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WO2024243224A2 - Approche multi-omique pour évaluer l'hétérogénéité du lupus érythémateux disséminé dans une réponse de traitement - Google Patents

Approche multi-omique pour évaluer l'hétérogénéité du lupus érythémateux disséminé dans une réponse de traitement Download PDF

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WO2024243224A2
WO2024243224A2 PCT/US2024/030394 US2024030394W WO2024243224A2 WO 2024243224 A2 WO2024243224 A2 WO 2024243224A2 US 2024030394 W US2024030394 W US 2024030394W WO 2024243224 A2 WO2024243224 A2 WO 2024243224A2
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cells
methylation
hypomethylation
lupus
monocytes
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WO2024243224A9 (fr
WO2024243224A3 (fr
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Joel Guthridge
Judith Ann JAMES
Joan MERRILL
Miles Smith
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Oklahoma Medical Research Foundation
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/154—Methylation markers

Definitions

  • the present invention relates in general to the field of treatments for systemic lupus erythematosus, and more particularly, to a multi-omic approach to assess lupus heterogeneity in treatment response.
  • a method for determining or diagnosing a disease, classifying a stage of a disease, or treating a disease or methods for assessing the efficacy of a therapy for treating a disease based on the measurement and the computational analysis of various disease-specific biomarkers by measuring, in a processed sample from the subject, a set of circulating biomarkers comprising an extra-cellular vesicle (EV) miRNA, an EV mRNA, a circulating cell-free DNA, a circulating tumor DNA, and a protein biomarker specific for the disease or the condition; applying a machine learning algorithm on the set of circulating biomarkers to generate an output indicative of a disease or a condition state of the subject; determining whether the subject has the disease or the condition based upon the output so generated; and treating the subject as needed.
  • the method involves obtaining both nucleic acids and proteins to make an assessment.
  • an aspect of the present disclosure relates to a method to select lupus patients that will be responsive to a treatment with an inhibitor of CD80/86 immune cell costimulation, comprising: determining whether a patient has a genetic predisposition to being responsive to the treatment with the inhibitor of CD80/86 immune cell costimulation by: obtaining or having obtained a whole blood sample from the patient with lupus; separating T cells, B cells, and monocytes; performing or having performed a methylation assay on a genome of at least one of the T cells, B cells, or monocytes to detect hypomethylation at one or more CpG biomarkers selected from; Cg04612171; Cg 16733676; Cg02901522; or Cg 11986743; determining if the patient has hypomethylation at the one or more CpG biomarkers when compared to a subject that does not have lupus in the at least one of the T cells, B cells, and monocyte
  • the inhibitor of CD80/86 immune cell costimulation is Abatacept or Belatacept.
  • the hypomethylation is detected for any 2, 3 or 4 of Cg04612171 and Cgl6733676; Cg02901522; and Cgl 1986743.
  • the hypomethylation is detected for any 2 or 3 of the T cells, B cells, and monocytes.
  • the hypomethylation is detected for any 2 or 3 of the T cells, B cells, and monocytes and is detected for any 2, 3 or 4 of Cg04612171 and Cgl6733676; Cg02901522; and Cgl 1986743.
  • the hypomethylation is detected in T cells and there is an increase in T cell receptor (TCR) signaling genes, a decrease in T cell proliferation, or both.
  • TCR T cell receptor
  • the hypomethylation is detected in monocytes and there is an increase in expression of genes in retinoic acid pathway genes.
  • the hypomethylation is detected in B cells and there is a decrease in B cell receptor (BCR) activity, Fc gamma receptor IIB (FCyRIIB) activity, or both.
  • hypomethylation is detected by bisulfite sequencing, high-performance liquid chromatography-ultraviolet (HPLC-UV), liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), enzyme-linked immunosorbent assay (ELISA), long interspersed nuclear elements (LINE-1) and Pyrosequencing, amplification fragment length polymorphism (AFLP), restriction fragment length polymorphism (RFLP), luminometric methylation assay, bead-chip, bead-chip array, microarray, methylsensitive cut counting, or luminometric methylation.
  • HPLC-UV high-performance liquid chromatography-ultraviolet
  • LC-MS/MS liquid chromatography coupled with tandem mass spectrometry
  • ELISA enzyme-linked immunosorbent assay
  • LINE-1 long interspersed nuclear elements
  • Pyrosequencing amplification fragment length polymorphism
  • AFLP amplification fragment length polymorphism
  • RFLP restriction fragment length polymorphism
  • the hypomethylation of Cg04612171 and Cgl6733676; Cg02901522; and Cgl 1986743, affects expression of Sine Oculis Binding Protein Homolog (SOBP), Solute Carrier Family 25 Member 24 (SLC25A24), Plasmacytoma Variant Translocation 1 (PVT1), or Beta- 1,4-Galactosyltransf erase 6 (B4GALT6), respectively.
  • SOBP Sine Oculis Binding Protein Homolog
  • SLC25A24 Solute Carrier Family 25 Member 24
  • PVT1 Plasmacytoma Variant Translocation 1
  • B4GALT6 Beta- 1,4-Galactosyltransf erase 6
  • the lupus is systemic lupus erythematosus, Cutaneous lupus erythematosus, drug-induced lupus, or neonatal lupus.
  • the method further comprises the step of monitoring a methylation status of the one or more CpG biomarkers at one or more points in time and based on a change in methylation discontinuing or restarting treatment with the inhibitor of CD80/86 immune cell costimulation.
  • the method further comprises the step of monitoring a methylation status of the one or more CpG biomarkers at one or more points in time of a patient undergoing treatment with the inhibitor of CD80/86 immune cell costimulation and based on a change in methylation selecting an alternative treatment.
  • an aspect of the present disclosure relates to a method of treating lupus patients that will be responsive for a treatment with an inhibitor of CD80/86 immune cell costimulation, comprising: determining whether a patient has a genetic predisposition to being responsive to the treatment with the inhibitor of CD80/86 immune cell costimulation: obtaining or having obtained a whole blood sample from the patient with lupus; separating T cells, B cells, and monocytes; performing or having performed a methylation assay on a genome of at least one of the T cells, B cells, or monocytes to detect methylation at one or more CpG biomarkers selected from; Cg04612171; Cgl6733676; Cg02901522; or Cgl 1986743; determining if the lupus patient has hypomethylation at the one or more CpG biomarkers when compared to a subject that does not have lupus in the at least one of the T cells, B cells
  • the inhibitor of CD80/86 immune cell costimulation is Abatacept or Belatacept.
  • the hypomethylation is detected for any 2, 3 or 4 of Cg04612171 and Cg 16733676; Cg02901522; and Cg 11986743.
  • the hypomethylation is detected for any 2 or 3 of the T cells, B cells, and monocytes.
  • the hypomethylation is detected for any 2 or 3 of the T cells, B cells, and monocytes and is detected for any 2, 3 or 4 of Cg04612171 and Cg 16733676; Cg02901522; and Cgl 1986743.
  • hypomethylation is detected by bisulfite sequencing, high-performance liquid chromatography-ultraviolet (HPLC-UV), liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), enzyme-linked immunosorbent assay (ELISA), long interspersed nuclear elements (LINE-1) and Pyrosequencing, amplification fragment length polymorphism (AFLP), restriction fragment length polymorphism (RFLP), luminometric methylation assay, bead-chip, bead-chip array, microarray, methyl-sensitive cut counting, or luminometric methylation.
  • HPLC-UV high-performance liquid chromatography-ultraviolet
  • LC-MS/MS liquid chromatography coupled with tandem mass spectrometry
  • ELISA enzyme-linked immunosorbent assay
  • LINE-1 long interspersed nuclear elements
  • Pyrosequencing amplification fragment length polymorphism
  • AFLP amplification fragment length polymorphism
  • RFLP restriction fragment length polymorphism
  • the method further comprises the step of monitoring a methylation status of the one or more CpG biomarkers at one or more points in time and based on a change in methylation discontinuing or restarting treatment with the inhibitor of CD80/86 immune cell costimulation.
  • the method further comprises the step of monitoring a methylation status of the one or more CpG biomarkers at one or more points in time of a patient undergoing treatment with the inhibitor of CD80/86 immune cell costimulation and based on a change in methylation selecting an alternative treatment.
  • an aspect of the present disclosure relates to a CTLA4/T cell activation methylation signature predicting responsiveness to an inhibitor of CD80/86 immune cell costimulation, the signature comprising: hypomethylation in a genome of at least one of T cells, B cells, or monocytes in one or more CpG biomarkers selected from; Cg04612171; Cgl6733676; Cg02901522; or Cgl 1986743; wherein the hypomethylation at the one or more CpG biomarkers is compared to a subject that does not have lupus in the at least one of the T cells, B cells, and monocytes.
  • the inhibitor of CD80/86 immune cell costimulation is Abatacept or Belatacept.
  • the hypomethylation is detected for any 2, 3 or 4 of Cg04612171 and Cgl 6733676; Cg02901522; and Cgl 1986743.
  • the hypomethylation is detected for any 2 or 3 of the T cells, B cells, and monocytes.
  • the hypomethylation is detected for any 2 or 3 of the T cells, B cells, and monocytes and is detected for any 2, 3 or 4 of Cg04612171 and Cg 16733676; Cg02901522; and Cgl 1986743.
  • the hypomethylation is detected in T cells and there is an increase in T cell receptor (TCR) signaling genes and a decrease in T cell proliferation.
  • TCR T cell receptor
  • the hypomethylation is detected in monocytes and there is an increase in expression of genes in retinoic acid pathway genes.
  • the hypomethylation is detected in B cells and there is a decrease in B cell receptor (BCR) activity, Fc gamma receptor IIB (FCyRIIB) activity.
  • hypomethylation is detected by bisulfite sequencing, high-performance liquid chromatography-ultraviolet (hypomethylation -UV), liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), enzyme-linked immunosorbent assay (ELISA), long interspersed nuclear elements (LINE-1) and Pyrosequencing, amplification fragment length polymorphism (AFLP), restriction fragment length polymorphism (RFLP), luminometric methylation assay, bead-chip, bead-chip array, microarray, methyl-sensitive cut counting, or luminometric methylation.
  • LC-MS/MS liquid chromatography coupled with tandem mass spectrometry
  • ELISA enzyme-linked immunosorbent assay
  • LINE-1 long interspersed nuclear elements
  • LINE-1 long interspersed nuclear elements
  • Pyrosequencing amplification fragment length polymorphism
  • AFLP amplification fragment length polymorphism
  • RFLP restriction fragment length polymorphism
  • hypomethylation of Cg04612171 and Cgl6733676; Cg02901522; and Cgl 1986743, affects expression of Sine Oculis Binding Protein Homolog (SOBP), Solute Carrier Family 25 Member 24 (SLC25A24), Plasmacytoma Variant Translocation 1 (PVT1), or Beta- 1,4-Galactosyltransf erase 6 (B4GALT6), respectively.
  • SOBP Sine Oculis Binding Protein Homolog
  • SLC25A24 Solute Carrier Family 25 Member 24
  • PVT1 Plasmacytoma Variant Translocation 1
  • B4GALT6 Beta- 1,4-Galactosyltransf erase 6
  • an aspect of the present disclosure relates to a method for diagnosing lupus patients responsive to treatment with an inhibitor of CD80/86 immune cell costimulation, comprising: determining whether a patient has a genetic predisposition to being responsive to the treatment with the inhibitor of CD80/86 immune cell costimulation: obtaining or having obtained a whole blood sample from the patient with lupus; separating T cells, B cells, and monocytes; performing or having performed a methylation assay on a genome of at least one of the T cells, B cells, or monocytes to detect methylation at one or more CpG biomarkers selected from; Cg04612171; Cgl6733676; Cg02901522; or Cgl 1986743; and determining if the lupus patient has hypomethylation at the one or more CpG biomarkers when compared to a subject that does not have lupus in the at least one of the T cells, B cells, and mon
  • the lupus is systemic lupus erythematosus, Cutaneous lupus erythematosus, drug-induced lupus, or neonatal lupus.
  • the method further comprises the step of monitoring a methylation status of the one or more CpG biomarkers at one or more points in time and based on a change in methylation discontinuing or restarting treatment with the inhibitor of CD80/86 immune cell costimulation.
  • the method further comprises the step of monitoring a methylation status of the one or more CpG biomarkers at one or more points in time of a patient undergoing treatment with the inhibitor of CD80/86 immune cell costimulation and based on a change in methylation selecting an alternative treatment.
  • FIG. 1 Overview of total samples and study design. Flowchart depicting the overall systems biology methodology applied to the samples and processing implemented in this study.
  • PBMCs were fractionated into major immune cell populations for parallel epigenomic and transcriptomic profiling. These data were integrated and collectively analyzed to develop a model predicting lupus response to abatacept at screening.
  • ABT abatacept
  • CTX cyclophosphamide
  • R responder
  • NR non-responder.
  • FIG. 2 Comparison of cell type-specific methylation differences of top 4 performing CpGs by abatacept response. Violin dot plots of methylation differences (as beta values) by abatacept response of top 4 CpGs in each cell type. Red violins are for responders (R) and blue violins are for non-responders (NR). Asterisks indicate significance of Benjamini -Hochberg corrected p- value of Mann-Whitney U-Tests: * ⁇ 0.05, ** ⁇ 0.01, *** ⁇ 0.001, **** ⁇ 0.0001.
  • FIG. 4 Genomic regions near cg04612171.
  • UCSC Genome Browser view of the genomic and regulatory context of near the cg04612171 CpG. Tracks show the location of the CpG of interest, GENCODE annotated transcripts, CpG islands, ENCODE predicted cis-regulatory elements, Encode activating histone acetylation, density of ReMap transcription factor binding sites, and common SNPs (MAF > 1%) in dbSNP. Coordinates are presented from the hg38 build of the human genome.
  • FIG. 5 Methylation linkage in cg04612171 with nearby CpG sites.
  • Linkage plot depicting linkage between methylation at the site of interest, cg04612171, and surrounding CpG sites as the coefficient of determination (R2) between the methylation M-values. Linkage was calculated to CpG sites within lOkbp independently in each cell type assayed. Color of each point indicates annotated genetic region of each CpG site.
  • FIG. 6 Genomic regions near cgl6733676.
  • UCSC Genome Browser view of the genomic and regulatory context of near the cgl6733676 CpG. Tracks show the location of the CpG of interest, GENCODE annotated transcripts, CpG islands, ENCODE predicted cis-regulatory elements, Encode activating histone acetylation, density of ReMap transcription factor binding sites, and common SNPs (MAF > 1%) in dbSNP. Coordinates are presented from the hg38 build of the human genome.
  • FIG. 7 Methylation linkage in cgl6733676 with nearby CpG sites.
  • Linkage plot depicting linkage between methylation at the site of interest, cgl6733676, and surrounding CpG sites as the coefficient of determination (R2) between the methylation M-values. Linkage was calculated to CpG sites within lOkbp independently in each cell type assayed. Color of each point indicates annotated genetic region of each CpG site.
  • FIG. 8 Genomic regions near cg02901522.
  • UCSC Genome Browser view of the genomic and regulatory context of near the cg02901522 CpG. Tracks show the location of the CpG of interest, GENCODE annotated transcripts, CpG islands, ENCODE predicted cis-regulatory elements, Encode activating histone acetylation, density of ReMap transcription factor binding sites, and common SNPs (MAF > 1%) in dbSNP. Coordinates are presented from the hg38 build of the human genome.
  • FIG. 9 Methylation linkage in cg02901522 with nearby CpG sites.
  • Linkage plot depicting linkage between methylation at the site of interest, cg02901522, and surrounding CpG sites as the coefficient of determination (R2) between the methylation M-values. Linkage was calculated to CpG sites within 25kbp independently in each cell type assayed. Color of each point indicates annotated genetic region of each CpG site.
  • FIG. 10 Genomic regions near cgl 1986743.
  • UCSC Genome Browser view of the genomic and regulatory context of near the cgl 1986743 CpG. Tracks show the location of the CpG of interest, GENCODE annotated transcripts, CpG islands, ENCODE predicted cis-regulatory elements, Encode activating histone acetylation, density of ReMap transcription factor binding sites, and common SNPs (MAF > 1%) in dbSNP. Coordinates are presented from the hg38 build of the human genome.
  • FIG. 11 Methylation linkage in cgl 1986743 with nearby CpG sites.
  • Linkage plot depicting linkage between methylation at the site of interest, cgl 1986743, and surrounding CpG sites as the coefficient of determination (R2) between the methylation M-values. Linkage was calculated to CpG sites within 50kbp independently in each cell type assayed. Color of each point indicates annotated genetic region of each CpG site.
  • FIG. 12 Correlation of T cell RNA transcripts with parallel methylation at CpGs predictive of abatacept response. Volcano plots showing the results of correlation analysis of methylation at each of four predictive CpGs with parallel RNA-seq data in T cells. The slope of each correlated relationship (unit change in transcript per unit change in methylation) is plotted on the x-axis, and values on the y-axis represent negative log-transforms of p-values adjusted for multiple comparisons. Horizontal and vertical dashed lines indicate cutoffs for significance. Positive- and negative-associated transcripts passing these cutoffs are marked in red and green, respectively. The highest positive- and negative-associated transcripts are labeled on each plot.
  • FIG. 13 Ingenuity Canonical Pathway Analysis of T cell transcriptome correlations with methylation at predictive CpGs. Summary of most significant results comparing Canonical Pathway Analysis of by QIAGEN’s Ingenuity® Pathway Analysis (IP A). The most significantly correlated T cell transcripts with methylation at each CpG of interest were input into IPA with previously noted cutoffs. Rows represent the most significant results across all analyses, and columns represent the correlations with each CpG site noted at the bottom. Color indicates the activation z-score, which represents a predicted change in pathway activity correlating with methylation.
  • FIG. 14 Ingenuity Canonical Pathway Analysis of B cell transcriptome correlations with methylation at 3 CpG sites of interest. Summary of most significant results comparing Canonical Pathway Analysis of by QIAGEN’s Ingenuity® Pathway Analysis (IP A). The most significantly correlated B cell transcripts with methylation at each CpG of interest were input into IPA with previously noted cutoffs. Rows represent the most significant results across all analyses, and columns represent the correlations with each CpG site noted at the bottom. Color indicates the activation z-score, which represents a predicted change in pathway activity correlating with methylation.
  • FIG. 15 Ingenuity Canonical Pathway Analysis of monocyte transcriptome correlations with methylation at CpG sites of interest. Summary of most significant results comparing Canonical Pathway Analysis of by QIAGEN’s Ingenuity® Pathway Analysis (IPA). The most significantly correlated monocyte transcripts with methylation at each CpG of interest were input into IPA with previously noted cutoffs. Rows represent the most significant results across all analyses, and columns represent the correlations with each CpG site noted at the bottom. Color indicates the activation z-score, which represents a predicted change in pathway activity correlating with methylation.
  • SLE Systemic Lupus Erythematosus
  • SLE is a complex autoimmune disease often necessitating complex management.
  • treatment options for patients are limited.
  • federal regulators in the United States have approved six medications for the treatment of SLE.
  • Existing regimens involve combinations of antimalarials, corticosteroids, and immunosuppressants depending on the clinical manifestations and the prescriber’s preferences. While these treatments can be effective at easing symptoms, SLE patients still have increased morbidity and mortality due to drug toxicity and persistent disease activity 1,2 .
  • belimumab, anifrolumab and voclosporin trials of over 30 promising agents over the past decades have resulted in disproportionate failure 3 ' 10 .
  • Abatacept is an example of a biologic therapy that is a fusion protein of the cytotoxic T- lymphocyte associated protein 4 (CTLA4) and the fragment crystallizable (Fc) region of IgG.
  • CTL4 cytotoxic T- lymphocyte associated protein 4
  • Fc fragment crystallizable region of IgG.
  • This medication targets the co-stimulation of lymphocytes by antigen presenting cells by disrupting the interaction of B7 molecules (CD80 and CD86) with CD28 and has received regulatory approval for the treatment of rheumatoid arthritis, psoriatic arthritis, and juvenile idiopathic arthritis. Since abatacept is predicted to induce tolerance in autoreactive T cells, several randomized phase II controlled trials have assessed the efficacy of abatacept in the treatment of SLE 19 ' 21 .
  • DNA methylation the covalent addition of a CH3- moiety to cytosine bases by DNA-methyltransferases, is one of a number of epigenetic modifications that can have profound impacts on subsequent transcription. Methylation of sites located within promoters and enhancers can block the accessibility of these motifs and prevent the binding of transcription factors and activation of genes in response to intra- and extracellular stimuli.
  • Recent bulk and single-cell transcriptomic approaches in lupus have identified associations between inflammatory and interferon signatures with disease activity, ancestry, and autoantibody profiles 32 ' 34 .
  • An integrated multi-omic analysis of serum has recently hinted at dysregulated sphingolipid metabolism driving SLE activity 35 , but otherwise, there have been few attempts to integrate findings across multiple profiles to establish systems-based mechanisms of disease characterizing SLE.
  • the present inventors isolated immune populations in bulk for a systems-focused analysis.
  • the inventors were able to normalize signal differences due to cell type composition while maintaining a global perspective of the circulating immune environment.
  • the inventors also incorporated samples from the Abatacept and Cyclophosphamide Combination Therapy for Lupus Nephritis (ACCESS) trial, which provided a more suitable cohort for discovery.
  • This trial specifically assessed the efficacy of abatacept as adjunctive therapy for lupus nephritis when co-administered with standard-of-care treatment low- dose cyclophosphamide followed by azathioprine.
  • patients could receive up to 320 mg total methylprednisolone by intramuscular injection as needed over the first month of the trial to suppress disease activity and manage symptoms. All background medications aside from some oral prednisone ( ⁇ 20 mg/day) were discontinued. Patients whose disease activity was sufficiently suppressed by steroids at one month were randomized 1 :1 to receive weekly 125 mg abatacept or placebo by subcutaneous injection. This study was approved by the Oklahoma Medical Research Foundation (OMRF) institutional review board. All subjects provided written informed consent.
  • OMRF Oklahoma Medical Research Foundation
  • ABC Subjects Eligible patients were men and women between 18 and 70 years or age that met at least four 1997 revised American College of Rheumatology (ACR) criteria for SLE. All had moderate to severe arthritis consistent with BILAG-2004 A or B with at least three swollen and tender joints. Patients with severe disease, including acute nephritis, CNS lupus, or any conditions requiring cyclophosphamide, biologies, or IV bolus steroids > 500 mg were excluded. Overall, 76 patients were screened, of whom 66 met entry criteria and improved sufficiently with intramuscular steroids to comprise the intent-to-treat population for randomization.
  • BICLA BILAG-based Combined Lupus Assessment
  • SLED Al SLE Disease Activity Index
  • PGA Global Assessment
  • BICLA response is defined as at least one BILAG-2004 letter grade improvement in each organ without an increase in SLED Al and no more than a 10% increase in PGA.
  • This SLED Al measure is a hybrid index of the SLEDAI-2K44 and the SELENA- SLEDAI45.
  • SRI SLE Responder Index
  • SLEDAI improvement, resolution of arthritis by SLEDAI, musculoskeletal improvement by BILAG, and achievement of low disease activity by a SLEDAI ⁇ 2 or Lupus Low Disease Activity Score (LLDAS).
  • SRI SLE Responder Index
  • SLEDAI improvement Resolution of arthritis by SLEDAI
  • musculoskeletal improvement by BILAG achievement of low disease activity by a SLEDAI ⁇ 2 or Lupus Low Disease Activity Score (LLDAS).
  • LDAS Lupus Low Disease Activity Score
  • Patients were classified as non-responders prior to or at month 6 if: (1) they dropped out of the study for any reason (lost to follow-up, adverse effects, etc.), (2) their disease flared, or (3) they initiated a new SLE medication outside of the allowed protocol treatment. Upon loss of response, patients could elect for any standard of care. After month 3, this could include open label abatacept.
  • BICLA and SRI indices are limited by the inherent pitfalls of their underlying instruments: the BILAG and SLEDAI, respectively.
  • additional measures of flare severity employing visual analogue scales were used as exploratory endpoints. These were the Lupus Foundation of America-Rapid Evaluation of Activity in Lupus (LFA-REAL) and the SELENA SLEDAI Physician’s Global Assessment (SSPGA). These measures have been shown to be reliable surrogates of more commonly used endpoints 46 .
  • ACCESS defined separate categories of complete response (CR), partial response (PR), and no response (NR) to abatacept at a 6-month time point. These were based on cutoffs for urine protein-to-creatinine ratio, serum creatinine improvement, and adherence to a prednisone taper.
  • CR and NR patients in ACCESS were treated as abatacept responders and non- responders, respectively. Some partial responders demonstrated significant clinical improvement over their baseline scores, but did not meet the original cutoff criteria defined for CR or NR. These patients were treated as responders in this study.
  • PBMCs peripheral blood mononuclear cells
  • Serum serum was aliquoted and stored at -80°C for future analysis.
  • PBMCs were isolated using Lymphocyte Separation Medium (Corning), and frozen at a concentration of 10 million/vial in freezing media (20% human serum/10% DMSO in RPMI) for storage in liquid nitrogen until future analysis.
  • Serum soluble mediator assessment Serum levels of TGF-01, APRIL, BMP2, MIP-la, Eotaxin, IL-2Ra, GCSF, IFN-a, IFN-y, IL-IRa, IL-5, IL-7, IL-10, IL-17, IL-33, 0-NGF, Resistin, E-selectin, TNF-a, VCAM-1, BAFF, MCP-1, MIP-1 , CD40L, FasL, ICAM, IFN-0, IL- la, IL-4, IL-6, IL-8, IL-12p70, IL-21, LIF, PDGF-BB, SCF, Syndecan-1, TRAIL, VEGF, MIG, Fas, IL-2, IL-15, Leptin, TNF-RII, IP-10, IL-1 , IL-13, IL-23, TNF-RI, and CCL7 were assessed using custom xMAP Human Magnetic Luminex assays (R
  • CCL7 was not measured in the ABC samples, but all other analytes listed were analyzed in serum samples from both samples. Data were analyzed on the Bio-Rad BioPlex200® array system (Bio-Rad Technologies), with a lower boundary of 50 beads per analyte per sample. The common 50 analytes shared between trials were used for downstream analysis.
  • Monocytes were isolated from the CD19- fraction from the previous step with CD14 MicroBeads (Miltenyi Biotec) according to the manufacturer protocol.
  • T cells were isolated from the CD14- fraction from the previous step with the Pan T-Cell Biotin-AB Cocktail and Pan T-Cell MicroBead Cocktail (Miltenyi Biotec) according to the manufacturer's protocol. After isolation and counting of each cell type, at least 150,000 cells were resuspended in 50 pL of separation buffer and 50 pL of DNA/RNA Shield 2X Concentrate (Zymo Research). Samples were frozen at -80°C.
  • RNA Integrity Number RINs for the samples had a median of 7.4, with the interquartile range falling between 6.6 and 8.2.
  • RNA-seq RNA sequencing
  • Advanta RNA-Seq XT NGS Library Prep Kit II 101-9959
  • cDNA libraries were generated from 20 ng total RNA per sample, which were randomized across integrated fluidic circuits (IFCs) by cell type and abatacept response status.
  • the Advanta RNA-Seq XT NGS kit generates dual-indexed, randomly primed cDNA libraries from the poly(A) RNA contained within a total RNA sample.
  • the Atlas IFC and Juno SystemTM were used to automate poly(A) RNA capture by oligo-dT beads, washing, elution, fragmentation, and first strand cDNA synthesis using random primers. This was followed by second-strand cDNA synthesis with a template switch oligomer.
  • Illumina adaptor and index barcodes were added to each library to enable sequencing demultiplexing.
  • Individual sample libraries were isolated from the Atlas IFC and quantified for normalization via qPCR using the KAPA Library Quantification Kit for Illumina Platforms 480 (Roche) along with qPCR Primer Premix provided with the Advanta RNA-Seq XT NGS Library Prep Kit II.
  • Sequencing was carried out by the OMRF Clinical Genomics Center on an Illumina NovaSeq 6000. Separate sets of “A” and “B” indices generated from individual Advanta kits were combined when possible to generate pools of up to 96 uniquely indexed samples. Pooled libraries were submitted for sequencing on individual lanes of an S4 flowcell to give an approximate depth of 50 million paired-end reads/library for cell type libraries.
  • Samples were then held at 4°C for at least 10 minutes and up to 20 hours. Samples were transferred to Silicon-A Binding Plates containing 400 pL of M-Binding Buffer before being centrifuged at 5000 ref for 5 minutes. Converted DNA was washed with 400 pL of M-Wash Buffer, and plates were centrifuged on top of Collection Plates at 5000 ref for 5 minutes. Collection Plates were emptied as needed in subsequent steps to prevent overflow. M-Desulphonation Buffer (200 pL) was added to each well and samples were incubated for 15 minutes at room temperature before being centrifuged at 5000 ref for 5 minutes.
  • RNA sequencing Raw basecall (.bcl) files were converted to fastqs and demultiplexed using bcl2fastq (v2.20.0.422). Fastq files were further processed via a custom Nextflow pipeline48. Reads were trimmed via bbduk (39.01) to remove adapter sequences, poly(A) sequences, and rRNA sequences. Trimmed reads were pseudoaligned with salmon (1.3.0) 49 to the GROG 8 build (GENCODE 32) 50 of the human transcriptome for transcript quantification. Downstream analysis of quantitated counts was carried out in R (4.1.0) 51 EdgeR (3.34.0) 52 and limma (3.48.3) 53 were used to normalize transcript count data per sample, accounting for library size, cell type, and responder status in the design.
  • DNA methylation arrays were preprocessed in R using the minfi (1.38.0) 54 package. Data was normalized with the preprocessFunNorm function. QC was performed by calculating detection p-values for each sample across all probes with the detectionP function. Samples with mean p-values > 0.01 were removed from further analysis, and probes with any p-values > 0.01 were also removed from further analysis. Normalized values were returned as the proportion of total sites methylated (beta values).
  • the best model was used to predict the classification on the testing split. Performance as area under the curve (AUC) of the receiver operator characteristic (ROC) and feature usage statistics of the 40 bootstrapped models were collected for reporting. Data for plotting ROC curves was generated using the ROCR (1.0-11) 56 package. “Averaged” ROC curve points were generated by taking the mean sensitivity and specificity values generated by ROCR across the 40 bootstrapped models.
  • AUC area under the curve
  • ROC receiver operator characteristic
  • CpG annotation Annotation of CpGs was done through the missMethyl (1.26.1) 57 package, which provides chromosomal locations of probe sites, near variants, regional annotations, and nearby genes for Infmium Methyl ationEPIC array probes.
  • the local genomic impact of methylation at critical sites identified by machine learning was studied similarly to genetic linkage between nearby genomic sites. This methylation linkage was calculated as the coefficient of determination (R2) between the methylation beta values at a CpG of interest and the beta values of nearby CpGs. In this way, a higher linkage score (R2) indicates a greater association in the methylation of proximal CpGs assayed.
  • Interomic analysis was performed by correlating methylation at CpG sites of interest with transcriptomic data. To do this, normalized methylation as beta values were transformed to M-values by a logit transformation (Equation 1). Linear models were then built with the limma package correlating methylation M-values at each CpG site with normalized cell type-specific transcription data. P-values in this approach were corrected for multiple comparisons by Benjamini -Hochberg adjustment. Effect sizes reported by these tests indicated the slope of the linear model fit to these relationships.
  • Differential methylation analysis Differential methylation analysis of probe sites was also performed using the limma package. Normalized methylation M-values, as calculated above, were used for cell type-specific analysis. To account for confounding technical batch effects, surrogate variable analysis (SVA) was employed to identify 2 technical covariates of the data not captured by cell type or abatacept response status. These technical covariates were incorporated into the final linear model of differential methylation. Filtering of these data was performed using the CpG annotation noted above, with regional annotations of “TSS200” and “TSS1500” indicating probes falling within 200 bp and 200-1500 bp, respectively, of transcription start sites (TSSs).
  • SVA surrogate variable analysis
  • a systems biology approach to assess drug response in two trials of abatacept in lupus was to develop a predictive model of abatacept response in lupus using data from the screening visits of these clinical trials.
  • a systems biology approach was chosen to dissect contributions from underrepresented circulating immune populations (FIG. 1). This allowed for less frequent cell types (i.e. B cell subsets) to be analyzed with the same depth as more abundant cell types, effectively normalizing heterogeneity due to cell type abundances.
  • PBMCs and serum were collected from screening visits of identified responders and non-responders in the ABC and ACCESS trials.
  • Soluble mediators were assessed in serum by a 50-analyte panel by BioRad Bioplex ANA2200 assay.
  • PBMCs were fractionated by successive antibody-conjugated magnetic bead-based isolations to obtain major immune populations of B cells, monocytes, and T cells.
  • Genomic DNA and total RNA were sequentially extracted from these bulk populations and used to generate parallel transcriptome and epigenome profiles. These molecular profiles were then used for an integrated analysis to develop a model of abatacept response incorporating findings across multiple cell types and omics layers.
  • Machine learning identified a four-marker methylation signature distinguishing abatacept responders and non-responders at screening.
  • the cohort of ACCESS screening samples was considerably larger and more balanced than the ABC samples.
  • this trial was also predicted to have greater measurable biological differences between responders and non-responders than in the ABC trial, which examined SLE patients with active, but less severe disease.
  • the inventors implemented a machine learning approach to first develop a model of abatacept response in ACCESS trial patients from DNA methylation. After final QC, 865,859 CpG sites across 119 total samples from ACCESS were used as input for machine learning model development.
  • a bootstrap approach was utilized to generate 40 random splits of the samples into 70/30 training/testing portions for development and testing of generalized linear models (GLMs) as described in methods.
  • LLMs generalized linear models
  • These 40 initial models robustly separated responder samples from non-responder samples, with an average AUC of the ROC for test samples of 0.982 and an average test error of 6.36%.
  • the inventors examined the frequency of marker usage across bootstrapped divisions, noting that the use of a set of 13 CpGs was shared among more than half of the models generated.
  • the inventors applied a machine learning approach to a combined cohort of ABC and ACCESS.
  • Bootstrapped models separated responder samples from non-responder samples robustly, with an average AUC of 0.976 and an average error of 6.78%.
  • These four CpGs had also shown the most consistent pattern of methylation between responders and non-responders across both the ABC and the ACCESS trial, confirming the evaluation of the previous signature.
  • the ABC and ACCESS trials studied the efficacy of abatacept in different clinical contexts regarding lupus severity and end-organ involvement.
  • the models developed on all T cells were assessed on samples from the ACCESS and ABC trials independently.
  • the models distinguished response in ACCESS T cells very consistently, with an average AUC of 0.974 and an average error rate of 10.2%.
  • Model performance on ABC T cells had a smaller average AUC of 0.868 and an average error rate of 24.1%.
  • the parsimonious models trained on the ABC T cell samples maintained performance when validated on T cell samples from the ACCESS trial, and vice-versa. While the model consistently performs better on ACCESS samples than on ABC samples, possibly due to the overrepresentation of these samples, the model was robust despite the very different clinical contexts of the samples examined. This is supported by the similar patterns of methylation between studies.
  • Identified methylation markers represent both epigenetic and genetic influence at predicted genomic regulatory regions.
  • the inventors examined the methylation status of the top four features selected in model development across cell types and response status in the dataset (FIG. 2). All four markers exhibited a statistically significant difference in proportion of sites methylated (beta values) in all isolated PBMC fractions.
  • the top performing marker, cg04612171 had a continuous distribution of methylation beta values ranging from 0.45 to 0.85, as expected of a single CpG assayed in a single cell type across a population.
  • the inventors examined the methylation at these four sites for annotated activity and inspected their impact on their respective local methylation landscapes.
  • the cg04612171 CpG site falls within a putative cis-regulatory element in the first intron of the Sine Oculis Binding Protein Homolog (SOBP) gene (FIG. 4). This region is also dense with H3K27 acetylation and reported ChlP-seq transcription factor binding sites. Methylation at this locus appears to be linked to methylation at CpGs further downstream in the gene body of SOBP, primarily in T cells (FIG. 5).
  • SOBP Sine Oculis Binding Protein Homolog
  • Marker cgl6733676 corresponds to an upstream shore of the CpG island in the gene body of Solute Carrier Family 25 Member 24 (SLC25 A42) (FIG. 6). While this site appears to not reside within any known or approximate regulatory region, it does have a strong apparent linkage with methylation status near the TSS of SLC25 A24 (FIG. 7). This relationship is maintained across cell types but is most prominent in T cells and monocytes. Correspondingly, normalized counts from RNA-seq of SLC25A25 across cell types similarly correlates with methylation at this locus in T cells and monocytes (FIG. 8).
  • the CpG at this site contains a highly common SNP which appears to correspond to bimodal levels of methylation across cell types.
  • the association of this SNP both with methylation at the proximal TSS and also with the transcript level of the target gene implies function as a cis-acting methylation quantitative trait locus (cis- meQTL). Presence of the minor allele impairs methylation at this site, which is linked to decreased methylation at the TSS, resulting in lower levels of the SLC25A24 transcript.
  • the CpG corresponding to the cg02901522 probe is located on chromosome 8 within an intronic region of the Plasmacytoma Variant Translocation 1 (PVT1) transcript (FIG. 9).
  • PVT1 Plasmacytoma Variant Translocation 1
  • This is a long non-coding RNA (IncRNA) transcript with multiple isoforms and is located downstream of MYC, a transcription factor governing global cellular transcription. This site is located in another predicted regulatory site in an intron preceding the fifth annotated exon. Methylation at this SNP- associated CpG appears to be linked to a nearby CpG containing another common variant in T cells (FIG. 10). This represents a T cell-specific methylation linkage at this region.
  • IncRNA Plasmacytoma Variant Translocation 1
  • cgl 1986743 represents a CpG in another putative regulatory site located near the 5’ UTR of B4GALT6 (FIG. 11). It does not appear to be linked to proximal methylation or with transcription of the immediate gene (FIG. 12).
  • the methylation signature was identified as predictive of abatacept response in lupus patients contains four sites with predicted regulatory activity. Evidence at one site predicts function as an unannotated meQTL but the impacts of methylation at the other sites remain largely uncharacterized.
  • T cells for cg04612171 methylation the top positively correlated genes included TCR variant genes, XCL2, FASLG, and VCAM1, while the top negatively correlated genes included myeloid-associated genes LYZ, CD300E, SPI1, and CSF3R. Methylation in the two variant-linked CpGs correlated most strongly with markers of T cell activation including ICOS, CD28, and CD6, as well as the regulatory markers LEF1 and PLXNA4 (FIG. 13). B cells had major positive associations between cg04612171 methylation in Ig light chain genes, while the top negative associations included CD36, PTGER2, and ITGA6.
  • Variant-associated methylation was linked most strongly to markers of B cell lineage, MS4A1, CD79A, BLK, BANK1, PAX5, and CD 19.
  • FOLR3, WNT5A, and CCL7 were among the most positive associations with cg04612171 methylation in monocytes, and lymphoid markers LEF1, PAX5, and ZAP70 were among the most negative correlations.
  • Methylation in all 3 variant CpGs had the strongest positive correlations with myeloid lineage and activation markers such as CD14, S100A8/9, CD68, VCAN, and TREM1. The number of strong transcriptomic correlations observed across cell types with methylation at these markers provides a signature associated with functional differences in major immune subpopulations.
  • Ingenuity pathway analysis was implemented to identify canonical pathways and associated activity in the methylation-correlated transcripts. Cutoffs for significance and slope of the linear regression were applied. In T cells, the most significant and strongest canonical pathways associated with methylation at three loci were “CTLA4 regulation of CD8+ T cell- mediated cytotoxicity”, “T Cell Receptor Signaling” and “Regulation of IL-2 production by T cells”, indicating a major association with T cell signaling and its regulation (FIG. 14). In addition to enrichment analysis, IPA also provides a prediction of activity in the associated pathway, an activity z-score, by comparing the provided correlation direction with the expected gene change associated with activity in the pathway.
  • the negative z-score less than -2 indicates that activity in the CTLA4 pathway is predicted to be negatively correlated with methylation in three marker CpGs. Likewise, T cell receptor activity and IL-2 production are predicted to be positively associated with methylation at the same loci. This indicates a critical link between the modeled loci and the regulation of the costimulatory signal to T cell activation which is targeted by abatacept. More methylation at the CpGs of interest is positively correlated with T cell activation via the T cell receptor, which is predictive of non-response to abatacept.
  • FIG. 15 Ingenuity Canonical Pathway Analysis of monocyte transcriptome correlations with methylation at CpG sites of interest. Summary of most significant results comparing Canonical Pathway Analysis of by QIAGEN’s Ingenuity® Pathway Analysis (IPA). The most significantly correlated monocyte transcripts with methylation at each CpG of interest were input into IPA with previously noted cutoffs. Rows represent the most significant results across all analyses, and columns represent the correlations with each CpG site noted at the bottom. Color indicates the activation z-score, which represents a predicted change in pathway activity correlating with methylation.
  • BCR activity is significantly enriched in genes correlated with CpG methylation, the direction of the activity correlation is inconsistent between markers. BCR activity is negatively correlated with methylation at the SOBP gene body site, and it is positively correlated with methylation at two variant-associated CpG sites.
  • Monocyte transcripts correlated with epigenetics are enriched in “Phagosome formation”, “Cytokine storm” and “RXR activation” canonical pathways (FIG. 16).
  • cell-specific methylation signatures are predictive of the responsiveness to a drug that targets the co-stimulatory pathway.
  • the genes impacted by the methylation signature can be directly mapped to both upregulated expression and downregulated expression of the predicted drug-targeted genes through pathways in the cell.
  • the methylation signature predicts response very well in two clinical trials with very disparate patient characteristics undergoing treatment and different impacts of background medications.
  • a systems immunology approach leveraging multi-omic data and machine learning can be a powerful approach to better understanding underlying mechanisms and can be used for precision medicine.
  • program storage devices e.g., digital data storage media, which are machine or computer-readable and encode machine-executable or computer-executable programs of instructions, wherein said instructions perform some or all of the steps of said above-described methods.
  • the program storage devices may be, e.g., digital memories, magnetic storage media such as magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media.
  • the embodiments are also intended to cover computers programmed to perform said steps of the above-described methods.
  • modules may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with the appropriate software.
  • the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared.
  • module should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application-specific integrated circuit (ASIC), field- programmable gate array (FPGA), read-only memory (ROM) for storing software, random access memory (RAM), and nonvolatile storage. Other hardware, conventional and/or custom, may also be included.
  • DSP digital signal processor
  • ASIC application-specific integrated circuit
  • FPGA field- programmable gate array
  • ROM read-only memory
  • RAM random access memory
  • nonvolatile storage Other hardware, conventional and/or custom, may also be included.
  • the words “comprising” (and any form of comprising, such as “comprise” and “comprises”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”) or “containing” (and any form of containing, such as “contains” and “contain”) are inclusive or open- ended and do not exclude additional, unrecited elements or method steps.
  • “comprising” may be replaced with “consisting essentially of’ or “consisting of’.
  • the phrase “consisting essentially of’ requires the specified integer(s) or steps as well as those that do not materially affect the character or function of the claimed invention.
  • the term “consisting” is used to indicate the presence of the recited integer (e.g., a feature, an element, a characteristic, a property, a method/process step or a limitation) or group of integers (e.g., feature(s), element(s), characteristic(s), propertie(s), method/process steps or limitation(s)) only.
  • A, B, C, or combinations thereof refers to all permutations and combinations of the listed items preceding the term.
  • “A, B, C, or combinations thereof’ is intended to include at least one of: A, B, C, AB, AC, BC, or ABC, and if order is important in a particular context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB.
  • expressly included are combinations that contain repeats of one or more item or term, such as BB, AAA, AB, BBC, AAABCCCC, CBBAAA, CABABB, and so forth.
  • the skilled artisan will understand that typically there is no limit on the number of items or terms in any combination, unless otherwise apparent from the context.
  • words of approximation such as, without limitation, “about”, “substantial” or “substantially” refers to a condition that when so modified is understood to not necessarily be absolute or perfect but would be considered close enough to those of ordinary skill in the art to warrant designating the condition as being present.
  • the extent to which the description may vary will depend on how great a change can be instituted and still have one of ordinary skilled in the art recognize the modified feature as still having the required characteristics and capabilities of the unmodified feature.
  • a numerical value herein that is modified by a word of approximation such as “about” may vary from the stated value by at least ⁇ 1, 2, 3, 4, 5, 6, 7, 10, 12 or 15%.
  • compositions and/or methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the compositions and/or methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit, and scope of the invention. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope, and concept of the invention as defined by the appended claims.
  • Embodiment 1 A method to select lupus patients that will be responsive to a treatment with an inhibitor of CD80/86 immune cell costimulation, comprising: determining whether a patient has a genetic predisposition to being responsive to the treatment with the inhibitor of CD80/86 immune cell costimulation by: obtaining or having obtained a whole blood sample from the patient with lupus; separating T cells, B cells, and monocytes; performing or having performed a methylation assay on a genome of at least one of the T cells, B cells, or monocytes to detect hypomethylation at one or more CpG biomarkers selected from;
  • Cgl 1986743 determining if the patient has hypomethylation at the one or more CpG biomarkers when compared to a subject that does not have lupus in the at least one of the T cells, B cells, and monocytes, and administering the inhibitor of CD80/86 immune cell costimulation if the patient has hypomethylation of the one or more of the CpG biomarkers in the least one of the T cells, B cells, and monocytes; or discontinuing treatment with the inhibitor of CD80/86 immune cell costimulation if the patient does not have hypomethylation of the one or more of the CpG biomarkers in the least one of the T cells, B cells, and monocytes.
  • Embodiment 2 The method of embodiment 1, wherein the inhibitor of CD80/86 immune cell costimulation is Abatacept or Belatacept.
  • Embodiment 3 The method of embodiments 1 or 2, wherein the hypomethylation is detected for any 2, 3 or 4 of Cg04612171 and Cg 16733676; Cg02901522; and Cgl 1986743.
  • Embodiment 4 The method of any one of embodiments 1 to 3, wherein the hypomethylation is detected for any 2 or 3 of the T cells, B cells, and monocytes.
  • Embodiment 5 The method of any one of embodiments 1 to 4, wherein the hypomethylation is detected for any 2 or 3 of the T cells, B cells, and monocytes and is detected for any 2, 3 or 4 of Cg04612171 and Cgl 6733676; Cg02901522; and Cgl 1986743.
  • Embodiment 6 The method of any one of embodiments 1 to 5, wherein the hypomethylation is detected in T cells and there is an increase in T cell receptor (TCR) signaling genes, a decrease in T cell proliferation, or both.
  • TCR T cell receptor
  • Embodiment 7 The method any one of embodiments 1 to 6, wherein the hypomethylation is detected in monocytes and there is an increase in expression of genes in retinoic acid pathway genes.
  • Embodiment 8 The method any one of embodiments 1 to 7, wherein the hypomethylation is detected in B cells and there is a decrease in B cell receptor (BCR) activity, Fc gamma receptor IIB (FCyRIIB) activity, or both.
  • BCR B cell receptor
  • FCyRIIB Fc gamma receptor IIB
  • Embodiment 9 The method of any one of embodiments 1 to 8, wherein the hypomethylation is detected by bisulfite sequencing, high-performance liquid chromatographyultraviolet (HPLC-UV), liquid chromatography coupled with tandem mass spectrometry (LC- MS/MS), enzyme-linked immunosorbent assay (ELISA), long interspersed nuclear elements (LINE-1) and Pyrosequencing, amplification fragment length polymorphism (AFLP), restriction fragment length polymorphism (RFLP), luminometric methylation assay, bead-chip, bead-chip array, microarray, methyl-sensitive cut counting, or luminometric methylation.
  • HPLC-UV high-performance liquid chromatographyultraviolet
  • LC- MS/MS liquid chromatography coupled with tandem mass spectrometry
  • ELISA enzyme-linked immunosorbent assay
  • LINE-1 long interspersed nuclear elements
  • LINE-1 Pyrosequencing
  • AFLP amplification fragment length polymorphism
  • RFLP restriction
  • Embodiment 10 The method any one of embodiments 1 to 9, wherein the hypomethylation of Cg04612171 and Cgl6733676; Cg02901522; and Cgl 1986743, affects expression of Sine Oculis Binding Protein Homolog (SOBP), Solute Carrier Family 25 Member 24 (SLC25A24), Plasmacytoma Variant Translocation 1 (PVT1), or Beta-1, 4- Galactosyltransferase 6 (B4GALT6), respectively.
  • SOBP Sine Oculis Binding Protein Homolog
  • SLC25A24 Solute Carrier Family 25 Member 24
  • PVT1 Plasmacytoma Variant Translocation 1
  • Beta-1 4- Galactosyltransferase 6
  • Embodiment 11 The method of any one of embodiments 1 to 10, wherein the lupus is systemic lupus erythematosus, Cutaneous lupus erythematosus, drug-induced lupus, or neonatal lupus.
  • Embodiment 12 The method any one of embodiments 1 to 11, further comprising the step of monitoring a methylation status of the one or more CpG biomarkers at one or more points in time and based on a change in methylation discontinuing or restarting treatment with the inhibitor of CD80/86 immune cell costimulation.
  • Embodiment 15 The method of embodiment 14, wherein the inhibitor of CD80/86 immune cell costimulation is Abatacept or Belatacept.
  • Embodiment 16 The method of embodiments 14 or 15, wherein the hypomethylation is detected for any 2, 3 or 4 of Cg04612171 and Cg 16733676; Cg02901522; and Cgl 1986743.
  • Embodiment 17 The method of embodiments 14 or 16, wherein the hypomethylation is detected for any 2 or 3 of the T cells, B cells, and monocytes.
  • Embodiment 22 A CTLA4/T cell activation methylation signature predicting responsiveness to an inhibitor of CD80/86 immune cell costimulation, the signature comprising: hypomethylation in a genome of at least one of T cells, B cells, or monocytes in one or more CpG biomarkers selected from;
  • Embodiment 23 The signature of embodiment 22, wherein the inhibitor of CD80/86 immune cell costimulation is Abatacept or Belatacept.
  • Embodiment 24 The signature of embodiments 22 or 23, wherein the hypomethylation is detected for any 2, 3 or 4 of Cg04612171 and Cgl6733676; Cg02901522; and Cgl 1986743.
  • Embodiment 25 The signature of embodiments 22 to 24, wherein the hypomethylation is detected for any 2 or 3 of the T cells, B cells, and monocytes.
  • Embodiment 26 The signature of embodiments 22 to 25, wherein the hypomethylation is detected for any 2 or 3 of the T cells, B cells, and monocytes and is detected for any 2, 3 or 4 of Cg04612171 and Cgl 6733676; Cg02901522; and Cgl 1986743.
  • Embodiment 27 The signature of embodiments 22 to 26, wherein the hypomethylation is detected in T cells and there is an increase in T cell receptor (TCR) signaling genes and a decrease in T cell proliferation.
  • TCR T cell receptor
  • Embodiment 28 The signature of embodiments 22 to 27, wherein the hypomethylation is detected in monocytes and there is an increase in expression of genes in retinoic acid pathway genes.
  • Embodiment 29 The signature of embodiments 22 to 28, wherein the hypomethylation is detected in B cells and there is a decrease in B cell receptor (BCR) activity,
  • FCyRIIB Fc gamma receptor IIB
  • Embodiment 30 The signature of embodiments 22 to 29, wherein the hypomethylation is detected by bisulfite sequencing, high-performance liquid chromatographyultraviolet (hypomethylation -UV), liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), enzyme-linked immunosorbent assay (ELISA), long interspersed nuclear elements (LINE-1) and Pyrosequencing, amplification fragment length polymorphism (AFLP), restriction fragment length polymorphism (RFLP), luminometric methylation assay, bead-chip, bead-chip array, microarray, methyl-sensitive cut counting, or luminometric methylation.
  • LC-MS/MS liquid chromatography coupled with tandem mass spectrometry
  • ELISA enzyme-linked immunosorbent assay
  • LINE-1 long interspersed nuclear elements
  • LINE-1 amplification fragment length polymorphism
  • RFLP restriction fragment length polymorphism
  • luminometric methylation assay bead-chip, bead-
  • Embodiment 31 The signature of embodiments 22 to 30, wherein the hypomethylation of Cg04612171 and Cgl6733676; Cg02901522; and Cgl 1986743, affects expression of Sine Oculis Binding Protein Homolog (SOBP), Solute Carrier Family 25 Member 24 (SLC25A24), Plasmacytoma Variant Translocation 1 (PVT1), or Beta-1, 4-
  • SOBP Sine Oculis Binding Protein Homolog
  • SLC25A24 Solute Carrier Family 25 Member 24
  • PVT1 Plasmacytoma Variant Translocation 1
  • Beta-1 4-
  • Embodiment 32 A method for diagnosing lupus patients responsive to treatment with an inhibitor of CD80/86 immune cell costimulation, comprising: determining whether a patient has a genetic predisposition to being responsive to the treatment with the inhibitor of CD80/86 immune cell costimulation: obtaining or having obtained a whole blood sample from the patient with lupus; separating T cells, B cells, and monocytes; performing or having performed a methylation assay on a genome of at least one of the T cells, B cells, or monocytes to detect methylation at one or more CpG biomarkers selected from;
  • the lupus patient has hypomethylation at the one or more CpG biomarkers when compared to a subject that does not have lupus in the at least one of the T cells, B cells, and monocytes, wherein the lupus is systemic lupus erythematosus, Cutaneous lupus erythematosus, drug-induced lupus, or neonatal lupus, wherein patients with hypomethylation responsive to treatment with the inhibitor of CD80/86 immune cell costimulation.
  • Embodiment 33 The method of claim 32, wherein the lupus is systemic lupus erythematosus, Cutaneous lupus erythematosus, drug-induced lupus, or neonatal lupus.
  • Embodiment 34 The method of embodiments 32 or 33, further comprising the step of monitoring a methylation status of the one or more CpG biomarkers at one or more points in time and based on a change in methylation discontinuing or restarting treatment with the inhibitor of CD80/86 immune cell costimulation.

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Abstract

L'invention concerne des méthodes de traitement de patients atteints de lupus qui consistent à déterminer si un patient a une prédisposition génétique à réagir au traitement avec l'inhibiteur de la costimulation des cellules immunitaires CD80/86 en obtenant ou en ayant obtenu un échantillon de sang total du patient atteint de lupus, en séparant les lymphocytes T, les lymphocytes B et les monocytes, et en effectuant ou en ayant effectué un dosage de méthylation sur le génome d'au moins l'un des lymphocytes T, des lymphocytes B ou des monocytes, et en effectuant ou en ayant effectué une analyse de la méthylation sur le génome de l'au moins l'un des lymphocytes T, des lymphocytes B ou des monocytes, et en administrant l'inhibiteur de la costimulation de cellules immunitaires CD80/86 si le patient a une hypométhylation du ou des biomarqueurs CpG dans l'au moins l'un des lymphocytes T, des lymphocytes B et des monocytes.
PCT/US2024/030394 2023-05-24 2024-05-21 Approche multi-omique pour évaluer l'hétérogénéité du lupus érythémateux disséminé dans une réponse de traitement Pending WO2024243224A2 (fr)

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