WO2020194211A1 - Methods and compositions for monitoring acute exacerbation of copd - Google Patents

Abstract

Described are methods and compositions for prognosing and/or diagnosing acute exacerbations of chronic obstructive pulmonary disease (AECOPD) in a subject. The expression of one or more biomarkers in a biomarker panel is used to determine a biomarker score, where a higher biomarker score in a subject sample compared to a control sample indicates that the subject has or is likely to develop AECOPD. Also provided are methods of treating AECOPD based on the biomarker score.

Description

METHODS AND COMPOSITIONS FOR MONITORING ACUTE

EXACERBATION OF COPD

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to U.S. Provisional Application No. 62/823,867, filed on March 26, 2019, which is hereby incorporated by reference in its entirety.

SEQUENCE LISTING

[0002] The instant application contains a Sequence Listing which has been submitted

electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on February 25, 2020, is named 097513-1179445_(000610WO)_SL.txt and is 158,953 bytes in size.

BACKGROUND OF THE INVENTION

[0003] Chronic obstructive pulmonary disease (COPD) affects approximately 328 million individuals worldwide(l). In 2013, it was the 4th leading cause of death, accounting for 2.9 million deaths or approximately 5% of the global mortality (2). Most of these deaths occur during periods of acute worsening of symptoms, which are called acute exacerbations of COPD (AECOPD). Currently, there are no clinical tools to prognose AECOPDs and diagnosis is based on clinical acumen. Although AECOPDs are diagnosed as discrete events, in reality, they most likely reflect in part underlying physiological“disease activity” of patients. Most AECOPDs are diagnosed when the symptoms are severe and require urgent medical attention. By this time, some patients require emergency room visit or hospitalization. It would be desirable to detect AECOPDs earlier to enable interventions before patients require urgent care.

BRIEF SUMMARY OF THE INVENTION

[0004] 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.

[0005] In a first aspect, 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;

wherein 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), ZNF827 (SEQ ID NO 23), KMT2A (SEQ ID NO 24), CBLB (SEQ ID NO 25), CCL28 (SEQ ID NO 26), TMEM116 (SEQ ID NO 27), TRAF5 (SEQ ID NO 28), CD3E (SEQ ID NO 29), DCAF4 (SEQ ID NO 30), ITK (SEQ ID NO 31), TET1 (SEQ ID NO 32), SKAP1 (SEQ ID NO 33), GOSR2 (SEQ ID NO 34), and RORA (SEQ ID NO 35).

[0006] In some embodiments, the the biomarker score is determined based on the weighted contributions of the biomarkers in the panel using the formula: biomarker score = intercept + å ₌₁ weight k*biomarkerk.

[0007] In some embodiments, 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.

[0008] In some embodiments, 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.

[0009] In some embodiments, 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.

[0010] In some embodiments, 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.

[0011] In some embodiments, 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%. [0012] In some embodiments, the method further comprises providing a course of treatment based on the prognosis and/or diagnosis. In some embodiments, the course of treatment is selected from short-acting beta2-agonists, long-acting bronchodilators, oral steroids, oxygen therapy, and/or antibiotics.

[0013] In another aspect, a kit for detecting a panel of biomarkers in a blood sample obtained from a subject having COPD is provided, the kit comprising:

(i) a plurality of reagents for detecting the panel of biomarkers in claim 1;

(ii) a positive control sample; and

(iii) instructions for using the plurality of reagents to detect the biomarker.

[0014] In some embodiments, the instructions comprise instructions for conducting a gene sequencing assay.

[0015] In another aspect, a method of treating acute exacerbation of chronic obstructive pulmonary disease (AECOPD) in a subject is provided, the method 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;

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; and

b) treating the AECOPD by administering a course of treatment to the subject.

[0016] In some embodiments, the course of treatment is selected from short-acting beta2- agonists, long-acting bronchodilators, oral steroids, oxygen therapy, and/or antibiotics. In some embodiments, the biomarker score is determined based on the weighted contributions of the biomarkers in the panel using the formula: biomarker score = intercept +

weight k*biomarkerk. In some embodiments, 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.

[0017] In another aspect, 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. [0018] In some embodiments, 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.

[0019] In some embodiments, 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 (SEQ ID NO 22), ZNF827 (SEQ ID NO 23), KMT2A (SEQ ID NO 24), CBLB (SEQ ID NO 25), CCL28 (SEQ ID NO 26), TMEM116 (SEQ ID NO 27), TRAF5 (SEQ ID NO 28), CD3E (SEQ ID NO 29), DCAF4 (SEQ ID NO 30), ITK (SEQ ID NO 31), TET1 (SEQ ID NO 32), SKAPl (SEQ ID NO 33), GOSR2 (SEQ ID NO 34), and RORA (SEQ ID NO 35).

[0020] In some embodiments, 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.

[0021] In some embodiments, 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.

[0022] In some embodiments, 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 bionarkers 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. 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. In some embodiments, the formula for determining a biomarker score is: biomarker score = intercept + å ₌₁ weight k*biomarkerk. [0023] In some embodiments, 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.

[0024] In some embodiments, 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), OR7E14P (SEQ ID NO 22), ZNF827 (SEQ ID NO 23), KMT2A (SEQ ID NO 24), CBLB (SEQ ID NO 25), CCL28 (SEQ ID NO 26), TMEM116 (SEQ ID NO 27), TRAF5 (SEQ ID NO 28), CD3E (SEQ ID NO 29), DCAF4 (SEQ ID NO 30), ITK (SEQ ID NO 31), TET1 (SEQ ID NO 32), SKAPl (SEQ ID NO 33), GOSR2 (SEQ ID NO 34), and RORA (SEQ ID NO 35).

[0025] In some embodiments, 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.

[0026] In some embodiments, 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.

[0027] In some embodiments, 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.

[0028] In some embodiments, 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.

[0029] In some embodiments, 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. In particular embodiments, 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.

[0030] In some embodiments of the methods described herein, 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.

[0031] In some embodiments of the methods, 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%).

[0032] The methods described herein may be implemented using one or more computers.

[0033] In some embodiments, the methods further include obtaining the subject sample from the subject. The subject sample may be a blood sample.

[0034] In some embodiments, the methods further include providing a course of treatment based on the prognosis and/or diagnosis. In some embodiments, the course of treatment includes administering a thereapeutically effective amount of a drug or pharmaceutical agent to the subject. In some embodiments, the drug or pharmaceutical agent is selected from short-acting beta2- agonists, long-acting bronchodilators, oral steroids, oxygen therapy, and/or antibiotics.

[0035] 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.

[0036] The 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.

[0037] In another aspect, 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. In some embodiments, 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). In some embodiments, the instructions comprise instructions for conducting a gene sequencing assay. In some embodiments, 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%).

[0038] In another aspect, 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. In some embodiments, the reagents comprise gene sequencing reagents targeting at least one biomarker. In some embodiments, 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).

[0039] In another aspect, 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.

[0040] In another aspect, 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.

[0041] In yet another aspect, 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.

[0042] These and other features of the present teachings will become more apparent from the description herein. While the present teachings are described in conjunction with various embodiments, it is not intended that the present teachings be limited to such embodiments. On the contrary, the present teachings encompass various alternatives, modifications, and equivalents, as will be appreciated by those of skill in the art.

BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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. In our disease activity model of AECOPD, 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.

[0044] 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.

[0045] FIG. 1C. Subject selection for the ECLIPSE cohort. The division of the ECLIPSE blood samples we received into the module discovery and biomarker discovery subcohorts was on the basis of timepoint. Over a year elapsed between the microarray analyses of the two subcohorts, and they were clinically different. The month 3 samples included many subjects who had exacerbated more than twice in the previous year (“frequent exacerbator”), which is known to predict future exacerbation. The year 1 samples were chosen such that subjects had no more than one exacerbation in the previous year, and thus, were a more optimal cohort for finding useful blood-based markers to prognose AECOPD.

[0046] 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).

[0047] 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)

[0048] 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.

DEFINITIONS

[0050] Most of the words used in this specification have the meaning that would be attributed to those words by one skilled in the art. Words specifically defined in the specification have the meaning provided in the context of the present teachings as a whole, and as are typically understood by those skilled in the art. In the event that a conflict arises between an art-understood definition of a word or phrase and a definition of the word or phrase as specifically taught in this specification, the specification shall control.

[0051] As used in the specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0052] Terms used in the claims and specification are defined as set forth below unless otherwise specified.

[0053] "Marker," "markers," "biomarker," or "biomarkers," 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. In particular embodiments, 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.

[0054] 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). In some aspects, 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.

[0055] The term“prognosing” refers to an act of predicting or forecasting the likely occurrence of a disease or ailment ( e.g ., AECOPD) in a subject. The disclosure provides methods that may be used for prognosing whether the subject is likely to develop AECOPD. In some embodiments, a prognosis may indicate that the subject having COPD is likely or unlikely to develop AECOPD. As described herein, 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.

[0056] 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.

[0057] A "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. The term "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.

[0058] In particular aspects, the sample is a blood sample from the subject.

[0059] 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.

[0060] In some embodiments, 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.

[0061] "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.

[0062] The term "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. For example, "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 (e.g, a gene listed in Tables 4A, 4B, 4C) 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.

[0063] The term“receiver operating characteristic” (ROC) refers to the performance of a classifier system as its discrimination threshold is varied.

DETAILED DESCRIPTION OF THE INVENTION

[0064] Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) result in considerable morbidity and mortality. While early diagnosis of AECOPD could potentially prevent long-standing complications, a blood-based biomarker for AECOPD has yet to be developed for clinical practice. Described herein are methods and compositions useful for diagnosing AECOPD, as well as distinguishing AECOPD from stable or convalescent clinical states of COPD. In some embodiments, the biomarkers are genes (e.g, genes listed in Tables 4A, 4B, 4C), for example, genes present in a blood sample obtained from a subject.

[0065] Described herein are 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. As described in detail in the Examples, 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. These modules were used for biomarker discovery of imminent exacerbators (defined as those experiencing an exacerbation within 60 days after blood draw) versus non-exacerbators (defined as those with no exacerbations for 365+ days after blood draw); and during an active exacerbation versus during stable periods. The performance of the discovered biomarkers was further replicated in an independent cohort.

[0066] Twenty-three gene modules were identified, and three of these modules yielded biomarker panels ( e.g ., the three biomarker panels, salmon, green, and lightcyan, listed in Tables 4A, 4B, 4C) that discriminated: (a) imminent exacerbators from non-exacerbators with an area under the receiver operating characteristics curve (AUC) ranging 0.79 - 0.98 in the discovery cohort; (b) between exacerbation and stable disease states, when tested off-the-shelf in an independent cohort, with an AUC of 0.76 - 0.82; and (c) between exacerbation and stable disease states, also confirmed in a large independent cohort, with an AUC of 0.74 - 0.84. 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.

METHODS FOR PROGNOSING, DIAGNOSING, AND/OR MONITORING AECOPD

[0067] 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. [0068] Tables 4A, 4B, 4C lists three panels of biomarkers (e.g. , salmon, green, and lightcyan panels) that may be used in the methods. In some embodiments of 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. In some embodiments, 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.

[0069] In some embodiments of the methods, 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. In some embodiments, 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.

[0070] In some embodiments of the methods, 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. In some embodiments, 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. In some embodiments, the methods use all of the biomarkers in the lightcyan panel in Table 4C.

[0071] In other embodiments, 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.

[0072] In methods described herein, 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. In some embodiments, 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. In some embodiments, the biomarker score is optimized to detect AECOPD with a sensitivity of at least 70% and/or a specificity of at least 85%. In some embodiments, 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%. In some embodiments, 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%. In some embodiments, 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) of assays that use the biomarkers described herein (e.g., biomarkers listed in Tables 4A, 4B, 4C) is greater than using the other markers, e.g, C-reactive protein (CRP).

[0073] In some embodiments, 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).

CLINICAL TESTS AND FACTORS

[0074] In some embodiments of the methods described herein, 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. Moreover, arterial blood gas analysis may be performed to measure oxygen delivery from the lungs into the blood and the removal of carbon dioxide.

[0075] 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. Examples of 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. [0076] 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.

[0077] 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. In some embodiments, 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).

ASSAYS

[0078] Examples of assays for detecting biomarkers (e.g, the genes in Tables 4A, 4B, 4C) 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. Patent Publications US2003/0013091, US2007/0166708, US2010/0015607, US2010/0261026, US2010/0262374, US2010/0112710, US2010/0047924, US2014/0371088, US2011/0086774, and US2020/0040385, which are incorporated by reference herein. In some embodiments of the methods described herein, 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 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. In some embodiments, measuring the amount of the polynucleotides may be accomplished by using one or more of the assays described herein. [0079] 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.

In one embodiment, 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.

[0080] In some embodiments, the expression level of a biomarker ( e.g ., a gene in Tables 4A, 4B, 4C) in a sample (e.g., a blood sample) may be determined using sequencing assays that target the biomarker. Examples of 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

[0081] In one aspect, 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. 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 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. 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 lightcyan panel of Table 4C.

[0082] In some embodiments, the 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. In some embodiments, the instructions include instructions for conducting a gene sequencing assay. In particular embodiments, the 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%). In some embodiments, 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%).

[0083] 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. In some embodiments, 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).

COMPUTER IMPLEMENTATION

[0084] 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.

[0085] In some embodiments, 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.

[0086] 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.

[0087] In some embodiments, 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.

[0088] 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.

[0089] In some embodiments, 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.

[0090] In one embodiment, 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. In some embodiments, a display is coupled to the graphics adapter. In one embodiment, the functionality of the chipset is provided by a memory controller hub and an I/O controller hub. In another embodiment, the memory is coupled directly to the processor instead of the chipset.

[0091] 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.

[0092] As is known in the art, a computer can have different and/or other components than those described previously. In addition, the computer can lack certain components. Moreover, the storage device can be local and/or remote from the computer (such as embodied within a storage area network (SAN)).

[0093] As is known in the art, the computer is adapted to execute computer program modules for providing functionality described herein. As used herein, the term "module" refers to computer program logic utilized to provide the specified functionality. Thus, a module can be implemented in hardware, firmware, and/or software. In one embodiment, program modules are stored on the storage device, loaded into the memory, and executed by the processor.

[0094] Embodiments of the entities described herein can include other and/or different modules than the ones described here. In addition, the functionality attributed to the modules can be performed by other or different modules in other embodiments. Moreover, this description occasionally omits the term "module" for purposes of clarity and convenience.

TREATMENTS

[0095] In some embodiments, the above methods further comprise providing a course of treatment based on the results of the prognostic methods. In some embodiments, 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.

[0096] Examples of antibiotics include, for mild to moderate exacerbations:

Doxy cy cline (Vibramycin), 100 mg twice daily

Trimethoprim-sulfamethoxazole (Bactrim DS, Septra DS), one tablet twice daily

Amoxicillin-clavulanate potassium(Augmentin), one 500 mg/125 mg tablet three times daily or one 875 mg/125 mg tablet twice daily

Macrolides:

Clarithromycin (Biaxin), 500 mg twice daily

Azithromycin (Zithromax), 500 mg initially, then 250 mg daily

Fluoroquinolones:

Levofloxacin (Levaquin), 500 mg daily Gatifloxacin (Tequin), 400 mg daily

Moxifloxacin (Avelox), 400 mg daily.

[0097] For moderate to severe exacerbations:

Cephalosporins:

Ceftriaxone (Rocephin), 1 to 2 g IV daily

Cefotaxime (Claforan), 1 g IV every 8 to 12 hours

Ceftazidime (Fortaz), 1 to 2 g IV every 8 to 12 hours

Antipseudomonal penicillins:

Piperacillin-tazobactam (Zosyn), 3.375 g IV every 6 hours

Ticarcillin-clavulanate potassium (Timentin), 3.1 g IV every 4 to 6 hours

Fluoroquinolones:

Levofloxacin, 500 mg IV daily

Gatifloxacin, 400 mg IV daily

Aminoglycoside:

Tobramycin (Tobrex), 1 mg per kg IV every 8 to 12 hours, or 5 mg per kg IV daily.

EXAMPLES

EXAMPLE 1

[0098] This Example describes the development of biomarkers that can distinguish AECOPD from a convalescent state.

Methods

Study Populations.

[0099] 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).

[0100] We first used 226 ECLIPSE subjects (blood collected at 3 month post-enrollment), who have not been exacerbating at time of blood draw, to identify distinct gene co-expression modules for COPD subjects. Next, we selected 142 subjects with blood collected at 1 year post-enrollment, with <1 AECOPD in the previous year and no recent exacerbation. Of these patients, 20 experienced an AECOPD within 60 days post-blood draw; these patients were defined as “imminent exacerbators” (IE). The other 122 experienced no AECOPD for at least 1 year post- blood draw and were defined as non-exacerbators (NE). This subcohort of 142 subjects was used for biomarker discovery.

[0101] 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. To test this hypothesis, we used clinical data and blood samples obtained in the Rapid Transition Patient (RTP) cohort. Briefly, 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. 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. Blood samples for this study were collected at baseline (day of admission to the hospital for AECOPD) as well as at day 3 and 7 post-admission, at discharge, and at day 30 and 90 post-admission if they were clinically stable at those times (representing convalescence). Patients who were stable and free of AECOPD for at least 1 month were enrolled at the SPH COPD clinic. The RTP study was registered at ClinicalTrials.gov (NCT02050022) and approved by the University of British Columbia Clinical Research Ethics Board (certificate numbers HI 1-00786 and H13-00790). For biomarker selection, we used 249 subject-timepoints (from 168 unique subjects enrolled between July 2012 and October 2014) that were analyzed as a pilot study. An additional 480 subject-timepoints (from 371 unique subjects enrolled between July 2012 and January 2017, independent from 168 subjects) were analyzed later and used for replication.

[0102] 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.

Sample Processing

[0103] All blood samples were collected in PAXgene (PreAnalytix, Switzerland) and EDTA tubes, and stored at -80°C. A complete blood count, including leukocyte differential (CBC/diff) was obtained from the EDTA blood. Total RNA was extracted from PAXgene blood on the QIAcube (Qiagen, Germany), using the PAXgene Blood miRNA kit from PreAnalytix, according to the manufacturer’s instructions. 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.

Statistical Analysis

[0104] Statistical analyses were performed in R(4), using packages sourced from CRAN and Bioconductor(5). Patient demographics were assessed using ANOVA and the Kruskal-Wallis test for continuous variables, and Fisher’s exact test for categorical variables. Unless otherwise noted, we have reported uncorrected p-values and consider p<0.05 to indicate statistical significance.

[0105] Pre-Processing. Raw CEL files were processed using the oligo package(6) to assess quality and perform Robust Multi-array Average normalization^), with summarization at the transcript cluster level. Gene expression data from ECLIPSE and RTP were normalized together to enable comparisons. The ComBat algorithm(8) was used to correct for plate effects, without incorporating any phenotypic information.

[0106] 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.

[0107] To ensure biologically meaningful results, 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.

[0108] Gene Regulatory Module Identification. We carried out an untargeted discovery of co expressed gene modules using the gene expression data of the first 226 ECLIPSE subjects, using Weighted Gene Co-expression Network Analysis (WGCNA)(10). This approach identifies novel pathways and mechanisms specific to COPD, as co-expression may serve as a surrogate for regulatory relationships. Unsigned correlations were used and minimum module size was set at 50 genes.

[0109] Pathway Enrichment Analysis. The gene modules identified by WGCNA were compared to approximately 13,000 annotated gene sets from the Broad Institute’s MSigDB collections(l l, 12), 350 Blood Transcription Modules(13), and 73 tissue-specific gene sets(14). A hypergeometric test was used to identify annotated genes sets that were statistically over- represented in our modules, with correction for multiple testing performed using the Benjamini- Hochberg procedure(15).

[0110] 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. We applied elastic net regularized logistic regression(17) to each list of candidate genes, separately for each module, to build classifiers for IE versus NE. Elastic net selected the genes to be included in the biomarker panel (listed in Table 4A, 4B, 4C) and assigned weights to each (listed in tables 5A, 5B, 5C).

[0111] Area under the receiver operating characteristics curve (AUC) was selected as the primary performance metric, as it summarizes discriminative power independently of decision thresholds.

[0112] 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.

[0113] 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.

Results

[0114] 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. There was a significant difference (p<0.05) in FEV1, FEV1 %predicted, and lymphocytes between IE and NE, and consistent differences between AECOPD and stable COPD subjects with respect to inhaled corticosteroid use, white blood cells, and N-terminal prohormone of brain natriuretic peptide.

Table 1. Study population demographics

continuous variables, analysis of variance was used to calculate p-values for group differences. For categorical variables, Fisher’s exact test was used to calculate p-values for group differences.)

[0115] Normalizing the Human Gene 1.1 ST microarray data and summarizing it at the transcript cluster level produced 33,397 probe sets. T-SNE revealed a small set of samples from both ECLIPSE and RTP cohorts, which clustered separately from the rest of the samples assayed

(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. We then removed un-annotated probe sets (11,041) and transcripts assigned to multiple genes (2,121), then summarized genes with multiple probe set mappings (676). The resulting expression data consisted of 19,245 transcripts with unique gene symbols (hereafter“genes”).

[0116] 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.

Table 2. WGCNA modules and their biological significance

Table 3. Module-based biomarker discovery, biomarker selection, and biomarker replication performance

*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.

[0117] When these six biomarker panels were applied off-the-shelf to the first RTP subcohort, four showed a peak in predicted disease activity at the time of AECOPD followed by gradually decreasing levels at day 3, 30, and 90, and in stable COPD subjects. Of these panels, three (derived from the salmon, green, and lightcyan WGCNA modules) demonstrated AUC >0.75 for discriminating AECOPD from stable COPD. The AUCs and patterns described here can be seen in Table 3 and FIG. 2. Finally, the performance of these three selected biomarker panels was confirmed in the second RTP subcohort in AECOPD, day 30, day 90, and stable COPD samples, with AUCs ranging 0.74 - 0.84 between AECOPD and stable COPD (Table 3, FIG. 3). The genes that comprise these three biomarker panels are listed in Tables 4A, 4B, 4C.

Table 4A. Genes in the top 3 biomarker panels

Table 4B. Genes in the top 3 biomarker panels

Table 4C. Genes in the top 3 biomarker panels

Table 5A. Salmon Panel Weights

Table 5B. Green Panel Weights

Table 5C. Light Cyan Panel Weights

[0118] Biological characterization of these three modules revealed that, while entirely distinct at the gene level, there was a significant functional overlap between the salmon and lightcyan modules, with both modules reflecting T-cell activation and differentiation, as well as recruitment (e.g, 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)). Comparing module membership to cell-specific gene signatures derived from peripheral whole blood(14), we again found the salmon and lightcyan modules to have very similar profiles: significantly enriched for genes characteristic of CD4+ and CD8+ T-cells, as well as Treg cells. The salmon module was significantly enriched in NK cell-specific genes, while the lightcyan module was not. While the green module did not correspond well to any BTM, it was significantly enriched in genes involved in endoplasmic reticulum stress and unfolded protein responses (MSigDB Hallmark Collection: Unfolded Protein Response).

Discussion

[0119] 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).

[0120] The purpose of this work was to identify biomarkers of AECOPD to track disease activity, which might be used to anticipate exacerbation and guide interventions for patient care. Hospitalization due to AECOPD is a major cost to the healthcare system and the ability to anticipate episodes of increased disease activity would result in significant healthcare savings(23- 26). We leveraged two large COPD patient cohorts and gene expression profiling in peripheral blood to identify biomarkers of AECOPD. Starting from 23 COPD-specific co-expressed gene modules, each representing distinct functional biological units coordinately regulated in stable COPD, we constructed biomarker panels predictive of imminent AECOPD, identified which of these signatures tracked with disease activity, and confirmed this behavior in an independent replication cohort of over 300 COPD subjects.

[0121] We identified three suitable panels out of 23, which had good performance in cross- validation when predicting AECOPD within 60 days (CV AUC = 0.71, 0.68, and 0.67, respectively), and strong off-the-shelf classification performance in distinguishing AECOPD from stable COPD controls (AUC = 0.84, 0.77, and 0.74, respectively). Moreover, the temporal patterns of the biomarker scores obtained from these panels suggest that they may also be used to monitor the journey from AECOPD to convalescence in COPD patients. These results are particularly interesting given that the panels were not trained to pick up such patterns. Rather, they were trained in a different COPD cohort using a related, but different, phenotype (upcoming but non- symptomatic AECOPD), yet performed very well in tracking convalescence in an independent cohort.

[0122] 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).

Comparison with existing Predictive Markers of AECOPD

[0123] Several sociodemographic, physiological, psychological, and clinical factors are associated with a higher risk of hospitalizations in COPD(30, 31). Multi-dimensional indices that capture these risk factors, such as St George's Respiratory Questionnaire (SGRQ)(32), the Clinical COPD Questionnaire (CCQ)(33), BODE (body mass index (BMI), airflow obstruction, dyspnea, and exercise capacity)(34), BODEX (BMI, airflow obstruction, dyspnea, and previous severe exacerbations)(35), ADO (age, dyspnea, and airflow obstruction)(36), DOSE (dyspnea, airflow obstruction, smoking status, and exacerbation frequency)(37), CODEX (comorbidity, obstruction, dyspnea, and previous severe exacerbations)(38), and SCOPEX (short-term [6-month] risk of COPD exacerbations)(22) scores have all been assessed for their ability to predict exacerbation in the medium-long term (1 year) (AUC = 0.65 - 0.69). Risk of exacerbation naturally varies throughout the course of the disease, and disease stage itself has been shown to be moderately predictive of AECOPD (AUC = 0.69). However, these scores are meant to capture long term/inherent risk rather than predict AECOPD in the short-term.

[0124] Modifiable biomarkers have also been investigated. Thomsen and others(39, 40) found that elevated CRP, fibrinogen, and leukocyte counts relative to baseline levels were associated with frequent exacerbation (1.2-, 1.7-, and 3.7-fold for 1, 2, and 3 elevated markers, respectively). Addition of these inflammatory markers to a model including basic demographic and clinical factors significantly improved classification performance (AUC = 0.73) when trying to identify frequent exacerbators. Eosinophilia has also been associated with an increased risk of severe exacerbations among individuals with COPD in the general population (1.76-fold)(41). Urokinase- type plasminogen activator receptor is elevated during AECOPD relative to baseline and can discriminate day 1 of AECOPD from day 7(42). The ability of these modifiable biomarkers to identify imminent AECOPD has yet to be evaluated. [0125] Short-term re-exacerbation risk may be predicted using various demographic and clinical factors(43), but in stable COPD this task is more challenging. By leveraging telehealth monitoring of breathing rate, oxygen saturation, or other respiratory signals, several groups were able to predict imminent AECOPD with relatively high accuracy (76.0% - 84.7%)(44-46). However, performance metrics were based on re substitution of the training data and are overly optimistic. Furthermore, because these measure respiratory symptoms, they may detect AECOPD too late for effective intervention to prevent hospitalization.

Comparison with existing AECOPD diagnostic markers

[0126] Many studies have attempted to identify molecular biomarkers diagnostic of AECOPD, as summarized in a recent review by our group(47).

[0127] 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.

[0128] Hurst et al. found that CRP performed best (AUC = 0.73) out of 36 plasma biomarkers, for discriminating between AECOPD and stable COPD(49). Combining CRP with other biomarkers did not significantly increase performance, but combining CRP with observation of at least one major clinical symptom improved performance greatly (AUC = 0.88, 95% Cl 0.82 - 0.93). Given the definition of exacerbation required at least one major symptom, this is not unexpected. One of our biomarker panels performs comparably. However, we believe that a diagnostic which does not depend on clinical assessment of symptoms has different utility. Moreover, CRP alone does not track with AECOPD severity or outcome(50).

[0129] Copeptin, on the other hand, has been associated with disease severity and outcomes in COPD and may be more specific, at least relative to heart failure(51-53).

[0130] We have preliminarily assessed our signatures in chronic heart failure versus healthy subjects (data not shown) and have seen no differentiation, which suggests that the biomarkers identified herein differentiate between co-morbid conditions with overlapping symptoms.

Conclusion

[0131] We have discovered blood-based transcriptomic biomarker panels that can be used for predicting upcoming AECOPD and tracking convalescence. REFERENCES

E Vos T, Flaxman AD, Naghavi M, Lozano R, Michaud C, Ezzati M, Shibuya K, Salomon JA, Abdalla S, Aboyans V, Abraham J, Ackerman I, Aggarwal R, Ahn SY, Ali MK, AlMazroa MA, Alvarado M, Anderson HR, Anderson LM, Andrews KG, Atkinson C, Baddour LM, Bahalim AN, Barker-Collo S, Barrero LH, Bartels DH, Basanez M-G, Baxter A, Bell ML, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990- 2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet 2012;380:2163-2196. . GBD 2013 Mortality and Causes of Death Collaborators. Global, regional, and national age- sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet Lond Engl 2015;385: 117-171.

3. Vestbo J, Anderson W, Coxson HO, Crim C, Dawber F, Edwards L. Evaluation of COPD longitudinally to identify predictive surrogate end-points (eclipse). Eur Re spir J 2008;31 :. . R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2008.

5. Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oles AK, Pages H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods 2015;12: 115-121.

6. Carvalho BS, Irizarry RA. A framework for oligonucleotide microarray preprocessing.

Bioinforma OxfEngl 2010;26:2363-2367.

7. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP.

Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostat OxfEngl 2003;4:249-264.

8. Chen C, Grennan K, Badner J, Zhang D, Gershon E, Jin L, Liu C. Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods. PLoS ONE 2011;6:. van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res 2008;9:2579- 2605. Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. StatAppl Genet Mol Biol 2005;4:. Subramanian A, Tamayo P, Mootha VK, Mukheijee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: A knowledge- based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci 2005;102: 15545-15550. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011;27: 1739-1740. Li S, Rouphael N, Duraisingham S, Romero- Steiner S, Presnell S, Davis C. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat Immunol 2013 ; 15 : . Gene enrichment profiles reveal T-cell development, differentiation, and lineage-specific transcription factors including ZBTB25 as a novel NF-AT r... - PubMed - NCBI. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. JR Stat Soc Ser B Methodol 1995;57:289-300. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015;43:e47. Zou H, Hastie T. Regularization and variable selection via the elastic net. JR Stat Soc Ser B Stat Methodol 2005;67:301-320. Biomarker Development in COPD: Moving From P Values to Products to Impact Patient Care. Chest 2017;151 :455-467. Morgan WC, Hodge HL. Diagnostic Evaluation of Dyspnea. Am Fam Physician 1998;57:711. Celli BR, Decramer M, Wedzicha JA, Wilson KC, Agusti AA, Criner GJ, MacNee W, Make BJ, Rennard SI, Stockley RA, Vogelmeier C, Anzueto A, Au DH, Barnes PJ, Burgel P-R, Calverley PM, Casanova C, Clini EM, Cooper CB, Coxson HO, Dusser DJ, Fabbri LM, Fahy B, Ferguson GT, Fisher A, Fletcher MJ, Hayot M, Hurst JR, Jones PW, et al. An official American Thoracic Society/European Respiratory Society statement: research questions in COPD. Eur Respir Rev OffJEur Respir Soc 2015;24: 159-172. Curtis JL, Martinez CH. Preventing COPD exacerbations: new options for a crucial and growing problem. Fed Pract Health Care ProfVA DoD PHS 2014;31 : 18S-24S. Make BJ, Eriksson G, Calverley PM, Jenkins CR, Postma DS, Peterson S, Ostlund O, Anzueto A. A score to predict short-term risk of COPD exacerbations (SCOPEX). Int J Chron Obstruct Pulmon Dis 2015;10:201-209. Strassels SA, Smith DH, Sullivan SD, Mahajan PS. The Costs of Treating COPD in the United States. Chest 2001;119:344-352. Andersson F, Borg S, Jansson SA, Jonsson AC, Ericsson A, Priitz C, Ronmark E, Lundback B. The costs of exacerbations in chronic obstructive pulmonary disease (COPD). Respir Med 2002;96:700-708. Toy EL, Gallagher KF, Stanley EL, Swensen AR, Duh MS. The economic impact of exacerbations of chronic obstructive pulmonary disease and exacerbation definition: a review. COPD 2010;7:214-228. Mulpuru S, McKay J, Ronksley PE, Thavom K, Kobewka DM, Forster AJ. Factors contributing to high-cost hospital care for patients with COPD. Int J Chron Obstruct Pulmon Dis 2017;12:989-995. He B. Viruses, endoplasmic reticulum stress, and interferon responses. Cell Death Differ 2006;13:393-403. Hewitt R, Fame H, Ritchie A, Luke E, Johnston SL, Mallia P. The role of viral infections in exacerbations of chronic obstructive pulmonary disease and asthma. Ther Adv Respir Dis 2016;10: 158-174. Hutchinson AF, Ghimire AK, Thompson MA, Black JF, Brand CA, Lowe AJ, Smallwood DM, Vlahos R, Bozinovski S, Brown GV, Anderson GP, Irving LB. A community -based, time-matched, case-control study of respiratory viruses and exacerbations of COPD. Respir Med 2007; 101 :2472-2481. Garcia-Aymerich J, Monso E, Marrades RM, Escarrabill J, Felez MA, Sunyer J, Anto JM, EFRAM Investigators. Risk factors for hospitalization for a chronic obstructive pulmonary disease exacerbation. EFRAM study. Am J Respir Crit Care Med 2001;164: 1002-1007. Gudmundsson G, Gislason T, Janson C, Lindberg E, Hallin R, Ulrik CS, Brondum E, Nieminen MM, Aine T, Bakke P. Risk factors for rehospitalisation in COPD: role of health status, anxiety and depression. Eur Respir J 2005;26:414-419. Osman LM, Calder C, Robertson R, Friend JA, Legge JS, Douglas JG. Symptoms, quality of life, and health service contact among young adults with mild asthma. Am J Respir Crit Care Med 2000;161 :498-503. van der Molen T, Willemse BWM, Schokker S, ten Hacken NHT, Postma DS, Juniper EF. Development, validity and responsiveness of the Clinical COPD Questionnaire. Health Qual Life Outcomes 2003 ; 1 : 13. Celli BR, Cote CG, Marin JM, Casanova C, Montes de Oca M, Mendez RA, Pinto Plata V, Cabral HJ. The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl JMed 2004;350: 1005-1012. Soler-Cataluna JJ, Martinez-Garcia MA, Sanchez LS, Tordera MP, Sanchez PR. Severe exacerbations and BODE index: two independent risk factors for death in male COPD patients. Respir Med 2009; 103 : 692-699. Puhan MA, Garcia-Aymerich J, Frey M, ter Riet G, Anto JM, Agusti AG, Gomez FP, Rodriguez-Roisin R, Moons KGM, Kessels AG, Held U. Expansion of the prognostic assessment of patients with chronic obstructive pulmonary disease: the updated BODE index and the ADO index. Lancet Lond Engl 2009;374:704-711. Jones RC, Donaldson GC, Chavannes NH, Kida K, Dickson-Spillmann M, Harding S, Wedzicha JA, Price D, Hyland ME. Derivation and validation of a composite index of severity in chronic obstructive pulmonary disease: the DOSE Index. Am J Respir Crit Care Med 2009;180: 1189-1195. Almagro P, Soriano JB, Cabrera FJ, Boixeda R, Alonso-Ortiz MB, Barreiro B, Diez-Manglano J, Murio C, Heredia JL, Working Group on COPD, SpanishSociety of Internal Medicine*. Short- and medium-term prognosis in patients hospitalized for COPD exacerbation: the CODEX index. Chest 2014;145:972-980. Thomsen M, Ingebrigtsen TS, Marott JL, Dahl M, Lange P, Vestbo J, Nordestgaard BG. Inflammatory biomarkers and exacerbations in chronic obstructive pulmonary disease. JAMA 2013;309:2353-2361. Thomsen M, Dahl M, Lange P, Vestbo J, Nordestgaard BG. Inflammatory biomarkers and comorbidities in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2012;186:982-988. Vedel-Krogh S, Nielsen SF, Lange P, Vestbo J, Nordestgaard BG. Blood Eosinophils and Exacerbations in Chronic Obstructive Pulmonary Disease. The Copenhagen General Population Study. Am J Respir Crit Care Med 2016; 193 :965-974. Gumus A, Altintas N, Cinarka H, Kirbas A, Haziroglu M, Karatas M, Sahin U. Soluble urokinase-type plasminogen activator receptor is a novel biomarker predicting acute exacerbation in COPD. Int J Chron Obstruct Pulmon Dis 2015;10:357-365. Liu D, Peng S-H, Zhang J, Bai S-H, Liu H-X, Qu J-M. Prediction of short term re exacerbation in patients with acute exacerbation of chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2015;10: 1265-1273. Yanez AM, Guerrero D, Perez de Alejo R, Garcia-Rio F, Alvarez-Sala JL, Calle-Rubio M, de Molina RM, Valle Falcones M, Ussetti P, Sauleda J, Garcia EZ, Rodriguez-Gonzalez-Moro JM, Franco Gay M, Torrent M, Agusti A. Monitoring breathing rate at home allows early identification of COPD exacerbations. Chest 2012;142: 1524-1529. Pedone C, Chiurco D, Scarlata S, Incalzi RA. Efficacy of multiparametric telemonitoring on respiratory outcomes in elderly people with COPD: a randomized controlled trial. BMC Health ServRes 2013; 13:82. Sanchez-Morillo D, Fernandez-Granero MA, Jimenez AL. Detecting COPD exacerbations early using daily telemonitoring of symptoms and k-means clustering: a pilot study. Med Biol Eng Comput 2015;53:441-451. Chen Y-WR, Leung JM, Sin DD. A Systematic Review of Diagnostic Biomarkers of COPD Exacerbation. PLOS ONE 2016;l l :e0158843. Bafadhel M, McKenna S, Terry S, Mistry V, Reid C, Haidar P, McCormick M, Haidar K, Kebadze T, Duvoix A, Lindblad K, Patel H, Rugman P, Dodson P, Jenkins M, Saunders M, Newbold P, Green RH, Venge P, Lomas DA, Barer MR, Johnston SL, Pavord ID, Brightling CE. Acute exacerbations of chronic obstructive pulmonary disease: identification of biologic clusters and their biomarkers. Am J Respir Crit Care Med 2011;184:662-671. 49. Hurst JR, Donaldson GC, Perera WR, Wilkinson TMA, Bilello JA, Hagan GW, Vessey RS, Wedzicha JA. Use of Plasma Biomarkers at Exacerbation of Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med 2006; 174:867-874.

50. Antonescu-Turcu AL, Tomic R. C-reactive protein and copeptin: prognostic predictors in chronic obstructive pulmonary disease exacerbations. Curr Opin Pulm Med 2009;15: 120-125.

51. Winther JA, Brynildsen J, Hoiseth AD, Strand H, Foiling I, Christensen G, Nygard S, Rosjo H, Omland T. Prognostic and diagnostic significance of copeptin in acute exacerbation of chronic obstructive pulmonary disease and acute heart failure: data from the ACE 2 study. Respir Res 2017; 18 : 184.

52. Stolz D, Christ-Crain M, Morgenthaler NG, Leuppi J, Miedinger D, Bingisser R, Miiller C, Struck J, Miiller B, Tamm M. Copeptin, C-reactive protein, and procalcitonin as prognostic biomarkers in acute exacerbation of COPD. Chest 2007; 131 : 1058-1067.

53. Lacoma A, Prat C, Andreo F, Lores L, Ruiz-Manzano J, Ausina V, Dominguez J. Value of procalcitonin, C-reactive protein, and neopterin in exacerbations of chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2011;6: 157-169.

[0132] All patents, patent applications, and other publications, including GenBank Accession

Numbers, cited in this application are incorporated by reference in their entirety for all purposes.

Claims

WHAT IS CLAIMED IS:

1. A method for prognosing, diagnosing, and/or monitoring acute exacerbations of chronic obstructive pulmonary disease (AECOPD) in a subject, 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;

wherein 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), ZNF827 (SEQ ID NO 23), KMT2A (SEQ ID NO 24), CBLB (SEQ ID NO 25), CCL28 (SEQ ID NO 26), TMEM116 (SEQ ID NO 27), TRAF5 (SEQ ID NO 28), CD3E (SEQ ID NO 29), DCAF4 (SEQ ID NO 30), ITK (SEQ ID NO 31), TET1 (SEQ ID NO 32), SKAP1 (SEQ ID NO 33), GOSR2 (SEQ ID NO 34), and RORA (SEQ ID NO 35).

2. The method of claim 1, wherein the the biomarker score is determined based on the weighted contributions of the biomarkers in the panel using the formula: biomarker score = intercept + å ₌₁ weight _k*biomarken_.

3. The method of claim 1, wherein 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.

4. The method of claim 3, wherein 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 4A.

5. The method of claim 4, wherein 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.

6. The method of any one of claims 1 to 5, wherein the biomarker score is greater in a subject having or who is likely to develop AECOPD than in a control subject who is in a stable or convalescent state of COPD or without COPD.

7. The method of any one of claims 1 to 5, wherein a biomarker score in the subject sample greater than -1.198 indicates that the subject has or is likely to develop AECOPD.

8. The method of any one of claims 1 to 5, wherein 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%.

9. The method of claims 1 to 5, further comprising providing a course of treatment based on the prognosis and/or diagnosis.

10. The method of claim 9, wherein the course of treatment is selected from short-acting beta2-agonists, long-acting bronchodilators, oral steroids, oxygen therapy, and/or antibiotics.

11. A kit for detecting the panel of biomarkers in claim 1 in a blood sample obtained from a subject having COPD, comprising:

(i) a plurality of reagents for detecting the panel of biomarkers in claim 1;

(ii) a positive control sample; and

(iii) instructions for using the plurality of reagents to detect the biomarker.

12. The kit of claim 11, wherein the instructions comprise instructions for conducting a gene sequencing assay.

13. 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;

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; and

b) treating the AECOPD by administering a course of treatment to the subject.

14. The method of claim 13, wherein the course of treatment is selected from short-acting beta2-agonists, long-acting bronchodilators, oral steroids, oxygen therapy, and/or antibiotics.

15. The method of claim 13, wherein the the biomarker score is determined based on the weighted contributions of the biomarkers in the panel using the formula: biomarker score = intercept + å^=i weight k*biomarkerk.

16. The method of claim 13, wherein 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.

17. The method of claim 13, wherein a biomarker score in the subject sample greater than -1.198 indicates that the subject has or is likely to develop AECOPD.