one of Group 1, Group 4, Group 5, Group 6, Group 7, or Group 8.

[0020] In various embodiments, the quantitative data is determined by one of RT-qPCR (quantitative reverse transcription polymerase chain reaction), qPCR (quantitative polymerase chain reaction), PCR (polymerase chain reaction), RT-PCR (reverse transcription polymerase chain reaction), SDA (strand displacement amplification), RPA (recombinase polymerase amplification), MDA (multiple displacement amplification), HDA (helicase-dependent amplification), LAMP (loop-mediated isothermal amplification), RCA (rolling circle amplification), NASBA (nucleic acid sequence-based amplification) and any other isothermal or thermocycling amplification reaction.

[0021] In various embodiments, the quantitative data is determined by contacting the sample with a reagent; generating a plurality of complexes between the reagent and a plurality of biomarkers in the sample; and detecting the plurality of complexes to obtain a dataset associated with the sample, the dataset comprising the quantitative data. In various embodiments, the classification of the subject is determined by determining a classification-specific score for the subject with respect to at least one candidate classification of the subject; and determining the classification of the subject with a patient subtype classifier based on the classification-specific score.

[0022] In various embodiments, determining the classification-specific score comprises: determining a first subscore of quantitative data for the subject for one or more biomarkers of the candidate classification (wherein the quantitative data for the subject for one or more biomarkers of the candidate classification is increased compared to the quantitative data for the one or more biomarkers for one or more control subjects); determining a second subscore of quantitative expression for the subject for one or more additional biomarkers of the candidate classification (wherein the quantitative data for the subject for one or more additional biomarkers of the candidate classification is decreased compared to the quantitative data for the one or more additional biomarkers for one or more control subjects); and determining the difference between the first subscore and the second subscore (the first and second geometric subscores are optionally scaled, and the difference comprises the classification-specific score for the subject). In various embodiments, one or both of the first subscore and the second subscore are geometric means.

[0023] In various embodiments, the patient subtype classifier is a machine learning model. In various embodiments, the machine learning model is a support vector machine (SVM). In various embodiments, the support vector machine receives one or more classification-specific scores as input and outputs the classification of the subject. In various embodiments, the patient subtype classifier determines the classification of the subject by comparing the classification-specific scores with one or more thresholds; and determining the classification of the subject based on the comparison. In various embodiments, at least one of the one or more thresholds is a fixed value. In various embodiments, at least one of the one or more thresholds is determined using training samples, and at least one threshold represents the value on the ROC curve that is closest to the maximum sensitivity or maximum specificity.

[0024] In various embodiments, the methods disclosed herein further comprise normalizing the quantitative data based on quantitative data for one or more housekeeping genes prior to using the patient subtype classifier to determine the subject's classification. In various embodiments, the candidate subject classifications include subtype A, subtype B, and subtype C. In various embodiments, at least one biomarker set is Group 1, and the patient subtype classifier has an average accuracy of at least 82.93%. In various embodiments, at least one biomarker set is Group 2, and the patient subtype classifier has an average accuracy of at least 89.6%. In various embodiments, at least one biomarker set is Group 3, and the patient subtype classifier has an average accuracy of at least 86.3%. In various embodiments, at least one biomarker set is Group 4, and the patient subtype classifier has an average accuracy of at least 98.3%.

[0025] In various embodiments, the treatment recommendation identified for the subject further comprises corticosteroid therapy, no corticosteroid therapy, or no treatment recommendation. In various embodiments, the treatment recommendation comprises no corticosteroid therapy, and no corticosteroid therapy is identified by determining that the statistical significance of the reduction in mortality of subjects exhibiting host response dysregulation who are not provided with corticosteroid therapy is equal to or exceeds a threshold statistical significance. In various embodiments, the treatment recommendation comprises no corticosteroid therapy, and no corticosteroid therapy is identified by determining that the subject's classification comprises a subtype that is likely to respond poorly to corticosteroid therapy. In various embodiments, the subtype is subtype A or subtype C. In various embodiments, the treatment recommendation comprises corticosteroid therapy, and corticosteroid therapy is identified by determining that the statistical significance of the reduction in mortality of subjects exhibiting host response dysregulation who are provided with corticosteroid therapy is equal to or exceeds a threshold statistical significance.

[0026] In various embodiments, the treatment recommendation includes corticosteroid therapy, and the corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to respond well to corticosteroid therapy. In various embodiments, the subtype is subtype B.

[0027] In various embodiments, the identified treatment recommendation for the subject includes no treatment recommendation, and the no treatment recommendation is identified by at least determining that the statistical significance of the reduction in mortality of subjects exhibiting host response dysregulation who are not provided with corticosteroid therapy is less than a threshold statistical significance; and determining that the statistical significance of the reduction in mortality of subjects exhibiting host response dysregulation who are provided with corticosteroid therapy is less than a threshold statistical significance. In various embodiments, statistical significance includes a p-value, and the threshold statistical significance includes at least 0.1.

[0028] In various embodiments, the treatment recommendation identified for the subject includes no corticosteroid therapy, the host response dysregulation includes a host response dysregulation not caused by an infection, and at least one biomarker set is Group 1 or Group 4. In various embodiments, no corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to respond poorly to corticosteroid therapy. In various embodiments, the subtype is subtype A or subtype C.

[0029] In various embodiments, the treatment recommendation identified for the subject further comprises no treatment recommendation, wherein the no treatment recommendation is identified by determining that the subject's classification comprises subtype B. In various embodiments, the treatment recommendation identified for the subject comprises no corticosteroid therapy, the host response dysregulation comprises sepsis, and at least one biomarker set is one of Group 2, Group 3, or Group 4.

[0030] In various embodiments, the absence of corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to respond poorly to corticosteroid therapy. In various embodiments, the subtype is subtype A. In various embodiments, the treatment recommendation identified for the subject further includes no treatment recommendation, and the absence of treatment recommendation is identified by determining that the subject's classification includes a subtype that is likely to not respond to corticosteroid therapy. In various embodiments, the subtype is subtype B or subtype C.

[0031] In various embodiments, the treatment recommendation identified for the subject further includes no corticosteroid therapy, and the host response dysregulation includes a host response dysregulation not caused by an infection, and at least one biomarker set is Group 2. In various embodiments, no corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to respond poorly to corticosteroid therapy. In various embodiments, the subtype is subtype C.

[0032] In various embodiments, the treatment recommendation identified for the subject further includes no treatment recommendation, where the no treatment recommendation is identified by determining that the subject's classification includes a subtype that is likely to be unresponsive to corticosteroid therapy. In various embodiments, the subtype is subtype A or subtype B. In various embodiments, the treatment recommendation identified for the subject includes no corticosteroid therapy, where the host response dysregulation includes a host response dysregulation not caused by an infection, and at least one biomarker set is group 3. In various embodiments, no corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to be unresponsive to corticosteroid therapy. In various embodiments, the subtype is subtype A or subtype C. In various embodiments, the treatment recommendation identified for the subject further includes corticosteroid therapy, where the corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to be responsive to corticosteroid therapy. In various embodiments, the subtype is subtype B.

[0033] Further disclosed herein is a method for identifying a candidate treatment, the method comprising: accessing a differentially expressed gene database comprising the fold change in gene level between patients of different subtypes; determining a threshold number of differentially expressed genes in at least a first subtype patient compared to a second subtype patient, wherein each differentially expressed gene is involved in a common biological pathway; and determining a candidate treatment that is likely to be effective for patients of the first subtype, wherein the candidate treatment is effective in modulating the expression of at least one of the differentially expressed genes in patients of the first subtype.In various embodiments, the differentially expressed gene database is generated by obtaining labeled patient data, where the label of the labeled patient data identifies patients classified into one of two or more subtypes; and generating a differentially expressed gene database for at least one or more genes by determining the fold change in gene level between at least the patient data with a label indicating the first subtype and the patient data with a label indicating the second subtype. In various embodiments, the labels of the labeled patient data are generated by applying cluster analysis or by applying a patient subtype classifier. In various embodiments, the threshold number of genes is at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, at least 9 genes, or at least 10 genes. In various embodiments, determining candidate therapies for patients of the first subtype further includes analyzing one or both of pharmacokinetic data including data on candidate therapies; and host response pathobiology data including data on patients of the first subtype.

[0034] Further disclosed herein is a non-transitory computer-readable medium for determining a patient subtype, which, when executed by a processor, causes the processor to obtain quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets, where Group 1 includes biomarker 1, biomarker 2, and biomarker 3, where biomarker 1 is one or more of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, or MMP8, where biomarker 2 is one or more of SERPINB1 or GSPT1, and where biomarker 3 is one or more of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, or TOMM70A, and Group 2 includes biomarker 4, biomarker 5, and biomarker 6. Group 3 includes biomarker 6, wherein biomarker 4 is one or more of ZNF831, MME, CD3G or STOM, biomarker 5 is one or more of ECSIT, LAT or NCOA4, and biomarker 6 is one or more of SLC1A5, IGF2BP2 or ANXA3; Group 3 includes biomarker 7, biomarker 8 and biomarker 9, wherein biomarker 7 is one or more of C14orf159 or PUM2, and biomarker 8 is one or more of Group 4 includes biomarker 10, biomarker 11, and biomarker 12, wherein biomarker 10 is one or more of MSH2, DCTD, or MMP8, biomarker 11 is one or more of HK3, UCP2, or NUP88, and biomarker 12 is one or more of GABARAPL2 or CASP4;Group 5 includes biomarker 13, biomarker 14, and biomarker 15, where biomarker 13 is one or more of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G, biomarker 14 is one or more of EPB42, GSPT1, LAT, HK3, or SERPINB1, and biomarker 15 is one or more of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; a non-transitory computer-readable medium containing instructions for causing a patient subtype classifier to determine a classification of a subject based on the quantitative data;

[0035] In various embodiments, at least one biomarker set is Group 5 and biomarker 13 is one or more of STOM, MME, BNT3A2, or HLA-DPA1. In various embodiments, at least one biomarker set is Group 5 and biomarker 14 is one or more of EPB42, GSPT1, LAT, HK3, or SERPINB1. In various embodiments, at least one biomarker set is Group 5 and biomarker 15 is one or more of SLC1A5, IGF2BP2, or ANXA3.

[0036] Further disclosed herein is a non-transitory computer readable medium for determining a treatment recommendation for a patient, the medium, when executed by a processor, causing the processor to: A non-transitory computer-readable medium comprising instructions for obtaining quantitative data for two or more biomarkers selected from the group consisting of ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2, ANXA3, C14orf159, PUM2, EPB42, RPS6KA5, GBP2, MSH2, DCTD, HK3, UCP2, NUP88, GABARAPL2 and CASP4; and determining a classification of the subject based on the quantitative data using a patient subtype classifier.

[0037] Further disclosed herein is a non-transitory computer readable medium for determining a therapy recommendation for a patient, which, when executed by a processor, causes the processor to obtain quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets, where Group 1 comprises two or more biomarkers selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, and TOMM70A; and Group 2 comprises ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A. Group 5, IGF2BP2, and ANXA3; Group 3 comprises two or more biomarkers selected from the group consisting of C14orf159, PUM2, EPB42, RPS6KA5, EPB42, and GBP2; Group 4 comprises two or more biomarkers selected from the group consisting of MSH2, DCTD, MMP8, HK3, UCP2, NUP88, GABARAPL2, and CASP4; and Group 5 comprises two or more biomarkers selected from the group consisting of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, CD3G, EPB42, GSPT1, LAT, HK3, SERPINB1, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, and TNFRSF1A); a non-transitory computer-readable medium comprising instructions for causing a patient subtype classifier to determine a classification of a subject based on quantitative data. In various embodiments, the instructions further include instructions that, when executed by the processor, cause the processor to identify a treatment recommendation for the subject based at least in part on the classification.

[0038] Also disclosed herein is a non-transitory computer readable medium for determining a treatment recommendation for a subject, which, when executed by a processor, causes the processor to obtain a classification of the subject exhibiting host response dysregulation, wherein the classification comprises obtaining, or having obtained, quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets (wherein Group 1 comprises biomarker 1, biomarker 2, and biomarker 3; Biomarker 1 is one or more of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK or MMP8, biomarker 2 is one or more of SERPINB1 or GSPT1, biomarker 3 is one or more of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3 or TOMM70A, and group 2 is biomarker 1. Group 3 includes biomarker 4, biomarker 5 and biomarker 6, where biomarker 4 is one or more of ZNF831, MME, CD3G or STOM, biomarker 5 is one or more of ECSIT, LAT or NCOA4, and biomarker 6 is one or more of SLC1A5, IGF2BP2 or ANXA3; Group 3 includes biomarker 7, biomarker 8 and biomarker 9, where biomarker 7 is one or more of C14orf159 or PUM2; Biomarker 8 is one or more of EPB42 or RPS6KA5, and biomarker 9 is one or more of EPB42 or GBP2; Group 4 includes biomarker 10, biomarker 11, and biomarker 12, where biomarker 10 is one or more of MSH2, DCTD, or MMP8, biomarker 11 is one or more of HK3, UCP2, or NUP88, and biomarker 12 is one or more of GABARAPL2 or CASP4;Group 5 includes biomarker 13, biomarker 14, and biomarker 15, where biomarker 13 is one or more of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G, biomarker 14 is one or more of EPB42, GSPT1, LAT, HK3, or SERPINB1, and biomarker 15 is one or more of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and determined by using a patient subtype classifier to determine a classification based on the quantitative data; and a non-transitory computer-readable medium comprising instructions for identifying a treatment recommendation for the subject based at least in part on the classification;

[0039] In various embodiments, the subject's host response dysregulation comprises one of sepsis and a host response dysregulation not caused by infection. In various embodiments, the subject's classification comprises one of subtype A or subtype B. In various embodiments, the subject's classification comprises one of subtype A, subtype B, or subtype C. In various embodiments, in response to a subject's classification comprising subtype A, the treatment recommendation identified for the subject comprises at least no immunosuppressive therapy. In various embodiments, in response to a subject's classification comprising subtype A, the treatment recommendation identified for the subject further comprises at least no corticosteroid therapy. In various embodiments, the treatment recommendation identified for the subject further comprises no hydrocortisone.

[0040] In various embodiments, the treatment recommendation identified for the subject, depending on the classification of the subject as including subtype B, includes at least one of no treatment recommendation, immunostimulatory therapy, suppression of immunomodulatory therapy, blockade of immunosuppressive therapy, blockade of complement activation therapy, and anti-inflammatory therapy. In various embodiments, the treatment recommendation identified for the subject, depending on the classification of the subject as including subtype B, includes at least one of a checkpoint inhibitor, a blocker of a complement component, a blocker of a complement component receptor, and a blocker of an inflammatory cytokine. In various embodiments, the treatment recommendation identified for the subject further comprises at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulator, IL-22 agonist, IFN-alpha regulator, IFN-lambda regulator, IFN-alpha 2b stimulator, anti-C5a, anti-C3a, anti-C5aR, anti-C3aR, anti-TNF-alpha and anti-IL-6, anti-HMGB1, ST2 antibody, IL-33 antibody. In various embodiments, depending on the subject's classification, including subtype C, the treatment recommendation identified for the subject comprises at least one of no treatment recommendation, immunostimulatory therapy, suppression of immunomodulatory therapy, blockade of immunosuppressive therapy, modulator of coagulation therapy, and modulator of vascular permeability therapy.

[0041] In various embodiments, in response to a subject's classification including subtype C, the treatment recommendation identified for the subject further comprises at least one of a checkpoint inhibitor and an anticoagulant. In various embodiments, the treatment recommendation identified for the subject further comprises at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulator, IL-22 agonist, IFN-alpha regulator, IFN-lambda regulator, IFN-alpha 2b stimulator, activated protein C, antithrombin, and thrombomodulin.

[0042] In various embodiments, the instructions that cause the processor to obtain the quantitative data further comprise instructions that, when executed by the processor, cause the processor to obtain a sample from a subject exhibiting host response dysregulation, the sample comprising a plurality of biomarkers; and determine the quantitative data from the obtained sample. In various embodiments, the obtained sample comprises a blood sample from the subject.

[0043] In various embodiments, the subject exhibiting a dysregulated host response does not exhibit shock, and at least one set of biomarkers is one of Group 1, Group 3, or Group 4. In various embodiments, the subject exhibiting a dysregulated host response further exhibits shock, and at least one set of biomarkers is one of Group 1, Group 2, Group 4, Group 5, Group 6, Group 7, or Group 8. In various embodiments, the subject exhibiting a dysregulated host response is an adult subject, and at least one set of biomarkers is one of Group 1, Group 2, Group 3, Group 5, Group 6, Group 7, or Group 8. In various embodiments, the subject exhibiting a dysregulated host response is a pediatric subject, and at least one set of biomarkers is one of Group 1, Group 4, Group 5, Group 6, Group 7, or Group 8. In various embodiments, the quantitative data is determined by one of RT-qPCR (quantitative reverse transcription polymerase chain reaction), qPCR (quantitative polymerase chain reaction), PCR (polymerase chain reaction), RT-PCR (reverse transcription polymerase chain reaction), SDA (strand displacement amplification), RPA (recombinase polymerase amplification), MDA (multiple displacement amplification), HDA (helicase-dependent amplification), LAMP (loop-mediated isothermal amplification), RCA (rolling circle amplification), NASBA (nucleic acid sequence-based amplification) and any other isothermal or thermocycling amplification reaction.

[0044] In various embodiments, the quantitative data is determined by contacting the sample with a reagent; forming a plurality of complexes between the reagent and a plurality of biomarkers in the sample; and detecting the plurality of complexes to obtain a dataset associated with the sample that includes the quantitative data.

[0045] In various embodiments, the classification of a subject is determined by: determining a classification-specific score for the subject for at least one candidate classification of the subject; and using a patient subtype classifier to determine the classification of the subject based on the classification-specific score.In various embodiments, the instruction for causing a processor to determine a classification-specific score, when executed by the processor, further comprises the following instructions: determine a first subscore of the quantitative data for the subject for one or more biomarkers of the candidate classification (wherein the quantitative data for the subject for one or more biomarkers of the candidate classification is increased compared with the quantitative data for one or more biomarkers for one or more control subjects); determine a second subscore of the quantitative expression for the subject for one or more additional biomarkers of the candidate classification (wherein the quantitative data for the subject for one or more additional biomarkers of the candidate classification is decreased compared with the quantitative data for one or more additional biomarkers for one or more control subjects); determine the difference between the first subscore and the second subscore (the first and second geometric subscores are optionally scaled, and the difference comprises the classification-specific score for the subject).In various embodiments, one or both of the first subscore and the second subscore is a geometric mean value. In various embodiments, the patient subtype classifier is a machine learning model. In various embodiments, the machine learning model is a support vector machine (SVM). In various embodiments, the support vector machine receives one or more classification-specific scores as input and outputs a classification of the subject.

[0046] In various embodiments, the patient subtype classifier compares classification-specific scores with one or more thresholds; and determines the classification of the subject based on the comparison.In various embodiments, at least one of the one or more thresholds is fixed.In various embodiments, at least one of the one or more thresholds is determined using training samples, and at least one threshold represents the value on the ROC curve that is closest to maximum sensitivity or maximum specificity.In various embodiments, before using the patient subtype classifier to determine the classification of the subject, the quantitative data is further normalized based on the quantitative data of one or more housekeeping genes.

[0047] In various embodiments, the candidate subject classifications include subtype A, subtype B, and subtype C. In various embodiments, at least one biomarker set is Group 1, and the patient subtype classifier has an average accuracy of at least 82.93%. In various embodiments, the patient subtype classifier has an average accuracy of at least 89.6%. In various embodiments, the patient subtype classifier has an average accuracy of at least 86.3%. In various embodiments, at least one biomarker set is Group 4, and the patient subtype classifier has an average accuracy of at least 98.3%.

[0048] In various embodiments, the treatment recommendation identified for the subject further comprises corticosteroid therapy, no corticosteroid therapy, or no treatment recommendation.In various embodiments, the treatment recommendation comprises no corticosteroid therapy, and no corticosteroid therapy is identified by determining that the statistical significance of the reduction in mortality of the subject who is not provided with corticosteroid therapy and shows host response dysregulation is equal to or exceeds a threshold statistical significance.In various embodiments, the treatment recommendation comprises no corticosteroid therapy, and no corticosteroid therapy is identified by determining that the subject's classification comprises a subtype that is likely to respond poorly to corticosteroid therapy.In various embodiments, the subtype is subtype A or subtype C.

[0049] In various embodiments, the treatment recommendation includes corticosteroid therapy, and the corticosteroid therapy is identified by determining that the statistical significance of the reduction in mortality rate of subjects who are provided with corticosteroid therapy and exhibit host response dysregulation is equal to or exceeds a threshold statistical significance. In various embodiments, the treatment recommendation includes corticosteroid therapy, and the corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to respond well to corticosteroid therapy. In various embodiments, the subtype is subtype B.

[0050] In various embodiments, the identified treatment recommendation for the subject includes no treatment recommendation, and the no treatment recommendation is identified by at least determining that the statistical significance of the reduction in mortality of subjects exhibiting host response dysregulation who are not provided with corticosteroid therapy is less than a threshold statistical significance; and determining that the statistical significance of the reduction in mortality of subjects exhibiting host response dysregulation who are provided with corticosteroid therapy is less than a threshold statistical significance. In various embodiments, statistical significance includes a p-value, and the threshold statistical significance includes at least 0.1.

[0051] In various embodiments, the treatment recommendation identified for the subject includes no corticosteroid therapy, the host response dysregulation includes a host response dysregulation not caused by an infection, and at least one biomarker set is Group 1 or Group 4. In various embodiments, no corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to respond poorly to corticosteroid therapy. In various embodiments, the subtype is subtype A or subtype C.

[0052] In various embodiments, the identified treatment recommendation for the subject further includes no treatment recommendation, where the no treatment recommendation is identified by determining that the subject's classification includes subtype B. In various embodiments, the identified treatment recommendation for the subject includes no corticosteroid therapy, the host response dysregulation includes sepsis, and the at least one biomarker set is one of Group 2, Group 3, or Group 4. In various embodiments, the no corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to respond poorly to corticosteroid therapy. In various embodiments, the subtype is subtype A.

[0053] In various embodiments, the identified treatment recommendation for the subject further comprises no treatment recommendation, wherein the no treatment recommendation is identified by determining that the subject's classification comprises a subtype that is likely to be non-responsive to corticosteroid therapy. In various embodiments, the subtype is subtype B or subtype C.

[0054] In various embodiments, the treatment recommendation identified for the subject further includes no corticosteroid therapy, and the host response dysregulation includes a host response dysregulation not caused by an infection, and at least one biomarker set is Group 2. In various embodiments, no corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to respond poorly to corticosteroid therapy. In various embodiments, the subtype is subtype C.

[0055] In various embodiments, the identified treatment recommendation for the subject further comprises no treatment recommendation, wherein the no treatment recommendation is identified by determining that the subject's classification comprises a subtype that is likely to be non-responsive to corticosteroid therapy. In various embodiments, the subtype is subtype A or subtype B.

[0056] In various embodiments, the treatment recommendation identified for the subject includes no corticosteroid therapy, the host response dysregulation includes a host response dysregulation not caused by an infection, and at least one biomarker set is Group 3. In various embodiments, no corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to be unresponsive to corticosteroid therapy. In various embodiments, the subtype is subtype A or subtype C.

[0057] In various embodiments, the treatment recommendation identified for the subject further includes corticosteroid therapy, wherein the corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to be responsive to corticosteroid therapy. In various embodiments, the subtype is subtype B.

[0058] Further disclosed herein is a non-transitory computer-readable medium for identifying a candidate treatment, the non-transitory computer-readable medium comprising instructions, when executed by a processor, to cause the processor to access a differentially expressed gene database containing fold changes in gene levels between patients of different subtypes; determine at least a threshold number of differentially expressed genes in patients of a first subtype compared to patients of a second subtype, where each differentially expressed gene is involved in a common biological pathway; and determine a candidate treatment that is likely to be effective for patients of the first subtype, where the candidate treatment is effective in modulating the expression of at least one of the differentially expressed genes in patients of the first subtype. In various embodiments, the differentially expressed gene database is generated by obtaining labeled patient data, where the labels of the labeled patient data identify patients classified into one of two or more subtypes; and generating a differentially expressed gene database for at least one or more genes by determining at least the fold changes in gene levels between patient data with a label indicating the first subtype and patient data with a label indicating the second subtype. In various embodiments, the labels of the labeled patient data are generated by applying cluster analysis or by applying a patient subtype classifier. In various embodiments, the threshold number of genes is at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, at least 9 genes, or at least 10 genes. In various embodiments, determining a candidate therapy for a patient of the first subtype further includes analyzing one or both of: therapeutic pharmacokinetic data including data on the candidate therapy; and host response pathobiology data including data on patients of the first subtype.

[0059] Also disclosed herein is a system for determining a patient subtype, comprising a set of reagents used to determine quantitative data for at least one set of biomarkers from a test sample from a subject, wherein the at least one biomarker set is selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets, wherein Group 1 includes biomarker 1, biomarker 2, and biomarker 3, and wherein biomarker 1 is selected from the group consisting of EVL, BTN3A, BTN4A, BTN5A, BTN6A, BTN7A, BTN8A, BTN9A, BTN10A, BTN11A, BTN12A, BTN13A, BTN14A, BTN15A, BTN16A, BTN17A, BTN18A, BTN19A, BTN10B, BTN11C, BTN12A, BTN13A, BTN14A, BTN15A, BTN16A, BTN17B, BTN18B, BTN19A, BTN19B, BTN19C, BTN19D, BTN19E, BTN19F ... 2, biomarker 1 is one or more of HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK or MMP8, biomarker 2 is one or more of SERPINB1 or GSPT1, biomarker 3 is one or more of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3 or TOMM70A, and group 2 is biomarker 4, biomarker Group 3 includes biomarker 7, biomarker 8, and biomarker 9, wherein biomarker 7 is one or more of C14orf159 or PUM2, and biomarker 9 is one or more of SLC1A5, IGF2BP2, or ANXA3. Group 8 is one or more of EPB42 or RPS6KA5, and biomarker 9 is one or more of EPB42 or GBP2; Group 4 includes biomarker 10, biomarker 11, and biomarker 12, where biomarker 10 is one or more of MSH2, DCTD, or MMP8, biomarker 11 is one or more of HK3, UCP2, or NUP88, and biomarker 12 is one or more of GABARAPL2 or CASP4;a set of reagents, wherein Group 5 comprises biomarker 13, biomarker 14, and biomarker 15, wherein biomarker 13 is one or more of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G, biomarker 14 is one or more of EPB42, GSPT1, LAT, HK3, or SERPINB1, and biomarker 15 is one or more of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and a device configured to receive a mixture of one or more reagents in the set and a test sample and measure quantitative data for at least one set of biomarkers from the test sample; and a computer system communicatively coupled to the device for obtaining the quantitative data for at least one set of biomarkers and using a patient subtype classifier to determine a classification of the subject based on the quantitative data;

[0060] In various embodiments, at least one biomarker set is Group 5 and biomarker 13 is one or more of STOM, MME, BNT3A2, or HLA-DPA1. In various embodiments, biomarker 14 is one or more of EPB42, GSPT1, LAT, HK3, or SERPINB1. In various embodiments, at least one biomarker set is Group 5 and biomarker 15 is one or more of SLC1A5, IGF2BP2, or ANXA3.

[0061] Further disclosed herein is a system for determining patient subtypes, the system comprising: a) a patient subtype selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, TOMM70A, ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2, ANXA3, C14orf159, PUM2, EPB42, RPS6KA5, GBP2, MSH2, and the like; , DCTD, HK3, UCP2, NUP88, GABARAPL2, and CASP4; and an apparatus configured to receive a mixture of one or more reagents in the set and a test sample and measure quantitative data for at least one set of biomarkers from the test sample; and a computer system communicatively coupled to the apparatus for obtaining the quantitative data for the at least one set of biomarkers and determining a classification of the subject based on the quantitative data using a patient subtype classifier.

[0062] Also disclosed herein is a system for determining a patient subtype, comprising a set of reagents used to determine quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets, wherein Group 1 is selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9or Group 1 comprises two or more biomarkers selected from the group consisting of f78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3 and TOMM70A, Group 2 comprises two or more biomarkers selected from the group consisting of ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2 and ANXA3, and Group 3 comprises C14orf159, PUM2, EPB42, RPS6KA5, EPB4 2 and GBP2; Group 4 comprising two or more biomarkers selected from the group consisting of MSH2, DCTD, MMP8, HK3, UCP2, NUP88, GABARAPL2 and CASP4; and Group 5 comprising two or more biomarkers selected from the group consisting of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, CD3G, EPB42, GSPT1, LAT, HK3, SERPINB1, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2 and TNFRSF1A; and a device configured to receive a mixture of one or more reagents in the set and a test sample and to measure quantitative data for at least one set of biomarkers from the test sample; and a computer system communicatively coupled to the device for obtaining the quantitative data for the at least one set of biomarkers and determining a classification of the subject based on the quantitative data using a patient subtype classifier.In various embodiments, the computer system is configured to identify a treatment recommendation for the subject based at least in part on the classification.

[0063] Also disclosed herein is a system for determining a treatment recommendation for a patient, comprising obtaining a classification of a subject exhibiting host response dysregulation, wherein the classification is obtained from the subject, or has obtained, quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets (wherein Group 1 includes biomarker 1, biomarker 2, and biomarker 3, and biomarker 1 includes EVL, BTN3A, Biomarker 2 is one or more of HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK or MMP8, biomarker 2 is one or more of SERPINB1 or GSPT1, biomarker 3 is one or more of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3 or TOMM70A, and group 2 is biomarker 4, biomarker Group 3 includes biomarker 7, biomarker 8, and biomarker 9, where biomarker 7 is one or more of C14orf159 or PUM2, and biomarker 9 is one or more of SLC1A5, IGF2BP2, or ANXA3. Group 4 includes biomarker 5 and biomarker 6, where biomarker 4 is one or more of ZNF831, MME, CD3G, or STOM, where biomarker 5 is one or more of ECSIT, LAT, or NCOA4, and where biomarker 6 is one or more of SLC1A5, IGF2BP2, or ANXA3. Group 8 is one or more of EPB42 or RPS6KA5, and biomarker 9 is one or more of EPB42 or GBP2; Group 4 includes biomarker 10, biomarker 11, and biomarker 12, where biomarker 10 is one or more of MSH2, DCTD, or MMP8, biomarker 11 is one or more of HK3, UCP2, or NUP88, and biomarker 12 is one or more of GABARAPL2 or CASP4;Group 5 includes biomarker 13, biomarker 14, and biomarker 15, where biomarker 13 is one or more of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G; biomarker 14 is one or more of EPB42, GSPT1, LAT, HK3, or SERPINB1; and biomarker 15 is one or more of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and determined by using a patient subtype classifier to determine a classification based on the quantitative data; and a computer system configured to identify a treatment recommendation for the subject based at least in part on the classification.

[0064] In various embodiments, the subject's host response dysregulation comprises one of sepsis and a host response dysregulation not caused by an infection. In various embodiments, the subject's classification comprises one of subtype A or subtype B. In various embodiments, the subject's classification comprises one of subtype A, subtype B, or subtype C. In various embodiments, in response to a subject's classification comprising subtype A, the treatment recommendation identified for the subject comprises at least no immunosuppressive therapy.

[0065] In various embodiments, depending on the classification of the subject as including subtype A, the treatment recommendation identified for the subject further comprises at least no corticosteroid therapy. In various embodiments, the treatment recommendation identified for the subject further comprises no hydrocortisone. In various embodiments, depending on the classification of the subject as including subtype B, the treatment recommendation identified for the subject further comprises at least one of no treatment recommendation, immunostimulatory therapy, suppression of immunomodulatory therapy, blockade of immunosuppressive therapy, blockade of complement activation therapy, and anti-inflammatory therapy. In various embodiments, depending on the classification of the subject as including subtype B, the treatment recommendation identified for the subject further comprises at least one of a checkpoint inhibitor, a complement component blocker, a complement component receptor blocker, and a pro-inflammatory cytokine blocker. In various embodiments, the treatment recommendation identified for the subject further comprises at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulator, IL-22 agonist, IFN-alpha regulator, IFN-lambda regulator, IFN-alpha 2b stimulator, anti-C5a, anti-C3a, anti-C5aR, anti-C3aR, anti-TNF-alpha and anti-IL-6, anti-HMGB1, ST2 antibody, IL-33 antibody.

[0066] In various embodiments, depending on the subject's classification as including subtype C, the treatment recommendation identified for the subject includes at least one of no treatment recommendation, immunostimulatory therapy, suppression of immunomodulatory therapy, blockade of immunosuppressive therapy, a modulator of coagulation therapy, and a modulator of vascular permeability therapy. In various embodiments, depending on the subject's classification as including subtype C, the treatment recommendation identified for the subject further includes at least one of a checkpoint inhibitor and an anticoagulant. In various embodiments, the treatment recommendation identified for the subject further includes at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a modulator, IL-22 agonist, IFN-alpha modulator, IFN-lambda modulator, IFN-alpha 2b stimulator, activated protein C, antithrombin, and thrombomodulin.

[0067] In various embodiments, the sample comprises a blood sample from the subject. In various embodiments, the subject exhibiting a dysregulated host response does not exhibit shock, and at least one set of biomarkers is one of Group 1, Group 3, or Group 4. In various embodiments, the subject exhibiting a dysregulated host response further exhibits shock, and at least one set of biomarkers is one of Group 1, Group 2, Group 4, Group 5, Group 6, Group 7, or Group 8. In various embodiments, the subject exhibiting a dysregulated host response is an adult subject, and at least one set of biomarkers is one of Group 1, Group 2, Group 3, Group 5, Group 6, Group 7, or Group 8. In various embodiments, the subject exhibiting a dysregulated host response is a pediatric subject, and at least one set of biomarkers is one of Group 1, Group 4, Group 5, Group 6, Group 7, or Group 8.

[0068] In various embodiments, the quantitative data is determined by one of RT-qPCR (quantitative reverse transcription polymerase chain reaction), qPCR (quantitative polymerase chain reaction), PCR (polymerase chain reaction), RT-PCR (reverse transcription polymerase chain reaction), SDA (strand displacement amplification), RPA (recombinase polymerase amplification), MDA (multiple displacement amplification), HDA (helicase-dependent amplification), LAMP (loop-mediated isothermal amplification), RCA (rolling circle amplification), NASBA (nucleic acid sequence-based amplification) and any other isothermal or thermocycling amplification reaction.

[0069] In various embodiments, the classification of a subject is determined by: determining a classification-specific score for the subject for at least one candidate classification of the subject; and using a patient subtype classifier to determine the classification of the subject based on the classification-specific score.In various embodiments, determining the classification-specific score further comprises: determining a first subscore of the quantitative data for the subject for one or more biomarkers of the candidate classification (wherein the quantitative data for the subject for one or more biomarkers of the candidate classification is increased compared with the quantitative data for one or more biomarkers of one or more control subjects); determining a second subscore of the quantitative expression for the subject for one or more additional biomarkers of the candidate classification (wherein the quantitative data for the subject for one or more additional biomarkers of the candidate classification is decreased compared with the quantitative data for one or more additional biomarkers of one or more control subjects); and determining the difference between the first subscore and the second subscore (the first and second geometric subscores are optionally scaled, and the difference comprises the classification-specific score for the subject).In various embodiments, one or both of the first subscore and the second subscore is a geometric mean value.

[0070] In various embodiments, the patient subtype classifier is a machine learning model. In various embodiments, the machine learning model is a support vector machine (SVM). In various embodiments, the support vector machine receives one or more classification-specific scores as input and outputs the classification of the subject. In various embodiments, the patient subtype classifier determines the classification of the subject by comparing the classification-specific scores with one or more thresholds; and determining the classification of the subject based on the comparison.

[0071] In various embodiments, at least one of the one or more thresholds is fixed value.In various embodiments, at least one of the one or more thresholds is determined using training sample, and at least one threshold represents the value on ROC curve that is closest to maximum sensitivity or maximum specificity.In various embodiments, before using patient subtype classifier to determine subject classification, said quantitative data is normalized based on the quantitative data of one or more housekeeping genes.

[0072] In various embodiments, the candidate subject classifications include subtype A, subtype B, and subtype C. In various embodiments, at least one biomarker set is Group 1, and the patient subtype classifier has an average accuracy of at least 82.93%. In various embodiments, the patient subtype classifier has an average accuracy of at least 89.6%. In various embodiments, the patient subtype classifier has an average accuracy of at least 86.3%. In various embodiments, at least one biomarker set is Group 4, and the patient subtype classifier has an average accuracy of at least 98.3%.

[0073] In various embodiments, the treatment recommendation identified for the subject further comprises corticosteroid therapy, no corticosteroid therapy, or no treatment recommendation. In various embodiments, the treatment recommendation comprises no corticosteroid therapy, and no corticosteroid therapy is identified by determining that the statistical significance of the reduction in mortality of subjects exhibiting host response dysregulation who are not provided with corticosteroid therapy is equal to or exceeds a threshold statistical significance. In various embodiments, the treatment recommendation comprises no corticosteroid therapy, and no corticosteroid therapy is identified by determining that the subject's classification comprises a subtype that is likely to respond poorly to corticosteroid therapy. In various embodiments, the subtype is subtype A or subtype C. In various embodiments, the treatment recommendation comprises corticosteroid therapy, and corticosteroid therapy is identified by determining that the statistical significance of the reduction in mortality of subjects exhibiting host response dysregulation who are provided with corticosteroid therapy is equal to or exceeds a threshold statistical significance.

[0074] In various embodiments, the treatment recommendation includes corticosteroid therapy, and the corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to respond well to corticosteroid therapy. In various embodiments, the subtype is subtype B.

[0075] In various embodiments, the identified treatment recommendation for the subject includes no treatment recommendation, and the no treatment recommendation is identified by at least determining that the statistical significance of the reduction in mortality of subjects exhibiting host response dysregulation who are not provided with corticosteroid therapy is less than a threshold statistical significance; and determining that the statistical significance of the reduction in mortality of subjects exhibiting host response dysregulation who are provided with corticosteroid therapy is less than a threshold statistical significance. In various embodiments, statistical significance includes a p-value, and the threshold statistical significance includes at least 0.1.

[0076] In various embodiments, the treatment recommendation identified for the subject includes no corticosteroid therapy, the host response dysregulation includes a host response dysregulation not caused by an infection, and at least one biomarker set is Group 1 or Group 4. In various embodiments, no corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to respond poorly to corticosteroid therapy. In various embodiments, the subtype is subtype A or subtype C.

[0077] In various embodiments, the treatment recommendation identified for the subject further comprises a no treatment recommendation, wherein the no treatment recommendation is identified by determining that the subject's classification comprises subtype B.

[0078] In various embodiments, the treatment recommendation identified for the subject includes no corticosteroid therapy, the host response dysregulation includes sepsis, and at least one biomarker set is one of Group 2, Group 3, or Group 4. In various embodiments, no corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to respond poorly to corticosteroid therapy. In various embodiments, the subtype is subtype A.

[0079] In various embodiments, the identified treatment recommendation for the subject further comprises no treatment recommendation, wherein the no treatment recommendation is identified by determining that the subject's classification comprises a subtype that is likely to be non-responsive to corticosteroid therapy. In various embodiments, the subtype is subtype B or subtype C.

[0080] In various embodiments, the treatment recommendation identified for the subject further includes no corticosteroid therapy, and the host response dysregulation includes a host response dysregulation not caused by an infection, and at least one biomarker set is Group 2. In various embodiments, no corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to respond poorly to corticosteroid therapy. In various embodiments, the subtype is subtype C.

[0081] In various embodiments, the identified treatment recommendation for the subject further comprises no treatment recommendation, wherein the no treatment recommendation is identified by determining that the subject's classification comprises a subtype that is likely to be non-responsive to corticosteroid therapy. In various embodiments, the subtype is subtype A or subtype B.

[0082] In various embodiments, the treatment recommendation identified for the subject includes no corticosteroid therapy, the host response dysregulation includes a host response dysregulation not caused by an infection, and at least one biomarker set is Group 3. In various embodiments, no corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to be unresponsive to corticosteroid therapy. In various embodiments, the subtype is subtype A or subtype C.

[0083] In various embodiments, the treatment recommendation identified for the subject further includes corticosteroid therapy, wherein the corticosteroid therapy is identified by determining that the subject's classification includes a subtype that is likely to be responsive to corticosteroid therapy. In various embodiments, the subtype is subtype B.

[0084] Further disclosed herein is a system for identifying candidate therapies, the system including: a storage device for storing a differentially expressed gene database comprising fold changes in gene levels between patients of different subtypes; accessing one or more fold changes in gene levels corresponding to differentially expressed genes in the differentially expressed gene database; determining at least a threshold number of differentially expressed genes in patients of a first subtype compared to patients of a second subtype, wherein each of the differentially expressed genes is involved in a common biological pathway; and determining candidate therapies that are likely to be effective for patients of the first subtype, wherein the candidate therapies are effective in modulating the expression of at least one of the differentially expressed genes in patients of the first subtype. In various embodiments, the differentially expressed gene database is generated by obtaining labeled patient data (wherein the labels of the labeled patient data identify patients classified into one of two or more subtypes); and determining a fold change in gene levels between at least patient data having a label indicating a first subtype and patient data having a label indicating a second subtype, thereby generating a differentially expressed gene database for at least one or more genes. In various embodiments, the labels of the labeled patient data are generated by applying cluster analysis or by applying a patient subtype classifier. In various embodiments, the threshold number of genes is at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, or at least ten genes. In various embodiments, determining candidate therapies for patients of the first subtype further includes analyzing one or both of pharmacokinetic data including data regarding the candidate therapies; and host response pathobiology including data regarding patients of the first subtype.

[0085] Further disclosed herein is a kit for determining a patient subtype, comprising a set of reagents for determining quantitative data for at least one set of biomarkers from a test sample derived from a subject, wherein the at least one set of biomarkers is selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets, wherein Group 1 includes biomarker 1, biomarker 2, and biomarker 3, and wherein biomarker 1 is selected from the group consisting of EVL, BTN3A2, HLA - Biomarker 1 is one or more of DPA1, IDH3A, ACBD3, EXOSC10, SNRK or MMP8, biomarker 2 is one or more of SERPINB1 or GSPT1, biomarker 3 is one or more of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3 or TOMM70A, and Group 2 is biomarker 4, biomarker 5 and and biomarker 6, wherein biomarker 4 is one or more of ZNF831, MME, CD3G, or STOM, wherein biomarker 5 is one or more of ECSIT, LAT, or NCOA4, and wherein biomarker 6 is one or more of SLC1A5, IGF2BP2, or ANXA3; and group 3 includes biomarker 7, biomarker 8, and biomarker 9, wherein biomarker 7 is one or more of C14orf159 or PUM2, and biomarker 8 is one or more of SLC1A5, IGF2BP2, or ANXA3. is one or more of EPB42 or RPS6KA5, and biomarker 9 is one or more of EPB42 or GBP2; group 4 includes biomarker 10, biomarker 11 and biomarker 12, and biomarker 10 is one or more of MSH2, DCTD or MMP8, biomarker 11 is one or more of HK3, UCP2 or NUP88, and biomarker 12 is one or more of GABARAPL2 or CASP4;a set of reagents, wherein Group 5 comprises biomarker 13, biomarker 14, and biomarker 15, wherein biomarker 13 is one or more of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G, biomarker 14 is one or more of EPB42, GSPT1, LAT, HK3, or SERPINB1, and biomarker 15 is one or more of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and instructions for using the set of reagents to determine quantitative data for at least one of the set of biomarkers;

[0086] In various embodiments, at least one biomarker set is Group 5 and biomarker 13 is one or more of STOM, MME, BNT3A2, or HLA-DPA1. In various embodiments, at least one biomarker set is Group 5 and biomarker 14 is one or more of EPB42, GSPT1, LAT, HK3, or SERPINB1. In various embodiments, at least one biomarker set is Group 5 and biomarker 15 is one or more of SLC1A5, IGF2BP2, or ANXA3.

[0087] Further disclosed herein is a kit for determining a patient subtype, the kit comprising: a first antibody against a gene encoding an antibody that is selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, TOMM70A, ZNF831, MME, CD3G, STOM, ECSIT, L A kit comprising: a set of reagents for determining quantitative data for two or more biomarkers selected from the group consisting of AT, NCOA4, SLC1A5, IGF2BP2, ANXA3, C14orf159, PUM2, EPB42, RPS6KA5, GBP2, MSH2, DCTD, HK3, UCP2, NUP88, GABARAPL2 and CASP4; and instructions for using the set of reagents to determine quantitative data for at least one of the set of biomarkers.

[0088] Further disclosed herein is a kit for determining a patient subtype, comprising a set of reagents for determining quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets, wherein Group 1 comprises two or more biomarkers selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, and TOMM70A; and Group 2 comprises the group consisting of ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2, and ANXA3. wherein Group 1 comprises two or more biomarkers selected from the group consisting of C14orf159, PUM2, EPB42, RPS6KA5, EPB42, and GBP2; Group 2 comprises two or more biomarkers selected from the group consisting of MSH2, DCTD, MMP8, HK3, UCP2, NUP88, GABARAPL2, and CASP4; and Group 3 comprises two or more biomarkers selected from the group consisting of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, CD3G, EPB42, GSPT1, LAT, HK3, SERPINB1, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, and TNFRSF1A; and instructions for using the set of reagents to determine quantitative data for at least one of the set of biomarkers.

[0089] In various embodiments, the instructions include instructions for determining quantitative data by performing one of RT-qPCR (quantitative reverse transcription polymerase chain reaction), qPCR (quantitative polymerase chain reaction), PCR (polymerase chain reaction), RT-PCR (reverse transcription polymerase chain reaction), SDA (strand displacement amplification), RPA (recombinase polymerase amplification), MDA (multiple displacement amplification), HDA (helicase-dependent amplification), LAMP (loop-mediated isothermal amplification), RCA (rolling circle amplification), NASBA (nucleic acid sequence-based amplification), and any other isothermal or thermocycling amplification reaction.

[0090] In various embodiments, the set of reagents comprises at least three primer sets for amplifying at least three biomarkers, wherein the at least three primer sets comprise pairs of single-stranded DNA primers for amplifying at least three biomarkers, wherein at least one of the at least three biomarkers is selected from the group consisting of the biomarkers EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, ZNF831, MME, CD3G, STOM, C14orf159, PUM2, MSH2, DCTD, BNT3A2, or HLA-DPA1, and wherein at least one of the at least three biomarkers is selected from the group consisting of: At least one biomarker is selected from the group consisting of the biomarkers SERPINB1, GSPT1, ECSIT, LAT, NCOA4, EPB42, RPS6KA5, HK3, UCP2 or NUP88, and at least one biomarker of the at least three biomarkers is selected from the group consisting of the biomarkers MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, PRPF3, TOMM70A, EPB42, GABARAPL2, CASP4, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1 or TNFRSF1A.

[0091] In various embodiments, at least one of the at least three primer sets is selected from the group consisting of a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 7 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 8, a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 9 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 10, a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 11 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 12, and a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 13 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 14; and at least one of the at least three primer sets is selected from the group consisting of a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 15 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 16, and a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 17 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 18, and a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 19 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 20, wherein at least one of the at least three primer sets is selected from the group consisting of a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 1 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 2; a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 3 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 4, and a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO.a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 5 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 6.

[0092] In various embodiments, at least one of the at least three primer sets is selected from the group consisting of a forward primer comprising SEQ ID NO. 7 and a reverse primer comprising SEQ ID NO. 8, a forward primer comprising SEQ ID NO. 9 and a reverse primer comprising SEQ ID NO. 10, a forward primer comprising SEQ ID NO. 11 and a reverse primer comprising SEQ ID NO. 12, and a forward primer comprising SEQ ID NO. 13 and a reverse primer comprising SEQ ID NO. 14; at least one of the at least three primer sets is selected from the group consisting of a forward primer comprising SEQ ID NO. 15 and a reverse primer comprising SEQ ID NO. 16, a forward primer comprising SEQ ID NO. 17 and a reverse primer comprising SEQ ID NO. 18, and a forward primer comprising SEQ ID NO. 19 and a reverse primer comprising SEQ ID NO. 20; and at least one of the at least three primer sets is selected from the group consisting of a forward primer comprising SEQ ID NO. 1 and a reverse primer comprising SEQ ID NO. 2; 3 and a reverse primer comprising SEQ ID NO. 4; and a forward primer comprising SEQ ID NO. 5 and a reverse primer comprising SEQ ID NO. 6.

[0093] In various embodiments, at least one of the at least three primer sets is selected from the group consisting of a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 21 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 22, and a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 23 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 24; at least one of the at least three primer sets is selected from the group consisting of a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 25 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 26, and a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 29 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 30; and at least one of the at least three primer sets is selected from the group consisting of a forward primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 25 and a reverse primer comprising at least 15 contiguous nucleotides of SEQ ID NO. 26, and a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 27 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 28.

[0094] In various embodiments, at least one of the at least three primer sets is selected from the group consisting of a forward primer comprising SEQ ID NO. 21 and a reverse primer comprising SEQ ID NO. 22, and a forward primer comprising SEQ ID NO. 23 and a reverse primer comprising SEQ ID NO. 24; at least one of the at least three primer sets is selected from the group consisting of a forward primer comprising SEQ ID NO. 25 and a reverse primer comprising SEQ ID NO. 26, and a forward primer comprising SEQ ID NO. 29 and a reverse primer comprising SEQ ID NO. 30; and at least one of the at least three primer sets is selected from the group consisting of a forward primer comprising SEQ ID NO. 25 and a reverse primer comprising SEQ ID NO. 26, and a forward primer comprising SEQ ID NO. 27 and a reverse primer comprising SEQ ID NO. 28.

[0095] In various embodiments, the set of reagents comprises at least three primer sets for amplifying at least three biomarkers, each primer set of the at least three primer sets comprising a forward outer primer, a backward outer primer, a forward inner primer, a backward inner primer, a forward loop primer, and a backward loop primer for amplifying one of the at least three biomarkers, wherein at least one of the at least three biomarkers is selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, ZNF831, MME, CD3G, STOM, C14orf159, PUM2, MSH2, and DCTD. , BNT3A2 or HLA-DPA1, and at least one biomarker of the at least three biomarkers is selected from the group consisting of SERPINB1, GSPT1, ECSIT, LAT, NCOA4, EPB42, RPS6KA5, HK3, UCP2 or NUP88, and at least one biomarker of the at least three biomarkers is selected from the group consisting of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, PRPF3, TOMM70A, EPB42, GABARAPL2, CASP4, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1 or TNFRSF1A.

[0096] In various embodiments, at least one of the at least three primer sets comprises a forward outer primer, a backward outer primer, a forward inner primer, a backward inner primer, a forward loop primer, and a backward loop primer, each configured to allow amplification of at least one biomarker selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, ZNF831, MME, CD3G, STOM, C14orf159, PUM2, MSH2, DCTD, BNT3A2, or HLA-DPA1, SERPINB1, GSPT1, ECSIT, LAT, NCOA4, EPB42, RPS6KA5, HK3, UCP2, or NUP88. and forward outer primers, backward outer primers, forward inner primers, backward inner primers, forward loop primers and backward loop primers, each configured to allow amplification of at least one biomarker selected from the group consisting of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, PRPF3, TOMM70A, EPB42, GABARAPL2, CASP4, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1 or TNFRSF1A. [The present invention 1001] 1. A method for determining a patient subtype, comprising the steps of: obtaining or having obtained quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4 or Group 5 biomarker sets; Group 1 includes biomarker 1, biomarker 2, and biomarker 3; Biomarker 1 is one of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, or MMP8; Biomarker 2 is one of SERPINB1 or GSPT1; Biomarker 3 is one of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, or TOMM70A; Group 2 includes biomarker 4, biomarker 5, and biomarker 6; Biomarker 4 is one of ZNF831, MME, CD3G, or STOM; Biomarker 5 is one of ECSIT, LAT, or NCOA4; Biomarker 6 is one of SLC1A5, IGF2BP2, or ANXA3; Group 3 includes biomarker 7, biomarker 8, and biomarker 9; Biomarker 7 is one of C14orf159 or PUM2; Biomarker 8 is one of EPB42 or RPS6KA5; Biomarker 9 is one of EPB42 or GBP2; Group 4 includes biomarker 10, biomarker 11, and biomarker 12; the biomarker 10 is one of MSH2, DCTD, or MMP8; biomarker 11 is one of HK3, UCP2, or NUP88; Biomarker 12 is one of GABARAPL2 or CASP4; Group 5 includes biomarker 13, biomarker 14, and biomarker 15; Biomarker 13 is one of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G; biomarker 14 is one of EPB42, GSPT1, LAT, HK3, or SERPINB1; Biomarker 15 is one of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and determining a classification of the subject based on said quantitative data using a patient subtype classifier; A method comprising: [The present invention 1002] 1001. The method of claim 1001, wherein at least one biomarker set is Group 5 and biomarker 13 is one of STOM, MME, BNT3A2, or HLA-DPA1. [The present invention 1003] The method of any one of claims 1001 to 1002, wherein at least one biomarker set is Group 5 and biomarker 14 is one of EPB42, GSPT1, LAT, HK3, or SERPINB1. [The present invention 1004] The method of any of claims 1001 to 1003, wherein at least one biomarker set is Group 5 and biomarker 15 is one of SLC1A5, IGF2BP2 or ANXA3. [The present invention 1005] 1. A method for determining a treatment recommendation for a patient, comprising the steps of: obtaining or having obtained quantitative data for two or more biomarkers selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, TOMM70A, ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2, ANXA3, C14orf159, PUM2, EPB42, RPS6KA5, GBP2, MSH2, DCTD, HK3, UCP2, NUP88, GABARAPL2 and CASP4; and determining a classification of the subject based on said quantitative data using a patient subtype classifier; A method comprising: [The present invention 1006] 1. A method for determining a treatment recommendation for a patient, comprising the steps of: obtaining or having obtained quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4 or Group 5 biomarker sets; Group 1 comprises two or more biomarkers selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3 and TOMM70A; Group 2 comprises two or more biomarkers selected from the group consisting of ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2 and ANXA3; Group 3 comprises two or more biomarkers selected from the group consisting of C14orf159, PUM2, EPB42, RPS6KA5, EPB42 and GBP2; Group 4 comprises two or more biomarkers selected from the group consisting of MSH2, DCTD, MMP8, HK3, UCP2, NUP88, GABARAPL2 and CASP4; Group 5 comprises two or more biomarkers selected from the group consisting of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, CD3G, EPB42, GSPT1, LAT, HK3, SERPINB1, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, and TNFRSF1A; and determining a classification of the subject based on said quantitative data using a patient subtype classifier; A method comprising: [The present invention 1007] identifying a treatment recommendation for the subject based at least in part on said classification. The method of any one of 1001 to 1006 of the present invention, further comprising: [The present invention 1008] 1. A method for determining a treatment recommendation for a patient, comprising the steps of: obtaining a classification of subjects exhibiting host response dysregulation, The classification is obtaining or having obtained quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4 or Group 5 biomarker sets; Group 1 includes biomarker 1, biomarker 2, and biomarker 3; Biomarker 1 is one of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, or MMP8; Biomarker 2 is one of SERPINB1 or GSPT1; Biomarker 3 is one of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, or TOMM70A; Group 2 includes biomarker 4, biomarker 5, and biomarker 6; Biomarker 4 is one of ZNF831, MME, CD3G, or STOM; Biomarker 5 is one of ECSIT, LAT, or NCOA4; Biomarker 6 is one of SLC1A5, IGF2BP2, or ANXA3; Group 3 includes biomarker 7, biomarker 8, and biomarker 9; Biomarker 7 is one of C14orf159 or PUM2; Biomarker 8 is one of EPB42 or RPS6KA5; Biomarker 9 is one of EPB42 or GBP2; Group 4 includes biomarker 10, biomarker 11, and biomarker 12; the biomarker 10 is one of MSH2, DCTD, or MMP8; biomarker 11 is one of HK3, UCP2, or NUP88; Biomarker 12 is one of GABARAPL2 or CASP4; Group 5 includes biomarker 13, biomarker 14, and biomarker 15; Biomarker 13 is one of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G; biomarker 14 is one of EPB42, GSPT1, LAT, HK3, or SERPINB1; Biomarker 15 is, will be, or has been one of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and determining the classification based on the quantitative data using a patient subtype classifier; the stage determined by identifying a treatment recommendation for the subject based at least in part on the classification. A method comprising: [The present invention 1009] The method of claim 1008, wherein the subject's host response dysregulation comprises one of sepsis and a host response dysregulation not caused by an infection. [The present invention 1010] The method of any of claims 1001 to 1009, wherein the classification of the subject comprises one of subtype A or subtype B. [The present invention 1011] The method of any of claims 1001 to 1009, wherein the classification of the subject comprises one of subtype A, subtype B, or subtype C. [The present invention 1012] 1012. The method of claim 1010 or 1011, wherein, depending on the classification of the subject as comprising subtype A, the treatment recommendation identified for said subject comprises at least no immunosuppressive therapy. [The present invention 1013] 1012. The method of claim 1010 or 1011, wherein, depending on the classification of the subject as comprising subtype A, the treatment recommendation identified for said subject further comprises at least no corticosteroid therapy. [The present invention 1014] The method of claim 1013, wherein the treatment recommendations identified for the subject further include at least one without hydrocortisone. [The present invention 1015] The method of any one of claims 1010 to 1011, wherein, depending on the classification of the subject as including subtype B, the treatment recommendation identified for the subject comprises at least one of no treatment recommendation, immunostimulatory therapy, suppression of immunomodulatory therapy, blockade of immunosuppressive therapy, blockade of complement activation therapy, and anti-inflammatory therapy. [The present invention 1016] The method of any one of claims 1010 to 1011, wherein, depending on the classification of the subject as including subtype B, the treatment recommendation identified for the subject further comprises at least one of a checkpoint inhibitor, a complement component blocker, a complement component receptor blocker, and an inflammatory cytokine blocker. [The present invention 1017] 1016. The method of claim 1016, wherein the treatment recommendation identified for the subject further comprises at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulator, IL-22 agonist, IFN-alpha regulator, IFN-lambda regulator, IFN-alpha 2b stimulator, anti-C5a, anti-C3a, anti-C5aR, anti-C3aR, anti-TNF-alpha, and anti-IL-6, anti-HMGB1, ST2 antibody, IL-33 antibody. [The present invention 1018] The method of the present invention 1011, wherein, in response to classification of the subject as including subtype C, the treatment recommendation identified for the subject comprises at least one of no treatment recommendation, immunostimulatory therapy, suppression of immunomodulatory therapy, blockade of immunosuppressive therapy, modulator of coagulation therapy, and modulator of vascular permeability therapy. [The present invention 1019] 1012. The method of claim 1011, wherein, in response to a classification of the subject comprising subtype C, the treatment recommendation identified for the subject further comprises at least one of a checkpoint inhibitor and an anticoagulant. [The present invention 1020] 1019. The method of claim 1019, wherein the treatment recommendation identified for the subject further comprises at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulator, IL-22 agonist, IFN-alpha regulator, IFN-lambda regulator, IFN-alpha 2b stimulator, activated protein C, antithrombin, and thrombomodulin. [The present invention 1021] administering or having administered a treatment to the subject based on said treatment recommendation. Any of the methods of 1007 to 1020 of the present invention, further comprising: [The present invention 1022] The stage at which quantitative data is obtained or acquired is obtaining a sample containing a plurality of biomarkers from a subject exhibiting a dysregulated host response; and Determining quantitative data from the samples obtained Any of the methods of the present inventions 1001 to 1021, comprising: [The present invention 1023] The method of claim 1022, wherein the obtained sample comprises a blood sample from the subject. [The present invention 1024] The method of any of claims 1001 or 1007-1023, wherein the subject exhibiting host response dysregulation does not exhibit shock and at least one set of biomarkers is one of Group 1, Group 3 or Group 4. [The present invention 1025] Any of the methods of 1001 or 1007-1023, wherein the subject exhibiting host response dysregulation further exhibits shock and at least one biomarker set is one of Group 1, Group 2, Group 4, Group 5, Group 6, Group 7 or Group 8. [The present invention 1026] Any of the methods of 1001 or 1007 to 1023, wherein the subject exhibiting host response dysregulation is an adult subject and at least one biomarker set is one of Group 1, Group 2, Group 3, Group 5, Group 6, Group 7 or Group 8. [The present invention 1027] Any of the methods of 1001 or 1007 to 1023, wherein the subject exhibiting host response dysregulation is a pediatric subject and at least one biomarker set is one of Group 1, Group 4, Group 5, Group 6, Group 7 or Group 8. [The present invention 1028] 10. The method of any of claims 1001 to 1027, wherein the quantitative data is determined by one of RT-qPCR (quantitative reverse transcription polymerase chain reaction), qPCR (quantitative polymerase chain reaction), PCR (polymerase chain reaction), RT-PCR (reverse transcription polymerase chain reaction), SDA (strand displacement amplification), RPA (recombinase polymerase amplification), MDA (multiple displacement amplification), HDA (helicase dependent amplification), LAMP (loop-mediated isothermal amplification), RCA (rolling circle amplification), NASBA (nucleic acid sequence-based amplification) and any other isothermal or thermocycling amplification reaction. [The present invention 1029] Quantitative data, contacting the sample with a reagent; forming a plurality of complexes between the reagent and a plurality of biomarkers in the sample; and detecting the plurality of complexes to obtain a data set associated with the sample, the data set including the quantification data. Any of the methods of 1001 to 1028 of the present invention, which is determined by the method. [The present invention 1030] The classification of the subject is determining a class-specific score for said subject with respect to at least one candidate class for said subject; determining a classification of the subject by a patient subtype classifier based on the classification-specific score. Any of the methods of 1001 to 1029 of the present invention, which is determined by the method. [The present invention 1031] Determining a classification-specific score includes: determining a first sub-score of quantitative data for the subject with respect to one or more biomarkers of the candidate classification, wherein the quantitative data for the subject with respect to the one or more biomarkers of the candidate classification is increased compared to the quantitative data with respect to the one or more biomarkers for one or more control subjects; determining a second subscore of quantitative expression for the subject for one or more additional biomarkers of the candidate classification, wherein the quantitative data for the subject for the one or more additional biomarkers of the candidate classification is decreased compared to the quantitative data for the one or more additional biomarkers for the one or more control subjects; and determining the difference between the first subscore and the second subscore, wherein the first and second geometric subscores are optionally subjected to scaling, and the difference comprises the class-specific score for the subject. The method of the present invention 1030, comprising: [The present invention 1032] The method of claim 1031, wherein one or both of the first subscore and the second subscore is a geometric mean value. [The present invention 1033] The method of any of claims 1001 to 1032, wherein the patient subtype classifier is a machine learning model. [The present invention 1034] The method of the present invention 1033, wherein the machine learning model is a support vector machine (SVM). [This invention 1035] The method of this invention 1034, wherein the support vector machine receives one or more classification-specific scores as input and outputs a classification of interest. [The present invention 1036] The patient subtype classifier comparing the classification-specific scores to one or more thresholds; and determining a classification of the subject based on said comparison. determining the classification of the subject by The method of the present invention 1030 or 1031. [This invention 1037] The method of the present invention 1036, wherein at least one of the one or more thresholds is a fixed value. [The present invention 1038] The method of the present invention 1036, wherein at least one of the one or more thresholds is determined using training samples, and the at least one threshold represents a value on the ROC curve that is closest to maximum sensitivity or maximum specificity. [This invention 1039] Before using the patient subtype classifier to determine the classification of the subject, normalizing said quantitative data based on quantitative data for one or more housekeeping genes. Any of the methods of claims 1001 to 1038, further comprising: [The present invention 1040] The method of any of claims 1030 to 1039, wherein the candidate classification of the subject comprises subtype A, subtype B, and subtype C. [This invention 1041] The method of any of claims 1001 or 1007-1040, wherein at least one biomarker set is Group 1 and the patient subtype classifier has an average accuracy of at least 82.93%. [The present invention 1042] The method of any of claims 1001 or 1007-1040, wherein at least one biomarker set is Group 2 and the patient subtype classifier has an average accuracy of at least 89.6%. [This invention 1043] The method of any of claims 1001 or 1007-1040, wherein at least one biomarker set is Group 3 and the patient subtype classifier has an average accuracy of at least 86.3%. [This invention 1044] The method of any of claims 1001 or 1007-1040, wherein at least one biomarker set is Group 4 and the patient subtype classifier has an average accuracy of at least 98.3%. [This invention 1045] The method of any one of claims 1007 to 1008, wherein the treatment recommendation identified for the subject further comprises corticosteroid therapy, no corticosteroid therapy, or no treatment recommendation. [The present invention 1046] Treatment recommendations include no corticosteroid therapy; The absence of corticosteroid therapy determining that the statistical significance of the reduction in mortality in subjects exhibiting host response dysregulation who are not provided corticosteroid therapy is equal to or exceeds a threshold statistical significance; Identified by, The method of the present invention 1045. [This invention 1047] Treatment recommendations include no corticosteroid therapy; The absence of corticosteroid therapy Determining that the subject classification includes subtypes that are likely to respond poorly to corticosteroid therapy. Identified by, The method of the present invention 1045. [This invention 1048] The method of claim 1047, wherein the subtype is subtype A or subtype C. [This invention 1049] Treatment recommendations include corticosteroid therapy; the corticosteroid therapy determining that the statistical significance of the reduction in mortality in subjects exhibiting host response dysregulation who are provided with corticosteroid therapy is equal to or exceeds a threshold statistical significance; Identified by, The method of the present invention 1045. [The present invention 1050] Treatment recommendations include corticosteroid therapy; the corticosteroid therapy Determining that the subject classification includes subtypes that are likely to respond favorably to corticosteroid therapy. Identified by, The method of the present invention 1045. [This invention 1051] The method of claim 1050, wherein the subtype is subtype B. [This invention 1052] the treatment recommendation identified for the subject includes no treatment recommendation; The treatment is not recommended, but at least determining that the statistical significance of the reduction in mortality rate in subjects exhibiting host response dysregulation who are not provided corticosteroid therapy is below a threshold statistical significance; and determining that the statistical significance of the reduction in mortality in subjects exhibiting host response dysregulation who are provided with corticosteroid therapy is below a threshold statistical significance; Identified by, The method of the present invention 1045. [This invention 1053] 1053. The method of any of claims 1046 to 1052, wherein the statistical significance comprises a p-value and the threshold statistical significance comprises at least 0.1. [This invention 1054] the treatment recommendation identified for the subject includes no corticosteroid therapy; the host response dysregulation includes a host response dysregulation that is not caused by an infection; At least one biomarker set is in Group 1 or Group 4, The method of the present invention 1045. [This invention 1055] Without corticosteroid therapy, Determining that the subject classification includes subtypes that are likely to respond poorly to corticosteroid therapy. Identified by, The method of the present invention 1054. [This invention 1056] The method of claim 1055, wherein the subtype is subtype A or subtype C. [This invention 1057] the treatment recommendations identified for the subject further comprise no treatment recommendation; No treatment is recommended, Determining that the classification of the subject includes subtype B. Identified by, The method of the present invention 1045. [This invention 1058] the treatment recommendation identified for the subject includes no corticosteroid therapy; Dysregulated host response, including sepsis, at least one biomarker set is in one of Group 2, Group 3 or Group 4; The method of the present invention 1045. [This invention 1059] Without corticosteroid therapy, Determining that the subject classification includes subtypes that are likely to respond poorly to corticosteroid therapy. Identified by, The method of the present invention 1058. [The present invention 1060] The method of claim 1059, wherein the subtype is subtype A. [This invention 1061] the treatment recommendations identified for the subject further comprise no treatment recommendation; No treatment is recommended, determining that the subject classification includes a subtype that is likely to be non-responsive to corticosteroid therapy; Identified by, The method of the present invention 1045. [This invention 1062] The method of claim 1061, wherein the subtype is subtype B or subtype C. [This invention 1063] the treatment recommendation identified for the subject further comprises no corticosteroid therapy; the host response dysregulation includes a host response dysregulation that is not caused by an infection; At least one biomarker set is in Group 2, The method of the present invention 1045. [This invention 1064] Without corticosteroid therapy, Determining that the subject classification includes subtypes that are likely to respond poorly to corticosteroid therapy. Identified by, The method of the present invention 1063. [This invention 1065] The method of the present invention 1064, wherein the subtype is subtype C. [The present invention 1066] the treatment recommendations identified for the subject further comprise no treatment recommendation; No treatment is recommended, determining that the subject classification includes a subtype that is likely to be non-responsive to corticosteroid therapy; Identified by, The method of the present invention 1045. [This invention 1067] The method of claim 1066, wherein the subtype is subtype A or subtype B. [The present invention 1068] the treatment recommendation identified for the subject includes no corticosteroid therapy; the host response dysregulation includes a host response dysregulation that is not caused by an infection; At least one biomarker set is in Group 3, The method of the present invention 1045. [The present invention 1069] Without corticosteroid therapy, Determining that the subject classification includes subtypes that are likely to be non-responsive to corticosteroid therapy. Identified by, The method of the present invention 1068. [The present invention 1070] The method of claim 1069, wherein the subtype is subtype A or subtype C. [This invention 1071] the treatment recommendations identified for the subject further comprise corticosteroid therapy; the corticosteroid therapy determining that the subject classification includes a subtype that is likely to be responsive to corticosteroid therapy; Identified by, The method of the present invention 1045. [This invention 1072] The method of claim 1071, wherein the subtype is subtype B. [This invention 1073] 1. A method for identifying candidate therapeutics, comprising the steps of: accessing a differentially expressed gene database containing fold changes in gene levels between patients of different subtypes; determining at least a threshold number of genes that are differentially expressed in patients of a first subtype compared to patients of a second subtype, wherein each of the differentially expressed genes is involved in a common biological pathway; and determining a candidate therapy that is likely to be effective for patients of said first subtype, wherein said candidate therapy is effective in modulating the expression of at least one of the genes that are differentially expressed in patients of said first subtype; A method comprising: [This invention 1074] The differentially expressed gene database includes: obtaining labeled patient data, wherein labels in the labeled patient data identify patients that fall into one of two or more subtypes; generating a differentially expressed gene database for at least one or more genes by determining a fold change in gene level between at least patient data having a label indicating a first subtype and patient data having a label indicating a second subtype; The method of the present invention 1073, which is produced by [This invention 1075] The method of invention 1073 or 1074, wherein the labels of the labeled patient data are generated by applying a cluster analysis or by applying a patient subtype classifier. [This invention 1076] Any of the methods of claims 1073 to 1075, wherein the threshold number of genes is at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, or at least ten genes. [This invention 1077] determining candidate treatments for patients with a first subtype; Pharmacokinetic data, including data regarding the candidate therapeutic; and host response pathobiology, including data regarding patients of said first subtype Analyzing one or both of Any of the methods of claims 1073 to 1076, further comprising: [This invention 1078] 1. A non-transitory computer-readable medium for determining a patient subtype, comprising: When executed by a processor, the processor: obtaining quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets; Group 1 includes biomarker 1, biomarker 2, and biomarker 3; Biomarker 1 is one of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, or MMP8; Biomarker 2 is one of SERPINB1 or GSPT1; Biomarker 3 is one of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, or TOMM70A; Group 2 includes biomarker 4, biomarker 5, and biomarker 6; Biomarker 4 is one of ZNF831, MME, CD3G, or STOM; Biomarker 5 is one of ECSIT, LAT, or NCOA4; Biomarker 6 is one of SLC1A5, IGF2BP2, or ANXA3; Group 3 includes biomarker 7, biomarker 8, and biomarker 9; Biomarker 7 is one of C14orf159 or PUM2; Biomarker 8 is one of EPB42 or RPS6KA5; Biomarker 9 is one of EPB42 or GBP2; Group 4 includes biomarker 10, biomarker 11, and biomarker 12; the biomarker 10 is one of MSH2, DCTD, or MMP8; biomarker 11 is one of HK3, UCP2, or NUP88; Biomarker 12 is one of GABARAPL2 or CASP4; Group 5 includes biomarker 13, biomarker 14, and biomarker 15; Biomarker 13 is one of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G; biomarker 14 is one of EPB42, GSPT1, LAT, HK3, or SERPINB1; obtaining, wherein biomarker 15 is one of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and determining a classification of the subject based on said quantitative data using a patient subtype classifier; An order to 1. A non-transitory computer-readable medium comprising: [This invention 1079] 1078. The non-transitory computer readable medium of the present invention, wherein at least one biomarker set is Group 5 and biomarker 13 is one of STOM, MME, BNT3A2, or HLA-DPA1. [The present invention 1080] The non-transitory computer readable medium of any one of claims 1078 to 1079, wherein at least one biomarker set is Group 5 and biomarker 14 is one of EPB42, GSPT1, LAT, HK3, or SERPINB1. [This invention 1081] The non-transitory computer readable medium of any of claims 1078 to 1080, wherein at least one biomarker set is Group 5 and biomarker 15 is one of SLC1A5, IGF2BP2 or ANXA3. [This invention 1082] 1. A non-transitory computer-readable medium for determining a therapy recommendation for a patient, comprising: When executed by a processor, the processor: obtaining quantitative data for two or more biomarkers selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, TOMM70A, ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2, ANXA3, C14orf159, PUM2, EPB42, RPS6KA5, GBP2, MSH2, DCTD, HK3, UCP2, NUP88, GABARAPL2, and CASP4; and determining a classification of the subject based on said quantitative data using a patient subtype classifier; An order to 1. A non-transitory computer-readable medium comprising: [This invention 1083] 1. A non-transitory computer-readable medium for determining a therapy recommendation for a patient, comprising: When executed by a processor, the processor: obtaining quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker sets; Group 1 comprises two or more biomarkers selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3 and TOMM70A; Group 2 comprises two or more biomarkers selected from the group consisting of ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2 and ANXA3; Group 3 comprises two or more biomarkers selected from the group consisting of C14orf159, PUM2, EPB42, RPS6KA5, EPB42 and GBP2; Group 4 comprises two or more biomarkers selected from the group consisting of MSH2, DCTD, MMP8, HK3, UCP2, NUP88, GABARAPL2 and CASP4; obtaining Group 5, wherein the group 5 comprises two or more biomarkers selected from the group consisting of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, CD3G, EPB42, GSPT1, LAT, HK3, SERPINB1, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, and TNFRSF1A; and determining a classification of the subject based on said quantitative data using a patient subtype classifier; An order to 1. A non-transitory computer-readable medium comprising: [This invention 1084] instructions that, when executed by a processor, cause the processor to identify a treatment recommendation for the subject based at least in part on the classification. The non-transitory computer-readable medium of any one of 1078 to 1083 of the present invention further comprises: [This invention 1085] 1. A non-transitory computer-readable medium for determining a treatment recommendation for a subject, comprising: When executed by a processor, the processor: obtaining a classification of said subject exhibiting host response dysregulation, The classification is as follows: obtaining or having obtained quantitative data for at least one biomarker set selected from the group consisting of Group 1, Group 2, Group 3, Group 4 or Group 5 biomarker sets; Group 1 includes biomarker 1, biomarker 2, and biomarker 3; Biomarker 1 is one of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, or MMP8; Biomarker 2 is one of SERPINB1 or GSPT1; Biomarker 3 is one of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, or TOMM70A; Group 2 includes biomarker 4, biomarker 5, and biomarker 6; Biomarker 4 is one of ZNF831, MME, CD3G, or STOM; Biomarker 5 is one of ECSIT, LAT, or NCOA4; Biomarker 6 is one of SLC1A5, IGF2BP2, or ANXA3; Group 3 includes biomarker 7, biomarker 8, and biomarker 9; Biomarker 7 is one of C14orf159 or PUM2; Biomarker 8 is one of EPB42 or RPS6KA5; Biomarker 9 is one of EPB42 or GBP2; Group 4 includes biomarker 10, biomarker 11, and biomarker 12; the biomarker 10 is one of MSH2, DCTD, or MMP8; biomarker 11 is one of HK3, UCP2, or NUP88; Biomarker 12 is one of GABARAPL2 or CASP4; Group 5 includes biomarker 13, biomarker 14, and biomarker 15; Biomarker 13 is one of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G; biomarker 14 is one of EPB42, GSPT1, LAT, HK3, or SERPINB1; Biomarker 15 is, will be, or has been one of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and determining a classification of the subject based on said quantitative data using a patient subtype classifier; to obtain, as determined by identifying a treatment recommendation for the subject based at least in part on the classification. An order to 1. A non-transitory computer-readable medium comprising: [The present invention 1086] The non-transitory computer readable medium of the present invention 1085, wherein the subject's host response dysregulation includes one of sepsis and a host response dysregulation not caused by an infection. [This invention 1087] The non-transitory computer-readable medium of any of claims 1078 to 1086, wherein the classification of the subject includes one of subtype A or subtype B. [This invention 1088] The non-transitory computer-readable medium of any of claims 1078 to 1086, wherein the classification of the subject includes one of subtype A, subtype B, or subtype C. [This invention 1089] The non-transitory computer readable medium of any one of claims 1087 to 1088, wherein, depending on the classification of the subject as including subtype A, the treatment recommendation identified for the subject includes at least no immunosuppressive therapy. [The present invention 1090] The non-transitory computer readable medium of any one of claims 1087 to 1088, wherein, in response to a classification of the subject comprising subtype A, the treatment recommendation identified for the subject further comprises at least no corticosteroid therapy. [This invention 1091] The non-transitory computer readable medium of the present invention 1090, wherein the treatment recommendations identified for the subject further include at least one without hydrocortisone. [This invention 1092] The non-transitory computer readable medium of the present invention 1087 or 1088, wherein, depending on the classification of the subject to include subtype B, the treatment recommendation identified for the subject includes at least one of no treatment recommendation, immune stimulating therapy, suppression of immune modulating therapy, blocking of immune suppressive therapy, blocking of complement activation therapy, and anti-inflammatory therapy. [This invention 1093] The non-transitory computer readable medium of the present invention 1087 or 1088, wherein, depending on the classification of the subject including subtype B, the treatment recommendation identified for the subject further includes at least one of a checkpoint inhibitor, a complement component blocker, a complement component receptor blocker, and an inflammatory cytokine blocker. [This invention 1094] 1093. The non-transitory computer readable medium of the present invention, wherein the treatment recommendation identified for the subject further comprises at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulatory factor, IL-22 agonist, IFN-alpha regulatory factor, IFN-lambda regulatory factor, IFN-alpha 2b stimulator, anti-C5a, anti-C3a, anti-C5aR, anti-C3aR, anti-TNF-alpha and anti-IL-6, anti-HMGB1, ST2 antibody, IL-33 antibody. [This invention 1095] The non-transitory computer readable medium of the present invention 1088, wherein, in response to classification of the subject as including subtype C, the treatment recommendation identified for the subject comprises at least one of no treatment recommendation, immune stimulating therapy, suppression of immune modulating therapy, blocking of immune suppressive therapy, modulator of coagulation therapy, and modulator of vascular permeability therapy. [This invention 1096] The non-transitory computer readable medium of the present invention 1088, wherein, in response to a classification of the subject comprising subtype C, the treatment recommendation identified for the subject further comprises at least one of a checkpoint inhibitor and an anticoagulant. [This invention 1097] 1096. The non-transitory computer readable medium of the present invention, wherein the treatment recommendations identified for the subject further include at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulator, IL-22 agonist, IFN-alpha regulator, IFN-lambda regulator, IFN-alpha 2b stimulator, activated protein C, antithrombin, and thrombomodulin. [This invention 1098] instructions that cause the processor to obtain quantitative data, When executed by the processor, the processor: obtaining a sample containing a plurality of biomarkers from a subject exhibiting a dysregulated host response; and determining quantitative data from the obtained samples; An order to The non-transitory computer-readable medium of any one of 1078 to 1097 of the present invention further comprises: [This invention 1099] The non-transitory computer readable medium of the present invention 1098, wherein the obtained sample comprises a blood sample from the subject. [The present invention 1100] The non-transitory computer readable medium of any of 1078 or 1084 to 1099, wherein the subject exhibiting host response dysregulation does not exhibit shock and at least one biomarker set is one of Group 1, Group 3 or Group 4. [The present invention 1101] The non-transitory computer readable medium of any of 1078 or 1084-1099, wherein the subject exhibiting host response dysregulation further exhibits shock, and at least one biomarker set is one of Group 1, Group 2, Group 4, Group 5, Group 6, Group 7, or Group 8. [The present invention 1102] The non-transitory computer readable medium of any of 1078 or 1084 to 1099, wherein the subject exhibiting host response dysregulation is an adult subject and at least one biomarker set is one of Group 1, Group 2, Group 3, Group 5, Group 6, Group 7 or Group 8. [The present invention 1103] The non-transitory computer readable medium of any of 1078 or 1084 to 1099, wherein the subject exhibiting host response dysregulation is a pediatric subject and at least one biomarker set is one of Group 1, Group 4, Group 5, Group 6, Group 7 or Group 8. [The present invention 1104] The non-transitory computer readable medium of any of claims 1078 to 1103, wherein the quantitative data is determined by one of RT-qPCR (quantitative reverse transcription polymerase chain reaction), qPCR (quantitative polymerase chain reaction), PCR (polymerase chain reaction), RT-PCR (reverse transcription polymerase chain reaction), SDA (strand displacement amplification), RPA (recombinase polymerase amplification), MDA (multiple displacement amplification), HDA (helicase dependent amplification), LAMP (loop-mediated isothermal amplification), RCA (rolling circle amplification), NASBA (nucleic acid sequence-based amplification) and any other isothermal or thermocycling amplification reaction. [This invention 1105] Quantitative data, contacting the sample with a reagent; forming a plurality of complexes between the reagent and a plurality of biomarkers in the sample; and detecting the plurality of complexes to obtain a data set associated with the sample, the data set including the quantification data. Any of the non-transitory computer-readable media of the present invention 1078 to 1104, as determined by the above. [The present invention 1106] The classification of the subject is determining a class-specific score for said subject with respect to at least one candidate class for said subject; determining a classification of the subject by a patient subtype classifier based on the classification-specific score. A non-transitory computer-readable medium according to any one of claims 1078 to 1105 of the present invention, as determined by the above. [This invention 1107] instructions for causing a processor to determine a class-specific score, When executed by the processor, the processor: determining a first sub-score of quantitative data for the subject with respect to one or more biomarkers of the candidate classification, wherein the quantitative data for the subject with respect to the one or more biomarkers of the candidate classification is increased compared to the quantitative data with respect to the one or more biomarkers for one or more control subjects; determining a second subscore of quantitative expression for the subject for one or more additional biomarkers of the candidate classification, wherein the quantitative data for the subject for the one or more additional biomarkers of the candidate classification is decreased compared to the quantitative data for the one or more additional biomarkers for the one or more control subjects; and determining a difference between the first subscore and the second subscore, wherein the first and second geometric subscores are optionally subjected to scaling, and wherein the difference comprises the class-specific score for the subject. An order to The non-transitory computer readable medium of the present invention 1106 further includes: [This invention 1108] The non-transitory computer readable medium of the present invention 1107, wherein one or both of the first sub-score and the second sub-score is a geometric mean value. [This invention 1109] The non-transitory computer-readable medium of any of claims 1078 to 1108, wherein the patient subtype classifier is a machine learning model. [The present invention 1110] The non-transitory computer-readable medium of the present invention 1109, wherein the machine learning model is a support vector machine (SVM). [The present invention 1111] A non-transitory computer readable medium of the present invention 1110, wherein a support vector machine receives as input one or more classification-specific scores and outputs a classification of interest. [The present invention 1112] The patient subtype classifier comparing the classification-specific scores to one or more thresholds; and determining a classification of the subject based on said comparison. determining the classification of the subject by A non-transitory computer-readable medium according to the present invention 1110 or 1111. [The present invention 1113] The non-transitory computer-readable medium of the present invention 1112, wherein at least one of the one or more thresholds is a fixed value. [This invention 1114] A non-transitory computer-readable medium of the present invention 1112, wherein at least one of the one or more thresholds is determined using training samples, and the at least one threshold represents a value on the ROC curve that is closest to maximum sensitivity or maximum specificity. [This invention 1115] Before using the patient subtype classifier to determine the classification of the subject, normalizing said quantitative data based on quantitative data for one or more housekeeping genes. The non-transitory computer-readable medium of any one of 1078 to 1114 of the present invention further comprises: [The present invention 1116] The non-transitory computer-readable medium of any of claims 1106 to 1115, wherein the candidate classification of the subject includes subtype A, subtype B, and subtype C. [This invention 1117] The non-transitory computer readable medium of any of 1078 or 1085-1116, wherein at least one biomarker set is Group 1 and the patient subtype classifier has an average accuracy of at least 82.93%. [This invention 1118] The non-transitory computer readable medium of any of 1078 or 1085-1116, wherein the patient subtype classifier has an average accuracy of at least 89.6%. [This invention 1119] The non-transitory computer readable medium of any of 1078 or 1085-1116, wherein the patient subtype classifier has an average accuracy of at least 86.3%. [The present invention 1120] The non-transitory computer readable medium of any of 1078 or 1085-1116, wherein at least one biomarker set is Group 4 and the patient subtype classifier has an average accuracy of at least 98.3%. [This invention 1121] The non-transitory computer readable medium of 1084 or 1085, wherein the treatment recommendation identified for the subject further comprises corticosteroid therapy, no corticosteroid therapy, or no treatment recommendation. [This invention 1122] Treatment recommendations include no corticosteroid therapy; The absence of corticosteroid therapy determining that the statistical significance of the reduction in mortality in subjects exhibiting host response dysregulation who are not provided corticosteroid therapy is equal to or exceeds a threshold statistical significance; Identified by, A non-transitory computer-readable medium of the present invention 1121. [This invention 1123] Treatment recommendations include no corticosteroid therapy; The absence of corticosteroid therapy Determining that the subject classification includes subtypes that are likely to respond poorly to corticosteroid therapy. Identified by, A non-transitory computer-readable medium of the present invention 1121. [This invention 1124] The non-transitory computer-readable medium of the present invention 1123, wherein the subtype is subtype A or subtype C. [Invention 1125] Treatment recommendations include corticosteroid therapy; the corticosteroid therapy determining that the statistical significance of the reduction in mortality in subjects exhibiting host response dysregulation who are provided with corticosteroid therapy is equal to or exceeds a threshold statistical significance; Identified by, A non-transitory computer-readable medium of the present invention 1121. [The present invention 1126] Treatment recommendations include corticosteroid therapy; the corticosteroid therapy Determining that the subject classification includes subtypes that are likely to respond favorably to corticosteroid therapy. Identified by, A non-transitory computer-readable medium of the present invention 1121. [This invention 1127] The non-transitory computer-readable medium of the present invention 1126, wherein the subtype is subtype B. [This invention 1128] the treatment recommendation identified for the subject includes no treatment recommendation; The treatment is not recommended, but at least determining that the statistical significance of the reduction in mortality rate in subjects exhibiting host response dysregulation who are not provided corticosteroid therapy is below a threshold statistical significance; and determining that the statistical significance of the reduction in mortality in subjects exhibiting host response dysregulation who are provided with corticosteroid therapy is below a threshold statistical significance; Identified by, A non-transitory computer-readable medium of the present invention 1121. [This invention 1129] The non-transitory computer readable medium of the present invention 1122-1128, wherein the statistical significance comprises a p-value and the threshold statistical significance comprises at least 0.1. [The present invention 1130] the treatment recommendation identified for the subject includes no corticosteroid therapy; the host response dysregulation includes a host response dysregulation that is not caused by an infection; At least one biomarker set is in Group 1 or Group 4, A non-transitory computer-readable medium of the present invention 1121. [This invention 1131] Without corticosteroid therapy, Determining that the subject classification includes subtypes that are likely to respond poorly to corticosteroid therapy. Identified by, A non-transitory computer-readable medium of the present invention 1130. [This invention 1132] The non-transitory computer-readable medium of the present invention 1131, wherein the subtype is subtype A or subtype C. [This invention 1133] the treatment recommendations identified for the subject further comprise no treatment recommendation; No treatment is recommended, Determining that the classification of the subject includes subtype B. Identified by, A non-transitory computer-readable medium of the present invention 1121. [This invention 1134] the treatment recommendation identified for the subject includes no corticosteroid therapy; Dysregulated host response, including sepsis, At least one biomarker set is one of Group 2, Group 3 or Group 4; A non-transitory computer-readable medium of the present invention 1121. [This invention 1135] Without corticosteroid therapy, Determining that the subject classification includes subtypes that are likely to respond poorly to corticosteroid therapy. Identified by, A non-transitory computer readable medium of the present invention 1134. [This invention 1136] The non-transitory computer-readable medium of the present invention 1135, wherein the subtype is subtype A. [This invention 1137] the treatment recommendations identified for the subject further comprise no treatment recommendation; No treatment is recommended, determining that the subject classification includes a subtype that is likely to be non-responsive to corticosteroid therapy; Identified by, A non-transitory computer-readable medium of the present invention 1121. [This invention 1138] The non-transitory computer-readable medium of the present invention 1137, wherein the subtype is subtype B or subtype C. [This invention 1139] the treatment recommendation identified for the subject further comprises no corticosteroid therapy; the host response dysregulation includes a host response dysregulation that is not caused by an infection; At least one biomarker set is in Group 2, A non-transitory computer-readable medium of the present invention 1121. [This invention 1140] Without corticosteroid therapy, Determining that the subject classification includes subtypes that are likely to respond poorly to corticosteroid therapy. Identified by, A non-transitory computer-readable medium of the present invention 1139. [This invention 1141] The non-transitory computer-readable medium of the present invention 1140, wherein the subtype is subtype C. [This invention 1142] the treatment recommendations identified for the subject further comprise no treatment recommendation; No treatment is recommended, determining that the subject classification includes a subtype that is likely to be non-responsive to corticosteroid therapy; Identified by, A non-transitory computer-readable medium of the present invention 1121. [This invention 1143] The non-transitory computer-readable medium of the present invention 1142, wherein the subtype is subtype A or subtype B. [This invention 1144] the treatment recommendation identified for the subject includes no corticosteroid therapy; the host response dysregulation includes a host response dysregulation that is not caused by an infection; At least one biomarker set is in Group 3, A non-transitory computer-readable medium of the present invention 1121. [Invention 1145] Without corticosteroid therapy, Determining that the subject classification includes subtypes that are likely to be non-responsive to corticosteroid therapy. Identified by, A non-transitory computer readable medium of the present invention 1144. [Invention 1146] The non-transitory computer-readable medium of the present invention 1145, wherein the subtype is subtype A or subtype C. [This invention 1147] the treatment recommendations identified for the subject further comprise corticosteroid therapy; the corticosteroid therapy determining that the subject classification includes a subtype that is likely to be responsive to corticosteroid therapy; Identified by, A non-transitory computer-readable medium of the present invention 1121. [This invention 1148] The non-transitory computer-readable medium of the present invention 1147, wherein the subtype is subtype B. [This invention 1149] 1. A non-transitory computer-readable medium for identifying candidate treatments, comprising: When executed by a processor, the processor: Accessing differentially expressed gene databases containing fold changes in gene levels between patients of different subtypes; determining at least a threshold number of genes that are differentially expressed in patients of a first subtype compared to patients of a second subtype, wherein each of the differentially expressed genes is involved in a common biological pathway; and determining a candidate therapy that is likely to be effective for patients with the first subtype, wherein the candidate therapy is effective in modulating the expression of at least one of the differentially expressed genes in patients with the first subtype; An order to 1. A non-transitory computer-readable medium comprising: [This invention 1150] The differentially expressed gene database obtaining labeled patient data, wherein labels in the labeled patient data identify patients that fall into one of two or more subtypes; generating a differentially expressed gene database for at least one or more genes by determining a fold change in gene level between at least patient data having a label indicating a first subtype and patient data having a label indicating a second subtype; A non-transitory computer-readable medium of the present invention 1149, generated by [This invention 1151] The non-transitory computer readable medium of the present invention 1149 or 1150, wherein the labels of the labeled patient data are generated by applying a cluster analysis or by applying a patient subtype classifier. [This invention 1152] The non-transitory computer readable medium of any of claims 1149 to 1151, wherein the threshold number of genes is at least 3 genes, at least 4 genes, at least 5 genes, at least 6 genes, at least 7 genes, at least 8 genes, at least 9 genes, or at least 10 genes. [This invention 1153] determining candidate treatments for patients with the first subtype; Pharmacokinetic data, including data regarding the candidate therapeutic; and host response pathobiology, including data regarding patients of said first subtype Analyzing one or both of The non-transitory computer-readable medium of any one of 1149 to 1152 of the present invention further comprises: [This invention 1154] 1. A system for determining a patient subtype, comprising: a set of reagents used to determine quantitative data for at least one set of biomarkers from a test sample derived from a subject, wherein the at least one set of biomarkers is selected from the group consisting of a Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker set; Group 1 includes biomarker 1, biomarker 2, and biomarker 3; Biomarker 1 is one of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, or MMP8; Biomarker 2 is one of SERPINB1 or GSPT1; Biomarker 3 is one of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, or TOMM70A; Group 2 includes biomarker 4, biomarker 5, and biomarker 6; Biomarker 4 is one of ZNF831, MME, CD3G, or STOM; Biomarker 5 is one of ECSIT, LAT, or NCOA4; Biomarker 6 is one of SLC1A5, IGF2BP2, or ANXA3; Group 3 includes biomarker 7, biomarker 8, and biomarker 9; Biomarker 7 is one of C14orf159 or PUM2; Biomarker 8 is one of EPB42 or RPS6KA5; Biomarker 9 is one of EPB42 or GBP2; Group 4 includes biomarker 10, biomarker 11, and biomarker 12; the biomarker 10 is one of MSH2, DCTD, or MMP8; biomarker 11 is one of HK3, UCP2, or NUP88; Biomarker 12 is one of GABARAPL2 or CASP4; Group 5 includes biomarker 13, biomarker 14, and biomarker 15; Biomarker 13 is one of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G; biomarker 14 is one of EPB42, GSPT1, LAT, HK3, or SERPINB1; a set of reagents, wherein biomarker 15 is one of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and a device configured to receive a mixture of one or more reagents in the set and the test sample and to measure quantitative data for the at least one set of biomarkers from the test sample; and a computer system communicatively coupled to the device for obtaining the quantitative data for the at least one biomarker set and for determining a classification of the subject based on the quantitative data using a patient subtype classifier. Including, the system. [This invention 1155] The system of the present invention 1154, wherein at least one biomarker set is Group 5 and biomarker 13 is one of STOM, MME, BNT3A2 or HLA-DPA1. [Invention 1156] The system of invention 1154 or 1155, wherein at least one biomarker set is Group 5 and biomarker 14 is one of EPB42, GSPT1, LAT, HK3 or SERPINB1. [This invention 1157] The system of any of inventions 1154 to 1156, wherein at least one biomarker set is Group 5 and biomarker 15 is one of SLC1A5, IGF2BP2, or ANXA3. [This invention 1158] 1. A system for determining a patient subtype, comprising: a set of reagents used to determine quantitative data for two or more biomarkers selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, TOMM70A, ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2, ANXA3, C14orf159, PUM2, EPB42, RPS6KA5, GBP2, MSH2, DCTD, HK3, UCP2, NUP88, GABARAPL2, and CASP4; and a device configured to receive a mixture of one or more reagents in the set and a test sample and to measure quantitative data for at least one of the set of biomarkers from the test sample; and a computer system communicatively coupled to the device for obtaining the quantitative data for the at least one biomarker set and for determining a subject classification based on the quantitative data using a patient subtype classifier. Including, the system. [This invention 1159] 1. A system for determining a patient subtype, comprising: A set of reagents used to determine quantitative data for at least one biomarker set selected from the group consisting of a Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker set, Group 1 comprises two or more biomarkers selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3 and TOMM70A; Group 2 comprises two or more biomarkers selected from the group consisting of ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2 and ANXA3; Group 3 comprises two or more biomarkers selected from the group consisting of C14orf159, PUM2, EPB42, RPS6KA5, EPB42 and GBP2; Group 4 comprises two or more biomarkers selected from the group consisting of MSH2, DCTD, MMP8, HK3, UCP2, NUP88, GABARAPL2 and CASP4; Group 5 is a set of reagents comprising two or more biomarkers selected from the group consisting of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, CD3G, EPB42, GSPT1, LAT, HK3, SERPINB1, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, and TNFRSF1A; and a device configured to receive a mixture of one or more reagents in the set and a test sample and to measure the quantitative data for the at least one set of biomarkers from the test sample; and a computer system communicatively coupled to the device for obtaining the quantitative data for the at least one biomarker set and for determining a subject classification based on the quantitative data using a patient subtype classifier. Including, the system. [The present invention 1160] The system of any of claims 1154-1159, wherein the computer system is configured to identify a treatment recommendation for the subject based at least in part on said classification. [This invention 1161] 1. A system for determining a therapy recommendation for a patient, comprising: a computer system; The computer system obtaining a classification of subjects exhibiting host response dysregulation; and Identifying a treatment recommendation for the subject based at least in part on the classification. It is configured as follows: The classification is as follows: obtaining, or having obtained, quantitative data for at least one biomarker set obtained from a subject selected from the group consisting of a Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker set; Group 1 includes biomarker 1, biomarker 2, and biomarker 3; Biomarker 1 is one of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, or MMP8; Biomarker 2 is one of SERPINB1 or GSPT1; Biomarker 3 is one of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, or TOMM70A; Group 2 includes biomarker 4, biomarker 5, and biomarker 6; Biomarker 4 is one of ZNF831, MME, CD3G, or STOM; Biomarker 5 is one of ECSIT, LAT, or NCOA4; Biomarker 6 is one of SLC1A5, IGF2BP2, or ANXA3; Group 3 includes biomarker 7, biomarker 8, and biomarker 9; Biomarker 7 is one of C14orf159 or PUM2; Biomarker 8 is one of EPB42 or RPS6KA5; Biomarker 9 is one of EPB42 or GBP2; Group 4 includes biomarker 10, biomarker 11, and biomarker 12; the biomarker 10 is one of MSH2, DCTD, or MMP8; biomarker 11 is one of HK3, UCP2, or NUP88; Biomarker 12 is one of GABARAPL2 or CASP4; Group 5 includes biomarker 13, biomarker 14, and biomarker 15; Biomarker 13 is one of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G; biomarker 14 is one of EPB42, GSPT1, LAT, HK3, or SERPINB1; Biomarker 15 is, will be, or has been one of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and determining said classification based on said quantitative data using a patient subtype classifier; It is determined by system. [This invention 1162] The system of the present invention 1161, wherein the subject's host response dysregulation includes one of sepsis and host response dysregulation not caused by infection. [This invention 1163] The system of any of claims 1154 to 1162, wherein the classification of the subject includes one of subtype A or subtype B. [This invention 1164] The system of any of claims 1154 to 1162, wherein the classification of the subject includes one of subtype A, subtype B, or subtype C. [Invention 1165] The system of the present invention 1163 or 1164, wherein, depending on the classification of the subject as including subtype A, the treatment recommendation identified for the subject includes at least no immunosuppressive therapy. [Invention 1166] The system of claim 1163 or 1164, wherein, in response to classification of the subject as including subtype A, the treatment recommendation identified for the subject further includes at least no corticosteroid therapy. [This invention 1167] The system of the present invention 1166, wherein the treatment recommendations identified for the subject further include at least one without hydrocortisone. [Invention 1168] The system of the present invention 1163 or 1164, wherein, depending on the classification of the subject as including subtype B, the treatment recommendation identified for the subject includes at least one of no treatment recommendation, immune stimulating therapy, suppression of immune modulating therapy, blockage of immune suppressive therapy, blockage of complement activation therapy, and anti-inflammatory therapy. [This invention 1169] The system of the present invention 1163 or 1164, wherein, depending on the classification of the subject as including subtype B, the treatment recommendation identified for the subject further includes at least one of a checkpoint inhibitor, a complement component blocker, a complement component receptor blocker, and an inflammatory cytokine blocker. [This invention 1170] 1163. The system of claim 1163, wherein the treatment recommendation identified for the subject further comprises at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulatory factor, IL-22 agonist, IFN-alpha regulatory factor, IFN-lambda regulatory factor, IFN-alpha 2b stimulator, anti-C5a, anti-C3a, anti-C5aR, anti-C3aR, anti-TNF-alpha, and anti-IL-6, anti-HMGB1, ST2 antibody, IL-33 antibody. [This invention 1171] The system of the present invention 1164, wherein, in response to classification of the subject as including subtype C, the treatment recommendation identified for the subject includes at least one of no treatment recommendation, immune stimulating therapy, suppression of immune modulating therapy, blockade of immune suppressive therapy, modulator of coagulation therapy, and modulator of vascular permeability therapy. [This invention 1172] The system of the present invention 1164, wherein, in response to the subject's classification as including subtype C, the treatment recommendation identified for the subject further comprises at least one of a checkpoint inhibitor and an anticoagulant. [This invention 1173] 1172. The system of claim 1172, wherein the treatment recommendation identified for the subject further comprises at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulator, IL-22 agonist, IFN-alpha regulator, IFN-lambda regulator, IFN-alpha 2b stimulator, activated protein C, antithrombin, and thrombomodulin. [This invention 1174] The system of any one of claims 1154 to 1163, wherein the sample comprises a blood sample from the subject. [Invention 1175] The system of any of claims 1154 or 1161-1174, wherein the subject exhibiting host response dysregulation does not exhibit shock and at least one biomarker set is one of Group 1, Group 3 or Group 4. [Invention 1176] Any of the systems of the present invention 1154 or 1161-1174, wherein the subject exhibiting host response dysregulation further exhibits shock and at least one biomarker set is one of Group 1, Group 2, Group 4, Group 5, Group 6, Group 7 or Group 8. [This invention 1177] The system of any of 1154 or 1161-1174, wherein the subject exhibiting host response dysregulation is an adult subject and at least one biomarker set is one of Group 1, Group 2, Group 3, Group 5, Group 6, Group 7 or Group 8. [This invention 1178] The system of any of 1154 or 1161-1174, wherein the subject exhibiting host response dysregulation is a pediatric subject and at least one biomarker set is one of Group 1, Group 4, Group 5, Group 6, Group 7 or Group 8. [This invention 1179] The system of any of claims 1154 to 1178, wherein the quantitative data is determined by one of RT-qPCR (quantitative reverse transcription polymerase chain reaction), qPCR (quantitative polymerase chain reaction), PCR (polymerase chain reaction), RT-PCR (reverse transcription polymerase chain reaction), SDA (strand displacement amplification), RPA (recombinase polymerase amplification), MDA (multiple displacement amplification), HDA (helicase-dependent amplification), LAMP (loop-mediated isothermal amplification), RCA (rolling circle amplification), NASBA (nucleic acid sequence-based amplification) and any other isothermal or thermocycling amplification reaction. [This invention 1180] The classification of the subject is determining a class-specific score for said subject with respect to at least one candidate class for said subject; determining a classification of the subject by a patient subtype classifier based on the classification-specific score. Any of the systems of the present inventions 1154 to 1179, determined by the above. [This invention 1181] Determining a classification-specific score determining a first sub-score of quantitative data for the subject with respect to one or more biomarkers of the candidate classification, wherein the quantitative data for the subject with respect to the one or more biomarkers of the candidate classification is increased compared to the quantitative data with respect to the one or more biomarkers for one or more control subjects; determining a second subscore of quantitative expression for the subject for one or more additional biomarkers of the candidate classification, wherein the quantitative data for the subject for the one or more additional biomarkers of the candidate classification is decreased compared to the quantitative data for the one or more additional biomarkers for the one or more control subjects; and determining a difference between the first subscore and the second subscore, wherein the first and second geometric subscores are optionally subjected to scaling, and wherein the difference comprises the class-specific score for the subject. The system of the present invention 1180 further includes: [This invention 1182] The system of the present invention 1181, wherein one or both of the first subscore and the second subscore is a geometric mean value. [This invention 1183] The system of any of claims 1154 to 1182, wherein the patient subtype classifier is a machine learning model. [This invention 1184] The system of the present invention 1183, wherein the machine learning model is a support vector machine (SVM). [This invention 1185] The system of the present invention 1184, wherein the support vector machine takes one or more classification-specific scores as input and outputs a classification of interest. [Invention 1186] The patient subtype classifier comparing the classification-specific scores to one or more thresholds; and determining a classification of the subject based on said comparison. determining the classification of the subject by The system of the present invention 1180 or 1181. [This invention 1187] The system of the present invention 1186, wherein at least one of the one or more thresholds is a fixed value. [This invention 1188] At least one of the one or more thresholds is determined using training samples; the at least one threshold represents the value on the ROC curve closest to maximum sensitivity or maximum specificity; The system of the present invention 1186. [This invention 1189] Before using the patient subtype classifier to determine the classification of the subject, normalizing said quantitative data based on quantitative data for one or more housekeeping genes. Any of the systems of the present inventions 1154 to 1188 further comprising: [This invention 1190] The system of any one of claims 1180 to 1189, wherein the candidate classification of the subject includes subtype A, subtype B, and subtype C. [This invention 1191] The system of any of inventions 1154 or 1161-1190, wherein at least one biomarker set is Group 1 and the patient subtype classifier has an average accuracy of at least 82.93%. [This invention 1192] The system of any of inventions 1154 or 1161-1190, wherein the patient subtype classifier has an average accuracy of at least 89.6%. [This invention 1193] The system of any of inventions 1154 or 1161-1190, wherein the patient subtype classifier has an average accuracy of at least 86.3%. [This invention 1194] The system of any of inventions 1154 or 1161-1190, wherein at least one biomarker set is Group 4 and the patient subtype classifier has an average accuracy of at least 98.3%. [This invention 1195] The system of invention 1158 or 1161, wherein the treatment recommendation identified for the subject further comprises corticosteroid therapy, no corticosteroid therapy, or no treatment recommendation. [Invention 1196] Treatment recommendations include no corticosteroid therapy; The absence of corticosteroid therapy determining that the statistical significance of the reduction in mortality in subjects exhibiting host response dysregulation who are not provided corticosteroid therapy is equal to or exceeds a threshold statistical significance; Identified by, The system of the present invention 1195. [This invention 1197] Treatment recommendations include no corticosteroid therapy; The absence of corticosteroid therapy Determining that the subject classification includes subtypes that are likely to respond poorly to corticosteroid therapy. Identified by, The system of the present invention 1195. [This invention 1198] The system of the present invention 1197, wherein the subtype is subtype A or subtype C. [This invention 1199] Treatment recommendations include corticosteroid therapy; the corticosteroid therapy determining that the statistical significance of the reduction in mortality in subjects exhibiting host response dysregulation who are provided with corticosteroid therapy is equal to or exceeds a threshold statistical significance; Identified by, The system of the present invention 1195. [The present invention 1200] Treatment recommendations include corticosteroid therapy; the corticosteroid therapy Determining that the subject classification includes subtypes that are likely to respond favorably to corticosteroid therapy. Identified by, The system of the present invention 1195. [The present invention 1201] The system of the present invention 1200, wherein the subtype is subtype B. [This invention 1202] the treatment recommendation identified for the subject includes no treatment recommendation; The treatment is not recommended, but at least determining that the statistical significance of the reduction in mortality rate in subjects exhibiting host response dysregulation who are not provided corticosteroid therapy is below a threshold statistical significance; and determining that the statistical significance of the reduction in mortality in subjects exhibiting host response dysregulation who are provided with corticosteroid therapy is below a threshold statistical significance; Identified by, The system of the present invention 1195. [This invention 1203] The system of any of claims 1196-1202, wherein the statistical significance comprises a p-value and the threshold statistical significance comprises at least 0.1. [The present invention 1204] the treatment recommendation identified for the subject includes no corticosteroid therapy; the host response dysregulation includes a host response dysregulation that is not caused by an infection; At least one biomarker set is in Group 1 or Group 4, The system of the present invention 1195. [This invention 1205] Without corticosteroid therapy, Determining that the subject classification includes subtypes that are likely to respond poorly to corticosteroid therapy. Identified by, The system of the present invention 1204. [This invention 1206] The system of the present invention 1205, wherein the subtype is subtype A or subtype C. [This invention 1207] the treatment recommendations identified for the subject further comprise no treatment recommendation; No treatment is recommended, Determining that the classification of the subject includes subtype B. Identified by, The system of the present invention 1195. [This invention 1208] the treatment recommendation identified for the subject includes no corticosteroid therapy; Dysregulated host response, including sepsis, At least one biomarker set is one of Group 2, Group 3 or Group 4; The system of the present invention 1195. [This invention 1209] Without corticosteroid therapy, Determining that the subject classification includes subtypes that are likely to respond poorly to corticosteroid therapy. Identified by, The system of the present invention 1208. [The present invention 1210] The system of the present invention 1209, wherein the subtype is subtype A. [The present invention 1211] the treatment recommendations identified for the subject further comprise no treatment recommendation; No treatment is recommended, determining that the subject classification includes a subtype that is likely to be non-responsive to corticosteroid therapy; Identified by, The system of the present invention 1195. [The present invention 1212] The system of the present invention 1211, wherein the subtype is subtype B or subtype C. [This invention 1213] the treatment recommendation identified for the subject further comprises no corticosteroid therapy; the host response dysregulation includes a host response dysregulation that is not caused by an infection; At least one biomarker set is in Group 2, The system of the present invention 1195. [This invention 1214] Without corticosteroid therapy, Determining that the subject classification includes subtypes that are likely to respond poorly to corticosteroid therapy. Identified by, The system of the present invention 1213. [This invention 1215] The system of the present invention 1214, wherein the subtype is subtype C. [This invention 1216] the treatment recommendations identified for the subject further comprise no treatment recommendation; No treatment is recommended, determining that the subject classification includes a subtype that is likely to be non-responsive to corticosteroid therapy; Identified by, The system of the present invention 1195. [This invention 1217] The system of the present invention 1216, wherein the subtype is subtype A or subtype B. [This invention 1218] the treatment recommendation identified for the subject includes no corticosteroid therapy; the host response dysregulation includes a host response dysregulation that is not caused by an infection; At least one biomarker set is in Group 3, The system of the present invention 1195. [This invention 1219] Without corticosteroid therapy, Determining that the subject classification includes subtypes that are likely to be non-responsive to corticosteroid therapy. Identified by, The system of the present invention 1218. [The present invention 1220] The system of the present invention 1219, wherein the subtype is subtype A or subtype C. [This invention 1221] the treatment recommendations identified for the subject further comprise corticosteroid therapy; the corticosteroid therapy determining that the subject classification includes a subtype that is likely to be responsive to corticosteroid therapy; Identified by, The system of the present invention 1195. [This invention 1222] The system of the present invention 1221, wherein the subtype is subtype B. [This invention 1223] 1. A system for identifying candidate treatments, comprising: a storage device for storing a differentially expressed gene database including fold changes in gene levels between patients of different subtypes; and accessing one or more gene level fold changes corresponding to the differentially expressed genes in the differentially expressed gene database; determining at least a threshold number of genes that are differentially expressed in patients of a first subtype compared to patients of a second subtype, wherein each of the differentially expressed genes is involved in a common biological pathway; determining a candidate therapy that is likely to be effective for patients of the first subtype, wherein the candidate therapy is effective in modulating the expression of at least one of the genes that are differentially expressed in patients of the first subtype; A computing device configured to Including, the system. [This invention 1224] The differentially expressed gene database includes: obtaining labeled patient data, wherein labels in the labeled patient data identify patients that fall into one of two or more subtypes; generating said differentially expressed gene database for at least one or more genes by determining a fold change in gene level between at least patient data having a label indicating a first subtype and patient data having a label indicating a second subtype; The system of the present invention 1223 is generated by [This invention 1225] The system of the present invention 1223 or 1224, wherein the labels of the labeled patient data are generated by applying a cluster analysis or by applying a patient subtype classifier. [This invention 1226] 1226. The system of any of claims 1223 to 1225, wherein the threshold number of genes is at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, or at least ten genes. [This invention 1227] determining candidate treatments for patients with the first subtype; Pharmacokinetic data, including data regarding the candidate therapeutic; and host response pathobiology, including data regarding patients of said first subtype Analyzing one or both of The system of any one of 1223 to 1226 of the present invention further comprises: [This invention 1228] 1. A kit for determining a patient subtype, comprising: a set of reagents for determining quantitative data for at least one set of biomarkers from a test sample derived from a subject, wherein the at least one set of biomarkers is selected from the group consisting of a Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker set; Group 1 includes biomarker 1, biomarker 2, and biomarker 3; Biomarker 1 is one of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, or MMP8; Biomarker 2 is one of SERPINB1 or GSPT1; Biomarker 3 is one of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, or TOMM70A; Group 2 includes biomarker 4, biomarker 5, and biomarker 6; Biomarker 4 is one of ZNF831, MME, CD3G, or STOM; Biomarker 5 is one of ECSIT, LAT, or NCOA4; Biomarker 6 is one of SLC1A5, IGF2BP2, or ANXA3; Group 3 includes biomarker 7, biomarker 8, and biomarker 9; Biomarker 7 is one of C14orf159 or PUM2; Biomarker 8 is one of EPB42 or RPS6KA5; Biomarker 9 is one of EPB42 or GBP2; Group 4 includes biomarker 10, biomarker 11, and biomarker 12; the biomarker 10 is one of MSH2, DCTD, or MMP8; biomarker 11 is one of HK3, UCP2, or NUP88; Biomarker 12 is one of GABARAPL2 or CASP4; Group 5 includes biomarker 13, biomarker 14, and biomarker 15; Biomarker 13 is one of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G; biomarker 14 is one of EPB42, GSPT1, LAT, HK3, or SERPINB1; a set of reagents, wherein biomarker 15 is one of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and instructions for using said set of reagents to determine said quantitative data for said at least one set of biomarkers; Includes a kit. [This invention 1229] 1228. The kit of claim 1228, wherein at least one biomarker set is Group 5 and biomarker 13 is one of STOM, MME, BNT3A2, or HLA-DPA1. [This invention 1230] The kit of invention 1228 or 1229, wherein at least one biomarker set is Group 5 and biomarker 14 is one of EPB42, GSPT1, LAT, HK3 or SERPINB1. [This invention 1231] The kit of any one of claims 1228 to 1230, wherein at least one biomarker set is Group 5 and biomarker 15 is one of SLC1A5, IGF2BP2, or ANXA3. [This invention 1232] 1. A kit for determining a patient subtype, comprising: a set of reagents for determining quantitative data for two or more biomarkers selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, TOMM70A, ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2, ANXA3, C14orf159, PUM2, EPB42, RPS6KA5, GBP2, MSH2, DCTD, HK3, UCP2, NUP88, GABARAPL2, and CASP4; and instructions for using said set of reagents to determine said quantitative data for said at least one set of biomarkers; Includes a kit. [This invention 1233] 1. A kit for determining a patient subtype, comprising: A set of reagents for determining quantitative data for at least one biomarker set selected from the group consisting of a Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker set, Group 1 comprises two or more biomarkers selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3 and TOMM70A; Group 2 comprises two or more biomarkers selected from the group consisting of ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2 and ANXA3; Group 3 comprises two or more biomarkers selected from the group consisting of C14orf159, PUM2, EPB42, RPS6KA5, EPB42 and GBP2; Group 4 comprises two or more biomarkers selected from the group consisting of MSH2, DCTD, MMP8, HK3, UCP2, NUP88, GABARAPL2 and CASP4; Group 5 is a set of reagents comprising two or more biomarkers selected from the group consisting of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, CD3G, EPB42, GSPT1, LAT, HK3, SERPINB1, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, and TNFRSF1A; and instructions for using said set of reagents to determine said quantitative data for said at least one set of biomarkers; Includes a kit. [This invention 1234] The kit of any of claims 1228 to 1233, wherein the instructions for use include instructions for determining quantitative data by performing one of RT-qPCR (quantitative reverse transcription polymerase chain reaction), qPCR (quantitative polymerase chain reaction), PCR (polymerase chain reaction), RT-PCR (reverse transcription polymerase chain reaction), SDA (strand displacement amplification), RPA (recombinase polymerase amplification), MDA (multiple displacement amplification), HDA (helicase-dependent amplification), LAMP (loop-mediated isothermal amplification), RCA (rolling circle amplification), NASBA (nucleic acid sequence-based amplification), and any other isothermal or thermocycling amplification reaction. [This invention 1235] the set of reagents comprises at least three primer sets for amplifying at least three biomarkers; the at least three primer sets comprise pairs of single-stranded DNA primers for amplifying the at least three biomarkers; at least one of the at least three biomarkers is selected from the group consisting of the biomarkers EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, ZNF831, MME, CD3G, STOM, C14orf159, PUM2, MSH2, DCTD, BNT3A2 or HLA-DPA1; at least one biomarker of the at least three biomarkers is selected from the group consisting of the biomarkers SERPINB1, GSPT1, ECSIT, LAT, NCOA4, EPB42, RPS6KA5, HK3, UCP2 or NUP88; The kit of any of claims 1228 to 1234, wherein at least one of the at least three biomarkers is selected from the group consisting of the biomarkers MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, PRPF3, TOMM70A, EPB42, GABARAPL2, CASP4, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1 or TNFRSF1A. [This invention 1236] At least one of the at least three primer sets is a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 7 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 8; a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 9 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 10; a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 11 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 12; and a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 13 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 14; selected from the group consisting of At least one of the at least three primer sets comprises: a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 15 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 16; a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 17 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 18; and a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 19 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 20; selected from the group consisting of At least one of the at least three primer sets comprises: a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 1 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 2; a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 3 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 4; and a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 5 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 6; The kit of the present invention 1235 selected from the group consisting of: [This invention 1237] At least one of the at least three primer sets is a forward primer comprising SEQ ID NO. 7 and a reverse primer comprising SEQ ID NO. 8; a forward primer comprising SEQ ID NO. 9 and a reverse primer comprising SEQ ID NO. 10; a forward primer comprising SEQ ID NO. 11 and a reverse primer comprising SEQ ID NO. 12, and A forward primer containing SEQ ID NO. 13 and a reverse primer containing SEQ ID NO. 14 selected from the group consisting of At least one of the at least three primer sets comprises: a forward primer comprising SEQ ID NO. 15 and a reverse primer comprising SEQ ID NO. 16; a forward primer comprising SEQ ID NO. 17 and a reverse primer comprising SEQ ID NO. 18, and A forward primer containing SEQ ID NO. 19 and a reverse primer containing SEQ ID NO. 20 selected from the group consisting of At least one of the at least three primer sets comprises: a forward primer comprising SEQ ID NO. 1 and a reverse primer comprising SEQ ID NO. 2; a forward primer comprising SEQ ID NO. 3 and a reverse primer comprising SEQ ID NO. 4, and A forward primer containing SEQ ID NO. 5 and a reverse primer containing SEQ ID NO. 6 The kit of the present invention 1235 selected from the group consisting of: [This invention 1238] At least one of the at least three primer sets is a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 21 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 22; and a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 23 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 24; selected from the group consisting of At least one of the at least three primer sets comprises: a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 25 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 26; and a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 29 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 30; selected from the group consisting of At least one of the at least three primer sets comprises: a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 25 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 26; and a forward primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 27 and a reverse primer comprising at least 15 consecutive nucleotides of SEQ ID NO. 28; The kit of the present invention 1235 selected from the group consisting of: [This invention 1239] At least one of the at least three primer sets is a forward primer comprising SEQ ID NO. 21 and a reverse primer comprising SEQ ID NO. 22, and A forward primer containing SEQ ID NO. 23 and a reverse primer containing SEQ ID NO. 24 selected from the group consisting of At least one of the at least three primer sets comprises: a forward primer comprising SEQ ID NO. 25 and a reverse primer comprising SEQ ID NO. 26, and A forward primer containing SEQ ID NO. 29 and a reverse primer containing SEQ ID NO. 30 selected from the group consisting of At least one of the at least three primer sets comprises: a forward primer comprising SEQ ID NO. 25 and a reverse primer comprising SEQ ID NO. 26, and A forward primer containing SEQ ID NO. 27 and a reverse primer containing SEQ ID NO. 28 The kit of the present invention 1235 selected from the group consisting of: [This invention 1240] the set of reagents comprises at least three primer sets for amplifying at least three biomarkers; each primer set of the at least three primer sets comprises a forward outer primer, a backward outer primer, a forward inner primer, a backward inner primer, a forward loop primer, and a backward loop primer for amplifying one of the at least three biomarkers; at least one of the at least three biomarkers is selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, ZNF831, MME, CD3G, STOM, C14orf159, PUM2, MSH2, DCTD, BNT3A2 or HLA-DPA1; at least one biomarker of the at least three biomarkers is selected from the group consisting of SERPINB1, GSPT1, ECSIT, LAT, NCOA4, EPB42, RPS6KA5, HK3, UCP2 or NUP88; The kit of any of claims 1228 to 1234, wherein at least one biomarker of the at least three biomarkers is selected from the group consisting of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, PRPF3, TOMM70A, EPB42, GABARAPL2, CASP4, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1 or TNFRSF1A. [This invention 1241] At least one of the at least three primer sets is a forward outer primer, a backward outer primer, a forward inner primer, a backward inner primer, a forward loop primer and a backward loop primer, each configured to allow amplification of at least one biomarker selected from the group consisting of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, ZNF831, MME, CD3G, STOM, C14orf159, PUM2, MSH2, DCTD, BNT3A2 or HLA-DPA1; a forward outer primer, a backward outer primer, a forward inner primer, a backward inner primer, a forward loop primer, and a backward loop primer, each configured to allow amplification of at least one biomarker selected from the group consisting of SERPINB1, GSPT1, ECSIT, LAT, NCOA4, EPB42, RPS6KA5, HK3, UCP2, or NUP88; and a forward outer primer, a backward outer primer, a forward inner primer, a backward inner primer, a forward loop primer, and a backward loop primer, each configured to allow amplification of at least one biomarker selected from the group consisting of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, PRPF3, TOMM70A, EPB42, GABARAPL2, CASP4, SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, or TNFRSF1A; The kit of the present invention 1240 selected from the group consisting of: [Brief explanation of the drawings]

[0097] These and other features, aspects and advantages of the present disclosure will become better understood with reference to the following detailed description and accompanying drawings.

[0098] [Figure 1A] FIG. 1 is a block diagram of a process for identifying host response dysregulated patient subtypes, constructing a patient subtype classifier, and assessing the effectiveness of corticosteroid therapy for host response dysregulated patients based on the subtype classification identified using the patient subtype classifier, according to an embodiment. [Figure 1B] FIG. 1 is an overview of a system environment for determining therapy recommendations for a patient, according to an aspect. [Figure 2-1] For the full model, graphs show the individual accuracies determined for each combination of three biomarkers, one biomarker from each subtype. [Figure 2-2] See description of Figure 2-1. [Figure 2-3] See description of Figure 2-1. [Figure 2-4] See description of Figure 2-1. [Figure 3] Graph of the individual accuracies determined for each combination of three biomarkers, one biomarker from each subtype, for the SS model. [Figure 4] Graph of the individual accuracies determined for each combination of three biomarkers, one biomarker from each subtype, for the S model. [Figure 5] Graph of the individual accuracies determined for each combination of three biomarkers, one biomarker from each subtype, for the P model. [Figure 6A-1] Graph of the individual accuracies determined for each combination of three biomarkers, one biomarker from each subtype, for the SS.B1 model. [Figure 6A-2] See legend to Figure 6A-1. [Figure 6A-3] See legend to Figure 6A-1. [Figure 6B] Graph of the individual accuracies determined for each combination of three biomarkers, one biomarker from each subtype, for the SS.B2 model. [Figure 6C]1 is a graph of the individual accuracies determined for each combination of three biomarkers, one biomarker from each subtype, for the SS.B3 model. [Figure 6D] 1 is a graph of the individual accuracies determined for each combination of three biomarkers, one biomarker from each subtype, for the SS.B4 model. [Figure 7] 1 is an exemplary flow process for determining treatment hypotheses for patient subtypes, according to an embodiment. [Figure 8] According to certain embodiments, the conclusions of further analysis in Tables 6 and 7 are presented. [Figure 9] 1 shows a heatmap illustrating differential expression of genes from Table 6 for host response dysregulated patients with subtypes A, B, and C and healthy subjects without host response dysregulated, according to an embodiment. [Figure 10] According to one embodiment, the mortality risk of host response dysregulated patients with subtypes A, B and C is shown. [Figure 11] According to one embodiment, differential expression of genes in Table 7 associated with the pharmacology of hydrocortisone therapy (eg, modulation of the glucocorticoid receptor signaling pathway) for subtypes A, B, and C is shown. [Figure 12] According to certain embodiments, differential expression of genes in Table 7 associated with the pharmacology of checkpoint blockade therapy (e.g., modulation of immune checkpoints and related immune functions mediated by cytokines) for subtypes A, B, and C is shown, providing support for the hypothesis of differential response to checkpoint blockade therapy among subtypes A, B, and C. [Figure 13] According to certain embodiments, an example of a precision platform clinical trial design is provided. [Figure 14] 1 illustrates an exemplary workflow for using the patient subtype classifier described throughout this disclosure in targeting therapy for septic shock patients, according to certain aspects. [Figure 15]According to one embodiment, an exemplary host response dysregulated patient subtype classification test is shown using an FDA-cleared patient sample collection system (e.g., PAXgene Blood RNA System) and an FDA-cleared real-time PCR system (e.g., Thermo Fisher Quantstudio Dx System). [Figure 16] 1 illustrates an exemplary computer for implementing the methods described herein, according to certain aspects.

[0099] These figures depict various aspects of the present disclosure for purposes of illustration only. Those skilled in the art will readily recognize from the following description that alternative aspects of the structures and methods shown herein can be employed without departing from the principles of the disclosure described herein. DETAILED DESCRIPTION OF THE INVENTION

[0100] Detailed Description I. Definition In general, terms used in the claims and specification are intended to be interpreted as having the common meaning understood by a person skilled in the art. Certain terms are defined below to provide additional clarity. In the event of a conflict between the common meaning and a provided definition, the provided definition shall prevail.

[0101] The term "patient" or "subject" includes organisms, mammals, including humans or non-humans (e.g., non-human primates, dogs, cats, mice, cats, cows, horses, and pigs), whether in vivo, ex vivo, or in vitro, and whether male or female.

[0102] The term "sample" can include an aliquot of a bodily fluid, such as a single cell or multiple cells or cell fragments or a blood sample, obtained from a subject by means including venipuncture, excretion, ejaculation, massage, biopsy, needle aspiration, lavage sample, scraping, surgical incision, or by intervention or other means known in the art. Examples of bodily fluid aliquots include amniotic fluid, aqueous humor, bile, lymph, breast milk, interstitial fluid, blood, plasma, earwax (cerumen), Cowper's fluid (pre-ejaculatory fluid), chyle, chyme, female ejaculate, menstrual fluid, mucus, saliva, urine, vomit, tears, vaginal fluid, sweat, serum, semen, sebum, pus, pleural effusion, cerebrospinal fluid, synovial fluid, intracellular fluid, and vitreous humor.

[0103] The terms "marker," "markers," "biomarker," and "biomarkers" encompass, but are not limited to, lipids, lipoproteins, proteins, cytokines, chemokines, growth factors, peptides, nucleic acids, genes, and oligonucleotides, along with their associated complexes, metabolites, mutants, variants, polymorphisms, modifications, fragments, subunits, degradation products, elements, and other analyte- or sample-derived measures. In certain embodiments discussed herein, the biomarker is a gene. However, in alternative embodiments, the biomarker can include any other measurable substance in a sample from a subject. Markers can also include mutant proteins, mutant nucleic acids, copy number variations, and / or transcript variants in situations where such variations, copy number variations, and / or transcript variants are useful in generating predictive models or in predictive models developed using related markers (e.g., non-mutated versions of proteins or nucleic acids, alternative transcripts, etc.). In some embodiments, the biomarkers discussed throughout this disclosure can include nucleic acids, including DNA, modified (e.g., methylated) DNA, cDNA, and RNA, including coding RNA (e.g., mRNA) and non-coding RNA (e.g., sncRNA); proteins, including post-transcriptionally modified proteins (e.g., phosphorylated, glycosylated, myristylated proteins, etc.); nucleotides (e.g., adenosine triphosphate (ATP), adenosine diphosphate (ADP), and adenosine monophosphate (AMP)), including cyclic nucleotides such as cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP); biologics; ADCs; small molecules, such as oxidized and reduced nicotinamide adenine dinucleotide (NADP / NADPH); volatile compounds; and any combination thereof.

[0104] The term "antibody" is used in the broadest sense and specifically encompasses monoclonal antibodies (including full-length monoclonal antibodies), polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies), and antibody fragments that are antigen-binding so long as they exhibit the desired biological activity, e.g., antibodies or antigen-binding fragments thereof.

[0105] As used herein, the term "antibody fragment," and all grammatical variations thereof, is defined as a portion of an intact antibody that contains the antigen-binding site or variable region of the intact antibody, wherein the portion does not contain the constant heavy chain domains of the Fc region of the intact antibody (i.e., CH2, CH3, and CH4, depending on the antibody isotype). Examples of antibody fragments include Fab, Fab', Fab'-SH, F(ab')2, and Fv fragments; diabodies; and any antibody fragment that is a polypeptide having a primary structure consisting of an uninterrupted sequence of consecutive amino acid residues (referred to herein as a "single-chain antibody fragment" or "single-chain polypeptide").

[0106] The term "obtaining or having obtained quantitative data" encompasses obtaining a set of data determined from at least one sample. Obtaining a dataset encompasses obtaining a sample, processing the sample, and experimentally determining the data. The phrase also encompasses receiving a set of data from a third party, for example, who processed the sample, and experimentally determining the dataset. Additionally, the phrase encompasses mining data from at least one database or at least one publication, or a combination of a database and a publication. One skilled in the art can obtain a dataset through a variety of known methods, including storing in a storage memory.

[0107] Any terms not directly defined herein shall be understood to have the meaning generally associated with them as understood within the scope of the technology of the present disclosure. Certain terms are discussed herein to provide additional guidance to practitioners when describing the compositions, devices, methods, etc. of the present disclosure and how to make or use them. It will be recognized that the same thing can be stated in more than one way. Therefore, alternative language and synonyms can be used for any one or more of the terms discussed herein. No emphasis should be placed on whether a term is detailed or discussed herein. Some synonyms or alternative methods, materials, etc. are provided. The detailed description of one or a few synonyms or equivalents does not exclude the use of other synonyms or equivalents unless expressly stated. The use of examples, including examples of terms, is merely for illustrative purposes and does not limit the scope and meaning of the aspects of the disclosure herein.

[0108] Additionally, as used herein, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0109] II. Overview: Biomarker panels to guide therapies for host response dysregulation 1A is a block diagram of a process for identifying subtypes of host response dysregulated patients (line 1), constructing a patient subtype classifier (line 2), and evaluating the effectiveness of treatments for host response dysregulated patients based on the subtype classification identified using the patient subtype classifier (line 3), according to an embodiment. To identify subtypes of host response dysregulated patients, a working dataset compiled from historical transcriptome data from sepsis patients was created, as described in further detail below. Cluster analysis was then performed on the working dataset to identify subtypes of host response dysregulated patients based on differential expression of biomarkers. By labeling these clusters (e.g., subtype A, subtype B, subtype C, etc.), the data can be used for training and building models (line 2).

[0110] In various embodiments, the process of building a model for predicting a patient subtype, hereinafter referred to as a patient subtype classifier, involves using labeled data. The labeled data is analyzed to select biomarkers that provide information for predicting a particular patient subtype (e.g., "gene selection" shown in FIG. 1A). In various embodiments, the patient subtype classifier was trained using labeled training data. As shown in the embodiment in FIG. 1A, the patient subtype classifier (shown as a triangle) can be trained to classify patients into one of three subtypes (e.g., subtype A, subtype B, and subtype C). In some embodiments, the patient subtype classifier can predict fewer (e.g., two subtypes) or additional (e.g., more than three) subtypes. The patient subtype classifier can be validated using a test dataset (e.g., a dataset other than the labeled training data) to ensure sufficient performance of the classifier.

[0111] The trained patient subtype classifier can be deployed to classify specific patients. In one embodiment, the patient subtype classifier analyzes data from the data of one or more patients related to randomized controlled trials (RCTs), and outputs predictions for the patient. For example, the patient subtype classifier analyzes the biomarker expression quantification data for patients who are involved in randomized controlled trials, and classifies the patient into one of different subtypes.

[0112] IIA. System Environment Overview 1B shows an overview of a system environment for determining a therapy recommendation 140 for a patient 110, according to an embodiment. The system environment 100 provides a context for implementing a marker quantification assay 120 and a patient classification system 130.

[0113] In various embodiments, a test sample is obtained from a subject 110. The test sample is analyzed to determine quantitative values ​​of one or more biomarkers by performing a marker quantification assay 120. The marker quantification assay 120 can be a quantitative reverse transcription polymerase chain reaction (RT-PCR) assay, a microarray, a sequencing assay, or an immunoassay, examples of which are described in further detail below. The quantitative values ​​of the biomarkers can be quantified RT-PCR data, transcriptomics data, and / or RNA-seq data. The quantified expression values ​​of the biomarkers are provided to a patient classification system 130.

[0114] Generally, the patient classification system 130 includes one or more computers, such as the exemplary computer 1600 discussed below with respect to FIG. 16 . Thus, in various embodiments, the steps described in connection with the patient classification system 130 are performed in silico. The patient classification system 130 analyzes biomarker expression values ​​received from the marker quantification assays 120. In various embodiments, the patient classification system 130 determines a classification for the patient 110. For example, the classification for the patient 110 can be one of multiple subtypes characterized by the patient's 110's quantitative biomarkers. In various embodiments, the patient classification system 130 determines a therapy recommendation 140 for the patient 110. In such embodiments, the patient classification system 130 determines the therapy recommendation 140 for the patient 110 based on the classification of the patient 110.

[0115] In various embodiments, the patient classification system 130 applies a patient subtype classifier to predict a classification for the patient 110. In various embodiments, the patient subtype classifier can be a machine learning model. In such embodiments, the patient classification system 130 can train the patient subtype classifier using training data and / or deploy the patient subtype classifier to analyze the quantitative expression values ​​of the biomarkers for the patient 110.

[0116] In various embodiments, the marker quantification assay 120 and the patient classification system 130 can be employed by different parties. For example, a first party performs the marker quantification assay 120, which then provides the results to a second party that implements the patient classification system 130. For example, the first party can be a clinical laboratory that obtains a test sample from the subject 110 and performs the assay 120 on the test sample. The second party receives the biomarker expression values ​​resulting from the performed assay 120 and analyzes the expression values ​​using the patient classification system 130.

[0117] In various embodiments, the patient classification system 130 can be a distributed computing system implemented in a cloud computing environment. For example, the steps performed by the patient classification system 130 can be performed using systems at different geographic locations. In certain embodiments, the patient classification system 130 receives quantitative biomarker data from the marker quantification assays 120 at a first location. The patient classification system 130 transmits the quantitative biomarker data and uses a patient subtype classifier at a second location to analyze the quantitative biomarker data (e.g., cloud computing) to predict a classification. The patient classification system 130 can further transmit the classification back to the first location for subsequent use.

[0118] Cloud computing can be employed to provide on-demand access to a shared set of configurable computing resources. The shared set of configurable computing resources can be rapidly provisioned through virtualization, released with low management effort or service provider interaction, and then scaled accordingly. Cloud computing models can be composed of various characteristics, such as, for example, on-demand self-service, wide network access, resource sharing, rapid scalability, scalable services, and others. Cloud computing models can also expose various service models, such as, for example, software as a service ("SaaS"), platform as a service ("PaaS"), and infrastructure as a service ("IaaS"). Cloud computing models can also be deployed using different deployment models, such as private cloud, community cloud, public cloud, hybrid cloud, and others. In this specification and claims, a "cloud computing environment" is an environment in which cloud computing is employed.

[0119] In various embodiments, the marker quantification assay 120 and the patient classification system 130 are implemented in a critical care setting, whereby a treatment recommendation should be generated for the patient 110 within a maximum time period. In various embodiments, the maximum time period is 30 minutes. In various embodiments, the maximum time period is 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, or 12 hours. In other embodiments, the marker quantification assay 120 and the patient classification system 130 are not implemented in a critical care setting.

[0120] IIB. Methods for Determining Treatment Recommendations In various embodiments, the patient classification system 130 (described above in connection with FIG. 1B) analyzes quantitative data for a set of biomarkers, the quantitative data from a patient (e.g., patient 110 in FIG. 1B), and determines a treatment recommendation for the patient. Typically, the patient classification system 130 analyzes the quantitative data for the set of biomarkers and applies a patient subtype classifier that classifies the patient into a classification. The patient classification system 130 can determine a treatment recommendation for the patient based on the patient's classification.

[0121] The patient classification system 130 receives quantitative data from the marker quantification assays 120. In this case, the quantitative data from the marker quantification assays 120 can include quantitative levels of one or more biomarkers determined from samples obtained from the patient. In various embodiments, the patient classification system 130 normalizes the quantitative data. For example, the patient classification system 130 can normalize the quantitative data based on study-specific parameters (such that the data is normalized for the study) and / or based on parameters specific to the particular assay or platform used to generate the quantitative data. In various embodiments, the patient classification system 130 can normalize the quantitative data according to normalization parameters derived from healthy samples. In such embodiments, the resulting quantitative data is normalized across patients and studies at the end of the normalization process. Such embodiments involving normalizing the quantitative data can be implemented in a research setting (non-critical care setting). In some embodiments, the patient classification system 130 does not need to normalize the quantitative data before analysis by the patient subtype classifier. Such embodiments without normalizing the quantitative data can be implemented in a critical care setting where rapid analysis and classification of patients 110 is required. The patient classification system 130 analyzes quantitative data, which also includes normalized quantitative data herein below.

[0122] As one example, the patient classification system 130 analyzes quantitative data for a biomarker set derived from microarray analysis. The patient classification system 130 applies a patient subtype classifier to analyze the quantitative microarray data to classify patients, which can then be used to determine treatment recommendations. As another example, the patient classification system 130 analyzes qPCR data that measures the relative or absolute expression levels of biomarkers. In various embodiments, a normalization or calibration process is implemented. Using the quantitative data of the biomarker set, scores are calculated for different classifications (e.g., subtypes), which are then used for subtype assignment by the patient subtype classifier. As another example, the patient classification system 130 analyzes RNA sequencing data that includes the relative expression levels of model genes and their transcripts. Using sequencing read alignment methods (e.g., Hisat2 and Bowtie2), expression estimation methods (e.g., StringTie, Salmon), and normalization processes (e.g., quantile normalization), the estimated expression of the model genes can be used to calculate classification-specific scores for downstream classification by the patient subtype classifier. In various embodiments, the patient classification system 130 can use a normalization factor to convert quantitative data from a first type of assay into quantitative data from a second type of assay. For example, the patient classification system 130 can convert quantitative data from microarray data into either qPCR data or RNA sequencing data. The conversion can require one or more normalization factors, including a normalization or calibration process for qPCR data or a normalization process (e.g., quantile normalization) for RNA sequencing data. Thus, the patient classification system 130 can apply different patient subtype classifiers to analyze different types of quantitative data.

[0123] The patient classification system 130 implements a patient subtype classifier to analyze the quantitative data for the biomarkers. In one embodiment, the patient subtype classifier is a trained machine learning model. Thus, the patient subtype classifier can be trained to receive quantitative data of a set of biomarkers as input, analyze the input, and output a classification for the patient. In some embodiments, the patient subtype classifier is not a machine learning model. In various embodiments, the patient subtype classifier outputs a prediction of one classification for the patient out of X possible classifications. For example, the patient subtype classifier can output a prediction of a patient subtype for the patient out of X possible patient subtypes. In various embodiments, X can be two possible classifications. In various embodiments, X can be more than two possible classifications. In various embodiments, X can be 3, 4, 5, 6, 7, 8, 9, or 10 possible classifications. In various embodiments, X can be more than 10 possible classifications.

[0124] In some embodiments, the patient classification system 130 calculates a score from the quantitative data and then provides the calculated score as an input to the patient subtype classifier, which then determines a classification for the patient based on the calculated score.

[0125] In various embodiments, the patient classification system 130 calculates multiple scores, each score corresponding to a patient subtype (e.g., classification). For example, if the goal is to classify a patient into a classification from X possible classifications, the patient classification system 130 calculates X scores. The X scores are then provided as inputs to a patient subtype classifier to predict the classification. These scores are hereinafter referred to as classification-specific scores.

[0126] In various embodiments, to calculate the class-specific score, the patient classification system 130 determines subscores derived from the quantitative data of one or more biomarkers in the biomarker set and uses these subscores to determine the class-specific score. In one embodiment, the subscores are calculated from one or more biomarkers that are differentially expressed in the patient compared to a control value. In various embodiments, the control value can be a value derived from a different set of patients, such as healthy patients. In various embodiments, the control value can be a baseline value derived from the same patient (e.g., a baseline value corresponding to a time when the same patient was previously healthy).

[0127] In various embodiments, the patient classification system 130 determines a subscore determined from quantitative data of one or more biomarkers that are upregulated in the patient compared to control values. In various embodiments, the patient classification system 130 determines a subscore determined from quantitative data of one or more biomarkers that are downregulated in the patient compared to control values. In various embodiments, the patient classification system 130 determines a first subscore determined from quantitative data of one or more biomarkers that are upregulated in the patient compared to control values, and further determines a second subscore determined from quantitative data of one or more biomarkers that are downregulated in the patient compared to control values. In various embodiments, the subscore can be an aggregation of the quantitative data of one or more biomarkers. For example, the subscore can be the mean, median, or geometric mean of the quantitative data of one or more biomarkers. In various embodiments, the patient classification system 130 can further scale the subscore.

[0128] In various embodiments, the quantitative data for one or more biomarkers analyzed refers to biomarkers previously categorized as affecting the particular subtype for which a classification-specific score is being calculated. For example, if the patient classification system 130 is determining a classification specific to subtype A, the patient classification system 130 determines the subscore using quantitative data for biomarkers categorized as affecting subtype A. Examples of biomarkers categorized with a particular subtype are shown in Tables 1, 2A-2B, 3, and 4A-4D below. Specifically, row number 1 in each of Tables 1, 2A-2B, 3, and 4A-4D indicates a biomarker categorized as subtype A; row number 2 in each of Tables 1, 2A-2B, 3, and 4A-4D indicates a biomarker categorized as subtype B; and row number 3 in each of Tables 1, 2A-2B, 3, and 4A-4D indicates a biomarker categorized as subtype C.

[0129] In various embodiments, the patient classification system 130 combines one or more subscores to determine a class-specific score. For example, the patient classification system 130 can determine the difference between a first subscore and a second subscore. The difference can represent the class-specific score.

[0130] As a specific example, the patient classification system 130 can determine the class-specific score using the following steps: the patient classification system 130 determines a first geometric mean value of quantitative expression data for the subject for one or more biomarkers of the candidate class, where the quantitative expression data for the subject for the one or more biomarkers of the candidate class is increased compared to the quantitative expression data for the one or more biomarkers for one or more control subjects; the patient classification system 130 determines a second geometric mean value of quantitative expression for the subject for one or more additional biomarkers of the candidate class, where the quantitative expression data for the subject for the one or more additional biomarkers of the candidate class is decreased compared to the quantitative expression data for the one or more additional biomarkers for one or more control subjects; the patient classification system 130 determines a difference between the first and second geometric mean values, where the first and second geometric mean values ​​are optionally subjected to scaling. In this case, the difference can represent the class-specific score.

[0131] In various embodiments, the patient classification system 130 determines a plurality of class-specific scores and provides them as input to a patient subtype classifier. The patient subtype classifier analyzes the class-specific scores and outputs a classification for the patient. Embodiments of the patient subtype classifier are described in further detail below.

[0132] In various embodiments, based on the classification-specific scores, the patient subtype classifier outputs a classification. For example, the patient subtype classifier may analyze X classification-specific scores and output a prediction for one class from X possible classifications. Alternatively, the patient subtype classifier may analyze X classification-specific scores and output a prediction for one class from two possible classifications. As a specific example, the patient subtype classifier may analyze three classification-specific scores (e.g., those specific to subtype A, subtype B, and subtype C) and output a prediction for a class from two possible classifications (e.g., subtype A vs. non-subtype A, subtype B vs. non-subtype B, or subtype C vs. non-subtype C).

[0133] Generally, the classification determined by the patient subtype classifier guides the selection of a therapy recommendation. In various aspects, the therapy recommendation indicates whether the therapy is likely to be beneficial to the patient. In particular aspects, the disease of interest is sepsis, and therefore the therapy recommendation relates to whether corticosteroid therapy, such as hydrocortisone, is likely to be beneficial to the patient. In one aspect, the therapy recommendation can indicate whether the patient is likely to be a "good responder" or "non-responder" to the therapy. In one aspect, the therapy recommendation can indicate whether the patient is likely to be a "good responder," "poor responder," or "non-responder" to the therapy.

[0134] Examples of therapies include immune stimulatory therapy, suppression of immunomodulatory therapy, blockade of immunosuppressive therapy, blockade of complement activation therapy, anti-inflammatory therapy, checkpoint inhibitors, complement component blockers, complement component receptor blockers, pro-inflammatory cytokine blockers, modulators of coagulation therapy, and modulators of vascular permeability therapy. Additional examples of therapies include GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulatory factors, IL-22 agonists, IFN-alpha regulatory factors, IFN-lambda regulatory factors, IFN-alpha 2b stimulators, anti-C5a, anti-C3a, anti-C5aR, anti-C3aR, anti-TNF-alpha, and anti-IL-6, anti-HMGB1, ST2 antibody, IL-33 antibody, activated protein C, antithrombin, and thrombomodulin.

[0135] Additional examples of treatments and corresponding treatment recommendations for different patient subtypes (e.g., subtype A, subtype B, and subtype C) are provided below in Table 8. Specifically, treatment recommendations are provided in the column headed "Subtype Hypothesis," and support for the hypothesis is found in the column headed "Evidence." In summary, treatment recommendations determined by the patient classification system 130 can be provided to guide treatment for patients.

[0136] The impact of a particular treatment and patient subtype, such as those hypothesized in Table 8, may have been previously determined by analyzing a cohort of patients who have received a particular treatment. For example, such a patient cohort may have been involved in a clinical trial. Thus, patients may exhibit host response dysregulation and therefore be enrolled in the trial. Thus, patients in the clinical trial are classified by patient subtype (for example, using the above-mentioned method), and the patient's response to the treatment (for example, good response, poor response, no response) is tracked and recorded. For each subtype, the response of patients receiving the treatment is compared with that of control patients. If the comparison results in a statistically significant difference, the patients of this subtype are labeled as having a good response or a poor response to the treatment. If the comparison does not result in a statistically significant difference (for example, the p-value does not exceed a threshold), the patients of the subtype are labeled as not responding to the treatment. In various embodiments, the threshold for statistical significance is a p-value, and the p-value is any one of 0.01, 0.0.5, or 0.1.

[0137] In certain embodiments, the measurable value to be compared and statistical significance is determined is the mortality rate of patients. Thus, the mortality rate of patients receiving a treatment is compared with the mortality rate of control patients to determine whether there is statistical significance indicating the effectiveness of the treatment. For example, if patients of a subtype receiving a treatment show a statistically significant increase in survival time compared with control patients not receiving the treatment, the patients of this subtype can be identified as having a good response to the treatment. As another example, if patients of a subtype receiving a treatment show a statistically significant decrease in survival time compared with control patients not receiving the treatment, the patients of this subtype can be identified as having a poor response to the treatment. As yet another example, if patients of a subtype receiving a treatment do not show a statistically significant increase or decrease in survival time compared with control patients not receiving the treatment, the patients of this subtype can be identified as not responding to the treatment.

[0138] IIB. Methods for determining treatment hypotheses In various embodiments, the methods disclosed herein involve identifying treatment hypotheses for different patient subtypes. In various embodiments, the process of identifying treatment hypotheses is performed by the patient classification system 130. In some embodiments, the process of identifying treatment hypotheses is performed by a third-party system that provides treatment hypotheses to the patient classification system 130. In various embodiments, the treatment hypotheses are specific to the patient subtype. Thus, the treatment hypotheses are useful for identifying treatment recommendations, as described above in connection with FIG. 1B.

[0139] Generally, therapeutic hypotheses involve analyzing the differentially expressed genes across different subtypes.By identifying the pattern of differentially expressed genes that are linked to a certain known biological pathway, a certain patient subtype can be associated with a certain dysregulated pathway.The therapeutic hypotheses can be selected that include candidate therapeutics.In this case, candidate therapeutics can modulate the part of the dysregulated pathway, thereby representing the possible therapeutic means for treating a certain patient subtype.

[0140] Reference is now made to FIG. 7 , which illustrates an example of a flow process for determining a treatment hypothesis for a patient subtype, according to an embodiment. Generally, FIG. 7 illustrates the use of labeled data 610 to generate differentially expressed gene data 620. The differentially expressed gene data 620 can be used to identify a treatment hypothesis 650. In some embodiments, the differentially expressed gene data 620 is analyzed together with therapeutic agent data 630 and response pathobiology data 640 to determine a treatment hypothesis. In some embodiments, the differentially expressed gene data 620 is analyzed with one of the therapeutic agent data 630 or the response pathobiology data 640 to determine a treatment hypothesis 650. In some embodiments, only the differentially expressed gene data 620 is analyzed to determine a treatment hypothesis 650.

[0141] The labeled data 610 represents patient data labeled with one or more classifications. For example, the labeled data 610 can be labeled with patient subtypes (e.g., subtype A, subtype B, subtype C, etc.). In various embodiments, the patient data includes quantitative data for one or more biomarkers for the patient. In various embodiments, the patient data is clinical trial data, and thus the quantitative data for the one or more biomarkers can be data obtained from patients enrolled in a clinical trial.

[0142] Labels for labeled data can be pre-generated by various means. In various embodiments, labels for data can be generated using a model such as the patient subtype classifier described herein. For example, the patient subtype classifier can be used to analyze quantitative data of biomarkers from patients to predict a classification for the patient. Thus, the predicted classification for each patient can serve as a label for the labeled data. In various embodiments, labels for data can be generated by cluster analysis. For example, quantitative data of biomarkers can be analyzed by unsupervised clustering, thereby generating clusters of patients with similar expression of various biomarkers. Each cluster of patients can be labeled. In various embodiments, clusters can be labeled based on patient outcomes in clinical trials. For example, if a majority of patients in a cluster exhibit extended survival in response to a therapy, the cluster can be labeled as a subtype that responds to the therapy.

[0143] The differentially expressed gene data 620 includes gene-level fold changes in biomarker expression between patients of different subtypes. Using the labeled data 610, gene expression from patients of individual subtypes is aggregated and compared across subtypes. For example, a statistical measure of gene expression (e.g., mean, median, mode, geometric mean) for patients of a subtype can be determined. A statistical measure of gene expression for patients of a first subtype is compared to a statistical measure of gene expression for patients of a second subtype. This can be performed across different patient subtypes and across various genes. Thus, the differentially expressed gene data 620 includes gene-level fold changes of different biomarkers across different patient subtypes.

[0144] In various embodiments, differentially expressed gene data 620 includes fold changes in gene levels for at least 20 biomarkers. In various embodiments, differentially expressed gene data 620 includes fold changes in gene levels for at least 50 biomarkers. In various embodiments, differentially expressed gene data 620 includes fold changes in gene levels for at least 100 biomarkers, at least 200 biomarkers, at least 300 biomarkers, at least 400 biomarkers, at least 500 biomarkers, at least 1000 biomarkers, at least 2000 biomarkers, at least 3000 biomarkers, at least 4000 biomarkers, at least 5000 biomarkers, at least 10,000 biomarkers, at least 50,000 biomarkers, or at least 100,000 biomarkers.

[0145] In various embodiments, the differentially expressed gene data 620 can be represented as a database or table that records the fold change in gene levels between patients of different subtypes. Examples of such fold changes in gene levels between patient subtypes are shown in Table 7 below. Specifically, for each gene, the fold change (e.g., ratio) in gene level between different subtypes (e.g., subtype A / subtype B, denoted as "A / B") is shown.

[0146] To determine a treatment hypothesis 650, a pattern of gene-level fold changes is identified across the differentially expressed gene data 620. In various embodiments, the pattern of gene-level fold changes refers to at least a threshold number of genes that are differentially expressed in a first patient subtype compared to a second patient subtype. In various embodiments, the pattern of gene-level fold changes refers to at least a threshold number of genes that are overexpressed in a first patient subtype compared to a second patient subtype. In various embodiments, the pattern of gene-level fold changes refers to at least a threshold number of genes that are underexpressed in a first patient subtype compared to a second patient subtype.

[0147] In various embodiments, the threshold number of genes includes genes involved in a common biological pathway. Examples of biological pathways include, but are not limited to, innate immune pathways, chronic inflammatory pathways, acute inflammatory pathways, coagulation pathways, complement pathways, signal transduction pathways (e.g., TLR signal transduction pathways or glucocorticoid receptor signal transduction pathways), and others. In various embodiments, the involvement of genes in a particular biological pathway is curated from publicly available databases, such as the Reactome pathway database or the KEGG pathway database.

[0148] In various embodiments, the threshold number of genes involved in a common biological pathway is at least two genes. In various embodiments, the threshold number of genes is at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least 10 genes, at least 15 genes, at least 20 genes, at least 25 genes, at least 50 genes, at least 75 genes, at least 100 genes, at least 200 genes, at least 300 genes, at least 400 genes, at least 500 genes, or at least 1000 genes. In various embodiments, the threshold number of genes involved in a common biological pathway is two genes. In various embodiments, the threshold number of genes involved in a common biological pathway is 3 genes, 4 genes, 5 genes, 6 genes, 7 genes, 8 genes, 9 genes, 10 genes, 11 genes, 12 genes, 13 genes, 14 genes, 15 genes, 16 genes, 17 genes, 18 genes, 19 genes, 20 genes, 25 genes, 50 genes, 75 genes, 100 genes, 200 genes, 300 genes, 400 genes, 500 genes, 600 genes, 700 genes, 800 genes, 900 genes, or 1000 genes.

[0149] In summary, the pattern of gene-level fold changes, as indicated by a threshold number of genes involved in a common biological pathway, is useful for understanding the underlying biology that may be involved in patient subtypes. For example, genes involved in inflammation may be differentially expressed in subtype A compared to genes in subtype B. Therefore, subtype A can be associated with or characterized by inflammation-based processes.

[0150] The pattern of fold-change in gene levels between subtypes is analyzed to determine a therapeutic hypothesis 650, which in some scenarios includes a class of candidate therapies or candidate therapies themselves (including, for example, without limitation, drug therapies or gene therapies). For example, given the characterization of a particular patient subtype as being associated with an underlying biological pathway or process, targets involved in the biological pathway or process can serve as druggable targets. Thus, a class of candidate therapies or candidate therapies that modulate targets involved in a biological pathway can be promising therapeutic hypotheses 650. Examples of classes of therapies include, but are not limited to, immunostimulatory therapies, suppressing immunomodulatory therapies, blocking immunosuppressive therapies, blocking complement activation therapies, anti-inflammatory therapies, checkpoint inhibitors, complement component blockers, complement component receptor blockers, pro-inflammatory cytokine blockers, modulators of coagulation therapies, and modulators of vascular permeability therapies. Examples of candidate therapies include, but are not limited to, GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulators, IL-22 agonists, IFN-alpha regulators, IFN-lambda regulators, IFN-alpha 2b stimulators, anti-C5a, anti-C3a, anti-C5aR, anti-C3aR, anti-TNF-alpha, and anti-IL-6, anti-HMGB1, ST2 antibody, IL-33 antibody, activated protein C, antithrombin, and thrombomodulin.

[0151] Therapeutic drug data 630 is useful for developing therapeutic hypotheses for a particular class of therapy or a particular candidate therapy. Generally, therapeutic drug data 630 is useful for understanding what therapeutic effect, if any, may be provided by a class of therapy or candidate therapy. For example, therapeutic drug data 630 can include molecular data for the therapeutic drug, clinical pharmacology data for the therapeutic drug (e.g., pharmacokinetic and pharmacodynamic data), and / or data identifying therapeutic drugs useful for modulating activity in a particular biological pathway. For example, for a given candidate therapy (e.g., an anti-PD-1 inhibitor), therapeutic drug data 630 is useful for understanding how different patients respond to the anti-PD-1 inhibitor.

[0152] An example of therapeutic data 630 is shown in Figure 12. For example, blockade of PD-1 is expected to upregulate IL-7, and blockade of CTLA-4 is expected to upregulate INF-gamma, stimulating immune activity more broadly. In patients with downregulated immune activity, PD-L1 and CTLA-4 are upregulated, while IL-7 and INF-gamma are downregulated. Thus, blockade of PD-1 / PD-L1 would likely result in upregulation of IL-7 and blocking CTLA-4 upregulation of INF-gamma, stimulating immune activity more broadly.

[0153] Response pathobiology data 640 is useful for developing hypotheses regarding treatment efficacy without relying on specific candidate treatments that may benefit a particular patient subtype. In various embodiments, response pathobiology data 640 can include patient data corresponding to patients who were good responders. In various embodiments, response pathobiology data 640 includes patient data for patient subtypes indicating differential expression of biomarkers associated with certain biological activities. Differentially expressed biomarkers can be promising targets for modulation. For example, patients with subtype A dysregulated host response exhibit upregulation of biomarkers associated with innate immune activity involved in pathogen recognition (e.g., via recognition of pathogen-associated molecular patterns (PAMPs)), upregulation of biomarkers associated with innate immune modulation, and upregulation of biomarkers associated with adaptive immune activity. As another example, subtype B host response dysregulated patients exhibit upregulation of biomarkers associated with innate immune activity involved in the recognition of injury-associated molecular patterns (DAMPs), upregulation of biomarkers associated with DAMPs, upregulation of biomarkers associated with inflammation (e.g., TNF-alpha), upregulation of biomarkers associated with complement activity, downregulation of biomarkers associated with adaptive immune activity, upregulation of biomarkers associated with adaptive immune suppression, and upregulation of markers associated with an increased risk of acute kidney injury. As another example, subtype C patients exhibit downregulation of biomarkers associated with innate and adaptive immune activity, upregulation of biomarkers associated with DAMPs, upregulation of biomarkers associated with cell recruitment (e.g., G-CSF and GM-CSF), upregulation of biomarkers associated with an increased risk of thrombosis, and upregulation of biomarkers associated with coagulation.

[0154] The treatment hypothesis 650 can then be tested and validated for the patient subtype, for example, in preclinical or clinical studies and trials (e.g., randomized controlled trials) by providing the candidate treatment to subjects or patients of the subtype and monitoring their response.

[0155] IIC. Patient subtype classifier In various embodiments, the patient subtype classifier is a machine learning model that analyzes biomarker quantitative data or classification-specific scores derived from biomarker quantitative data and predicts classification. In various embodiments, the patient subtype classifier is any one of the following: a regression model (e.g., linear regression, logistic regression, or polynomial regression), a decision tree, a random forest, a support vector machine, a naive Bayes model, a k-means cluster, or a neural network (e.g., a feedforward network, a convolutional neural network (CNN), a deep neural network (DNN), an autoencoder neural network, a generative adversarial network, or a recurrent network (e.g., a long short-term memory network (LSTM), a bidirectional recurrent network, a deep bidirectional recurrent network), or any combination thereof.

[0156] The patient subtype classifier can be trained using machine learning implementation methods such as linear regression algorithms, logistic regression algorithms, decision tree algorithms, support vector machine classification, naive Bayes classification, K-nearest neighbor classification, random forest algorithms, deep learning algorithms, gradient boosting algorithms, and dimensionality reduction techniques such as manifold learning, principal component analysis, factor analysis, autoencoder regularization, and independent component analysis, or any combination thereof. In various embodiments, the patient subtype classifier is trained using a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm (e.g., partial supervision), weakly supervised, transfer, multi-task learning, or any combination thereof.

[0157] In various embodiments, the patient subtype classifier has one or more parameters, such as hyperparameters or model parameters. Hyperparameters are generally established before training. Examples of hyperparameters include a learning rate, a depth or leaves of a decision tree, the number of hidden layers in a deep neural network, the number of clusters in a k-means clustering, a penalty in a regression model, and a regularization parameter associated with a cost function. Model parameters are generally adjusted during training. Examples of model parameters include weights associated with nodes in a layer of a neural network, support vectors in a support vector machine, and coefficients in a regression model. To improve the predictive ability of the patient subtype classifier, the model parameters of the patient subtype classifier are trained (e.g., adjusted) using training data.

[0158] In some embodiments, the patient subtype classifier is a regression, such as logistic regression. The parameters of the logistic regression are trained using training data, so that when the logistic regression is applied, it outputs a classification based on different classification-specific scores. The parameters of the logistic regression can be trained to maximize the difference between different classifications (e.g., subtype A, subtype B, and subtype C).

[0159] In some embodiments, the patient subtype classifier is a support vector machine. The support vector machine is trained using a single hyperplane or a set of hyperplanes that maximizes the difference between X different classifications. In one embodiment, the support vector machine is trained using a single hyperplane or a set of hyperplanes that maximizes the difference between three different classifications (e.g., subtype A, subtype B, and subtype C). As a specific example, the support vector machine is trained using a set of hyperplanes that maximizes the difference between three different classification-specific scores (e.g., scores for each of subtype A, subtype B, and subtype C). Thus, the trained support vector machine can use hyperplanes to output a classification prediction when provided with biomarker quantitative data or classification-specific scores derived from biomarker quantitative data.

[0160] In some embodiments, the patient subtype classifier may be a non-machine learning model. The patient subtype classifier may adopt one or more thresholds for comparison with the classification-specific score. Depending on the comparison between the threshold and the classification-specific score, the patient subtype classifier outputs a predicted classification. In various embodiments, the threshold is classification-specific. Thus, there may be X thresholds to be compared with X classification-specific scores.

[0161] In some embodiments, the threshold value may be a fixed value (e.g., fixed value = 0). In this case, the class-specific score is compared to the fixed threshold, and the patient subtype classifier determines the classification based on the comparison. For example, assuming there are two class-specific scores, the patient subtype classifier may compare each of the first and second class-specific scores to the fixed threshold. In one embodiment, if the first class-specific score is greater than the fixed threshold and the second class-specific score is less than the fixed threshold, the patient subtype classifier may output a particular classification. Similar logic can be applied to determine a classification using more than two class-specific scores.

[0162] In some embodiments, the threshold value can be determined from training samples including data from classified patients (e.g., classified as subtype A, subtype B, and / or subtype C). Such a threshold value can be derived from a receiver operating curve (ROC) demonstrating the sensitivity / specificity of the model that classified the patients in the training samples. For example, a receiver operating curve demonstrating the sensitivity and specificity of the classifier is generated for patients in the training samples classified as subtype A. The threshold value can be the upper left portion of the plot, representing the closest point in the ROC to perfect sensitivity or specificity.

[0163] The classification-specific score is compared with the corresponding threshold, and based on the comparison, the patient subtype classifier determines the classification. For example, assuming there are two classification-specific scores for subtype A and subtype B, the patient subtype classifier may compare the classification-specific score for subtype A with the threshold for subtype A, and may further compare the classification-specific score for subtype B with the threshold for subtype B. Thus, based on the two comparisons, the patient subtype classifier determines the classification. In one embodiment, if the first classification-specific score is greater than the first threshold and the second classification-specific score is less than the second threshold, the patient subtype classifier can output a specific classification. Similar logic can be applied to determine a classification using more than two classification-specific scores and / or more than two thresholds. Examples of subtype-specific thresholds derived from training samples are listed in Table 18 below.

[0164] IID. Biomarker Panel The embodiments described herein involve the analysis of biomarkers. As described herein, a biomarker panel, also referred to as a biomarker set, can be implemented to analyze the values ​​of biomarkers for a patient. In various embodiments, the biomarker panel can be a multivariate biomarker panel. In such embodiments, the multivariate biomarker panel includes more than one biomarker. In various embodiments, the multivariate biomarker panel includes two biomarkers. In various embodiments, a multivariate biomarker panel comprises 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 biomarkers. In certain embodiments, a multivariate biomarker panel comprises three biomarkers. In certain embodiments, a multivariate biomarker panel comprises four biomarkers. In certain embodiments, the multivariate biomarker panel comprises 5 biomarkers. In certain embodiments, the multivariate biomarker panel comprises 6 biomarkers. In certain embodiments, the multivariate biomarker panel comprises 8 biomarkers. In certain embodiments, the multivariate biomarker panel comprises 10 biomarkers. In certain embodiments, the multivariate biomarker panel comprises 15 biomarkers. In certain embodiments, the multivariate biomarker panel comprises 16 biomarkers. In certain embodiments, the multivariate biomarker panel comprises 24 biomarkers.

[0165] In various embodiments, the multivariate biomarker panel comprises biomarkers selected from the following markers: EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, TOMM70A, ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2, ANXA3, C14orf159, PUM2, EPB42, RPS6KA5, GBP2, MSH2, DCTD, HK3, UCP2, NUP88, GABARAPL2, and CASP4.

[0166] In various embodiments, the multivariate biomarker panel comprises at least two biomarkers selected from the following markers: EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, TOMM70A, ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2, ANXA3, C14orf159, PUM2, EPB42, RPS6KA5, GBP2, MSH2, DCTD, HK3, UCP2, NUP88, GABARAPL2, and CASP4.

[0167] In various embodiments, the multivariate biomarker panel includes X biomarkers, where X is the number of possible classifications that the patient subtype classifier can predict. For example, for a patient subtype classifier that predicts three different subtypes (e.g., subtype A, subtype B, and subtype C), the multivariate biomarker panel can include three different biomarkers.

[0168] In various embodiments, the multivariate biomarker panel comprises a first biomarker selected from EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, a second biomarker selected from SERPINB1 and GSPT1, and a third biomarker selected from MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, and TOMM70A. An example of the accuracy of a multivariate biomarker panel implementing the above three biomarker combinations is shown in Figure 2.

[0169] In various embodiments, the multivariate biomarker panel comprises one or more biomarkers selected from EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, one or more biomarkers selected from SERPINB1 and GSPT1, and one or more biomarkers selected from MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, and TOMM70A.

[0170] In one embodiment, the multivariate biomarker panel comprises a first biomarker selected from ZNF831, MME, CD3G, and STOM, a second biomarker selected from ECSIT, LAT, and NCOA4, and a third biomarker selected from SLC1A5, IGF2BP2, and ANXA3. An example of the accuracy of a multivariate biomarker panel implementing the above three biomarker combinations is shown in Figure 3.

[0171] In one embodiment, the multivariate biomarker panel comprises a first biomarker selected from C14orf159 and PUM2, a second biomarker selected from EPB42 and RPS6KA5, and a third biomarker selected from GBP2. An example of the accuracy of a multivariate biomarker panel implementing the above three biomarker combinations is shown in Figure 4.

[0172] In one embodiment, the multivariate biomarker panel comprises a first biomarker selected from MSH2, DCTD, and MMP8, a second biomarker selected from HK3, UCP2, and NUP88, and a third biomarker selected from GABARAPL2 and CASP4. An example of the accuracy of a multivariate biomarker panel implementing the above three biomarker combinations is shown in Figure 5.

[0173] In one embodiment, the multivariate biomarker panel comprises a first biomarker selected from STOM, ZNF831, CD3G, MME, BTN3A2, and HLA-DPA1, a second biomarker selected from EPB42, GSPT1, LAT, HK3, and SERPINB1, and a third biomarker selected from GBP2, TNFRSF1A, SLC1A5, IGF2BP2, and ANXA3. An example of the accuracy of a multivariate biomarker panel implementing the above three biomarker combinations is shown in Figure 6A.

[0174] In one embodiment, the multivariate biomarker panel comprises a first biomarker selected from STOM, ZNF831, CD3G, MME, BTN3A2, and HLA-DPA1, a second biomarker selected from EPB42, GSPT1, LAT, HK3, and SERPINB1, and a third biomarker selected from GBP2, SLC1A5, IGF2BP2, and ANXA3. An example of the accuracy of a multivariate biomarker panel implementing the above three biomarker combinations is shown in Figure 6B.

[0175] In one embodiment, the multivariate biomarker panel comprises a first biomarker selected from STOM, MME, BTN3A2, HLA-DPA1, and EVL, a second biomarker selected from EPB42, GSPT1, LAT, HK3, and SERPINB1, and a third biomarker selected from BTN3A2, TNFRSF1A, SLC1A5, IGF2BP2, and ANXA3. An example of the accuracy of a multivariate biomarker panel implementing the above three biomarker combinations is shown in Figure 6C.

[0176] In one embodiment, the multivariate biomarker panel comprises a first biomarker selected from STOM, MME, BTN3A2, HLA-DPA1, and EVL, a second biomarker selected from EPB42, GSPT1, LAT, HK3, and SERPINB1, and a third biomarker selected from GBP2, SLC1A5, IGF2BP2, and ANXA3. An example of the accuracy of a multivariate biomarker panel implementing the above three biomarker combinations is shown in Figure 6D.

[0177] Although the above embodiments may refer to a "first biomarker," a "second biomarker," and / or a "third biomarker," the terms "first biomarker," "second biomarker," and / or "third biomarker" each encompass one or more biomarkers. For example, a "first biomarker" can refer to one or more biomarkers selected from EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, and MMP8. A "second biomarker" can refer to one or more biomarkers selected from SERPINB1 and GSPT1. A "third biomarker" can refer to one or more biomarkers selected from MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, and TOMM70A.

[0178] In one embodiment, the multivariate biomarker panel comprises 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 biomarkers selected from EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, MMP8, SERPINB1, GSPT1, MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, and TOMM70A.

[0179] In one embodiment, the multivariate biomarker panel comprises four, five, six, seven, eight, nine, or ten biomarkers selected from ZNF831, MME, CD3G, STOM, ECSIT, LAT, NCOA4, SLC1A5, IGF2BP2, and ANXA3.

[0180] In one embodiment, the multivariate biomarker panel comprises four or five biomarkers selected from C14orf159, PUM2, EPB42, RPS6KA5, and GBP2.

[0181] In one embodiment, the multivariate biomarker panel comprises four, five, six, seven, or eight biomarkers selected from MSH2, DCTD, MMP8, HK3, UCP2, NUP88, GABARAPL2, and CASP4.

[0182] In one embodiment, the multivariate biomarker panel comprises 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 biomarkers selected from STOM, ZNF831, CD3G, MME, BTN3A2, HLA-DPA1, EPB42, GSPT1, LAT, HK3, SERPINB1, GBP2, TNFRSF1A, SLC1A5, IGF2BP2, and ANXA3.

[0183] In one embodiment, the multivariate biomarker panel comprises 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 biomarkers selected from STOM, ZNF831, CD3G, MME, BTN3A2, HLA-DPA1, EPB42, GSPT1, LAT, HK3, SERPINB1, GBP2, SLC1A5, IGF2BP2, and ANXA3.

[0184] In one embodiment, the multivariate biomarker panel comprises 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 biomarkers selected from STOM, MME, BTN3A2, HLA-DPA1, EVL, EPB42, GSPT1, LAT, HK3, SERPINB1, BTN3A2, TNFRSF1A, SLC1A5, IGF2BP2, and ANXA3.

[0185] In one embodiment, the multivariate biomarker panel comprises 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 biomarkers selected from STOM, MME, BTN3A2, HLA-DPA1, EVL, EPB42, GSPT1, LAT, HK3, SERPINB1, GBP2, SLC1A5, IGF2BP2, and ANXA3.

[0186] IIE. Assay As shown in FIG. 1B, the system environment 100 involves implementing a marker quantification assay 120 for determining quantitative data for one or more biomarkers. Examples of assays (e.g., marker quantification assay 120) for one or more markers include DNA assays, microarrays, polymerase chain reaction (PCR), RT-PCR, Southern blots, Northern blots, antibody-binding assays, enzyme-linked immunosorbent assays (ELISAs), flow cytometry, protein assays, Western blots, nephelometry, turbidimetry, chromatography, mass spectrometry, immunoassays, including, but not limited to, RIA, immunofluorescence, immunochemiluminescence, immunoelectrochemiluminescence, or competitive immunoassays, and immunoprecipitation. Information from the assays can be quantitative and can be sent to a computer system, such as that described in more detail with reference to FIG. 16. Information can also be qualitative, such as observed patterns or fluorescence, which can be converted into a quantitative measure by a user or automatically by a reader or computer system.

[0187] In various embodiments, the assay can be any one of RT-qPCR (quantitative reverse transcription polymerase chain reaction), qPCR (quantitative polymerase chain reaction), PCR (polymerase chain reaction), RT-PCR (reverse transcription polymerase chain reaction), SDA (strand displacement amplification), RPA (recombinase polymerase amplification), MDA (multiple displacement amplification), HDA (helicase-dependent amplification), LAMP (loop-mediated isothermal amplification), RCA (rolling circle amplification), NASBA (nucleic acid sequence-based amplification), and any other isothermal or thermocycling amplification reaction. In certain embodiments, the assay is an RT-qPCR assay or a LAMP assay. For example, in a critical care setting where classification and treatment recommendations should be made for patients quickly (e.g., within 30 minutes or within 2 hours), the assay can be an RT-qPCR or LAMP assay that allows for rapid quantification of biomarkers in samples obtained from patients.

[0188] In various embodiments, the marker quantification assay 120 involves performing sequencing to obtain sequence reads (e.g., sequence reads for generating a sequencing library). The sequence reads can be quantified to determine quantitative data for biomarkers. Sequence reads can be achieved using commercially available next-generation sequencing (NGS) platforms, including platforms that perform sequencing by synthesis, sequencing by ligation, using reversible terminator chemistry, pyrosequencing using phosphorus-linked fluorescent nucleotides, or real-time sequencing. For example, the amplified nucleic acid can be sequenced on an Illumina MiSeq platform.

[0189] In pyrosequencing, a library of NGS fragments is clonally amplified in situ by capturing a single matrix molecule using granules coated with adaptor-complementary oligonucleotides. Granules containing the same type of matrix are each placed in a "water-in-oil" microbubble, and the matrix is ​​clonally amplified using a method called emulsion PCR. After amplification, the emulsion is broken, and the granules are placed into separate wells of a titration picoplate, which acts as a flow cell during the sequencing reaction. Four dNTP reagents are each sequentially dosed multiple times into the flow cell in the presence of a sequencing enzyme and a luminescent reporter such as luciferase. When the appropriate dNTP is added to the 3' end of the sequencing primer, the resulting ATP generates a flash of luminescence in the well, which is recorded using a CCD camera. Read lengths of 400 bases or more can be achieved, with up to 10 of the sequence being captured. 6It is possible to obtain reads up to 500 million base pairs (megabytes) of sequence. Further details about pyrosequencing are described in Voelkerding et al., Clinical Chem., 55: 641-658, 2009; MacLean et al., Nature Rev. Microbiol., 7: 287-296; U.S. Patent No. 6,210,891; U.S. Patent No. 6,258,568; each of which is incorporated herein by reference in its entirety.

[0190] On the Solexa / Illumina platform, sequencing data is generated in the form of short reads. In this method, fragments from an NGS fragment library are captured on the surface of a flow cell coated with oligonucleotide anchor molecules. The anchor molecules are used as PCR primers, but due to the length of the matrix and their proximity to other nearby anchor oligonucleotides, PCR extension leads to the formation of molecular "vaults" through hybridization of the molecules with neighboring anchor oligonucleotides and the formation of crosslinked structures on the flow cell surface. These DNA loops are denatured and excised. The linear strands are then sequenced using reversibly dyed terminators. The nucleotides contained in the sequence are determined by detecting their fluorescence after incorporation; each fluorescent agent and blocking agent is then removed before the next dNTP addition cycle. Further details about sequencing using the Illumina platform can be found in Voelkerding et al., Clinical Chem., 55: 641-658, 2009; MacLean et al., Nature Rev. Microbiol., 7: 287-296; U.S. Patent No. 6,833,246; U.S. Patent No. 7,115,400; and U.S. Patent No. 6,969,488; each of which is incorporated by reference herein in its entirety.

[0191] Sequencing of nucleic acid molecules using SOLiD technology involves the clonal amplification of libraries of NGS fragments using emulsion PCR. Subsequently, matrix-containing granules are immobilized on the derivatized surface of a glass flow cell and annealed with primers complementary to adapter oligonucleotides. However, instead of using labeled primers for 3' extension, primers are used to acquire 5' phosphate groups for ligation to test probes containing two probe-specific bases followed by six degenerate bases and one of four fluorescent labels. In the SOLiD system, test probes each have 16 possible combinations of two bases at their 3' end and one of four fluorescent dyes at their 5' end. The color of the fluorescent dye, and therefore the identity of each probe, corresponds to a specific color space coding system. After multiple cycles of probe alignment, probe ligation, and fluorescent signal detection, denaturation is performed, followed by a second sequencing cycle using a primer that is shifted by one base compared to the original primer. In this way, the matrix sequence can be reconstructed by calculation, and the matrix base is checked twice, which leads to increased accuracy.Further details about sequencing using SOLiD technology can be found in Voelkerding et al., Clinical Chem., 55: 641-658, 2009; MacLean et al., Nature Rev. Microbiol., 7: 287-296; U.S. Patent No. 5,912,148; U.S. Patent No. 6,130,073; each of which is incorporated by reference in its entirety.

[0192] In a specific embodiment, the HeliScope from Helicos BioSciences is used. Sequencing is achieved by adding polymerase and sequentially adding fluorescently labeled dNTP reagents. When switched on, a fluorescent signal corresponding to the dNTP appears, and a specific signal is acquired by a CCD camera before each dNTP addition cycle. Sequence read lengths vary from 25 to 50 nucleotides, with a total yield exceeding 1 billion nucleotide pairs per analytical cycle. Further details for performing sequencing using HeliScope can be found in Voelkerding et al., Clinical Chem., 55: 641-658, 2009; MacLean et al., Nature Rev. Microbiol., 7: 287-296; U.S. Patent Nos. 7,169,560; 7,282,337; 7,482,120; 7,501,245; 6,818,395; 6,911,345; and 7,501,245; each of which is incorporated by reference in its entirety.

[0193] In some embodiments, the Roche sequencing system 454 is used. Sequencing 454 involves two steps. In the first step, DNA is cleaved into fragments of approximately 300-800 base pairs, and these fragments have blunt ends. Oligonucleotide adapters are then ligated to the ends of the fragments. The adapters serve as primers for fragment amplification and sequencing. For example, adapters containing 5'-biotin tags can be used to attach fragments to DNA capture beads, such as streptavidin-coated beads. The granule-attached fragments are amplified by PCR within droplets of an oil-water emulsion. The result is multiple copies of clonally amplified DNA fragments on each bead. In the second step, the granules are captured in wells (several picoliters in volume). Pyrosequencing is performed in parallel on each DNA fragment. The addition of one or more nucleotides results in the generation of a light signal, which is recorded by a CCD camera in the sequencing instrument. The signal intensity is proportional to the number of nucleotides involved. Pyrosequencing utilizes pyrophosphate (PPi) that is released after the addition of nucleotide. PPi is converted into ATP using ATP sulfurylase in the presence of adenosine 5' phosphosulfate. Luciferase uses ATP to convert luciferin into oxyluciferin, and as a result of this reaction, light is generated, and this light is detected and analyzed. Further details of how to perform sequencing 454 can be found in Margulies et al. (2005) Nature 437: 376-380, which is incorporated herein by reference in its entirety.

[0194] Ion Torrent technology is a DNA sequencing method based on the detection of hydrogen ions released during DNA polymerization. Microwells contain fragments from the NGS fragment library to be sequenced. Beneath the microwell layer is an ultrasensitive ion sensor, the ISFET. All layers are housed within a semiconductor CMOS chip, similar to chips used in the electronics industry. When a dNTP is incorporated into the growing complementary strand, a hydrogen ion is released, exciting the ultrasensitive ion sensor. If homopolymer repeats are present in the template sequence, multiple dNTP molecules will be incorporated in one cycle. This results in the release of a corresponding amount of hydrogen atoms, proportional to a higher electrical signal. This technology differs from other sequencing technologies in that it does not use modified nucleotides or optical devices. Further details of Ion Torrent technology can be found in Science 327(5970): 1190 (2010); U.S. Patent Application Publication Nos. 20090026082, 20090127589, 20100301398, 20100197507, 20100188073, and 20100137143, each of which is incorporated by reference in its entirety.

[0195] In various embodiments, immunoassays designed to quantify markers can be used in screening, including multiplex assays. Measuring the concentration of target markers in a sample or fraction thereof can be achieved by a variety of specific assays. For example, traditional sandwich-type assays can be used in array, ELISA, RIA, and other formats. Other immunoassays include Ouchterlony plates, which provide convenient determination of antibody binding. Additionally, Western blots can be performed on protein gels or protein spots on filters, conveniently using labeling methods, optionally using marker-specific detection systems.

[0196] Protein-based assays using antibodies that specifically bind to polypeptides (e.g., markers) can be used to quantify marker levels in test samples obtained from subjects. In various embodiments, the antibodies that bind to markers can be monoclonal antibodies. In various embodiments, the antibodies that bind to markers can be polyclonal antibodies. For multiplexed analysis of markers, arrays containing one or more marker affinity reagents, e.g., antibodies, can be generated. Such arrays can be constructed containing antibodies against markers. Detection can utilize one or a panel of marker affinity reagents, e.g., a panel or cocktail of affinity reagents specific for one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, or more markers.

[0197] In various embodiments, determining quantitative expression data for each of the at least three biomarkers comprises contacting the sample with a reagent; generating a plurality of complexes between the reagent and the plurality of biomarkers in the sample; and detecting the plurality of complexes to obtain a dataset associated with the sample, wherein the dataset comprises quantitative expression data for the biomarkers. [Example]

[0198] III. Subtypes of patients with host response dysregulation Custom processing of 14 datasets from sepsis studies from the literature was performed to identify subtypes of host response dysregulation 46For each study, patients were classified as either adult or pediatric. A manual literature review was performed to distinguish between pediatric and adult patients. Adult patients were then classified as either sepsis (S) or septic shock (SS). Septic shock is a subset of sepsis. To distinguish between adult sepsis and adult septic shock patients, the patient vasopressor use rate (usually on day 1) reported in the literature was used. If the patient vasopressor use rate exceeded 50%, the entire study cohort was classified as septic shock. In contrast, if the patient vasopressor use rate was less than 50%, the entire study cohort was classified as sepsis. Based on these classifications of adult or pediatric and sepsis or septic shock, patient samples were classified as full samples (including samples from adult, pediatric, sepsis, and septic shock patients), SS samples (including only samples from adult septic shock patients), S samples (including only samples from adult sepsis patients), and P samples (including only samples from pediatric sepsis and septic shock patients).

[0199] After classification of patient samples from each literature study, biomarker expression data were normalized within each study and curated using methodologies specific to the study's array platform technology and the study's available data format. Healthy control and patient samples were processed using the COCONUT framework. 47 , which normalized samples on the same array platform and transformed patient expression data according to normalization parameters derived from healthy samples. The resulting expression data were quantile-normalized across patients and studies at the end of the normalization process.

[0200] The COINCIDE algorithm was then used to rank the genes based on their expression data. 47Then, for each set of classified patient samples (e.g., full samples, SS samples, S samples, and P samples), for each ranked gene subset (i.e., 100, 250, 500, 1000, 1500 genes, etc.), the COMMUNAL clustering algorithm is used to identify the optimal number of clusters as well as 47、49 , each patient sample was labeled 46 For each set of classified patient samples, we created a COMMUNAL cluster optimality map, where the X axis is the number of clusters, the Y axis is the number of genes included, and the Z axis is the average validity score.

[0201] The COMMUNAL cluster optimality map for the full model (including adult, pediatric, sepsis, and septic shock patient samples) showed three clusters for 574 of the 700 training samples. The remaining training samples were reported as indeterminate.

[0202] The COMMUNAL cluster optimality map for the SS model (which included only samples from adult septic shock patients) showed three clusters for 115 of the 165 training samples.

[0203] The COMMUNAL cluster optimality map for the S model (which included only adult sepsis patient samples) showed four clusters for 153 of the 308 training samples, but the fourth cluster did not show consistent results across different clustering algorithms.

[0204] The COMMUNAL cluster optimality map for the P model (which included only samples from pediatric sepsis and septic shock patients) showed three clusters for 180 of the 227 training samples.

[0205] A stable optimum was consistently observed with K = 3 clusters. Gene Ontology (GO) analysis was performed using patient expression data and cluster labels to characterize the properties and functionality of each cluster (hereafter referred to as "subtypes"). Subtypes were named A (lower mortality, adaptive immune activation), B (higher mortality, innate immune activation), and C (higher mortality, older age, and with clinical and molecular evidence of coagulopathy). 46 The biological functions indicated by GO analysis demonstrated distinct characteristics among different subtypes, indicating a high potential for directed treatment.

[0206] IV. Subtype Classifier for Host Response Dysregulation Patients We deployed eight classification models, including the SS.B1, SS.B2, SS.B3, and SS.B4 models, as well as the full model based on the full sample, the SS model based on the SS sample, the S model based on the S sample, and the P model based on the P sample. To train classification models based on the relevant samples, as detailed below, we determined training labels for each training sample using unsupervised clustering procedures including normalization, the COCONUT method, the COINCIDE method, and the COMMUNAL method.

[0207] The methodology for constructing the classifier was guided by several considerations, particularly in data transformation and normalization, that have the greatest impact on classifier performance. Specifically, the classifier was constructed based on the following considerations. First, because the progression of host response dysregulation is dynamic (e.g., patients can transition from one subtype to another over time), classification is time-dependent. Therefore, time-matched data were analyzed. The analyzed time-matched data included data from blood collected from patients within 24 hours of sepsis diagnosis. When time-series data existed, data from the first time point was used. Second, because the final classification was expected to be a measure of a small number of biomarkers selected from tens of thousands of biomarkers, down-selection of the most important biomarkers was achieved. Third, because the training set for the subtype classifier did not have any outcome labels based on a randomized, placebo-controlled trial design, a test dataset was selected as a test set dedicated to evaluating the performance of the classifier. Fourth, because the raw expression data from the VANISH study were measured using the Illumina platform and reported in a format different from that of the training set, the normalization used in the clustering process required special consideration. Fifth, the classification process applied similar data transformations to the training and test sets to achieve optimal performance. Finally, the transformation and normalization strategies that performed best for the clustering process, and even the training process, may not necessarily perform well in classifying subtypes to identify corticosteroid responses because the training set was not accompanied by outcome data.

[0208] Based on these six considerations, a classifier was constructed using a normalization scheme for both the training and test expression data. A platform normalization matrix was constructed from all genes in all healthy and sepsis samples. Because the number of samples in the matrix was large, individual sample expression data were quantile-normalized to the matrix as a perturbation. To train the classifier, expression data from the training set was batch-normalized, curated, and then normalized by the platform normalization matrix, as detailed below.

[0209] A set of potentially significant biomarkers was identified by significance analysis of microarrays (SAM) 48 As another example, a set of potentially significant biomarkers can be identified using qPCR or RNA sequencing data. qPCR measures the relative or absolute expression levels of the biomarkers. A normalization or calibration process is implemented. The RNA sequencing data measures the relative expression levels of model genes and their transcripts. Sequencing read alignment methods (e.g., Hisat2 and Bowtie2), expression estimation methods (e.g., StringTie, Salmon), and normalization processes (e.g., quantile normalization) are used to quantify the estimated expression of the model genes.

[0210] These sets of potentially significant biomarkers were narrowed down by at least two-fold change, and a forward search methodology was used to identify a small set of biomarkers for feature calculation. 51 Calculated features (e.g., differential gene expression summaries) 51The clustering labels for each sample were finally used to train a multi-class classifier implemented as e1071::svm with a radial kernel with gamma of 0.1 and cost of 10. Tables 1, 2A-2B, and 3 below list the identified genes for each classifier (e.g., full model, SS model, S model, and P model) and for each subtype (e.g., A, B, and C). Specifically, Table 1 lists genes for each subtype (e.g., A, B, and C) for the full model, Table 2A lists genes for each subtype (e.g., A, B, and C) for the SS model, Table 2B lists genes for each subtype (e.g., A, B, and C) for the S model, and Table 3 lists genes for each subtype (e.g., A, B, and C) for the P model. Note that in certain embodiments, the entire set of genes for a given model is used to train and / or test the model. However, in alternative embodiments, only a subset of the set of genes for a given model is used to train and / or test the model. For example, in some embodiments, at least one gene from each of subtypes A, B, and C (e.g., at least one gene from each of rows 1, 2, and 3 in one of Tables 1, 2A, 2B, and 3 below) may be used to train and / or test the model.

[0211] (Table 1) Biomarkers in the full model TIFF2025178312000002.tif75163

[0212] Table 2A. Biomarkers in the SS model TIFF2025178312000003.tif60166

[0213] Table 2B. Biomarkers in the S model TIFF2025178312000004.tif61166

[0214] Table 3. Biomarkers in the P model TIFF2025178312000005.tif61162

[0215] Further models were created to include at least one up-gene and one down-gene in the model, allowing for the calculation of assay scores based on relative gene expression. Two methods were applied based on forward selection and backward elimination. Forward selection is an iterative method that starts with no genes in the model. At each iteration, features that improve the model are added until adding new variables no longer improves model performance. Backward elimination includes all genes, and then removes the least important features at each iteration if there is an improvement in model performance. This is repeated until no improvement is observed from feature removal. As an example, the SS model was used as a starting point for creating alternative models. The metrics used to evaluate model performance were leave-one-out accuracy and model similarity in labeling patients compared to the full model.

[0216] In this exercise, elimination of variables produced excellent results. Tables 4A-4D show four additional models created by this method, named SS.B1, SS.B2, SS.B3, and SS.B4.

[0217] (Table 4A) SS.B1 TIFF2025178312000006.tif61162

[0218] (Table 4B) SS.B2 TIFF2025178312000007.tif61162

[0219] (Table 4C) SS.B3 TIFF2025178312000008.tif61162

[0220] (Table 4D) SS.B4 TIFF2025178312000009.tif61162

[0221] Table 4E. Identification of biomarkers included in each of the full, SS, S, P, SS.B1, SS.B2, SS.B3, and SS.B4 models. TIFF2025178312000010.tif253163TIFF2025178312000011.tif119163

[0222] Table 5 shows primer sets for amplifying genes identified by the SS model and listed in Table 2A above, primer sets for amplifying genes identified by the S model and listed in Table 3B above, and primer sets for amplifying genes identified by the SS.B2 model and listed in Table 4B above. Each primer set includes a pair of single-stranded DNA primers (i.e., a forward primer and a reverse primer) for amplifying a gene, for example, by RT-qPCR. In some embodiments, the entire sequence of the primers can be used to amplify related genes. In alternative embodiments, at least 15 contiguous nucleotides of the primer sequence can be used to amplify related genes. In certain embodiments, primer sequences other than those listed in Table 5 can be used to amplify one or more of the genes from Tables 1, 2A, 2B, 3, and 4A-4D.

[0223] Table 5: RT-qPCR primer sequences TIFF2025178312000012.tif255168TIFF2025178312000013.tif75169

[0224] In certain embodiments, genes may be amplified by methods other than RT-qPCR. For example, in some embodiments, genes may be amplified via LAMP (loop-mediated isothermal amplification). In such embodiments where genes are amplified via LAMP, the primer set for amplifying genes comprises a forward outer primer, a backward outer primer, a forward inner primer, a backward inner primer, a forward loop primer, and a backward loop primer.

[0225] A sensitivity analysis was performed for each classifier for each combination of three genes, selecting one gene from each subtype. Specifically, the accuracy of the classifiers was measured to demonstrate that each classifier had greater than 50% accuracy (e.g., greater than random chance) in identifying the subtype of interest using any combination of three genes, one gene from each subtype.

[0226] To calculate the accuracy of a given classifier for a given combination of three genes, the leave-one-out accuracy of the training samples of the training dataset on which the classifier was trained was calculated. The training dataset included N training samples, each of which included a label y and a feature x. For a combination of three genes, the leave-one-out accuracy of the classifier was calculated based on N calculations. N i The computation is performed by excluding training sample i during training of the classifier. The trained classifier is then used to find the feature x corresponding to the excluded training sample i from the training dataset. i Predictions about z i Then, the predicted z i Label y i For each of the three gene combinations, we calculated the leave-one-out accuracy for the classifier as the number of correct predictions, z, divided by N.

[0227] Figures 2-5 show the individual accuracies determined for each combination of three genes, one gene from each subtype, for the full, SS, S, and P models, respectively. Specifically, Figure 2 is a graph of the individual accuracies determined for each combination of three genes, one gene from each subtype, for the full model. Figure 3 is a graph of the individual accuracies determined for each combination of three genes, one gene from each subtype, for the SS model. Figure 4 is a graph of the individual accuracies determined for each combination of three genes, one gene from each subtype, for the S model. Figure 5 is a graph of the individual accuracies determined for each combination of three genes, one gene from each subtype, for the P model. As shown in Figures 2-5, for each combination of three genes, one gene from each subtype, each classifier demonstrated an accuracy greater than 50% (e.g., greater than random chance). Furthermore, the average accuracies for the full, SS, S, and P models were 82.93%, 89.6%, 86.3%, and 98.3%, respectively. Thus, each classifier demonstrated an average accuracy greater than 50% (e.g., greater than random chance). Each of Figures 2-5 includes the accuracy of a model incorporating all of the genes for a particular model (labeled "full" in each figure). For example, for the full model, incorporating all of the genes refers to the full model analyzing all of the biomarkers listed in Table 1. For the SS model, incorporating all of the genes refers to the SS model analyzing all of the biomarkers listed in Table 2A. For the S model, incorporating all of the genes refers to the S model analyzing all of the biomarkers listed in Table 2B. For the P model, incorporating all of the genes refers to the SS model analyzing all of the biomarkers listed in Table 3.

[0228] Figures 6A-6D are graphs of the individual accuracies determined for each combination of three biomarkers, one biomarker from each subtype, for the SS.B1, SS.B2, SS.B3, and SS.B4 models, respectively. These models demonstrate an accuracy of 89.57%. Each of Figures 6A-6D includes the accuracy of each model incorporating all of the genes for that particular model (labeled "Full" in each figure). For example, for the SS.B1 model, incorporating all of the genes refers to the SS.B1 model analyzing all of the biomarkers listed in Table 4A. For example, for the SS.B2 model, incorporating all of the genes refers to the SS.B2 model analyzing all of the biomarkers listed in Table 4B. For example, for the SS.B3 model, incorporating all of the genes refers to the SS.B3 model analyzing all of the biomarkers listed in Table 4C. For example, for the SS.B4 model, incorporating all of the genes refers to the SS.B4 model analyzing all of the biomarkers listed in Table D.

[0229] V. Identification of Therapeutic Agents for the Treatment of Host Response Dysregulated Patient Subtypes Based on the differential expression of biomarkers determined for each host response dysregulation subtype, the immune status of each subtype was determined. Specifically, subtype A was determined to be associated with an adaptive immune status, subtype B was determined to be associated with an innate immune status and a complement immune status, and subtype C was determined to be associated with a coagulopathic immune status. Biomarkers indicated to be associated with host response dysregulation, immune status, and the pharmacology of existing therapeutic drugs were then identified from the literature. Table 6 below shows a representative list of genes associated with host response dysregulation, immune status, and the pharmacology of existing therapeutic drugs identified from the literature.

[0230] Table 6. Representative examples of genes associated with host response dysregulation, immune status, and the pharmacology of existing therapeutic agents. TIFF2025178312000014.tif139163TIFF2025178312000015.tif226163TIFF2025178312000016.tif231163 TIFF2025178312000017.tif236163TIFF2025178312000018.tif246163TIFF2025178312000019.tif232163

[0231] For each gene in Table 6, the fold change in gene expression was calculated between subtypes. Specifically, linear regression was used for each subtyping model (full / S / SS / P) to compare each gene expression between A / B / C subtypes. To adjust for the batch effect of microarray datasets from different studies, the study ID was included in the linear regression model. From the linear regression model, the fold change in gene expression was calculated using the coefficient of subtype, and the Benjamini-Hochberg (BH) of the subtype was used. 53 Adjusted p-values ​​were used to indicate whether the expression differences were statistically significant. Table 7 below shows a representative dataset of the subtype fold changes in the expression of the genes in Table 6. The fold changes in gene expression between subtypes (e.g., fold change "A / B" = 2^(AB) [where A and B are log 2 (average expression) for the genes listed for given subtypes A and B]) are listed numerically in the table. Bold or underlined indicates a statistically significant fold change as determined by BH. Bold indicates upregulation, and underlined indicates downregulation. This dataset was then used to identify therapeutic candidates for the treatment of host response dysregulation, taking into account whether the gene is expected to be appreciably expressed in the blood.

[0232] Table 7. Representative examples of fold changes in gene expression between A / B / C subtypes TIFF2025178312000020.tif106170TIFF2025178312000021.tif241170TIFF20251783120 00022.tif240170TIFF2025178312000023.tif236170TIFF2025178312000024.tif243170 TIFF2025178312000025.tif231170TIFF2025178312000026.tif234170TIFF20251783120 00027.tif234170TIFF2025178312000028.tif242170TIFF2025178312000029.tif220170

[0233] Figure 8 shows the conclusions of this further analysis according to the embodiments of Tables 6 and 7. Subtype A host response dysregulated patients show upregulation of biomarkers associated with innate immune activity involved in pathogen recognition (e.g., via recognition of pathogen-associated molecular patterns (PAMPs)), upregulation of biomarkers associated with innate immune regulation, and upregulation of biomarkers associated with adaptive immune activity. Subtype B host response dysregulated patients show upregulation of biomarkers associated with innate immune activity involved in recognition of injury-associated molecular patterns (DAMPs), upregulation of biomarkers associated with DAMPs, upregulation of biomarkers associated with inflammation (e.g., TNF-alpha), upregulation of biomarkers associated with complement activity, downregulation of biomarkers associated with adaptive immune activity, upregulation of biomarkers associated with adaptive immune suppression, and upregulation of markers associated with increased risk of acute kidney injury. Subtype C patients show downregulation of biomarkers associated with innate and adaptive immune activity, upregulation of biomarkers associated with DAMPs, upregulation of biomarkers associated with cell recruitment (e.g., G-CSF and GM-CSF), upregulation of biomarkers associated with increased risk of thrombosis, and upregulation of biomarkers associated with coagulation.

[0234] These findings of differential expression of biomarkers between subtypes A, B, and C inform general treatment strategies. Figure 9 depicts a heatmap showing the differential expression of genes from Table 6 for host response dysregulated patients with subtypes A, B, and C, and for healthy subjects without host response dysregulation, according to an embodiment. As discussed below with respect to Figure 10, subtype A patients exhibit a relatively low mortality rate, which may be attributed to a relatively beneficial host response. Indeed, as shown in Figure 9, the differential expression of genes for host response dysregulated patients with subtype A most closely resembles the differential expression of genes for healthy subjects without host response dysregulation. Therefore, in subtype A patients, it may be beneficial to avoid immune modulators that exhibit immunosuppressive effects that suppress beneficial host responses. In subtype B patients, it may be beneficial to stimulate adaptive immune activity, attenuate innate immune stimulants (e.g., TNF-α), attenuate complement immune activity, attenuate DAMPs and / or block DAMP receptors, and activate PAMP receptors. In subtype C patients, it may be beneficial to stimulate adaptive immune activity, administer anticoagulants or agents that indirectly attenuate procoagulant factors, reduce vascular permeability, attenuate DAMPs and / or block DAMP receptors, and activate PAMP receptors.

[0235] 10 shows the risk of death for host response dysregulated patients with subtypes A, B, and C, according to an embodiment. As mentioned above, patients with subtype A exhibit a lower risk of death compared to patients with subtypes B and C. Furthermore, patients with subtype C exhibit a higher risk of death compared to patients with subtypes A and B. Therefore, subtyping models can be used as a prognostic indicator for assessing the risk of death for host response dysregulated patients.

[0236] VI. Evaluating Therapeutics for Host Response Dysregulated Patient Subtypes As described above, the genes in Tables 6 and 7 are associated with the pharmacology of existing therapeutic agents. For example, examples of existing therapeutic agents associated with certain genes are provided in Table 7. Analysis of these genes in Tables 6 and 7 according to subtype informs the use of existing therapeutic agents associated with these genes to treat host response dysregulated patients of that subtype. Specifically, Table 8 shows treatment hypotheses for systemic immune patients with subtypes A, B, and C, determined based on analysis of the differential expression of the genes in Table 7, according to an embodiment.

[0237] As a specific example, whereas anti-TNF-alpha failed to show benefit in previous sepsis clinical trials, analysis of gene differential expression according to subtype can provide information on which specific subtypes of patients may respond to anti-TNF-alpha. In this example, TNF genes were found to be upregulated in patients with subtype B, and therefore, subtype B patients may be specifically responsive to anti-TNF-alpha therapy.

[0238] Table 8 below summarizes an analysis of existing therapeutic agents that are expected to provide the desired therapeutic effect for subtypes A, B, and C described above.

[0239] Table 8. Representative Treatment Hypotheses for Host Response Dysregulated Patient Subtypes TIFF2025178312000030.tif66170TIFF2025178312000031.tif225170TIFF2025178312000032.tif237170TIFF2025178312000033.tif221170TIFF2025178312000034.tif237170TIFF2025178312000035.tif235170TIFF2025178312000036.tif218170TIFF2025178312000037.tif213170TIFF2025178312000038.tif240170TIFF2025178312000039.tif243170TIFF2025178312000040.tif227170TIFF2025178312000041.tif233170TIFF2025178312000042.tif236170TIFF2025178312000043.tif238170TIFF2025178312000044.tif224170TIFF2025178312000045.tif238170TIFF2025178312000046.tif236170TIFF2025178312000047.tif226170TIFF2025178312000048.tif245170TIFF2025178312000049.tif230170TIFF2025178312000050.tif237170TIFF2025178312000051.tif237170TIFF2025178312000052.tif238170TIFF2025178312000053.tif213170TIFF2025178312000054.tif225170TIFF2025178312000055.tif239170TIFF2025178312000056.tif228170TIFF2025178312000057.tif227170TIFF2025178312000058.tif197170TIFF2025178312000059.tif227170TIFF2025178312000060.tif60170

[0240] VI.A. Subtype A in Patients with Host Response Dysregulation VI.A.1. Corticosteroids As detailed above, septic patients who remain hypotensive and require vasopressors to maintain a mean arterial pressure of 65 mmHg or greater are characterized as having septic shock (a condition associated with an in-hospital mortality rate of over 40%). Septic shock patients who do not show clinical improvement (defined as having a systolic blood pressure of <90 mmHg for more than one hour after both adequate fluid resuscitation and vasopressor therapy) are considered refractory to vasopressor therapy and are therefore characterized as refractory septic shock patients. Corticosteroid therapy, such as hydrocortisone, is often administered to refractory septic shock patients, based on the rationale that such therapy may enable them to respond to vasopressors.

[0241] To evaluate the effectiveness of hydrocortisone therapy in sepsis patients with subtypes A, B, and C, the differential expression of genes in Table 7 associated with the pharmacology of hydrocortisone therapy was evaluated for subtypes A, B, and C. Specifically, FIG. 11 shows the differential expression of genes in Table 7 associated with the pharmacology of hydrocortisone therapy (e.g., modulation of the glucocorticoid receptor signaling pathway) for subtypes A, B, and C, according to an embodiment. As shown in FIG. 11, subtype A patients show more differential expression of genes associated with glucocorticoid receptor signaling than subtype B patients. Specifically, compared to subtype B patients, subtype A patients show downregulation of genes associated with positive regulation of the glucocorticoid receptor signaling pathway, but upregulation of genes associated with negative regulation of the glucocorticoid receptor signaling pathway. In other words, compared with subtype A patients, subtype B patients show upregulation of genes associated with positive regulation of the glucocorticoid receptor signaling pathway, but downregulation of genes associated with negative regulation of the glucocorticoid receptor signaling pathway.

[0242] We hypothesized that this differential expression of genes associated with glucocorticoid receptor signaling between subtype A, B, and C patients would result in differential efficacy of hydrocortisone therapy across different subtypes. To test this hypothesis, we analyzed multiple cohort datasets for differential expression and survival to assess the effect of hydrocortisone across different host response dysregulation subtypes. Specifically, we applied the constructed classifier discussed above to two placebo-controlled studies: the VANISH study to assess survival in patients receiving hydrocortisone therapy and the burn-induced SIRS study. 13、50 .

[0243] As discussed in detail below 31 To assess the response of host response dysregulated patients to hydrocortisone therapy, we applied the patient subtype classifier to transcriptome datasets from placebo-controlled hydrocortisone clinical trials in patients with sepsis and burn-induced SIRS, which failed to demonstrate differences in mortality between the treatment and placebo arms of the trials. Differential responses to hydrocortisone therapy were identified for different patient subtypes. Specifically, one patient subtype was shown to benefit from hydrocortisone, while one or both of the other patient subtypes were shown to worsen with hydrocortisone.

[0244] Test expression data from each study was normalized by the platform normalization matrix described above. 13, thereby allowing the test data to be more consistent with the training data. A classifier (e.g., full model, SS model, S model, and P model) was then applied to the normalized data, thereby classifying patients into A, B, and C subtypes. In contrast to the COCONUT method, the normalization approach described herein is simpler because it does not use a control, but instead employs a platform normalization matrix, thus selecting all of the samples from the matrix used by the target platform of the target sample, and then co-normalizing them together. Thus, each sample in the target sample was independently normalized with the normalization matrix of the sample array platform.

[0245] Survival and mortality rates were calculated at day 28 because survival and mortality labels were unavailable at other time points. Single-timepoint survival analyses were performed to observe differences in survival rates between the hydrocortisone therapy and placebo groups in each subtype. Binomial and chi-square tests with continuity correction were used to test for the significance of these differences. The reduction in mortality rate when hydrocortisone was omitted was calculated as 1 - (placebo mortality rate / hydrocortisone mortality rate). Conversely, the reduction in mortality rate when hydrocortisone was added was calculated as 1 - (hydrocortisone mortality rate / placebo mortality rate). In both cases, the denominator was the larger of the two.

[0246] Tables 9-16 below show survival analyses by test, by classifier (e.g., full model, SS model, S model, and P model), and by subtype (e.g., A, B, and C). Specifically, Tables 9 and 13 show survival analyses by subtype (e.g., A, B, and C) for the full model, Tables 10 and 14 show survival analyses by subtype (e.g., A, B, and C) for the SS model, Tables 11 and 15 show survival analyses by subtype (e.g., A, B, and C) for the S model, and Tables 12 and 16 show survival analyses by subtype (e.g., A, B, and C) for the P model.

[0247] Table 9. Survival analysis of the full model VANISH study TIFF2025178312000061.tif97161

[0248] Table 10. Survival analysis of the VANISH study in the SS model TIFF2025178312000062.tif96161

[0249] Table 11. Survival analysis of the VANISH test in the S model TIFF2025178312000063.tif97161

[0250] Table 12. Survival analysis of the VANISH test in the P model TIFF2025178312000064.tif97161

[0251] Table 13. Survival analysis of the full model burn-induced SIRS study TIFF2025178312000065.tif97161

[0252] Table 14. Survival analysis of burn-induced SIRS study in the SS model TIFF2025178312000066.tif97161

[0253] Table 15. Survival analysis of the S model burn-induced SIRS study TIFF2025178312000067.tif97161

[0254] Table 16. Survival analysis of the P model burn-induced SIRS study TIFF2025178312000068.tif97161

[0255] An alternative method for identifying patients who may be harmed by the immunosuppressive effects of hydrocortisone is based on employing A and B scores to identify patients who are predicted to exhibit increased immune activity and lower inflammation. Briefly, this method is based on classifying patients with high A scores and low B scores.

[0256] In one example, previously identified subtypes of sepsis patients were used to refine the model to identify these type A and type B patients. Two distinct sepsis response signatures (SRS1 and SRS2) were identified in five public studies (E-MTAB-4421, E-MTAB-4451, E-MTAB-5273, E-MTAB-5274, and E-MTAB-7581) using HumanHT-12 v4 BeadChips to generate gene expression profiles of patient samples. The processed data from these five studies were downloaded and processed using the statistical programming language and software environment R (version 3.6.3). The Bioconductor annotation package, IlluminaHumanv4.db (version 1.26.0), was used to annotate microarray probes and determine gene expression levels by each individual probe or the average of probes belonging to the same gene. To remove cohort bias, the Bioconductor package, limma (version 3.42.2), was used to remove batch effects. Using the genes of the ss.b2 panel, scores for subtypes A, B, and C were calculated by the geometric mean of up / down genes.

[0257] To build the classifier, we defined E-MTAB-4421, E-MTAB-4451, E-MTAB-5273, and E-MTAB-5274 as the training dataset and VANISH (E-MTAB-7581) as the test dataset. We used the features (scores of subtypes A, B, and C) and class labels (comparing SRS1 and SRS2) from the training dataset to build a machine learning classifier based on the support vector machine (SVM) method. SVM is a supervised machine learning method for classification analysis. The algorithm finds a single hyperplane or set of hyperplanes that maximizes the margin between the scores of subtypes A, B, and C. To capture nonlinear data, we used a kernel function. Using the R package e1071, we built an SVM classifier with the following parameters: method = "C-classification", kernel = "radial", gamma = 0.1, and cost = 10.

[0258] The accuracy of the classifier was evaluated by leave-one-out (LOO) cross-validation across the training dataset. The classifier was also applied to 117 control samples from the VANISH study. Patients predicted as type A (SRS2-like) showed a significant reduction in 28-day mortality when hydrocortisone was administered compared to placebo. These type A patients showed a 75.5% reduction in mortality in the placebo group compared to the hydrocortisone group (Fisher's exact test p-value 0.0093). The type A (SRS2-like) and type B (SRS1-like) classifiers showed an accuracy of 88.6%. Table 17 below shows the survival analysis by subtype for the SS.B2 model.

[0259] Table 17. Survival analysis of the VANISH study in the SS.B2 model TIFF2025178312000069.tif107128

[0260] In addition to the SVM method, a threshold can be employed to define the A label compared with the B label. We found that the subtype A score and the B score play an important role in classifying subtypes SRS1 and SRS2. Therefore, we applied a heuristic threshold (threshold = 0) to the subtype A and B scores to classify SRS1-like and SRS2-like in VANISH: samples with a subtype A score > 0 and a subtype B score < 0 were assigned the SRS2-like label, and the remaining samples were assigned the SRS1-like label. Patients predicted as SRS2-like using a simple heuristic threshold (threshold = 0) showed an 85.2% reduction in 28-day mortality in the placebo group compared with the hydrocortisone group (Fisher's exact test p-value 0.0159).

[0261] In addition to the heuristic threshold, we also derived thresholds for the scores of subtypes A and B using the training dataset. Similar to the SVM method, we defined E-MTAB-4421, E-MTAB-4451, E-MTAB-5273, and E-MTAB-5274 as the training dataset, and VANISH (E-MTAB-7581) as the test dataset. To identify the best threshold for the score of subtype A to classify the subtypes SRS1 and SRS2 in the training dataset, we fitted the subtype A scores and SRS subtype labels to a receiver operating characteristic (ROC) curve and identified the threshold for the score of subtype A (A threshold = -0.2664) that was the shortest point on the plot in the upper left part with perfect sensitivity or specificity. Using the same method, we selected the optimal B score threshold (B threshold = 0.3179) to classify the subtypes SRS1 and SRS2 in the training set. We applied the defined optimal thresholds for subtype A and B scores to the VANISH study: samples with subtype A scores above the A threshold and subtype B scores below the B threshold were labeled as SRS2-like, and the remaining VANISH study samples were labeled as SRS1-like. Using such classification, patients with an SRS2-like label showed an 81.7% reduction in 28-day mortality in the placebo group compared with the hydrocortisone group (Fisher's exact test p-value 0.0065).

[0262] Various thresholds can be employed to optimize for mortality reduction (mr) and the number of patients who may benefit (percentage of patients who are B). Table 18 below shows survival analysis by subtype for the SS.B2 model.

[0263] Table 18. Survival analysis of the VANISH study in the SS.B2 model TIFF2025178312000070.tif161163 【...

Claims

1. 1. A system for determining a therapy recommendation for a patient, comprising: a computer system; The computer system obtaining a classification of subjects exhibiting host response dysregulation; and Identifying a treatment recommendation for the subject based at least in part on the classification. It is configured as follows: The classification is as follows: obtaining, or having obtained, quantitative data for at least one biomarker set obtained from a subject selected from the group consisting of a Group 1, Group 2, Group 3, Group 4, or Group 5 biomarker set; Group 1 includes biomarker 1, biomarker 2, and biomarker 3; Biomarker 1 is one of EVL, BTN3A2, HLA-DPA1, IDH3A, ACBD3, EXOSC10, SNRK, or MMP8; Biomarker 2 is one of SERPINB1 or GSPT1; Biomarker 3 is one of MPP1, HMBS, TAL1, C9orf78, POLR2L, SLC27A3, BTN3A2, DDX50, FCHSD2, GSTK1, UBE2E1, TNFRSF1A, PRPF3, or TOMM70A; Group 2 includes biomarker 4, biomarker 5, and biomarker 6; Biomarker 4 is one of ZNF831, MME, CD3G, or STOM; Biomarker 5 is one of ECSIT, LAT, or NCOA4; Biomarker 6 is one of SLC1A5, IGF2BP2, or ANXA3; Group 3 includes biomarker 7, biomarker 8, and biomarker 9; Biomarker 7 is one of C14orf159 or PUM2; Biomarker 8 is one of EPB42 or RPS6KA5; Biomarker 9 is one of EPB42 or GBP2; Group 4 includes biomarker 10, biomarker 11, and biomarker 12; the biomarker 10 is one of MSH2, DCTD, or MMP8; biomarker 11 is one of HK3, UCP2, or NUP88; Biomarker 12 is one of GABARAPL2 or CASP4; Group 5 includes biomarker 13, biomarker 14, and biomarker 15; Biomarker 13 is one of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, or CD3G; biomarker 14 is one of EPB42, GSPT1, LAT, HK3, or SERPINB1; Biomarker 15 is, will be, or has been one of SLC1A5, IGF2BP2, ANXA3, GBP2, TNFRSF1, BTN3A2, or TNFRSF1A; and determining said classification based on said quantitative data using a patient subtype classifier; It is determined by system.

2. The system of claim 1, wherein the subject's host response dysregulation comprises one of sepsis and a host response dysregulation not caused by an infection.

3. the classification of the subject comprises one of subtype A or subtype B; if the subject's classification includes subtype A, the treatment recommendation identified for the subject includes at least no immunosuppressive therapy; If the subject's classification includes subtype B, the treatment recommendation identified for the subject includes at least one of no treatment recommendation, immunostimulatory therapy, suppression of immunomodulatory therapy, blockade of immunosuppressive therapy, blockade of complement activation therapy, and anti-inflammatory therapy.

3. The system of claim 1 or 2.

4. the classification of the subject comprises one of subtype A, subtype B, or subtype C; if the subject's classification includes subtype A, the treatment recommendation identified for the subject includes at least no immunosuppressive therapy; if the subject's classification comprises subtype B, the treatment recommendation identified for the subject comprises at least one of no treatment recommendation, immunostimulatory therapy, suppression of immunomodulatory therapy, blockade of immunosuppressive therapy, blockade of complement activation therapy, and anti-inflammatory therapy; If the subject's classification includes subtype C, the treatment recommendation identified for the subject includes at least one of no treatment recommendation, immunostimulatory therapy, suppression of immunomodulatory therapy, blockade of immunosuppressive therapy, modulator of coagulation therapy, and modulator of vascular permeability therapy.

3. The system of claim 1 or 2.

5. 5. The system of claim 3 or 4, wherein if the subject's classification comprises subtype A, the treatment recommendations identified for the subject further comprise at least no corticosteroid therapy.

6. 6. The system of claim 5, wherein the treatment recommendations identified for the subject further include at least one of no hydrocortisone.

7. 5. The system of claim 3 or 4, wherein if the subject's classification comprises subtype B, the treatment recommendations identified for the subject further comprise at least one of a checkpoint inhibitor, a complement component blocker, a complement component receptor blocker, and a pro-inflammatory cytokine blocker.

8. 4. The system of claim 3, wherein the treatment recommendations identified for the subject further comprise at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulatory factor, IL-22 agonist, IFN-alpha regulatory factor, IFN-lambda regulatory factor, IFN-alpha 2b stimulator, anti-C5a, anti-C3a, anti-C5aR, anti-C3aR, anti-TNF-alpha and anti-IL-6, anti-HMGB1, ST2 antibody, IL-33 antibody.

9. 5. The system of claim 4, wherein if the subject's classification comprises subtype C, the treatment recommendation identified for the subject further comprises at least one of a checkpoint inhibitor and an anticoagulant.

10. 10. The system of claim 9, wherein the treatment recommendations identified for the subject further comprise at least one of GM-CSF, anti-PD-1, anti-PD-L1, anti-CLTA-4, anti-CEACAM-1, anti-TIM-3, anti-BTLA, IL-7, INF-gamma, IFN-beta 1a regulator, IL-22 agonist, IFN-alpha regulator, IFN-lambda regulator, IFN-alpha 2b stimulator, activated protein C, antithrombin, and thrombomodulin.

11. 11. The system of any one of claims 1 to 10, wherein the sample comprises a blood sample from the subject.

12. (a) subjects exhibiting host response dysregulation do not exhibit shock and at least one set of biomarkers is in one of Group 1, Group 3, or Group 4; or (b) the subject exhibiting host response dysregulation further exhibits shock, and at least one biomarker set is one of Group 1, Group 2, Group 4, or Group 5; or (c) the subject exhibiting host response dysregulation is an adult subject, and at least one set of biomarkers is one of Group 1, Group 2, Group 3, or Group 5; or (d) the subject exhibiting host response dysregulation is a pediatric subject, and at least one biomarker set is one of Group 1, Group 4, or Group 5; 12. The system of any one of claims 1 to 11.

13. 13. The system of any one of claims 1 to 12, wherein the quantitative data is determined by one of RT-qPCR (quantitative reverse transcription polymerase chain reaction), qPCR (quantitative polymerase chain reaction), PCR (polymerase chain reaction), RT-PCR (reverse transcription polymerase chain reaction), SDA (strand displacement amplification), RPA (recombinase polymerase amplification), MDA (multiple displacement amplification), HDA (helicase dependent amplification), LAMP (loop-mediated isothermal amplification), RCA (rolling circle amplification), NASBA (nucleic acid sequence-based amplification) and any other isothermal or thermocycling amplification reaction.

14. The classification of the subject is determining a class-specific score for said subject with respect to at least one candidate class for said subject; determining a classification of the subject by a patient subtype classifier based on the classification-specific score. The system of any one of claims 1 to 13, wherein the value is determined by

15. Determining a classification-specific score determining a first sub-score of quantitative data for the subject with respect to one or more biomarkers of the candidate class, wherein the quantitative data for the subject with respect to the one or more biomarkers of the candidate class is increased compared to the quantitative data with respect to the one or more biomarkers for one or more control subjects; determining a second subscore of quantitative expression for the subject for one or more additional biomarkers of the candidate classification, wherein the quantitative data for the subject for the one or more additional biomarkers of the candidate classification is decreased compared to the quantitative data for the one or more additional biomarkers for the one or more control subjects; and determining the difference between the first subscore and the second subscore, wherein the first and second subscores are optionally scaled, and the difference comprises the class-specific score for the subject.

15. The system of claim 14, further comprising:

16. 16. The system of claim 15, wherein one or both of the first subscore and the second subscore is a geometric mean value.

17. 17. The system of any one of claims 1 to 16, wherein the patient subtype classifier is a machine learning model.

18. 20. The system of claim 17, wherein the machine learning model is a support vector machine (SVM).

19. 20. The system of claim 18, wherein the support vector machine receives as input one or more classification-specific scores and outputs a classification of the subject.

20. The patient subtype classifier comparing the class-specific scores to one or more thresholds; and determining a classification of the subject based on said comparison. determining the classification of the subject by 16. The system of claim 14 or 15.

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