WO2020194211A1 - Procédés et compositions pour surveiller l'exacerbation aiguë de la bpco - Google Patents

Procédés et compositions pour surveiller l'exacerbation aiguë de la bpco Download PDF

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WO2020194211A1
WO2020194211A1 PCT/IB2020/052828 IB2020052828W WO2020194211A1 WO 2020194211 A1 WO2020194211 A1 WO 2020194211A1 IB 2020052828 W IB2020052828 W IB 2020052828W WO 2020194211 A1 WO2020194211 A1 WO 2020194211A1
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seq
subject
aecopd
biomarker
biomarkers
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PCT/IB2020/052828
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English (en)
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Don D. SIN
Raymond T. NG
Bruce M. Mcmanus
Zsuzsanna Hollander
Virginia CHEN
Scott J. TEBBUTT
Casey P. SHANNON
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The University Of British Columbia
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6842—Proteomic analysis of subsets of protein mixtures with reduced complexity, e.g. membrane proteins, phosphoproteins, organelle proteins
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P11/00—Drugs for disorders of the respiratory system
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • 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
    • C12Q2537/00—Reactions characterised by the reaction format or use of a specific feature
    • C12Q2537/10—Reactions characterised by the reaction format or use of a specific feature the purpose or use of
    • C12Q2537/165—Mathematical modelling, e.g. logarithm, ratio
    • 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
    • C12Q2600/118—Prognosis of disease development
    • 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
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/12—Pulmonary diseases
    • G01N2800/122—Chronic or obstructive airway disorders, e.g. asthma COPD
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis
    • 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
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations

Definitions

  • COPD chronic obstructive pulmonary disease
  • the present disclosure provides methods and compositions for prognosing, diagnosing, and monitoring AECOPD in a subject.
  • a panel or combination of biomarkers can be used to reliably distinguish subjects with AECOPD from subjects in a stable or convalescent state of COPD, or from subjects without COPD.
  • the disclosure features a method for prognosing, diagnosing, and/or monitoring acute exacerbations of chronic obstructive pulmonary disease (AECOPD) in a subject, the method comprising: determining a biomarker score based on the expression level of biomarkers in a biomarker panel in a subject sample; wherein a higher biomarker score in the subject sample compared to a control sample indicates that the subject has or is likely to develop AECOPD;
  • AECOPD chronic obstructive pulmonary disease
  • the biomarker panel comprises the biomarkers TAMM41 (SEQ ID NO 1), ENOSF1 (SEQ ID NO 2), TSPYL1 (SEQ ID NO 3), PPIH (SEQ ID NO 4), PIGU (SEQ ID NO 5), DISP1 (SEQ ID NO 6), HLCS (SEQ ID NO 7), ALG9 (SEQ ID NO 8), FAHD2B (SEQ ID NO 9), ACKR3 (SEQ ID NO 10), TCTN2 (SEQ ID NO 11), SNHG17 (SEQ ID NO 12), CRHR1-IT1 (SEQ ID NO 13), SCML4 (SEQ ID NO 14), SEC22C (SEQ ID NO 15), CD3G (SEQ ID NO 16), ZNF767P (SEQ ID NO 17), THEMIS (SEQ ID NO 18), DCAF16 (SEQ ID NO 19), ACTA2-AS1 (SEQ ID NO 20), KLF12 (SEQ ID NO 21), OR7E14P (SEQ ID NO 22), ZNF8
  • the biomarker score is determined by obtaining the expression level of the biomarkers in the biomarker panel in a blood sample obtained from the subject.
  • the obtaining comprises (i) extracting polynucleotides from the subject sample; (ii) purifying the polynucleotides; (iii) measuring the amount of the
  • polynucleotides (iv) amplifying the polynucleotides using polymerase chain reaction; (v) sequencing the polynucleotides; and (vi) analyzing the sequences of the polynucleotides to annotate the polynucleotides with their corresponding biomarkers selected from Table 4 A.
  • measuring the amount of the polynucleotides comprises using a microarray, quantitative polymerase chain reaction (qPCR), reverse transcription qPCR (RT- qPCR), direct hybridization, NanoString nCounter® technology, and/or sequencing.
  • qPCR quantitative polymerase chain reaction
  • RT- qPCR reverse transcription qPCR
  • direct hybridization NanoString nCounter® technology
  • sequencing nanoString nCounter® technology
  • the biomarker score is greater in a subject who has or is likely to develop AECOPD than in a control subject who is in a stable or convalescent state of COPD or without COPD. In some embodiments, a biomarker score in the subject sample greater than -1.198 indicates that the subject has or is likely to develop AECOPD.
  • the sensitivity of prognosing and/or diagnosing AECOPD is at least 70% and/or the specificity of prognosing and/or diagnosing AECOPD is at least 85%.
  • the method further comprises providing a course of treatment based on the prognosis and/or diagnosis.
  • the course of treatment is selected from short-acting beta2-agonists, long-acting bronchodilators, oral steroids, oxygen therapy, and/or antibiotics.
  • kits for detecting a panel of biomarkers in a blood sample obtained from a subject having COPD comprising:
  • the instructions comprise instructions for conducting a gene sequencing assay.
  • a method of treating acute exacerbation of chronic obstructive pulmonary disease (AECOPD) in a subject comprising: a) selecting a subject who has or is likely to develop AECOPD by: determining a biomarker score based on the expression level of biomarkers in a biomarker panel in a subject sample;
  • the course of treatment is selected from short-acting beta2- agonists, long-acting bronchodilators, oral steroids, oxygen therapy, and/or antibiotics.
  • the biomarker score is determined by obtaining the expression level of the biomarkers in the biomarker panel in a blood sample obtained from the subject.
  • the disclosure features a method for prognosing, diagnosing, and monitoring acute exacerbations of chronic obstructive pulmonary disease (AECOPD) in a subject, comprising: obtaining the expression level of at least one biomarker selected from Tables 4A, 4B, 4C in a subject sample obtained from a subject; and comparing the expression level of the biomarker in the subject sample to the expression level of the corresponding biomarker in a control sample, wherein a determination that a higher expression level of the biomarker in the subject sample indicates that the subject has or is likely to develop AECOPD.
  • AECOPD chronic obstructive pulmonary disease
  • the obtaining comprises obtaining the expression levels of at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen or more biomarkers selected from Tables 4 A, 4B, 4C in the subject sample.
  • the biomarker is selected from the group consisting of TAMM41 (SEQ ID NO 1), ENOSF1 (SEQ ID NO 2), TSPYL1 (SEQ ID NO 3), PPIH (SEQ ID NO 4), PIGU (SEQ ID NO 5), DISP1 (SEQ ID NO 6), HLCS (SEQ ID NO 7), ALG9 (SEQ ID NO 8), FAHD2B (SEQ ID NO 9), ACKR3 (SEQ ID NO 10), TCTN2 (SEQ ID NO 11), SNHG17 (SEQ ID NO 12), CRHR1-IT1 (SEQ ID NO 13), SCML4 (SEQ ID NO 14), SEC22C (SEQ ID NO 15), CD3G (SEQ ID NO 16), ZNF767P (SEQ ID NO 17), THEMIS (SEQ ID NO 18), DCAF16 (SEQ ID NO 19), ACTA2-AS1 (SEQ ID NO 20), KLF12 (SEQ ID NO 21), OR7E14P (
  • the biomarker is selected from the group consisting of PPP2R1B, ATIC, DNAJC16, MLLT10, RTTN, WDR59, MESDC2, TAS2R4, INTS2, LMLN, PDSS2, GALNTl l, CDK6, NUP205, MKL2, MCM7, TRAF3, NOM1, ANGEL 1, WDR77, MTR, BRD9, ACAD9, NIPAL3, SUN1, GART, STT3A, MACF1, DROSHA, VPRBP, MBTPS1, LUC7L, WHSC1, HEATR1, MGA, SARS, INO80D, NAT 10, MCCC2, RBM14, XP05, NBAS, HNRNPAB, RAD51B, LARS2, RUVBL1, PAPD7, NFXl, TANG06, and UTP20.
  • the biomarker is selected from the group consisting of EPHX2, ACVR1C, METAPID, TAF4B, EN02, LDHB, PLAG1, PAQR8, GGT7, GPA33, HABP4, GCSAM, TRABD2A, RASGRF2, DOCK9, and CHMP7.
  • the method for prognosing, diagnosing, and/or monitoring acute exacerbations of chronic obstructive pulmonary disease (AECOPD) in a subject comprises: (i) obtaining the expression level of a plurality or panel of biomarkers selected from the biffkers listed in Tables 4A, 4B, and/or 4C, in a blood sample obtained from the subject; (ii) determining a biomarker score based on the expression levels using a formula; wherein a higher biomarker score based on the expression levels of the biomarkers in the biomarker panel in the subject sample compared to a control sample indicates that the subject has or is likely to develop AECOPD.
  • AECOPD chronic obstructive pulmonary disease
  • a biomarker score in the subject sample greater than -1.198 indicates that the subject has or is likely to develop AECOPD.
  • the method comprises: (i) obtaining the expression level of a panel of biomarkers comprising the biomarkers in Table 4A in a blood sample obtained from the subject; (ii) comparing the expression level of the biomarker panel in the subject sample to the expression level of the corresponding biomarker panel in a control sample, wherein a higher expression level of the biomarker panel in the subject sample indicates that the subject has or is likely to develop AECOPD.
  • the biomarker panel comprises or consists of the biomarkers TAMM41 (SEQ ID NO 1), ENOSF1 (SEQ ID NO 2), TSPYL1 (SEQ ID NO 3), PPIH (SEQ ID NO 4), PIGU (SEQ ID NO 5), DISP1 (SEQ ID NO 6), HLCS (SEQ ID NO 7), ALG9 (SEQ ID NO 8), FAHD2B (SEQ ID NO 9), ACKR3 (SEQ ID NO 10), TCTN2 (SEQ ID NO 11), SNHG17 (SEQ ID NO 12), CRHR1-IT1 (SEQ ID NO 13), SCML4 (SEQ ID NO 14), SEC22C (SEQ ID NO 15), CD3G (SEQ ID NO 16), ZNF767P (SEQ ID NO 17), THEMIS (SEQ ID NO 18), DCAF16 (SEQ ID NO 19), ACTA2-AS1 (SEQ ID NO 20), KLF12 (SEQ ID NO 21), OR7E14
  • the method comprises: (i) obtaining the expression level of a panel of biomarkers comprising the biomarkers in Table 4B in a blood sample obtained from the subject; (ii) comparing the expression level of the biomarker in the subject sample to the expression level of the corresponding biomarker in a control sample, wherein a determination that a higher expression level of the biomarker in the subject sample indicates that the subject is likely to develop or already has AECOPD.
  • the biomarker panel comprises or consists of the biomarkers PPP2R1B, ATIC, DNAJC16, MLLT10, RTTN, WDR59, MESDC2, TAS2R4, INTS2, LMLN, PDSS2, GALNTl l, CDK6, NUP205, MKL2, MCM7, TRAF3, NOM1, ANGEL 1, WDR77, MTR, BRD9, ACAD9, NIPAL3, SUN1, GART, STT3A, MACF1, DROSHA, VPRBP, MBTPS1, LUC7L, WHSC1, HEATR1, MGA, SARS, INO80D, NAT 10, MCCC2, RBM14, XP05, NBAS, HNRNPAB, RAD51B, LARS2, RUVBL1, PAPD7, NFXl, TANG06, and UTP20.
  • the method comprises: (i) obtaining the expression level of a panel of biomarkers comprising the biomarkers in Table 4C in a blood sample obtained from the subject; (ii) comparing the expression level of the biomarker in the subject sample to the expression level of the corresponding biomarker in a control sample, wherein a determination that a higher expression level of the biomarker in the subject sample indicates that the subject is likely to develop or already has AECOPD.
  • the biomarker panel comprises or consists of the biomarkers EPHX2, ACVR1C, METAP1D, TAF4B, EN02, LDHB, PLAG1, PAQR8, GGT7, GPA33, HABP4, GCSAM, TRABD2A, RASGRF2, DOCK9, and CHMP7.
  • the obtaining comprises (i) extracting polynucleotides from the subject sample; (ii) purifying the polynucleotides; (iii) measuring the amount of the polynucleotides; (iv) amplifying the polynucleotides using polymerase chain reaction; (v) sequencing the polynucleotides; and (vi) analyzing the sequences of the polynucleotides to annotate the polynucleotides with their corresponding biomarkers selected from Tables 4A, 4B, 4C.
  • measuring the amount of the polynucleotides comprises using a microarray, quantitative polymerase chain reaction (qPCR), reverse transcription qPCR (RT- qPCR), direct hybridization, NanoString nCounter® technology, and/or sequencing.
  • qPCR quantitative polymerase chain reaction
  • RT- qPCR reverse transcription qPCR
  • direct hybridization NanoString nCounter® technology
  • sequencing nanoString nCounter® technology
  • a biomarker score is significantly greater in a subject likely to develop or already has AECOPD than in a control subject who is in a stable or convalescent state of COPD or without COPD.
  • the sensitivity of prognosing and/or diagnosing AECOPD is at least 70% (e.g, at least 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 98%) and/or the specificity of prognosing and/or diagnosing AECOPD is at least 85% (e.g, at least 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%).
  • the methods further include obtaining the subject sample from the subject.
  • the subject sample may be a blood sample.
  • the methods further include providing a course of treatment based on the prognosis and/or diagnosis.
  • the course of treatment includes administering a thereapeutically effective amount of a drug or pharmaceutical agent to the subject.
  • the drug or pharmaceutical agent is selected from short-acting beta2- agonists, long-acting bronchodilators, oral steroids, oxygen therapy, and/or antibiotics.
  • the methods described herein may further include observing one or more symptoms selected from the group consisting of dyspnea, cough, and sputum production, in the subject, wherein the observation of one or more of the symptoms indicates that the subject is likely to develop or already has AECOPD.
  • control sample in the methods described herein may be obtained from a control subject who is in a stable or convalescent state of COPD or without COPD.
  • the disclosure features a kit for detecting at least one biomarker selected from Tables 4A, 4B, 4C in a subject sample obtained from a subject having COPD, comprising: (i) a plurality of reagents for detecting at least one biomarker selected from Tables 4A, 4B, 4C; (ii) a positive control sample; and (iii) instructions for using the plurality of reagents to detect the biomarker.
  • the reagents include flow cells, nucleotides, oligonucleotides, primers, nucleic acid adaptors, protein adaptors, sequencing barcodes, reverse transcriptase, DNA polymerase, ligase, luciferase, end repair enzymes, excision enzymes, DNA purification reagents (e.g ., clean-up reagents, filtration columns), DNA fragmentation reagents or tools (e.g., enzymes, beads), affinity tags, fluorophores, substrates for DNA binding or capture (e.g, beads), hybridization buffers, PCR buffer, other buffers (e.g, containing salts, detergents or alcohol).
  • DNA purification reagents e.g ., clean-up reagents, filtration columns
  • DNA fragmentation reagents or tools e.g., enzymes, beads
  • affinity tags e.g., fluorophores, substrates for DNA binding or capture
  • hybridization buffers PCR buffer, other
  • the instructions comprise instructions for conducting a gene sequencing assay.
  • the kit provides a prognostic and/or diagnostic accuracy having a sensitivity of at least 70% (e.g, at least 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 98%) and/or a specificity of at least 85% (e.g, at least 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%).
  • the disclosure features a composition for use in prognosing, diagnosing, and monitoring acute exacerbations of chronic obstructive pulmonary disease (AECOPD), comprising one or more reagents for detecting at least one biomarker selected from Tables 4A, 4B, 4C.
  • the reagents comprise gene sequencing reagents targeting at least one biomarker.
  • the reagents include flow cells, nucleotides, oligonucleotides, primers, nucleic acid adaptors, protein adaptors, sequencing barcodes, reverse transcriptase, DNA polymerase, ligase, luciferase, end repair enzymes, excision enzymes, DNA purification reagents (e.g, clean-up reagents, filtration columns), DNA fragmentation reagents or tools (e.g, enzymes, beads), affinity tags, fluorophores, substrates for DNA binding or capture (e.g, beads), hybridization buffers, PCR buffer, other buffers (e.g, containing salts, detergents or alcohol).
  • DNA purification reagents e.g, clean-up reagents, filtration columns
  • DNA fragmentation reagents or tools e.g, enzymes, beads
  • affinity tags e.g, fluorophores, substrates for DNA binding or capture
  • hybridization buffers PCR buffer, other buffers (e.g,
  • the disclosure features a computer-implemented method comprising: storing, in a storage memory, a dataset associated with a subject sample obtained from a subject having COPD; and analyzing, by a computer processor, the dataset to determine the expression level of at least one biomarker selected from Tables 4A, 4B, 4C, wherein the expression level of the biomarker in the subject sample indicates whether the subject is likely or unlikely to develop AECOPD or whether the subject already has AECOPD.
  • the disclosure features a system comprising: a storage memory for storing a dataset associated with a subject sample obtained from a subject having COPD; and a processor communicatively coupled to the storage memory for analyzing the dataset to determine the expression level of at least one biomarker selected from Tables 4 A, 4B, 4C, wherein the expression level of the biomarker in the subject sample indicates whether the subject is likely or unlikely to develop AECOPD or whether the subject already has AECOPD.
  • the disclosure features a computer-readable storage medium storing computer-executable program code, the program code comprising: program code for storing a dataset associated with a subject sample obtained from a subject having COPD; and program code for analyzing the dataset to determine the expression level of at least one biomarker selected from Tables 4A, 4B, 4C, wherein the expression level of the biomarker in the subject sample indicates whether the subject is likely or unlikely to develop AECOPD or whether the subject already has AECOPD.
  • FIG. 1A Disease activity model and cohorts used for this study. The various cohorts, subcohorts, and subjects analyzed in this study are shown in relation to the hypothesized AECOPD event timeline.
  • the underlying physiological processes of AECOPD begin well before clinical onset of symptoms. This period is termed“imminent exacerbation”. These physiological processes are also assumed to take some time to resolve after AECOPD treatment, represented as the period of convalescence.
  • FIG. IB Analyses performed for this study. The analyses begin with module discovery in ECLIPSE subjects using Weighted Gene Co-expression Network Analysis (WGCNA) (10), followed by biomarker discovery in a separate and non-overlapping subcohort of ECLIPSE subjects. Cross-validation was used to estimate performance, in order to select the most promising prognostic biomarker panels. These were further pruned by examining their off-the-shelf performance in RTP subjects. Finally, the best 3 biomarker panels were replicated in a separate and non-overlapping subcohort of RTP subjects.
  • WGCNA Weighted Gene Co-expression Network Analysis
  • FIG. 1C Subject selection for the ECLIPSE cohort.
  • FIG. 2 The top 3 biomarker panels by discovery performance were applied to independent samples from AECOPD, convalescing, and stable COPD subjects. All 3 panels predict higher disease levels in IE samples than NE samples, as well as high levels at AECOPD and lower levels during convalescence/stable COPD. AUCs and their significance are shown for both the AECOPD versus day 90 and AECOPD versus stable COPD comparisons. (* p ⁇ 0.1, ** p ⁇ 0.05, *** pO.Ol).
  • FIG. 3 Performance of other markers in tracking disease activity. Aside from the module-based biomarker discovery, we also performed discovery analyses on all the unique genes available on the platform (19,245). Performance, while still good, is not as strong and lacks the same biological coherence of the module-based approach.
  • Cell composition white blood cells, neutrophil %, basophil %, monocyte %, eosinophil %, lymphocyte %) and C-reactive protein (CRP) track with convalescing AECOPD, as they reflect inflammatory and immune processes, but are not able to prognose upcoming AECOPD.
  • FEV1 %predicted is slightly prognostic of AECOPD, as it indicates worse disease and higher likelihood of exacerbation, but does not appear to track with convalescence. (* p ⁇ 0.1, ** p ⁇ 0.05, *** p ⁇ 0.01)
  • FIG. 4 Internal replication performance in two subcohorts. What we call the replication cohort in this manuscript was run as two separate microarray experiments, separated by approximately 8 months. They were conceived as two separate replications by the authors, but for simplicity’s sake, they have been combined into a single replication. However, it is interesting to observe that top 3 biomarker panels replicate very well in both subcohorts, and their performance is consistent. [0049] FIG. 5. Differential expression on a module-by-module basis. Volcano plots of the genes in each module show that genes within each module tend to move in the same direction in imminent versus non-exacerbators, which is expected because of how these co-expression modules are derived. The largest module (turquoise) consists of a mix of both up- and down-regulated genes, likely because it contains the least“cleanly clustered” genes in the WGCNA analysis.
  • Marker refers generally to a molecule (e.g ., a gene, peptide, protein, carbohydrate, or lipid) that is expressed in a cell or tissue, which is useful for the prognosis, diagnosis, or monitoring of AECOPD.
  • a marker in the context of the present disclosure encompasses, for example, genes, cytokines, chemokines, growth factors, proteins, peptides, and metabolites, together with their related metabolites, mutations, variants, modifications, fragments, subunits, degradation products, elements, and other analytes or sample- derived measures.
  • markers in the context of the present disclosure encompass the genes listed in Tables 4A, 4B, 4C. Markers also encompass non-blood borne factors and non-analyte physiological markers of health status, and/or other factors or markers not measured from samples (e.g., biological samples such as bodily fluids), such as clinical parameters and traditional factors for clinical assessments.
  • To “analyze” includes measurement and/or detection of data associated with a marker (such as, e.g, presence or absence of a gene, or constituent expression or abundance levels) in the sample (or, e.g, by obtaining a dataset reporting such measurements, as described below).
  • an analysis can include comparing the measurement and/or detection of at least one marker in samples from a subject pre- and post-treatment or other control subject(s).
  • the markers of the present teachings can be analyzed by any of various conventional methods known in the art.
  • prognosing refers to an act of predicting or forecasting the likely occurrence of a disease or ailment (e.g ., AECOPD) in a subject.
  • AECOPD a disease or ailment
  • the disclosure provides methods that may be used for prognosing whether the subject is likely to develop AECOPD.
  • a prognosis may indicate that the subject having COPD is likely or unlikely to develop AECOPD.
  • a prognosis may be made by measuring the expression level of one or more biomarks listed in Tables 4A, 4B, 4C in a sample obtained from the subject.
  • a "subject” in the context of the present teachings is generally a mammal.
  • the subject is generally a patient.
  • the term "mammal” as used herein includes but is not limited to a human, non human primate, dog, cat, mouse, rat, cow, horse, and pig. Mammals other than humans can be advantageously used as subjects that represent animal models of AECOPD.
  • a subject can be male or female.
  • sample in the context of the present teachings refers to any biological sample that is isolated from a subject.
  • a sample can include, without limitation, a single cell or multiple cells, fragments of cells, an aliquot of body fluid, whole blood, platelets, serum, plasma, red blood cells, white blood cells or leucocytes, endothelial cells, tissue biopsies, synovial fluid, lymphatic fluid, ascites fluid, and interstitial or extracellular fluid.
  • sample also encompasses the fluid in spaces between cells, including mucous, sputum, semen, sweat, urine, or any other bodily fluids.
  • Blood sample can refer to whole blood or any fraction thereof, including blood cells, red blood cells, white blood cells or leucocytes, platelets, serum and plasma. Samples can be obtained from a subject by means including but not limited to venipuncture, excretion, massage, biopsy, needle aspirate, lavage, scraping, or intervention or other means known in the art.
  • the sample is a blood sample from the subject.
  • a “dataset” is a set of data (e.g., numerical values) resulting from evaluation of a sample.
  • the values of the dataset can be obtained, for example, by experimentally obtaining measures from a sample and constructing a dataset from these measurements; or alternatively, by obtaining a dataset from a service provider such as a laboratory, or from a database or a server on which the dataset has been stored.
  • a dataset may be obtained by obtaining a sample, and processing the sample to experimentally determine the data, e.g, via measuring, microarray, quantitative polymerase chain reaction (qPCR), reverse transcription qPCR (RT-qPCR), direct hybridization, NanoString nCounter® technology, and/or sequencing.
  • the phrase also encompasses receiving a set of data, e.g., from a third party that has processed the sample to experimentally determine the dataset. Additionally, the phrase encompasses mining data from at least one database or at least one publication or a combination of databases and publications.
  • Measuring or “measurement” in the context of the present teachings refers to determining the presence, absence, quantity, amount, or effective amount of a marker or other substance (e.g., one or more genes in Tables 4A, 4B, 4C) in a clinical or subject-derived sample, including the presence, absence, or concentration levels of such markers or substances, and/or evaluating the values or categorization of a subject's clinical parameters.
  • a marker or other substance e.g., one or more genes in Tables 4A, 4B, 4C
  • expression level refers to a value that represents a direct, indirect, or comparative measurement of the level of expression or abundance of a gene, peptide, polypeptide, or protein.
  • expression level can refer to a value that represents a direct, indirect, or comparative measurement of the gene expression level of a biomarker of interest (e.g, a biomarker listed in Tables 4A, 4B, 4C).
  • the term“expression level” can also include the relative or absolute amount, quantity, or abundance of a biomarker (e.g, a gene listed in Tables 4A, 4B, 4C) in a sample.
  • Determining the expression level of a gene may include determining whether the gene expression is up-regulated as compared to a control, down- regulated as compared to a control, or substantially unchanged as compared to a control.
  • ROC receiver operating characteristic
  • AECOPD chronic obstructive pulmonary disease
  • biomarkers that provide prognostic value or diagnostic accuracy in diagnosing AECOPD.
  • the biomarkers may be used to prognose, diagnose, or monitor AECOPD to enable clinicians to detect pre-clinical exacerbation before patients require hospital-based care.
  • gene expression profiling and systems biology methods were used in peripheral whole blood from two large clinical COPD cohorts to derive a blood-based biomarker signature of heightened disease activity.
  • Whole blood gene expression profiling was carried out in two large clinical cohorts, totaling 1097 samples. Unsupervised clustering was first applied to subjects with stable disease to identify co-regulated gene modules.
  • biomarker panels e.g ., the three biomarker panels, salmon, green, and lightcyan, listed in Tables 4A, 4B, 4C
  • AUC receiver operating characteristics curve
  • Tables 4A, 4B, 4C lists the blood-based biomarkers that predict imminent exacerbation, peak during exacerbation, and decrease during convalescence.
  • the biomarkers were composed of genes consistent with immune response to viral infection, which may underlie the majority AECOPDs. These biomarkers may thus reflect disease activity and, may be used to monitor AECOPD risk and recovery in COPD patients. These biomarkers may be used to more effectively manage COPD patient care and reduce AECOPD associated morbidity and mortality by allowing clinicians to anticipate such events and modify their course.
  • the methods described herein for prognosing, diagnosing, and/or monitoring AECOPD in a subject include obtaining the expression level of at least one biomarker (e.g., one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more biomarkers) selected from Tables 4A, 4B, 4C in a subject sample (e.g, a blood sample) obtained from a subject; and comparing the expression level of the biomarker in the subject sample to the expression level of the corresponding biomarker in a control sample, wherein a determination that a higher expression level of the biomarker in the subject sample indicates that the subject is likely to develop or already has AECOPD.
  • a biomarker e.g., one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more biomarkers
  • Tables 4A, 4B, 4C lists three panels of biomarkers (e.g. , salmon, green, and lightcyan panels) that may be used in the methods.
  • the biomarker is selected from the salmon panel.
  • the biomarker may be selected from the group consisting of TAMM41, ENOSF1, TSPYL1, PPIH, PIGU, DISP1, HLCS, ALG9, FAHD2B, ACKR3, TCTN2, SNHG17, CRHR1-IT1, SCML4, SEC22C, CD3G, ZNF767P, THEMIS, DCAF16, ACTA2-AS1, KLF12, OR7E14P, ZNF827, KMT2A, CBLB, CCL28, TMEM116, TRAF5, CD3E, DCAF4, ITK, TET1, SKAPl, GOSR2, and RORA.
  • the methods use one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more biomarkers from the salmon panel in Table 4 A. In some embodiments, the methods use all of the biomarkers in the salmon panel in Table 4 A.
  • the biomarker is selected from the green panel.
  • the biomarker may be selected from the group consisting of PPP2R1B, ATIC, DNAJC16, MLLTIO, RTTN, WDR59, MESDC2, TAS2R4, INTS2, LMLN, PDSS2, GALNTl l, CDK6, NUP205, MKL2, MCM7, TRAF3, NOM1, ANGEL 1, WDR77, MTR, BRD9, ACAD9, NIPAL3, SUN1, GART, STT3A, MACF1, DROSHA, VPRBP, MBTPS1, LUC7L, WHSC1, HEATR1, MGA, SARS, INO80D, NAT 10, MCCC2, RBM14, XP05, NBAS, HNRNPAB, RAD51B, LARS2, RUVBL1, PAPD7, NFXl, TANG06, and UTP20.
  • the methods use one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more biomarkers from the green panel in Table 4B. In some embodiments, the methods use all of the biomarkers in the green panel in Table 4B.
  • the biomarker is selected from the lightcyan panel.
  • the biomarker may be selected from the group consisting of EPHX2, ACVR1C, METAPID, TAF4B, EN02, LDHB, PLAG1, PAQR8, GGT7, GPA33, HABP4, GCSAM, TRABD2A, RASGRF2, DOCK9, and CHMP7.
  • the methods use one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more biomarkers from the lightcyan panel in Table 4C.
  • the methods use all of the biomarkers in the lightcyan panel in Table 4C.
  • the methods may use one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, or more biomarkers listed in Tables 4A, 4B, 4C. In some embodiments, the methods may use all of the biomarkers listed in Tables 4A, 4B, 4C.
  • a biomarker score may be significantly greater in a subject likely to develop or already has AECOPD than in a control subject, e.g., a control subject who is in a stable or convalescent state of COPD or without COPD.
  • a biomarker score is calculated based on the weighted contributions of the biomarker genes shown in Tables 4A, 4B, 4C, where the weights are listed in Tables 5A, 5B, 5C and the formula is: biomarker score eight k*biomarkerk.
  • the biomarker score is optimized to detect AECOPD with a sensitivity of at least 70% and/or a specificity of at least 85%.
  • the sensitivity of the biomarkers described herein for diagnosing AECOPD is at least 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 98%.
  • the specificity of the biomarkers described herein for diagnosing AECOPD is at least 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%.
  • the prognostic value or diagnostic accuracy e.g ., the sensitivity and/or specificity for diagnosing AECOPD, the ROC curve, or the area under the curve (AUC) estimate
  • the prognostic value or diagnostic accuracy is greater than using the other markers, e.g, C-reactive protein (CRP).
  • CRP C-reactive protein
  • the biomarkers provide an area under the curve (AUC) of greater than 0.60 (e.g, greater than 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, or 0.95).
  • AUC area under the curve
  • assessment of one or more clinical factors or variables in a subject and/or one or more clinical tests may be combined with the biomarkers (e.g, the genes in Tables 4A, 4B, 4C) analysis in the subject to diagnose AECOPD, track the progession of COPD (i.e., whether it is likely to develop AECOPD), and/or monitor treatment effectiveness.
  • a spirometry test may be used to test a subject’s lung function. Other lung function tests include measurement of lung volumes, diffusing capacity and pulse oximetry.
  • a chest X-ray and/or a CT scan may also be performed to detect, e.g, emphysema.
  • arterial blood gas analysis may be performed to measure oxygen delivery from the lungs into the blood and the removal of carbon dioxide.
  • One or more clinical factors in a subject may be assessed to aid in providing a prognosis, diagnosis, and/or monitoring of AECOPD in a subject.
  • relevant clinical factors or variables that may aid in providing a prognosis include, but are not limited to, forced expiratory volume in 1 second (FEV1) ⁇ 60% predicted, FEVl/forced vital capacity (FVC) ⁇ or equal to 70%, acute increase in dyspnea, sputum volume, and/or sputum purulence without an alternative explanation.
  • FEV1 forced expiratory volume in 1 second
  • FVC FVC
  • the expression level of one or more biomarkers e.g ., the genes in Tables 4A, 4B, 4C described herein may be indicated as a value.
  • a value can be one or more numerical values resulting from evaluation of a sample (e.g., a blood sample) obtained from a subject having COPD.
  • the values can be obtained, for example, by experimentally obtaining measures from the sample by an assay performed in a laboratory, or alternatively, obtaining a dataset (e.g, gene sequencing data) from a service provider such as a laboratory, or from a database or a server on which the dataset has been stored, e.g, on a storage memory.
  • the biomarker’s expression level can be included in a dataset (e.g, gene sequencing data) associated with a sample (e.g, a blood sample) obtained from a subject.
  • the dataset may include the relative expression level of the biomarker (e.g, the genes in Tables 4A, 4B, 4C) in the sample compared to a control sample (e.g, a control sample obtained from a control subject who is in a stable or convalescent state of COPD or without COPD).
  • Examples of assays for detecting biomarkers include, but are not limited to, microarray assays, quantitative polymerase chain reaction (qPCR), reverse transcription qPCR (RT-qPCR), direct hybridization, NanoString nCounter® technology, and/or sequencing assays.
  • NanoString nCounter® technology is described in Cesano, A. nCounter® PanCancer Immune Profiling Panel (NanoString Technologies, Inc., Seattle, WA). J. Immunotherapy Cancer 3, 42 (2015), and in U.S.
  • a subject sample e.g, a blood sample obtained from a subject
  • measuring the amount of the polynucleotides may be accomplished by using one or more of the assays described herein.
  • the information from the assay can be quantitative and sent to a computer system.
  • the information can also be qualitative, such as observing patterns or fluorescence, which can be translated into a quantitative measure by a user or automatically by a reader or computer system.
  • the subject can also provide information other than assay information to a computer system, such as race, height, weight, age, sex, family medical history and any other information that may be useful to a user, such as a clinical factor or variable described herein.
  • information other than assay information such as race, height, weight, age, sex, family medical history and any other information that may be useful to a user, such as a clinical factor or variable described herein.
  • the expression level of a biomarker e.g ., a gene in Tables 4A, 4B, 4C
  • a sample e.g., a blood sample
  • the expression level of a biomarker may be determined using sequencing assays that target the biomarker.
  • sequencing assays include, but are not limited to, single-molecule real time sequencing, ion semiconductor sequencing, pyrosequencing, sequencing by synthesis, sequencing by bridge amplification, sequencing by ligation, nanopore sequencing, chain termination sequencing, massively parallel signature sequencing, polony sequencing, heliscope single molecule sequencing, shotgun sequencing, SOLiD sequencing, Illumina sequencing, tunneling currents DNA sequencing, sequencing by hybridization, sequencing with mass spectrometry, microfluidic Sanger sequencing, and oligonucleotide extension sequencing.
  • One or more biomarkers in Tables 4A, 4B, 4C may be targeted by a sequencing assay.
  • kits can be made that contain reagents that can be used to quantify the biomarker(s) (e.g, the genes in Tables 4A, 4B, 4C) of interest.
  • the disclosure includes kits for detecting at least one biomarker (e.g, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more biomarkers) selected from Tables 4A, 4B, 4C in a subject sample (e.g, a blood sample) obtained from a subject having COPD.
  • kits may be designed for detecting one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more) biomarkers in the salmon panel of Table 4A. In some embodiments, the kits may be designed for detecting one or more (e.g, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more) biomarkers in the green panel of Table 4B.
  • kits may be designed for detecting one or more (e.g, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more) biomarkers in the lightcyan panel of Table 4C.
  • one or more e.g, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more
  • kits may include (i) a plurality of reagents for detecting at least one (e.g, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more) biomarker selected from Tables 4A, 4B, 4C; (ii) a positive control sample; and (iii) instructions for using the plurality of reagents to detect the biomarker.
  • the instructions include instructions for conducting a gene sequencing assay.
  • kits may provide a prognostic and/or diagnostic accuracy having a sensitivity of at least 70% (e.g, at least 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 98%).
  • the kits may provide a prognostic and/or diagnostic accuracy having a specificity of at least 85% (e.g., at least 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100%).
  • Reagents for detecting biomarkers in a sample may include, for example, lysis reagents for disrupting cells in the sample, reagents for extracting genetic material (e.g, nucleic acid binding beads), reagents for amplifying the genetic material using PCR (e.g, a forward primer, a reverse primer, a polymerase, dNTP mix, and amplification buffers), and reagents for performing nucleic acid or gene sequencing assays.
  • genetic material e.g, nucleic acid binding beads
  • reagents for amplifying the genetic material using PCR e.g, a forward primer, a reverse primer, a polymerase, dNTP mix, and amplification buffers
  • PCR e.g, a forward primer, a reverse primer, a polymerase, dNTP mix, and amplification buffers
  • the reagents include flow cells, nucleotides, oligonucleotides, primers, nucleic acid adaptors, protein adaptors, sequencing barcodes, reverse transcriptase, DNA polymerase, ligase, luciferase, end repair enzymes, excision enzymes, DNA purification reagents (e.g, clean-up reagents, filtration columns), DNA
  • fragmentation reagents or tools e.g, enzymes, beads
  • affinity tags e.g., fluorophores
  • substrates for DNA binding or capture e.g, beads
  • hybridization buffers e.g, PCR buffer, other buffers (e.g, containing salts, detergents or alcohol).
  • the disclosure also includes computer-implemented methods that include: storing, in a storage memory, a dataset associated with a subject sample (e.g, a blood sample) obtained from a subject having COPD; and analyzing, by a computer processor, the dataset (e.g, gene sequencing data) to determine the expression level of at least one biomarker (e.g, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more biomarkers) selected from Tables 4A, 4B, 4C, wherein the expression level of the biomarker in the subject sample indicates whether the subject is likely or unlikely to develop AECOPD or whether the subject already has AECOPD.
  • a subject sample e.g, a blood sample obtained from a subject having COPD
  • the dataset e.g, gene sequencing data
  • the computer-implemented methods described herein may also store and analyze a dataset (e.g, gene sequencing data) to determine the expression level of one or more biomarkers from Tables 4 A, 4B, 4C, wherein the expression level from the one or more biomarkers in the subject sample indicates whether the subject is likely or unlikely to develop AECOPD or whether the subject already has AECOPD.
  • a dataset e.g, gene sequencing data
  • Also described herein are systems including: a storage memory for storing a dataset (e.g, gene sequencing data) associated with a subject sample (e.g., a blood sample) obtained from a subject having COPD; and a processor communicatively coupled to the storage memory for analyzing the dataset to determine the expression level of at least one biomarker (e.g ., one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more biomarkers) selected from Tables 4A, 4B, 4C, wherein the expression level of the biomarker in the subject sample indicates whether the subject is likely or unlikely to develop AECOPD or whether the subject already has AECOPD.
  • a biomarker e.g., one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more biomarkers
  • the systems described herein may also include a storage memory and a processor for storing and analyzing a dataset (e.g., gene sequencing data) to determine the expression level of one or more biomarkers from Tables 4A, 4B, 4C, wherein the expression level from the one or more biomarkers in the subject sample indicates whether the subject is likely or unlikely to develop AECOPD or whether the subject already has AECOPD.
  • a dataset e.g., gene sequencing data
  • the disclosure further includes computer-readable storage media storing computer- executable program code that includes: program code for storing a dataset (e.g, gene sequencing data) associated with a subject sample (e.g, a blood sample) obtained from a subject having COPD; and program code for analyzing the dataset to determine the expression level of at least one biomarker (e.g, one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, or more biomarkers) selected from Tables 4 A, 4B, 4C, wherein the expression level of the biomarker in the subject sample indicates whether the subject is likely or unlikely to develop AECOPD or whether the subject already has AECOPD.
  • a dataset e.g, gene sequencing data
  • a subject sample e.g, a blood sample obtained from a subject having COPD
  • program code for analyzing the dataset to determine the expression level of at least one biomarker (e.g, one, two, three, four, five, six, seven, eight, nine,
  • the computer-readable storage media storing computer-executable program code may also include program codes for storing and analyzing a dataset (e.g, gene sequencing data) to determine the expression level of at least one biomarker selected from Tables 4A, 4B, 4C, wherein the expression level of the biomarker in the subject sample indicates whether the subject is likely or unlikely to develop AECOPD or whether the subject already has AECOPD.
  • a dataset e.g, gene sequencing data
  • a computer comprises at least one processor coupled to a chipset.
  • a memory, a storage device, a keyboard, a graphics adapter, a pointing device, and a network adapter can be coupled to the chipset.
  • a display is coupled to the graphics adapter.
  • the functionality of the chipset is provided by a memory controller hub and an I/O controller hub.
  • the memory is coupled directly to the processor instead of the chipset.
  • the storage device is any device capable of holding data, like a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device.
  • the memory holds instructions and data used by the processor.
  • the pointing device may be a mouse, track ball, or other type of pointing device, and is used in combination with the keyboard to input data into the computer system.
  • the graphics adapter displays images and other information on the display.
  • the network adapter couples the computer system to a local or wide area network.
  • a computer can have different and/or other components than those described previously.
  • the computer can lack certain components.
  • the storage device can be local and/or remote from the computer (such as embodied within a storage area network (SAN)).
  • SAN storage area network
  • module refers to computer program logic utilized to provide the specified functionality.
  • a module can be implemented in hardware, firmware, and/or software.
  • program modules are stored on the storage device, loaded into the memory, and executed by the processor.
  • Embodiments of the entities described herein can include other and/or different modules than the ones described here.
  • the functionality attributed to the modules can be performed by other or different modules in other embodiments.
  • this description occasionally omits the term "module" for purposes of clarity and convenience.
  • the above methods further comprise providing a course of treatment based on the results of the prognostic methods.
  • the course of treatment comprises short-acting beta2-agonists, such as albuterol; anticholinergic bronchodilators, such as ipratropium bromide; methylxanthines such as aminophylline and theophylline; long- acting bronchodilators; oral steroids such as prednisone and methylprednisone, expectorants, oxygen therapy, and/or antibiotics if indicated for a lung infection.
  • antibiotics include, for mild to moderate exacerbations:
  • Amoxicillin-clavulanate potassium(Augmentin) one 500 mg/125 mg tablet three times daily or one 875 mg/125 mg tablet twice daily
  • Clarithromycin (Biaxin), 500 mg twice daily
  • Azithromycin (Zithromax), 500 mg initially, then 250 mg daily
  • Cefotaxime (Claforan), 1 g IV every 8 to 12 hours
  • Ticarcillin-clavulanate potassium (Timentin), 3.1 g IV every 4 to 6 hours
  • Tobramycin 1 mg per kg IV every 8 to 12 hours, or 5 mg per kg IV daily.
  • This Example describes the development of biomarkers that can distinguish AECOPD from a convalescent state.
  • ECLIPSE Study To discover blood-based biomarkers that are predictive of imminent exacerbation, we used PAXgene blood collected from patients with COPD who participated in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study (3). The present biomarker study using the ECLIPSE samples was approved by the University of British Columbia Research Ethics Board number (HI 1-00786).
  • Rapid Transition Cohort We reasoned that predictive biomarkers identified in the ECLIPSE discovery subcohort should also be differentially expressed in blood samples of patients actively exacerbating, compared to stable COPD.
  • RTP Rapid Transition Patient
  • RTP included patients admitted for AECOPD to Vancouver General Hospital (VGH) or St. Paul’s Hospital (SPH) in Vancouver, Canada, who consented to be part of the study.
  • VGH General Hospital
  • SPH St. Paul’s Hospital
  • the diagnosis of AECOPD was based on clinical acumen of the physicians treating these patients (board-certified pulmonologists or general internists), and confirmed by an independent board-certified pulmonologist who did not participate in the care of these patients.
  • FIG. 1A An overview of the study cohorts and subjects’ blood collection, relative to the exacerbation timelime, is shown in FIG. 1A. How each study cohort was used with respect to biomarker discovery and replication is shown in FIG. IB. The sample selection process for the ECLIPSE cohort is depicted in FIG. 1C.
  • Human Gene 1.1 ST 96-well array plates (Affymetrix, United States) were used to measure mRNA abundance, and this was carried out at The Scripps Research Institute DNA Array Core Facility (TSRI; La Jolla, CA). Samples were pseudo-randomly assigned to plates to prevent confounding of phenotype with plate effects.
  • T-distributed Stochastic Neighbor Embedding (t-SNE)(9) was used to visualize the normalized data in a 2-dimensional representation to highlight local structures and patterns. This was used to check for the resolution of batch effects and observe any interesting patterns, especially with respect to exacerbation phenotypes.
  • transcript cluster-level data was summarized at the gene level using the most recent Human Gene 1.1 ST transcript cluster annotations (v36). Unannotated transcripts and multi-mapping transcripts were removed. Genes with multiple transcripts assigned to them were given a value equal to the average of their corresponding transcripts. The result of this summarization is referred to as the gene expression data.
  • Module-Based Biomarker Discovery There were 20 imminent exacerbators (IE; patients who exacerbated within 60 days post-blood draw) and 122 non-exacerbators (NE; patients who were exacerbation-free for >365 days post-blood draw) in the second ECLIPSE subcohort. These subjects’ data were not previously used to derive the co-expression modules. Differential gene expression between IE and NE patients was assessed on a per-module basis using the moderated t- test provided by the Linear Models for Microarray Data (LIMMA)(16) package. Genes with p ⁇ 0.05 were selected as candidate genes.
  • LIMMA Linear Models for Microarray Data
  • AUC Area under the receiver operating characteristics curve
  • Biomarker Panel Selection We selected the best biomarker panels using a two-step process. First, we identified biomarker panels with fewer than 50 genes(18) and those with cross- validated AUC over 0.65 for discriminating between IE and NE. Next, we applied these biomarker panels to the first RTP subcohort (168 subjects), which included 78 subjects with samples at time of AECOPD and 53 stable COPD subjects. This was without any modification to or refinement of the biomarker panels (i.e. “off-the-shelf’ by using the same biomarker formula). From this analysis, we identified the biomarker panels with the best off-the-shelf AUCs. Additionally, we looked for patterns consistent with our hypothesis that a true signature of COPD disease activity should rise with upcoming AECOPD, peak during onset, then fall during convalescence and remain low during periods of stability.
  • Biomarker Panel Replication The top biomarker panels were replicated in the second RTP subcohort, which included 209 subjects with samples at time of AECOPD and 67 stable COPD subjects. This cohort was non-overlapping with biomarker selection. We evaluated the success of the replication using off-the-shelf AUCs and visual assessment.
  • This study used 226 stable COPD subjects from the ECLIPSE cohort to derive gene co expression modules, 142 subjects from the ECLIPSE cohort (non-overlapping with the 226) to identify predictive biomarkers of AECOPD, 168 subjects from the RTP cohort to select the most promising biomarker panels of disease activity, and 371 (non-overlapping with the 168) additional subjects from the RTP cohort to replicate the top biomarker panels.
  • the demographics of these study populations are shown in Table 1.
  • FIGS. 5A and 5B As we were unable to relate this cluster to any of the phenotypes, clinical variables, or known sources of technical variation (e.g ., location on plate), and the cluster of samples was small enough, we opted to exclude them from downstream analyses.
  • the resulting expression data consisted of 19,245 transcripts with unique gene symbols (hereafter“genes”).
  • WGCNA identified 23 distinct modules of co-expressed genes in the 226 ECLIPSE subjects. The sizes of these modules and their biological annotations are included in Table 2. For each of the modules identified, we carried out differential gene expression analysis in 20 IE versus 122 NE from the ECLIPSE cohort, and built classifier panels using elastic net regression. We obtained out-of-sample AUC estimates of each of the resulting classifiers via stratified 10-fold cross-validation. The results of these biomarker discovery analyses are shown in Table 3. A total of six biomarker panels with ⁇ 50 genes had cross-validated AUC >0.65.
  • the 95% confidence interval for the AUC was calculated empirically for the biomarker discovery, using cross-validation performance.
  • the 95% confidence intervals for biomarker selection and replication were calculated by bootstrapping the out-of-sample probabilities with 1000 iterations per panel.
  • BTM modules T cell activation (I) (M7.1) and T cell activation (III) (M7.4), enriched in T cells (I) (M7.0) and enriched in T cells (II) (M223), T cell differentiation (M14), T cell differentiation (Th2) (M19), T cell surface signature (SO), cell adhesion (GO) (Ml 17), receptors, cell migration (Ml 09), and IL2, IL7, TCR network (M65)).
  • BTM modules T cell activation (I) (M7.1) and T cell activation (III) (M7.4), enriched in T cells (I) (M7.0) and enriched in T cells (II) (M223), T cell differentiation (M14), T cell differentiation (Th2) (M19), T cell surface signature (SO), cell adhesion (GO) (Ml 17), receptors, cell migration (Ml 09), and IL2, IL7, TCR network (M65)).
  • the salmon module was additionally enriched in natural killer (NK) cell-specific BTMs (e.g, BTM modules: enriched in NK cells (I) (M7.2), enriched in NK cells (III) (Ml 57), and enriched in NK cells (receptor activation) (M61.2)).
  • BTM modules enriched in NK cells (I) (M7.2), enriched in NK cells (III) (Ml 57), and enriched in NK cells (receptor activation) (M61.2)
  • NK cells natural killer cell-specific BTMs
  • BTM modules enriched in NK cells (I) (M7.2), enriched in NK cells (III) (Ml 57), and enriched in NK cells (receptor activation) (M61.2)
  • NK cells receptor activation
  • AECOPD Diagnosis of AECOPD is largely subjective and symptom-based, but symptoms tend to be non-specific and overlap with those of other co-morbidities(19). Even when correctly diagnosed, treatment is often not informed by the underlying etiology. Patients are often over- or under-treated, leading to significant morbidity and mortality(20). Prognosing and preventing AECOPD episodes is an important primary care goal(21), but, currently, no clinically useful test exists capable of prognosing short term AECOPD risk(22).
  • the three co-expression modules also tell a biological story.
  • the green module was enriched in genes related to the unfolded protein response and endoplasmic reticulum stress, a hallmark of viral infection(27), while the salmon and lightcyan modules reflected T-cell recruitment, activation, and differentiation.
  • the salmon module was additionally enriched in NK cell-specific genes. Taken together, these modules appear consistent with host response to viral infection, which are present in 22-64% of AECOPD(28) and have been causally linked to AECOPD(29).
  • Bafadel et al. reported on a number of potential single-molecule biomarkers for discriminating between AECOPD and stable COPD, and between bacterial, viral, and eosinophilic AECOPD and stable COPD(48). Their independent validation showed AUCs ranging 0.65 - 0.73 for distinguishing bacterial or viral AECOPD from stable COPD, but they found no single biomarker capable of discriminating between general AECOPD and stable COPD with AUC >0.70. Our biomarker panels achieve replication AUCs ranging 0.74 - 0.84 on this task.
  • Copeptin has been associated with disease severity and outcomes in COPD and may be more specific, at least relative to heart failure(51-53).
  • Carvalho BS Irizarry RA. A framework for oligonucleotide microarray preprocessing.
  • Soluble urokinase-type plasminogen activator receptor is a novel biomarker predicting acute exacerbation in COPD.

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Abstract

L'invention concerne des procédés et des compositions pour pronostiquer et/ou diagnostiquer des exacerbations aiguës de la bronchopneumopathie chronique obstructive (BPCO) chez un sujet. L'expression d'un ou de plusieurs biomarqueurs dans un panel de biomarqueurs est utilisée pour déterminer un score de biomarqueur, un score de biomarqueur supérieur dans un échantillon de sujet par comparaison avec un échantillon témoin indiquant que le sujet présente ou est susceptible de développer une BPCO. L'invention concerne également des méthodes de traitement d'une BPCO sur la base du score de biomarqueur.
PCT/IB2020/052828 2019-03-26 2020-03-25 Procédés et compositions pour surveiller l'exacerbation aiguë de la bpco WO2020194211A1 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114410772A (zh) * 2021-01-28 2022-04-29 中国医学科学院北京协和医院 慢阻肺急性加重易感基因及其在预测易感慢阻肺急性加重中的应用
CN114822827A (zh) * 2022-05-30 2022-07-29 北京大学第三医院（北京大学第三临床医学院） 一种慢性阻塞性肺疾病急性加重预测***和预测方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101274835B1 (ko) * 2011-01-03 2013-06-17 순천향대학교 산학협력단 만성 폐쇄성 폐질환의 급성 악화 진단용 바이오마커 조성물 및 바이오마커 검출 방법
WO2016168565A1 (fr) * 2015-04-16 2016-10-20 President And Fellows Of Harvard College Procédés pour le traitement de la broncho-pneumopathie chronique obstructive et/ou de surveillance du traitement
WO2016185385A1 (fr) * 2015-05-18 2016-11-24 The University Of British Columbia Procédés et systèmes permettant de détecter des biomarqueurs de protéine de plasma pour diagnostiquer une exacerbation aiguë de la broncho-pneumopathie chronique obstructive

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101274835B1 (ko) * 2011-01-03 2013-06-17 순천향대학교 산학협력단 만성 폐쇄성 폐질환의 급성 악화 진단용 바이오마커 조성물 및 바이오마커 검출 방법
WO2016168565A1 (fr) * 2015-04-16 2016-10-20 President And Fellows Of Harvard College Procédés pour le traitement de la broncho-pneumopathie chronique obstructive et/ou de surveillance du traitement
WO2016185385A1 (fr) * 2015-05-18 2016-11-24 The University Of British Columbia Procédés et systèmes permettant de détecter des biomarqueurs de protéine de plasma pour diagnostiquer une exacerbation aiguë de la broncho-pneumopathie chronique obstructive

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114410772A (zh) * 2021-01-28 2022-04-29 中国医学科学院北京协和医院 慢阻肺急性加重易感基因及其在预测易感慢阻肺急性加重中的应用
CN114822827A (zh) * 2022-05-30 2022-07-29 北京大学第三医院（北京大学第三临床医学院） 一种慢性阻塞性肺疾病急性加重预测***和预测方法
CN114822827B (zh) * 2022-05-30 2023-06-02 北京大学第三医院（北京大学第三临床医学院） 一种慢性阻塞性肺疾病急性加重预测***和预测方法

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