WO2022212890A1 - Diagnostic compagnon et thérapies pour réponse hôte dérégulée - Google Patents

Diagnostic compagnon et thérapies pour réponse hôte dérégulée Download PDF

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WO2022212890A1
WO2022212890A1 PCT/US2022/023128 US2022023128W WO2022212890A1 WO 2022212890 A1 WO2022212890 A1 WO 2022212890A1 US 2022023128 W US2022023128 W US 2022023128W WO 2022212890 A1 WO2022212890 A1 WO 2022212890A1
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classification
subtype
biomarker
subject
therapy
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PCT/US2022/023128
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Diego Ariel Rey
Leonardo Maestri Teixeira
Hugo Yk LAM
Bayo Yh LAU
Lijing YAO
Li Tai FANG
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Endpoint Health Inc.
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Publication of WO2022212890A1 publication Critical patent/WO2022212890A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K45/00—Medicinal preparations containing active ingredients not provided for in groups A61K31/00 - A61K41/00
    • A61K45/06—Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]

Definitions

  • Sepsis is an acute, life-threatening syndrome caused by a dysregulated immune response to infection. Approximately 1.7 million patients are diagnosed with sepsis each year. According to a recent study based on electronic medical record data from more than 7 million hospitalizations across 409 US hospitals, sepsis has an estimated 6% hospital admission rate. The average length of stay for septic patients is 75% greater than most other conditions, and its mortality accounts for more than 50% of hospital deaths in hospitals. Sepsis ranks as one of the highest costs among all hospital admissions, representing approximately 13% of total US hospital costs, or more than $24 billion in hospital expenses. Sepsis costs increase based on sepsis severity level and timing of clinical presentation (e.g., at the hospital admission or during the hospital stay).
  • refractory septic shock defined as a systolic blood pressure ⁇ 90 mmHg for more than one hour following both adequate fluid resuscitation and vasopressor therapy.
  • glucocorticoid therapy may provide improvement.
  • Corticosteroids remain a controversial therapy for sepsis patients. Specifically, current guidelines provide a weak recommendation for corticosteroids sepsis patients by stating that either steroids and no steroids are reasonable management options.
  • the anti-TNF-alpha is any of adalimumab, etanercept, infliximab, golimumab, certolizumab pegol (CIMZIA), diammonium glycyrrhizinate (GANLIXIN), ozoralizumab (OZORALIZUMAB), rhTNFR(m):Fc, SBT-104, SCB-808, SSS07, and TNF Mab (LFB).
  • CCMZIA certolizumab pegol
  • GANLIXIN diammonium glycyrrhizinate
  • OZORALIZUMAB ozoralizumab
  • rhTNFR(m):Fc SBT-104, SCB-808, SSS07, and TNF Mab (LFB).
  • the Tissue Factor inhibitor comprises an antibody comprising a VH CDR1 of SEQ ID NO: 93, a VH CDR2 of SEQ ID NO: 94, a VH CDR3 of SEQ ID NO: 95, a VL CDR 1 of SEQ ID NO: 96, a VL CDR2 of SEQ ID NO: 97, and a VL CDR3 of SEQ ID NO: 98.
  • the activated protein C comprises drotrecogin alfa (XIGRIS).
  • the activated protein C comprises SEQ ID NO: 102.
  • the quantitative data is determined by: contacting a 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 the quantitative data.
  • the classification of the subject is determined by: determining, for at least one candidate classification of the subject, a classification-specific score for the subject; determining, by the patient subtype classifier, based on the classification- specific score, the classification of the subject.
  • a non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to: obtain quantitative data for a plurality of biomarkers, wherein the plurality of biomarkers comprise two or more of STOM, MME, BNT3A2, HLA-DPA1, ZNF831, CD3G, EPB42, GSPT1, LAT, HK3, SERPINB1, SLC1A5, IGF2BP2, ANXA3, or GBP2; and determine a classification for the subject based on the quantitative data using a patient subtype classifier by generating one or more scores for the subject; and identify a therapy recommendation for the subject with sepsis based on the classification, wherein if the classification comprises a first subtype, the therapy recommendation is one of a PD-1 inhibitor, a PD-L1 inhibitor, or a IL-7 (e.g., HYLEUKIN-7 or CYT107), and wherein if the classification comprises a second subtype, the
  • the therapy recommendation comprises at least one of GM-CSF, anti-PD-1, anti- PD-L1, anti-CTLA-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 stimulant, activated protein C, antithrombin, thrombomodulin, and Tissue Factor inhibitor.
  • the antithrombin is an antithrombin-III, antithrombin alfa, and antithrombin gamma.
  • the instructions that cause the processor to identify the classification of the subject further comprises instructions that, when executed by the processor, cause the processor to: determine, for at least one candidate classification of the subject, a classification-specific score for the subject; determine, by the patient subtype classifier, based on the classification-specific score, the classification of the subject.
  • the instructions that cause the processor to determine the classification-specific score further comprises instructions that, when executed by the processor, cause the processor to: 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 the one or more biomarkers of the candidate classification are increased relative to the quantitative data for the 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 the one or more additional biomarkers of the candidate classification are decreased relative to the quantitative data for the one or more additional biomarkers for the one or more control subjects; and determine a difference between the first subscore and the second subscore, the first and second subscore optionally subject to scaling, and the difference comprising the classification-specific score for the subject.
  • the patient subtype classifier is a machine-learned model.
  • the machine-learned model is a support vector machine (SVM).
  • the support vector machine receives, as input, one or more classification-specific scores and outputs the classification of the subject.
  • the patient subtype classifier determines the classification of the subject by: comparing the classification-specific scores to one or more threshold values; and determining the classification of the subject based on the comparisons. In various embodiments, at least one of the one or more threshold values is a fixed value.
  • the anti-PD-L1 is any of envafolimab, lodapolimab, avelumab (BAVENCIO), durvalumab (IMFINZI), atezolizumab (TECENTRIQ), and HBM9167.
  • the anti-CTLA-4 is any of tremelimumab, zalifrelimab, ipilimumab (YERVOY) and HBM4003.
  • the IL-7 is any of HYLEUKIN-7 and CYT107.
  • the IL-7 comprises any one of SEQ ID NOs: 103-109.
  • the anti-TNF-alpha is any of adalimumab, etanercept, infliximab, golimumab, certolizumab pegol (CIMZIA), diammonium glycyrrhizinate (GANLIXIN), ozoralizumab (OZORALIZUMAB), rhTNFR(m):Fc, SBT-104, SCB-808, SSS07, and TNF Mab (LFB).
  • CCMZIA certolizumab pegol
  • GANLIXIN diammonium glycyrrhizinate
  • OZORALIZUMAB ozoralizumab
  • rhTNFR(m):Fc SBT-104, SCB-808, SSS07, and TNF Mab (LFB).
  • the Tissue Factor inhibitor comprises an antibody comprising a VH CDR1 of SEQ ID NO: 93, a VH CDR2 of SEQ ID NO: 94, a VH CDR3 of SEQ ID NO: 95, a VL CDR 1 of SEQ ID NO: 96, a VL CDR2 of SEQ ID NO: 97, and a VL CDR3 of SEQ ID NO: 98.
  • the activated protein C comprises drotrecogin alfa (XIGRIS).
  • the activated protein C comprises SEQ ID NO: 102.
  • the test sample is analyzed to determine quantitative values of one or more biomarkers by performing the marker quantification assay 120.
  • the marker quantification assay 120 may 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 biomarkers can be quantified RT-PCR data, transcriptomics data, and/or RNA-seq data.
  • the quantified expression values of the biomarkers are provided to the patient classification system 130.
  • the patient classification system 130 includes one or more computers, such as example computer 1600 as discussed below with respect to FIG.16. Therefore, in various embodiments, the steps described in reference to the patient classification system 130 are performed in silico.
  • the quantitative data of one or more biomarkers that are analyzed refer to biomarkers that have been previously categorized as influencing the particular subtype that the classification-specific score is being calculated for. For example, if the patient classification system 130 is determining a classification-specific for subtype A, the patient classification system 130 determines subscores using quantitative data of biomarkers that are categorized as influencing the subtype A. Examples of biomarkers that are categorized with certain subtypes are shown below in Tables 1, 2A-2B, 3, and 4A-4E.
  • the patient classification system 130 may determine a first subscore and a second subscore for a patient.
  • the first subscore is a measure of adaptive immune activity.
  • the second subscore is a measure of inflammatory response.
  • the patient classification system 130 can determine a difference between the first subscore and a second subscore and compares the difference to a statistical measure.
  • the statistical measure can be a median score representing the median difference of first subscores and second subscores across multiple patients.
  • patterns of gene level fold changes refer to at least a threshold number of genes that are underexpressed in a first patient subtype in comparison to a second patient subtype.
  • the threshold number of genes include genes that are involved in a common biological pathway.
  • Example biological pathways include, but are not limited to: innate immune pathways, chronic inflammation pathways, acute inflammation pathways, coagulation pathways, complement pathways, signaling pathways (e.g., TLR signaling pathway or glucocorticoid receptor signaling pathway), and the like.
  • the involvement of genes in certain biological pathways is curated from publicly available databases such as the Reactome Pathway Database or the KEGG Pathway database.
  • the patient subtype classifier is a regression, such as a logistic regression. Parameters of the logistic regression are trained using the training data such that when the logistic regression is applied, it outputs a classification based on the different classification-specific scores. The parameters of the logistic regression can be trained to maximize the differences between the different classifications (e.g., subtype A, subtype B, and subtype C).
  • the patient subtype classifier is a support vector machine. The support vector machine is trained with a single or a set of hyperplanes that maximizes the differences among the X different classifications.
  • the patient subtype classifier can output a particular classification. Similar logic can be applied for determining classifications using more than two classification-specific scores and/or more than two threshold values. Examples of subtype specific threshold values that are derived from training samples are described below in Table 21B. IID. Biomarker Panel [00163] 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 values of biomarkers for a patient. In various embodiments, a biomarker panel can be a multivariate biomarker panel.
  • Each primer set includes a pair of single-stranded DNA primers (i.e., a forward primer and a reverse primer) for amplifying one gene by, for example, RT-qPCR.
  • a forward primer and a reverse primer for amplifying one gene by, for example, RT-qPCR.
  • the entire sequence of a primer may be used in amplification of the associated gene.
  • at least 15 contiguous nucleotides of a primer sequence may be used in amplification of the associated gene.
  • primer sequences other than those provided in Table 5A or Table 5B can be used to amplify one or more of the genes from Tables 1, 2A, 2B, 3, and 4A- 4E.
  • Table 5A RT-qPCR Primer Sequences
  • FIGS.2-5 depict the individual accuracies determined for each combination of three genes, with one gene from each subtype, for the Full, SS, S, and P Models, respectively. Specifically, FIG.2 is a graph of the individual accuracies determined for each combination of three genes, with one gene from each subtype, for the Full Model.
  • FIG.9 depicts risk of mortality for dysregulated host response patients having subtypes A, B, and C, in accordance with an embodiment.
  • subtype A patients exhibit a low risk of morality, relative to subtype B and C patients.
  • subtype C patients exhibit a high risk of morality, relative to subtype A and B patients.
  • TNF- ⁇ binds to its receptor and initiates three pathway: 1) NF-kB (nuclear factor kappa-light-chain-enhancer of activated B cells) via IkB kinase (IKK), 2) activation of mitogen-activated protein kinase (MAPK) pathways including (a) c-Jun N-terminal kinases (JNKs), (b) p38 mitogen-activated protein kinases (p38 MAPK), (c) extracellular signal-regulated kinases (ERKs), and (3) induction of death signaling via Caspase 8 and Caspase 3.
  • IKK IkB kinase
  • MAPK mitogen-activated protein kinase
  • ERKs extracellular signal-regulated kinases
  • the data summarized in Table 8 indicates that patients of subtype B and IS may benefit from TNF- ⁇ inhibition and other anti-inflammatory targeted at other portions of the inflammatory response such as therapies targeting P38- ⁇ MAPK, caspases, STAT3, STAT5a, and STAT6, and JAKs.
  • immunosuppressive agents may be contraindicated in dysregulated immune response syndromes and instead anti-inflammatory agents such as these targeted to subtypes exhibiting an inflammatory response may be beneficial.
  • heparin can modulate the anti-inflammatory effects of both antithrombin and thrombomodulin through interaction with glycosaminoglycans and high mobility group box 1 with its receptor of advanced glycation end products, respectively [38,39].
  • Overt DIC was determined utilizing lab values (platelets, fibrinogen, D-dimer, Quicktest, and PT ratio/INR) available at enrollment and non-overt DIC included changes of platelet counts and PT (PT, Quick or PT ratio/INR) values over time between baseline and 24-h values.
  • ChIP-X Enrichment Analysis 3 ranks transcription factor (TF) enrichment. Top TFs identified when comparing genes that are over-expressed in B vs. A and C vs. A and when comparing genes that are under-expressed in A vs. not-A provide a list of potential therapeutic targets. Table 11 summarizes this list.
  • B. vs. A identifies the CEBPB gene that encodes a protein important in the regulation of genes involved in immune and inflammatory responses which binds to the IL-1 response element in the IL-6 gene as well as to regulatory regions of several acute-phase and cytokine genes and is capable of increasing the expression of IL-6, IL-4, IL-5, and TNF-alpha.
  • subtypes A, B, and C identified for sepsis patients by the Full, SS, S, and P Models can be assigned titles as follows. Because mortality reduction was statistically significant in the placebo group compared to the hydrocortisone therapy group for sepsis patients assigned to subtype A by the Full, SS, S, and P Models, sepsis patients assigned to subtype A by at least one of the Full, SS, S, and P Models can be colloquially referred to as “adversely responsive” to corticosteroid therapy.
  • sepsis patients assigned to subtype C by the Full Model can be colloquially referred to as “adversely responsive” to corticosteroid therapy.
  • sepsis patients assigned to subtype B by the Full Model can be colloquially referred to as “favorably responsive” to corticosteroid therapy.
  • patients subtyped as “favorably responsive” to corticosteroid therapy can be recommended treatment with corticosteroids, while patients subtyped as “adversely responsive” to corticosteroid therapy can be recommended no corticosteroid therapy, and while patients subtyped as “non-responsive” to corticosteroid therapy can be provided with no therapy recommendation.
  • the IL7 polypeptide comprises the following three disulfide bridges: Cys: 1-4 (Cys2-Cys92); 2-5 (Cys34-Cys129) and 3-6 (Cys47-Cys141).
  • Table 22H Additional IL-7 Therapeutic Sequence VI.D.6
  • Example Antithrombin As disclosed herein (e.g., in Table 22A), a patient classified with a particular patient classification can be identified as a candidate for receiving an antithrombin therapy.
  • Example antithrombin III include KYBERNIN P, AT-III, AMBINEX, THROMBATE III, NEUART, AT III KEDRION, ACLOTINE, KENKETU, NONTHRON, and ATENATIV.
  • FIG.13 depicts an example of a precision clinical trial design, in accordance with an embodiment.
  • FIG.13 depicts an example of a precision platform clinical trial design, in accordance with an embodiment. VII.C. Precision Care
  • the improved ability to subtype patients exhibiting dysregulated host response also enables the delivery of precision care.
  • a kit can comprise a set of reagents for generating a dataset via at least one protein detection assay (e.g., immunoassay or RT-PCR assay) that analyzes the test sample from the subject.
  • the set of reagents enable detection of quantitative expression levels of biomarkers described in any of Tables 1, 2A-2B, 3, and 4A-4E.
  • the reagents include one or more antibodies that bind to one or more of the markers. The antibodies may be monoclonal antibodies or polyclonal antibodies.
  • the reagents can include reagents for performing ELISA including buffers and detection agents.
  • a software module is implemented with a computer program product including a computer-readable non-transitory medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.
  • Embodiments may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer.
  • biomarker set selected from the group consisting of the biomarker sets of group 1, group 2, group 3, group 4, or group 5, 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, wherein group 2 comprises biomarker 4, biomarker 5, and biomarker 6, wherein biomarker 4, biomarker 5, and biomarker 6, wherein biomarker 6, wherein biomarker 6, wherein biomarker 6, wherein biomarker 6, wherein biomarker 6,
  • the infliximab is any of AVSOLA, FLIXABI, INFIMAB, INFLECTRA, Infliximab (BAX), Infliximab (BIOCAD), Infliximab (WOCK), Infliximab BS, IXIFI, Pro-S03, REMICADE, REMSIMA SC, STI-002.
  • the golimumab is any of BAT2506, SIMPONI, and SIMPONI ARIA.
  • the therapy recommendation identified for the subject comprises at least one of no therapy recommendation, immune stimulation therapy, suppression of immune regulation therapy, blocking of immune suppression therapy, modulators of coagulation therapy, and modulators of vascular permeability therapy.
  • the therapy recommendation identified for the subject further comprises at least one of a checkpoint inhibitor and an anticoagulant.
  • the therapy recommendation comprises a no corticosteroid therapy, wherein the no corticosteroid therapy is identified by determining that a statistical significance of a reduction in mortality of subjects exhibiting dysregulated host response not provided corticosteroid therapy is greater than or equal to a threshold statistical significance.
  • the therapy recommendation comprises a no corticosteroid therapy, wherein the no corticosteroid therapy is identified by determining that the classification of the subject comprises a subtype likely to be adversely responsive to corticosteroid therapy.
  • the subtype is subtype A or subtype C.
  • the subtype is subtype B.
  • a method for identifying a candidate therapeutic comprising: accessing a differentially expressed gene database comprising gene level fold changes between patients of different subtypes; determining at least a threshold number of genes are differentially expressed in patients of a first subtype in comparison to patients of a second subtype, wherein each of the differentially expressed genes is involved in a common biological pathway; and determining a candidate therapeutic likely to be effective for patients of the first subtype, wherein the candidate therapeutic is effective in modulating expression of at least one of the genes that are differentially expressed in patients of the first subtype.
  • the adalimumab is any of Adalimumab (CIPLA), Adalimumab (HUALAN), Adalimuab (KSHVBSC), Adalimumab (LGND), Adalimumab (MYCNX), Adalimumab (MYL), ADALY, ADFRAR, ADVIXA, AMAB, AMJEVILA, BCD-057, CHS-1420, CINNORA, CT-P17, DMB-3113, HLX03, HULIO, HUMIRA, HYRIMOZ, IBI303, IDACIO, LBAL, MABURA, MB612, ONS-3010, PBP1502, PLAMUMAB, Pro-S01, QLETI, and UBP1211.
  • the antithrombin-III is any of KYBERNIN P, AT-III, AMBINEX, THROMBATE III, NEUART, AT III KEDRION, ACLOTINE, KENKETU, NONTHRON, and ATENATIV.
  • the instructions that cause the processor to obtain quantitative data further comprises instructions that, when executed by the processor, cause the processor to: obtain a sample from a subject exhibiting dysregulated host response, wherein the sample comprises a plurality of biomarkers; and determine the quantitative data from the obtained sample.
  • the obtained sample comprises a blood sample from the subject.
  • the subtype is subtype C.
  • the therapy recommendation identified for the subject further comprises a no therapy recommendation, wherein the no therapy recommendation is identified by determining that the classification of the subject comprises a subtype like to be non-responsive to corticosteroid therapy.
  • the subtype is subtype A or subtype B.
  • the therapy recommendation identified for the subject comprises a no corticosteroid therapy, wherein the dysregulated host response comprises dysregulated host response not caused by infection, and wherein the at least one biomarker set is group 3.
  • the no corticosteroid therapy is identified by determining that the classification of the subject comprises a subtype like to be non-responsive to corticosteroid therapy.
  • the subtype is subtype A or subtype C.
  • the therapy recommendation identified for the subject further comprises corticosteroid therapy, wherein the corticosteroid therapy is identified by determining that the classification of the subject comprises a subtype likely to be responsive to corticosteroid therapy.
  • the subtype is subtype B.
  • the anti-TNF-alpha is any of adalimumab, etanercept, infliximab, golimumab, certolizumab pegol (CIMZIA), diammonium glycyrrhizinate (GANLIXIN), ozoralizumab (OZORALIZUMAB), rhTNFR(m):Fc, SBT-104, SCB-808, SSS07, and TNF Mab (LFB).
  • CCMZIA certolizumab pegol
  • GANLIXIN diammonium glycyrrhizinate
  • OZORALIZUMAB ozoralizumab
  • rhTNFR(m):Fc SBT-104, SCB-808, SSS07, and TNF Mab (LFB).
  • the adalimumab is any of Adalimumab (CIPLA), Adalimumab (HUALAN), Adalimuab (KSHVBSC), Adalimumab (LGND), Adalimumab (MYCNX), Adalimumab (MYL), ADALY, ADFRAR, ADVIXA, AMAB, AMJEVILA, BCD-057, CHS-1420, CINNORA, CT-P17, DMB-3113, HLX03, HULIO, HUMIRA, HYRIMOZ, IBI303, IDACIO, LBAL, MABURA, MB612, ONS-3010, PBP1502, PLAMUMAB, Pro-S01, QLETI, and UBP1211.
  • the therapy recommendation identified for the subject comprises at least one of no therapy recommendation, immune stimulation therapy, suppression of immune regulation therapy, blocking of immune suppression therapy, modulators of coagulation therapy, and modulators of vascular permeability therapy.
  • the therapy recommendation identified for the subject further comprises at least one of a checkpoint inhibitor and an anticoagulant.
  • the subject exhibiting dysregulated host response is an adult subject, and wherein the at least one biomarker set is one of group 1, group 2, group 3, group 5, group 6, group 7, or group 8. In various embodiments, the subject exhibiting dysregulated host response is a pediatric subject, and wherein the at least one biomarker set is one of group 1, group 4, group 5, group 6, group 7, or group 8.
  • the patient subtype classifier has an average accuracy of at least 86.3%. In various embodiments, the at least one biomarker set is group 4, and wherein the patient subtype classifier has an average accuracy of at least 98.3%.
  • the therapy recommendation identified for the subject further comprises corticosteroid therapy, no corticosteroid therapy, or no therapy recommendation. In various embodiments, the therapy recommendation comprises a no corticosteroid therapy, wherein the no corticosteroid therapy is identified by determining that a statistical significance of a reduction in mortality of subjects exhibiting dysregulated host response not provided corticosteroid therapy is greater than or equal to a threshold statistical significance.
  • the therapy recommendation comprises a corticosteroid therapy, wherein the corticosteroid therapy is identified by determining that the classification of the subject comprises a subtype likely to be favorably responsive to corticosteroid therapy.
  • the subtype is subtype B.
  • the therapy recommendation identified for the subject comprises a no therapy recommendation, wherein the no therapy recommendation is identified at least by: determining that a statistical significance of a reduction in mortality of subjects exhibiting dysregulated host response and not provided corticosteroid therapy is less than a threshold statistical significance; and determining that a statistical significance of a reduction in mortality of subjects exhibiting dysregulated host response and provided corticosteroid therapy is less than a threshold statistical significance.
  • the no corticosteroid therapy is identified by determining that the classification of the subject comprises a subtype like to be non-responsive to corticosteroid therapy.
  • the subtype is subtype A or subtype C.
  • the therapy recommendation identified for the subject further comprises corticosteroid therapy, wherein the corticosteroid therapy is identified by determining that the classification of the subject comprises a subtype likely to be responsive to corticosteroid therapy.
  • the subtype is subtype B.
  • the differentially expressed gene database is generated by: obtaining labeled patient data, wherein labels of the labeled patient data identify patients that are classified into one of two or more subtypes; generating the differentially expressed gene database for at least one or more genes by at least determining gene-level fold changes between patient data with a label indicating a first subtype and patient data with a label indicating a second subtype.
  • the labels of the labeled patient data are generated by applying a clustering analysis or by applying a patient subtype classifier.
  • at least 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.
  • determining a candidate therapeutic for patients of the first subtype further comprises: analyzing one or both of: therapeutic pharmacology data comprising data for the candidate therapeutic; and host response pathobiology comprising data for patients of the first subtype.
  • a kit for determining a patient subtype comprising: a set of reagents for determining quantitative data for at least one biomarker set from a test sample from a subject, the at least one biomarker set selected from the group consisting of the biomarker sets of group 1, group 2, group 3, group 4, or group 5, 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,
  • the at least one biomarker set is group 5, and wherein biomarker 13 is one or more of STOM, MME, BNT3A2, or HLA-DPA1.
  • the at least one biomarker set is group 5, and wherein biomarker 14 is one or more of EPB42, GSPT1, LAT, HK3, or SERPINB1.
  • the at least one biomarker set is group 5, and wherein biomarker 15 is one or more of SLC1A5, IGF2BP2, or ANXA3.
  • the instructions comprise instructions for determining the 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 thermocycled amplification reaction.
  • 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
  • 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 the at least three biomarkers, and 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, 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, and at least one biomarker of the at least three biomarkers is selected from the group consisting of the biomarkers SERPINB
  • the 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, 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 nu
  • At least one of the at least three primer sets is selected from the group consisting of: 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 of which is configured to enable 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 of which is configured to enable amplification of at least one biomarker selected from the group consisting of: SERPINB1, GSPT1, ECSIT, LAT, NCOA4, EP
  • GO enrichment analysis was applied to the up- and down-regulated gene sets of each subclass.
  • GO analyses were performed using the R package “clusterProfiler”.
  • the Fisher- exact test was used to generate enrichment P-values, which were adjusted by the Benjamin- Hochberg method.
  • the GO terms with an adjusted P-value ⁇ 0.05 were considered as significantly enriched GO terms for the gene set of interest.
  • the clinical characteristics of the subclasses were evaluated in the validation dataset to assess if they corresponded to their assigned immune-pathophysiological subclasses. [00408] Once the subclasses were established via unsupervised learning, all genes in the training data were extracted and quantile normalization was applied.
  • FIG.20 further shows the ROC plots for each subclass: immune-adaptive, immune-innate, and immune-coagulant.
  • a-c) are ROC curves for each subclass classification using the training set Leave-One-Out cross validation method. These results confirmed that gene-expression scores generated from just 15 genes had excellent performance for subclass identification.
  • Each of the scores were highly representative of the activity level of their assigned immune state according to the respective GO genes. Reference is now made to FIG.21 which shows that scores correlate with gene expression that are related to host-response-state GO terms.
  • a-c) figures show that scores by classifier genes have high correlations with score by genes belonging to host-response immune GO terms.
  • Interleukin-7 Ameliorates Immune Dysfunction and Improves Survival in a 2-Hit Model of Fungal Sepsis. The Journal of Infectious Diseases.2012. pp.606–616. doi:10.1093/infdis/jis383 59. Unsinger J, McGlynn M, Kasten KR, Hoekzema AS, Watanabe E, Muenzer JT, et al. IL-7 promotes T cell viability, trafficking, and functionality and improves survival in sepsis. J Immunol.2010;184: 3768–3779. 60. Kasten KR, Prakash PS, Unsinger J, Goetzman HS, England LG, Cave CM, et al.

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Abstract

L'invention concerne une méthode d'identification d'une recommandation de thérapie pour un sujet présentant une réponse hôte dérégulée, par exemple, une réponse immunitaire dérégulée manifestant cliniquement une sepsie. L'expression quantitative d'un panel de biomarqueurs est analysée pour classifier le sujet. Des exemples de classifications comprennent un sous-type actif immunitaire, un sous-type immunodéprimé ou un sous-type à hypercoagulation. Une recommandation de thérapie pour le sujet est identifiée sur la base, au moins en partie, de la classification.
PCT/US2022/023128 2021-04-02 2022-04-01 Diagnostic compagnon et thérapies pour réponse hôte dérégulée WO2022212890A1 (fr)

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Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012054589A2 (fr) * 2010-10-22 2012-04-26 T2 Biosystems, Inc. Dispositifs contenant des conduits et procédés pour le traitement et la détection d'analytes
WO2014004724A1 (fr) * 2012-06-26 2014-01-03 Board Of Regents, The University Of Texas System Plateforme génomique fonctionnelle efficace
US20170218456A1 (en) * 2014-07-23 2017-08-03 Ontario Institute For Cancer Research Systems, Devices and Methods for Constructing and Using a Biomarker
US20170247759A1 (en) * 2013-03-15 2017-08-31 Veracyte, Inc. Biomarkers for diagnosis of lung diseases and methods of use thereof
US10126305B2 (en) * 2013-06-25 2018-11-13 University of Pittsburg—Of the Commonwealth System of Higher Education Proteomic biomarkers of sepsis in elderly patients
US10329299B2 (en) * 2013-10-04 2019-06-25 Infinity Pharmaceuticals, Inc. Heterocyclic compounds and uses thereof
WO2020070700A2 (fr) * 2018-10-04 2020-04-09 Exogenus Therapeutics, Sa Compositions comprenant de petites vésicules extracellulaires dérivées de cellules mononucléaires du sang de cordon ombilical ayant des propriétés anti-inflammatoires et immunomodulatrices, et leur procédé d'obtention
US20200347456A1 (en) * 2017-10-02 2020-11-05 The Broad Institute, Inc. Methods and compositions for detecting and modulating an immunotherapy resistance gene signature in cancer
US20210001006A1 (en) * 2018-10-05 2021-01-07 Xenotherapeutics, Inc. Xenotransplantation products and methods
WO2021067773A1 (fr) * 2019-10-02 2021-04-08 Endpoint Health Inc. Panels de biomarqueurs de guidage de thérapie de réponse hôte dérégulée

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012054589A2 (fr) * 2010-10-22 2012-04-26 T2 Biosystems, Inc. Dispositifs contenant des conduits et procédés pour le traitement et la détection d'analytes
WO2014004724A1 (fr) * 2012-06-26 2014-01-03 Board Of Regents, The University Of Texas System Plateforme génomique fonctionnelle efficace
US20170247759A1 (en) * 2013-03-15 2017-08-31 Veracyte, Inc. Biomarkers for diagnosis of lung diseases and methods of use thereof
US10126305B2 (en) * 2013-06-25 2018-11-13 University of Pittsburg—Of the Commonwealth System of Higher Education Proteomic biomarkers of sepsis in elderly patients
US10329299B2 (en) * 2013-10-04 2019-06-25 Infinity Pharmaceuticals, Inc. Heterocyclic compounds and uses thereof
US20170218456A1 (en) * 2014-07-23 2017-08-03 Ontario Institute For Cancer Research Systems, Devices and Methods for Constructing and Using a Biomarker
US20200347456A1 (en) * 2017-10-02 2020-11-05 The Broad Institute, Inc. Methods and compositions for detecting and modulating an immunotherapy resistance gene signature in cancer
WO2020070700A2 (fr) * 2018-10-04 2020-04-09 Exogenus Therapeutics, Sa Compositions comprenant de petites vésicules extracellulaires dérivées de cellules mononucléaires du sang de cordon ombilical ayant des propriétés anti-inflammatoires et immunomodulatrices, et leur procédé d'obtention
US20210001006A1 (en) * 2018-10-05 2021-01-07 Xenotherapeutics, Inc. Xenotransplantation products and methods
WO2021067773A1 (fr) * 2019-10-02 2021-04-08 Endpoint Health Inc. Panels de biomarqueurs de guidage de thérapie de réponse hôte dérégulée

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