WO2016123058A1 - Biomarqueurs pour la détection du risque de tuberculose - Google Patents

Biomarqueurs pour la détection du risque de tuberculose Download PDF

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WO2016123058A1
WO2016123058A1 PCT/US2016/014840 US2016014840W WO2016123058A1 WO 2016123058 A1 WO2016123058 A1 WO 2016123058A1 US 2016014840 W US2016014840 W US 2016014840W WO 2016123058 A1 WO2016123058 A1 WO 2016123058A1
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WIPO (PCT)
Prior art keywords
biomarkers
infection
sample
days
level
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PCT/US2016/014840
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English (en)
Inventor
Thomas HRAHA
David G. STERLING
Urs A. OCHSNER
Nebojsa Janjic
Thomas Jens Scriba
Adam Garth PENN-NICHOLSON
Willem Albert HANEKOM
Daniel Edward ZAK
Ethan Greene THOMPSON
Original Assignee
Somalogic, Inc.
University Of Cape Town
Seattle Biomedical Research Institute D/B/A/ The Center For Infectious Disease Research
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Publication of WO2016123058A1 publication Critical patent/WO2016123058A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56911—Bacteria
    • G01N33/5695—Mycobacteria
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/195—Assays involving biological materials from specific organisms or of a specific nature from bacteria
    • G01N2333/35—Assays involving biological materials from specific organisms or of a specific nature from bacteria from Mycobacteriaceae (F)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2469/00—Immunoassays for the detection of microorganisms
    • G01N2469/10—Detection of antigens from microorganism in sample from host
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/50—Determining the risk of developing a disease
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the present application relates generally to biomarkers for determining the risk of a subject with latent tuberculosis (TB) infection developing active TB disease, and methods of use thereof.
  • the invention relates to one or more biomarkers, biomarker panels, methods, devices, reagents, systems, and/or kits for detecting and/or characterizing the risk of a subject with a latent TB infection developing active TB disease.
  • Tuberculosis is a disease caused by Mycobacterium tuberculosis and other disease causing mycobacteria.
  • the bacteria usually attack the lungs, but TB bacteria can attack any part of the body such as the kidney, spine, and brain. If not treated properly, TB disease can be fatal. Not everyone infected with TB bacteria becomes sick.
  • two TB-related conditions exist: latent TB infection and active TB disease. Both latent TB infection and active TB disease can be treated.
  • methods of determining the risk of a subject with latent tuberculosis (TB) infection developing active TB disease are provided.
  • a method comprises detecting the presence or level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or nine biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP- 10, KCNE2, and CXCL16 (soluble) in a sample (e.g., plasma, serum, urine, saliva, etc.) from the subject.
  • a sample e.g., plasma, serum, urine, saliva, etc.
  • the subject is identified as having a latent TB infection that is likely to transition into active TB disease if the level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight of the biomarkers is higher than a control level of the respective biomarker.
  • a method comprises detecting the presence or level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or ten biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample (e.g., plasma, serum, urine, saliva, etc.) from the subject.
  • a sample e.g., plasma, serum, urine, saliva, etc.
  • the subject is identified as having a latent TB infection that is likely to transition into active TB disease if the level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or nine of the biomarkers is higher than a control level of the respective biomarker.
  • a method comprises detecting the level of C9 and optionally one or more of AMBN, C5, MMP-1, D-dimer, SGICI, 2DMA, IP-10, KCNE2, and CXCL16 (soluble) in a sample from the subject. In some embodiments, a method comprises detecting the level of AMBN and optionally one or more of C5, MMP-1, D-dimer, SGICI, 2DMA, IP- 10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject.
  • a method comprises detecting the level of C5 and optionally one or more of AMBN, MMP-1, D-dimer, SGICI, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject. In some embodiments, a method comprises detecting the level of MMP-1 and optionally one or more of AMBN, C5, D-dimer, SGICI, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject.
  • a method comprises detecting the level of D-dimer and optionally one or more of AMBN, C5, MMP-1, SGICI, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject. In some embodiments, a method comprises detecting the level of SGICI and optionally one or more of AMBN, C5, MMP-1, D-dimer, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject.
  • a method comprises detecting the level of 2DMA and optionally one or more of AMBN, C5, MMP-1, D-dimer, SGICI, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject. In some embodiments, a method comprises detecting the level of IP-10 and optionally one or more of AMBN, C5, MMP-1, D- dimer, SGICI, 2DMA, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject.
  • a method comprises detecting the level of CXCL16 (soluble) and optionally one or more of AMBN, C5, MMP-1, D-dimer, SGICI, 2DMA, IP-10, KCNE2, and C9 in a sample from the subject. In some embodiments, a method comprises detecting the level of KCNE2 and optionally one or more of AMBN, C5, MMP-1, D-dimer, SGICI, 2DMA, IP-10, CXCL16 (soluble), and C9 in a sample from the subject.
  • detection of a particular level of AMBN, C5, MMP-1, D- dimer, SGICI, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and/or C9 in a sample (e.g., plasma, serum, urine, saliva, etc.) from the subject that is higher than a control level of the respective biomarker is indicative of and/or diagnostic for a latent TB infection that is likely to develop into active TB disease within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days transitioning to active TB infection.
  • a level of at least one biomarker selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and/or C9 that is higher than the level of the respective biomarker in a control sample indicates that a subject with latent TB infection is likely to develop active TB disease.
  • TB latent tuberculosis
  • methods of determining a likelihood of a latent tuberculosis (TB) infection in a subject transitioning to active TB disease comprising detecting the level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or at least eight, biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, and CXCL16 (soluble) in a sample from the subject, wherein the subject is identified as having a latent TB infection that is likely to develop into active TB disease within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days if the level of the respective biomarker is higher relative to a control level of the respective biomarker.
  • methods further comprise detecting the level of one or more biomarkers that are indicative of one or more of: the presence of latent TB infection, the presence of active TB disease, the strain of TB, the antibiotic resistance/sensitivity of TB, and/or the presence of other diseases.
  • methods comprise detecting the levels of 2 to 20 biomarkers, or 2 to 10 biomarkers, or 2 to 9 biomarkers, or 3 to 20 biomarkers, or 3 to 10 biomarkers, or 3 to 9 biomarkers, or 4 to 20 biomarkers, or 4 to 10 biomarkers, or 4 to 9 biomarkers, or 5 to 20 biomarkers, or 5 to 10 biomarkers, or 5 to 9 biomarkers.
  • methods of determining a likelihood of a latent TB infection in a subject transitioning to active TB disease comprising detecting the level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or ten biomarkers selected from AMBN, C5, MMP-1, D- dimer, SG1C1, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject, wherein the subject is identified as having a latent TB infection that is likely to develop into active TB disease within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days if the level of the respective biomarker is higher relative to a control level of the respective biomarker.
  • methods further comprise detecting the level of one or more biomarkers that are indicative of one or more of: the presence of latent TB infection, the presence of active TB disease, the strain of TB, the antibiotic resistance/sensitivity of TB, and/or the presence of other diseases.
  • methods comprise detecting the levels of 2 to 20 biomarkers, or 2 to 10 biomarkers, or 2 to 9 biomarkers, or 3 to 20 biomarkers, or 3 to 10 biomarkers, or 3 to 9 biomarkers, or 4 to 20 biomarkers, or 4 to 10 biomarkers, or 4 to 9 biomarkers, or 5 to 20 biomarkers, or 5 to 10 biomarkers, or 5 to 9 biomarkers.
  • methods of monitoring a latent TB infection in a subject for the likelihood of the latent TB infection transitioning to active TB disease comprising detecting the level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or nine biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, and CXCL16 (soluble) in a sample from the patient at a first time point, and measuring the level of the at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers at a second time point.
  • the level of the biomarkers is further from a control level at the second time point than the first time point, the likelihood of the latent TB infection transitioning to active TB disease has increased.
  • the likelihood of the latent TB infection transitioning to active TB disease has increased. In some embodiments, if the level of the biomarkers is lower at the second time point than at the first time point, the likelihood of the latent TB infection transitioning to active TB disease has decreased.
  • methods of monitoring a latent TB infection in a subject for the likelihood of the latent TB infection transitioning to active TB disease comprising detecting the level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or ten biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the patient at a first time point, and measuring the level of the at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or nine biomarkers at a second time point.
  • the likelihood of the latent TB infection transitioning to active TB disease within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days has increased. In some embodiments, if the level of the biomarkers is higher at the second time point than at the first time point, the likelihood of the latent TB infection transitioning to active TB disease has increased. In some embodiments, if the level of the biomarkers is nearer to a control level at the second time point than the first time point, the likelihood of the latent TB infection transitioning to active TB disease has decreased. In some embodiments, if the level of the biomarkers is lower at the second time point than at the first time point, the likelihood of the latent TB infection transitioning to active TB disease has decreased.
  • methods of monitoring treatment of a latent TB infection comprising detecting the level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or nine biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, and CXCL16 (soluble) in a sample from the patient at a first time point, administering at least one treatment for TB infection to the patient, and detecting the level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or nine biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, and CXCL16 (soluble) in a sample from the patient at a second time point, wherein the treatment is effective at reducing the likelihood of the latent TB infection transitioning to active
  • methods of monitoring treatment of a latent TB infection comprising detecting the level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or nine biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, and CXCL16 (soluble) in a sample from the patient at a first time point, administering at least one treatment for TB infection to the patient, and detecting the level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or ten biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the patient at a second time point.
  • the treatment is effective at reducing the likelihood of the latent TB infection transitioning to active TB disease if the level of the biomarkers is nearer to a control level, or is not further from a control level than, at the second time point compared to the first time point. In some embodiments, the treatment is effective at reducing the likelihood of the latent TB infection transitioning to active TB disease if the level of the biomarkers is lower at the second time point compared to the first time point.
  • the at least one treatment for TB infection is selected from the group consisting of isoniazid (INH), rifampin (RIF), rifapentine (RPT), ethambutol (EMB), pyrazinamide (PZA), and/or another approved TB therapeutic to the subject.
  • IH isoniazid
  • RIND rifampin
  • RPT rifapentine
  • EMB ethambutol
  • PZA pyrazinamide
  • a control level is the level of the respective biomarker in a subject or population of subjects with latent TB infection who are known not to have developed active TB within a particular time period. In some embodiments, a control level is the level of the respective biomarker in a subject or population of subjects with latent TB infection who are known not to have developed active TB within 540 days of sample collection. In some embodiments, a control level is the level of the respective biomarker in a subject or population of subjects with latent TB infection who are known not to have developed active TB within 2 years of sample collection.
  • methods further comprise performing one or more additional tests for TB infection.
  • additional tests for TB infection comprise chest x-ray.
  • each biomarker is a protein biomarker.
  • methods comprise contacting biomarkers of the sample from the subject or patient with a set of biomarker capture reagents, wherein each biomarker capture reagent of the set of biomarker capture reagents specifically binds to a different biomarker being detected.
  • each biomarker capture reagent is an antibody or an aptamer.
  • at least one aptamer is a slow off-rate aptamer.
  • At least one slow off-rate aptamer comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least 10 nucleotides with modifications.
  • each slow off-rate aptamer binds to its target protein with an off rate (t1 ⁇ 2) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.
  • the sample is a blood sample. In some embodiments, the sample is a serum sample.
  • a method for determining whether a a latent TB infection is likely to advance into active TB disease in a subject within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days comprises (a) forming a biomarker panel having N biomarker proteins selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, CXCL16 (soluble), and C9; or AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, and CXCL16 (soluble); and (b) detecting the level of each of the N biomarker proteins of the panel in a sample from the subject.
  • N biomarker panel having N biomarker proteins selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, CXCL16 (soluble), and C9; or AMBN, C5, MMP-1, D-
  • N is 1 to 9. In some embodiments, N is 2 to 9. In some embodiments, N is 3 to 9. In some embodiments, N is 4 to 9. In some embodiments, N is 5 to 9. In some embodiments, N is 6 to 9. In some embodiments, N is 7 to 9. In some embodiments, N is 8 to 9. In some
  • N is 9. In some embodiments, N is 2 to 8. In some embodiments, N is 3 to 7. In some embodiments, N is 4 to 6. In some embodiments, N is 1 to 8. In some embodiments, N is 2 to 8. In some embodiments, N is 3 to 8. In some embodiments, N is 4 to 8. In some embodiments, N is 5 to 8. In some embodiments, N is 6 to 8. In some embodiments, N is 7 to 8. In some embodiments, N is 8.
  • a method comprises forming a biomarker panel having X biomarker proteins, wherein N of the X biomarker proteins are selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9; or from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, and CXCL16 (soluble); and detecting the level of each of the X biomarker proteins of the panel in a sample from the subject.
  • X is 100 or fewer (e.g., ⁇ 90 biomarkers, ⁇ 80 biomarkers, ⁇ 70 biomarkers, ⁇ 60 biomarkers, ⁇ 50 biomarkers, ⁇ 40 biomarkers, ⁇ 30 biomarkers, ⁇ 20 biomarkers, ⁇ 15 biomarkers). In some embodiments, X is 10 or greater (e.g., >11 biomarkers, >12 biomarkers, >13 biomarkers, >14 biomarkers, >15 biomarkers, >20 biomarkers, >30 biomarkers, >40 biomarkers, >50 biomarkers).
  • X is between 10 and 100, between 10 and 90, between 10 and 80, between 10 and 70, between 10 and 60, between 10 and 50, between 10 and 40, between 10 and 30, between 10 and 20, or between 10 and 15.
  • N is between 1 and 9 (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9) or between 1 and 8 (e.g., 1, 2, 3, 4, 5, 6, 7, 8).
  • a set of biomarker proteins with a sensitivity + specificity value of 1.3 or greater, 1.35 or greater, 1.4 or greater, 1.45 or greater, 1.5 or greater is selected that comprises one or more biomarkers selected from: AMBN, C5, MMP-1, D- dimer, SG1C1, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9; or from: AMBN, C5, MMP-1, D-dimer, SGI CI, 2DMA, IP-10, KCNE2, and CXCL16 (soluble); or from: AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, KCNE2, and CXCL16 (soluble); or from AMBN, C5, MMP-1, C9, SG1C1, 2DMA, KCNE2, and CXCL16 (soluble).
  • one or more additional steps are taken upon identifying a subject as having a latent TB infection that is likely to transition into active TB disease within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days.
  • methods further comprise a subsequent step of treating said subject or patient for latent TB.
  • methods further comprise a subsequent step of treating said subject or patient for active TB disease.
  • methods further comprise a subsequent step of additional TB-diagnostic steps.
  • said additional TB-diagnostic steps comprise a chest x-ray.
  • methods further comprise generating a report indicating that said subject is likely to develop active TB disease within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days.
  • a subject with such a risk is treated for active TB disease before developing symptoms of active TB disease.
  • the each biomarker may be a protein biomarker.
  • the method may comprise contacting biomarkers of the sample from the subject or patient with a set of biomarker detection reagents. In any of the embodiments described herein, the method may comprise contacting biomarkers of the sample from the subject or patient with a set of biomarker capture reagents, wherein each biomarker capture reagent of the set of biomarker capture reagents specifically binds to a biomarker being detected. In some embodiments, each biomarker capture reagent of the set of biomarker capture reagents specifically binds to a different biomarker being detected. In any of the embodiments described herein, each biomarker capture reagent may be an antibody or an aptamer.
  • each biomarker capture reagent may be an aptamer.
  • at least one aptamer may be a slow off-rate aptamer.
  • at least one slow off-rate aptamer may comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least 10 nucleotides with modifications.
  • the modifications are hydrophobic modifications.
  • the modifications are hydrophobic base modifications.
  • one or more of the modifications may be selected from the modifications shown in Figure 52.
  • each slow off-rate aptamer binds to its target protein with an off rate (t1 ⁇ 2) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.
  • the sample may be a blood sample.
  • the blood sample is selected from a serum sample and a plasma sample.
  • the sample is a body fluid selected from tracheal aspirate fluid, bronchoalveolar fluid, bronchoalveolar lavage sample, blood or portion thereof, serum, plasma, urine, semen, saliva, tears, etc.
  • a method may further comprise treating the subject or patient for TB infection or TB disease.
  • treating the subject or patient for TB infection or TB disease comprises a treatment regimen of administering one or more of: isoniazid (INH), rifampin (RIF), rifapentine (RPT), ethambutol (EMB), pyrazinamide (PZA), and/or another approved TB therapeutic to the subject or patient.
  • IH isoniazid
  • RIND rifampin
  • RPT rifapentine
  • EMB ethambutol
  • PZA pyrazinamide
  • kits are provided.
  • a kit comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or ten aptamers, wherein each aptamer specifically binds to a target protein selected from AMBN, C5, MMP-1, D-dimer, SGICI, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9; or at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or nine aptamers, wherein each aptamer specifically binds to a target protein selected from AMBN, C5, MMP-1, D-dimer, SGICI, 2DMA, IP-10, KCNE2, and CXCL16 (soluble).
  • a kit comprises an aptamer that specifically binds C9 and optionally one or more aptamers that specifically bind one or more of AMBN, C5, MMP-1, D-dimer, SGICI, 2DMA, IP-10, KCNE2, and CXCL16 (soluble) in a sample from the subject.
  • a kit comprises an aptamer that specifically binds AMBN and optionally one or more aptamers that specifically bind one or more of C5, MMP-1, D-dimer, SGICI, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject.
  • a kit comprises an aptamer that specifically binds C5 and optionally one or more aptamers that specifically bind one or more of AMBN, MMP-1, D-dimer, SGICI, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject.
  • a kit comprises an aptamer that specifically binds MMP- and optionally one or more aptamers that specifically bind one or more of AMBN, C5, D-dimer, SGICI, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject.
  • a kit comprises an aptamer that specifically binds D-dimer and optionally one or more aptamers that specifically bind one or more of AMBN, C5, MMP-1, SGICI, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject.
  • a kit comprises an aptamer that specifically binds SGICI and optionally one or more aptamers that specifically bind one or more of AMBN, C5, MMP-1, D-dimer, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject.
  • a kit comprises an aptamer that specifically binds 2DMA and optionally one or more aptamers that specifically bind one or more of AMBN, C5, MMP-1, D-dimer, SGICI, IP-10, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject.
  • a kit comprises an aptamer that specifically binds IP-10 and optionally one or more aptamers that specifically bind one or more of AMBN, C5, MMP-1, D-dimer, SGICI, 2DMA, KCNE2, CXCL16 (soluble), and C9 in a sample from the subject.
  • a kit comprises an aptamer that specifically binds CXCL16 (soluble) and optionally one or more aptamers that specifically bind one or more of AMBN, C5, MMP-1, D-dimer, SGICI, 2DMA, IP-10, KCNE2, and C9 in a sample from the subject.
  • a kit comprises an aptamer that specifically binds KCNE2 and optionally one or more aptamers that specifically bind one or more of AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, CXCL16 (soluble), and C9 in a sample from the subject.
  • the kit comprises a total of 2 to 20 aptamers, or 2 to 10 aptamers, or 2 to 9 aptamers, or 3 to 20 aptamers, or 3 to 10 aptamers, or 3 to 9 aptamers, or 4 to 20 aptamers, or 4 to 10 aptamers, or 4 to 9 aptamers, or 5 to 20 aptamers, or 5 to 10 aptamers, or 5 to 9 aptamers.
  • a kit comprises X aptamers, wherein N aptamers specifically bind to a biomarker protein selected from AMBN, C5, MMP-1, D- dimer, SG1C1, 2DMA, IP-10, CXCL16 (soluble), and C9; or AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, and CXCL16 (soluble).
  • X is less than 100 (e.g., ⁇ 90, ⁇ 80, ⁇ 70, ⁇ 60, ⁇ 50, ⁇ 40, ⁇ 30, ⁇ 20, ⁇ 15).
  • X is 10 or more (e.g., >10, >11, >12, >13, >14, >15, >20, >30, >40, >50). In some embodiments, X is between 10 and 100, between 10 and 90, between 10 and 80, between 10 and 70, between 10 and 60, between 10 and 50, between 10 and 40, between 10 and 30, between 10 and 20, or between 10 and 15. In some embodiments, N is 1 to 9 (1, 2, 3, 4, 5, 6, 7, 8, 9). In some embodiments, N is 1 to 8 (1, 2, 3, 4, 5, 6, 7, 8).
  • compositions comprising proteins of a sample from a subject or patient and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or nine aptamers, wherein each aptamer specifically binds to a different target protein selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9.
  • compositions comprising proteins of a sample from a subject or patient and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight aptamers, wherein each aptamer specifically binds to a different target protein selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, and CXCL16 (soluble).
  • a kit or composition may comprise at least one aptamer that is a slow off-rate aptamer.
  • each aptamer of a kit or composition may be a slow off-rate aptamer.
  • at least one slow off-rate aptamer comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least 10 nucleotides with modifications.
  • at least one nucleotide with a modification is a nucleotide with a hydrophobic base modification.
  • each nucleotide with a modification is a nucleotide with a hydrophobic base modification.
  • each hydrophobic base modification is independently selected from the modification in Figure 52.
  • each slow off-rate aptamer in a kit binds to its target protein with an off rate (t1 ⁇ 2) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.
  • Figure 1 shows (left) beeswarm plots of sampling time for all cases in the discovery and verification sets for all time points, and (right) empirical cumulative distribution functions of time to diagnosis in discovery and verification.
  • Figure 2 shows boxplots of log2 transformed hybridization normalization scale factors for each plate (left), and cumulative distribution functions of raw normalization scale factors for each plate (right).
  • Figure 3 shows boxplots of Log2 transformed median normalization scale factors.
  • Figure 4 shows a subspace projection for each sample from a PC A performed using the top 50 ranked proteins which were observed to differentiate gender.
  • Figure 5 shows a plot of the empirical CDF of age (left) and a demographic table (right) for all TB Cases and Controls, 0 to 950 to beginning of treatment.
  • Figure 6 shows empirical CDFs for the top 9 ranked proteins comparing all TB case and all Control samples.
  • Figure 7 shows a plot of KS distances with class randomization statistics for the top 100 features.
  • Figure 8 shows a volcano plot of 3040 proteins from a univariate KS analysis comparing all TB Cases and all Controls..
  • Figure 9 shows RFU trajectories of individual TB cases overlaid onto a 'control band' created by interpolating the median, IQR and range of the control data.
  • the top axis corresponds to the controls and the bottom the TB cases. Time moves to the right.
  • Figure 10 shows a heat map of t-statistics arranged by hierarchical clustering for the top 200 t-statistics ranked by the median across all bins.
  • Figure 11 shows a heat map of t-statistics (left) and corresponding CDFs of cases and controls for subcluster A, which was selected based on inconsistencies in the 3TB/8Controls bin.
  • Figure 12 shows a heat map of t-statistics (left) and corresponding CDFs of cases and controls for subcluster B, which was selected based on inconsistencies in the 4TB/6Controls bin.
  • Figure 13 shows a heat map of t-statistics (left) and corresponding CDFs of cases and controls for subcluster C, which was selected because most proteins seemed to be
  • Figure 14 shows a heat map of t-statistics (left) and corresponding CDFs of cases and controls for subcluster D, which was selected based on most proteins being homogenously lower in the TB Cases.
  • Figure 15 shows a heat map of t-statistics (left) and corresponding CDFs of cases and controls for subcluster E, which was selected based on inconsistencies in several bins.
  • Figure 16 shows Linear fits for all TB cases are shown as a function of time to treatment.
  • the dark band corresponds to the interquartile range (IQR), while the lighter shaded region corresponds to the whiskers, or the nearest data point that's within the upper/lower quartile + 1.5*IQR. Data outside this range is considered an outlier.
  • IQR interquartile range
  • Figure 17 shows sample times for all TB subjects as a function of time to the beginning of treatment. Negative values are days on treatment.
  • Figure 18 shows a plot of the empirical CDF of age (left) and a demographic table (right) for TB Cases 0-180 days to beginning of treatment, and matched Controls.
  • Figure 19 shows empirical CDFs for the top 9 ranked proteins comparing non-TB vs. TB 0-180 days before treatment.
  • Figure 20 shows a KS Plot of KS distances with class randomization statistics for the top 50 features comparing non-TB vs. TB 0-180 days before treatment (pvalue
  • Figure 21 shows a volcano plot of 3040 proteins from a univariate KS analysis comparing TB Cases 0-180 days pre-Rx to matched controls.
  • Figure 22 shows RFU trajectories for the top markers found to distinguish non-TB vs. TB 0-180 days before treatment. Individual TB cases were overlaid onto a 'control band' created by interpolating the median, IQR and range of the control data. The top axis corresponds to the controls and the bottom to the TB cases. Time moves to the right.
  • Figure 23 shows a plot of the empirical CDF of age (left) and a demographic table (right) for TB Cases 180-360 days to beginning of treatment and matched Controls
  • Figure 24 shows empirical CDFs for the top 9 ranked proteins comparing non-TB vs. TB 180-360 days before treatment.
  • Figure 25 shows a KS Plot of KS distances with class randomization statistics for the top 50 features comparing non-TB vs. TB 180-360 days before treatment (pvalue
  • Figure 26 shows a volcano plot of 3040 proteins from a univariate KS analysis comparing TB Cases 180-360 days pre-Rx to matched controls.
  • Figure 27 shows RFU trajectories for the top markers found to distinguish non-TB vs. TB 180-360 days before treatment. Individual TB cases were overlaid onto a 'control band' created by interpolating the median, IQR and range of the control data. The top axis corresponds to the controls and the bottom the TB cases. Time moves to the right.
  • Figure 28 shows a plot of the empirical CDF of age (left) and a demographic table (right) for TB Cases 360-540 days to beginning of treatment and matched Controls
  • Figure 29 shows empirical CDFs for the top 9 ranked proteins comparing non-TB vs. TB 360-540 days before treatment.
  • Figure 31 shows a volcano plot of 3040 proteins from a univariate KS analysis comparing TB Cases 360-540 days pre-Rx to matched controls.
  • Figure 32 shows RFU trajectories for the top markers found to distinguish non-TB vs. TB 360-540 days before treatment. Individual TB cases were overlaid onto a 'control band' created by interpolating the median, IQR and range of the control data. The top axis corresponds to the controls and the bottom the TB cases. Time moves to the right.
  • Figure 33 shows a plot of the empirical CDF of age (left) and a demographic table (right) for TB Cases 540-700 days to beginning of treatment and matched Controls.
  • Figure 34 shows empirical CDFs for the top 6 ranked proteins comparing non-TB vs. TB 540-700 days before treatment.
  • Figure 36 shows a volcano plot of 3040 proteins from a univariate KS analysis comparing TB Cases 540-700 days pre-Rx to matched controls .
  • Figure 37 shows RFU trajectories for the top markers found to distinguish non-TB vs. TB 540-700 days before treatment. Individual TB cases were overlaid onto a 'control band' created by interpolating the median, IQR and range of the control data. The top axis corresponds to the controls and the bottom the TB cases. Time moves to the right.
  • Figure 38 shows stability paths (top) and regularization paths (bottom) for non-TB Controls vs. TB Cases 0-180 days pre-Rx.
  • Figure 39 shows empirical CDFs for the top 11 proteins whose maximum selection probability exceeded 50%.
  • Figure 40 shows CDFs of the 17 proteins included in model buildings comparing all 57 TB case samples to all 197 control samples.
  • Figure 41 shows box plots of cross-validated model performance
  • Figure 42 shows a bar graph of the frequency that each protein was included in an optimal model using forward and backward selection.
  • Figure 43 shows ROC with 95% bootstrap confidence intervals and decision boundary plot of the log-odds for all subjects colored by diagnosis. Blue dots are Control samples, red dots are TB cases, and hollow data points indicate a misclassification.
  • Figure 44 shows log-odds trajectories of individual TB cases overlaid onto a 'control band' created by the median, IQR and range of the control data (left) and responsiveness versus performance of the model (right).
  • Figure 45 shows a boxplot of time binned log-odds generated from another TB biomarker panel (9 proteins) and the 8-protein model described herein.
  • Figure 46 shows a seriated correlation matrix for all 17 proteins used in model building (left), and the scatter plots for a 3 protein cluster identified using a Spearman threshold of 0.7.
  • Figure 47 shows scatter plots for a 6 protein cluster identified using a Spearman threshold of 0.7.
  • Figure 48 shows a plot showing sorted log odds for the 8 marker model (solid data points) and the change when adding C9 to the model (hollow data points).
  • Figure 49 shows ROC with 95% bootstrap confidence intervals and decision boundary plot of the log-odds for all subjects colored by diagnosis. Control samples are above (left plot) and left (right plot) of the mean; TB cases are below (left plot) and right (right plot) of the mean; and hollow data points indicate a misclassification.
  • Figure 50 shows: log-odds trajectories of individual TB cases overlaid onto a 'control band' created by the median, IQR and range of the control data; Time moves to the right (right); and responsiveness of the model to time to diagnosis versus model performance (left).
  • Figure 51 shows a boxplot of time binned log-odds generated from another TB biomarker panel (9 proteins) and the 9-protein model described herein.
  • Figure 52 shows certain exemplary modified pyrimidines that may be incorporated into aptamers, such as slow off-rate aptamers.
  • Figure 53 illustrates a non-limiting exemplary computer system for use with various computer-implemented methods described herein.
  • Figure 54 illustrates a non-limiting exemplary aptamer assay that can be used to detect one or more biomarkers in a biological sample.
  • Figure 55 shows the univariate CDF for protein KCNE2.
  • Figure 56 shows box plots of cross-validated model performance
  • Figure 57 shows a bar graph of the frequency that each protein was included in an optimal model using forward and backward selection.
  • Figure 58 shows ROC with 95% bootstrap confidence intervals and decision boundary plot of the log-odds for all subjects colored by diagnosis.
  • Blue dots are Control samples (in the right panel, blue dots are to the left of the center vertical line), red dots are TB cases (in the right panel, red dots are to the right of the center vertical line), and hollow data points indicate a misclassifi cation.
  • Figure 59 shows log-odds trajectories of individual TB cases overlaid onto a 'control band' created by the median, IQR and range of the control data. Time moves to the right. Responsiveness of the model to time to diagnosis versus model performance.
  • Figure 60 shows a boxplot of time binned log-odds generated from the HR9 and 8 protein models.
  • Figure 61 shows a seriated correlation matrix for all 18 proteins used in model building.
  • Figure 62 shows the scatter plots for a 3 protein cluster identified using a Spearman threshold of 0.7 (left) and for a 6 protein cluster identified using a Spearman threshold of 0.7 (right).
  • Figure 63 is a plot showing sorted log odds for the 8 protein model (solid data points) and the change when adding C9 to the model (hollow data points).
  • Figure 64 shows a ROC plot with 95% bootstrap confidence intervals (right) and decision boundary plot of the log-odds for all subjects colored by diagnosis (left).
  • Blue dots are Control samples (in the left panel, blue dots are to the left of the center vertical line), red dots are TB cases (in the left panel, red dots are to the right of the center vertical line), and hollow data points indicate a misclassification.
  • Figure 65 shows a histogram of features selected in the optimal model during 18 runs of 5 -fold double cross validation.
  • Figure 66 shows histograms of model performance metrics across 18 runs of 5-fold double cross validation.
  • the terms “comprises,” “comprising,” “includes,” “including,” “contains,” “containing,” and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, product-by -process, or composition of matter that comprises, includes, or contains an element or list of elements may include other elements not expressly listed.
  • TB infection refers to the infection of an individual with any of a variety of TB disease-causing mycobacteria (e.g., Mycobacterium tuberculosis).
  • TB infection encompasses both "latent TB infection” (non-transmissible and without symptoms) and "active TB infection” (transmissible and symptomatic). Observable signs of active TB infection include, but are not limited to, chronic cough with blood-tinged sputum, fever, night sweats, and weight loss.
  • ⁇ can be a mammal or a non-mammal. In various embodiments, the individual is a mammal.
  • a mammalian individual can be a human or non-human. In various embodiments, the individual is a human.
  • a "non-infected” individual is one which has not been infected with a TB disease-causing mycobacterium (e.g., Mycobacterium tuberculosis), does not have either latent TB infection or active TB disease, and/or for whom TB infection is not detectable by conventional diagnostic methods.
  • a TB disease-causing mycobacterium e.g., Mycobacterium tuberculosis
  • a "subject at risk of TB infection” refers to a subject with or exposed to one or more risk factors for TB infection.
  • risk factors include HIV infection, poverty, geographic location, chronic lung disease, poverty, diabetes, genetic susceptibility, imprisonment, etc.
  • one or more biomarkers are provided for use either alone or in various combinations to detect TB infection and/or disease, to differentiate latent TB infection from active TB disease, to identify subjects at risk of transition from latent to active TB infection, etc.
  • Biomarkers and biomarker panels provided herein are particularly useful for distinguishing samples obtained from individuals with latent TB infection that will advance to active TB disease (or are at high risk of advancing to TB disease) from samples from individuals with latent TB infection that will not advance to active TB disease (or are at low risk of advancing to TB disease).
  • exemplary embodiments include one or more biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, and CXCL16 (soluble); or from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9; or from: AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, KCNE2, and CXCL16 (soluble); or from AMBN, C5, MMP-1, C9, SG1C1, 2DMA, KCNE2, and CXCL16 (soluble).
  • biomarkers for diagnosis of TB infection/disease e.g., biomarkers for diagnosis of TB infection/disease, biomarkers for identification of the strain of infection, biomarkers for identifying antibiotic resistant TB, etc.
  • panels of at least two, at least three, at least four, at least five, or at least 6 biomarkers, at least 7 biomarkers, at least 8 biomarkers, at least 9 biomarkers, at least 10 biomarkers, at least 11 biomarkers, at least 12 biomarkers, at least 13 biomarkers, at least 14 biomarkers, at least 15 biomarkers, at least 16 biomarkers, at least 17 biomarkers, at least 18 biomarkers, at least 19 biomarkers, at least 20 biomarkers are provided.
  • the number and identity of biomarkers in a panel are selected based on the sensitivity and specificity for the particular combination of biomarker values.
  • sensitivity and “specificity” are used herein with respect to the ability to correctly classify an individual, based on one or more biomarker levels detected in a biological sample.
  • Sensitivity indicates the performance of the biomarker(s) with respect to correctly classifying individuals as, for example at risk (e.g., high risk or likely) of transitioning from latent TB infection to active TB disease.
  • Specificity indicates the performance of the biomarker(s) with respect to correctly classifying individuals who have latent TB infection and are not at risk (e.g., low risk) of transitioning from latent TB infection to active TB disease. For example, 85% specificity and 90% sensitivity for a panel of markers used to test a set of control samples (such as samples from individuals with latent TB infections that did not advance to active TB disease) and test samples (such as samples from TB-infected individuals that developed active TB disease) indicates that 85% of the control samples were correctly classified as control samples by the panel, and 90% of the test samples were correctly classified as test samples by the panel.
  • AUC area-under-the-curve
  • the AUC value is derived from receiver operating characteristic (ROC) plots, which are exemplified herein.
  • the ROC curve is the plot of the true positive rate (sensitivity) of a test against the false positive rate (1- specificity) of the test.
  • area under the curve or "AUC” refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art.
  • AUC measures are useful for comparing the accuracy of a classifier across the complete data range.
  • ROC curves are useful for plotting the performance of a particular feature (e.g., any of the biomarkers described herein and/or any item of additional biomedical information) in distinguishing between two populations (e.g., cases in which subjects transitioned from latent to active TB vs. controls in which TB infection remained latent).
  • a particular feature e.g., any of the biomarkers described herein and/or any item of additional biomedical information
  • the feature data across the entire population e.g., all tested subject
  • the true positive and false positive rates for the data are calculated.
  • the true positive rate is determined by counting the number of cases above the value for that feature and then dividing by the total number of cases.
  • the false positive rate is determined by counting the number of controls above the value for that feature and then dividing by the total number of controls.
  • ROC curves can be generated for a single feature as well as for other single outputs, for example, a combination of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide a single sum value, and this single sum value can be plotted in a ROC curve. Additionally, any combination of multiple features, in which the combination derives a single output value, can be plotted in a ROC curve.
  • methods comprise contacting a sample or a portion of a sample from a subject with at least one capture reagent, wherein each capture reagent specifically binds a biomarker the levels of which are being detected.
  • the method comprises contacting the sample, or proteins from the sample, with at least one aptamer, wherein each aptamer specifically binds a biomarker, the levels of which are being detected.
  • a method comprises detecting the level of at least one biomarker from at least a first panel of biomarkers, the first panel comprising biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, and CXCL16 (soluble); or from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9; or from: AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, KCNE2, and CXCL16 (soluble); or from AMBN, C5, MMP-1, C9, SG1C1, 2DMA, KCNE2, and
  • methods further comprise detecting at least one biomarker from at least a second panel of biomarkers, the second panel comprising biomarkers for detection of TB infection, detection of active TB disease, characterization of the type, strain, and/or resistance/sensitivity of the TB infection, etc.
  • the subject and/or the infection are characterized according to the particular second panel being analyzed.
  • biomarkers identified herein provide a number of choices for subsets or panels of biomarkers that can be used to effectively characterize TB infection (e.g., characterize the risk of transition from latent to active). Selection of the appropriate number of such biomarkers may depend on the specific combination of biomarkers chosen. In addition, in any of the methods described herein, except where explicitly indicated, a panel of biomarkers may comprise additional biomarkers not listed herein.
  • a method comprises detecting the level of at least one biomarker, at least two biomarkers, at least three biomarkers, at least four biomarkers, at least five biomarkers, at least six biomarkers, at least seven biomarkers, or at least eight biomarkers, selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP- 10, KCNE2, and CXCL16 (soluble) in a sample from the subject.
  • a method comprises detecting the level of at least one biomarker, at least two biomarkers, at least three biomarkers, at least four biomarkers, at least five biomarkers, at least six biomarkers, at least seven biomarkers, at least eight biomarkers, or nine biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP-10, CXCL16 (soluble), and C9 in a sample from the subject.
  • a method comprises detecting the level of any number or combination of AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP- 10, KCNE2, and CXCL16 (soluble); or any number or combination of AMBN, C5, MMP-1, D-dimer, SGI CI, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9.
  • Bio sample “sample”, and “test sample” are used interchangeably herein to refer to any material, biological fluid, tissue, or cell obtained or otherwise derived from an individual. This includes blood (including whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, and serum), sputum, tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, lymph fluid, nipple aspirate, bronchial aspirate (e.g., bronchoalveolar lavage), bronchial brushing, synovial fluid, joint aspirate, organ secretions, cells, a cellular extract, and cerebrospinal fluid.
  • blood including whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, and serum
  • sputum tears, mucus
  • nasal washes nasal aspirate,
  • a blood sample can be fractionated into serum, plasma, or into fractions containing particular types of blood cells, such as red blood cells or white blood cells (leukocytes).
  • a sample can be a combination of samples from an individual, such as a combination of a tissue and fluid sample.
  • biological sample also includes materials containing homogenized solid material, such as from a stool sample, a tissue sample, or a tissue biopsy, for example.
  • biological sample also includes materials derived from a tissue culture or a cell culture.
  • any suitable methods for obtaining a biological sample can be employed; exemplary methods include, e.g., phlebotomy, swab (e.g., buccal swab), and a fine needle aspirate biopsy procedure.
  • tissue susceptible to fine needle aspiration include lymph node, lung, lung washes, BAL (bronchoalveolar lavage), thyroid, breast, pancreas, and liver.
  • Samples can also be collected, e.g., by micro dissection (e.g., laser capture micro dissection (LCM) or laser micro dissection (LMD)), bladder wash, smear (e.g., a PAP smear), or ductal lavage.
  • a "biological sample” obtained or derived from an individual includes any such sample that has been processed in any suitable manner after being obtained from the individual.
  • a biological sample may be derived by taking biological samples from a number of individuals and pooling them, or pooling an aliquot of each individual's biological sample.
  • the pooled sample may be treated as described herein for a sample from a single individual, and, for example, if high-risk TB infection is detected in the pooled sample, then each individual biological sample can be re-tested to identify the individual(s) with latent TB infection that is likely to transition into active TB disease within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days.
  • Target refers to any molecule of interest that may be present in a biological sample.
  • a "molecule of interest” includes any minor variation of a particular molecule, such as, in the case of a protein, for example, minor variations in amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component, which does not substantially alter the identity of the molecule.
  • target molecule refers to a set of copies of one type or species of molecule or multi-molecular structure.
  • Target molecules refer to more than one type or species of molecule or multi-molecular structure.
  • target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affybodies, antibody mimics, viruses, pathogens, toxic substances, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or portion of any of the foregoing.
  • a target molecule is a protein, in which case the target molecule may be referred to as a "target protein.”
  • a “capture agent' or “capture reagent” refers to a molecule that is capable of binding specifically to a biomarker.
  • a “target protein capture reagent” refers to a molecule that is capable of binding specifically to a target protein.
  • Nonlimiting exemplary capture reagents include aptamers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligand-binding receptors, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, synthetic receptors, and modifications and fragments of any of the aforementioned capture reagents.
  • a capture reagent is selected from an aptamer and an antibody.
  • antibody refers to full-length antibodies of any species and fragments and derivatives of such antibodies, including Fab fragments, F(ab')2 fragments, single chain antibodies, Fv fragments, and single chain Fv fragments.
  • antibody also refers to synthetically-derived antibodies, such as phage display-derived antibodies and fragments, affybodies, nanobodies, etc.
  • marker and “biomarker” are used interchangeably to refer to a target molecule that indicates or is a sign of a normal or abnormal process in an individual or of a disease or other condition in an individual. More specifically, a “marker” or
  • biomarker is an anatomic, physiologic, biochemical, or molecular parameter associated with the presence of a specific physiological state or process, whether normal or abnormal, and, if abnormal, whether chronic or acute. Biomarkers are detectable and measurable by a variety of methods including laboratory assays and medical imaging. In some embodiments, a biomarker is a target protein.
  • biomarker level and “level” refer to a measurement that is made using any analytical method for detecting the biomarker in a biological sample and that indicates the presence, absence, absolute amount or concentration, relative amount or concentration, titer, a level, an expression level, a ratio of measured levels, or the like, of, for, or corresponding to the biomarker in the biological sample.
  • level depends on the specific design and components of the particular analytical method employed to detect the biomarker.
  • a “control level” of a target molecule refers to the level of the target molecule in the same sample type from an individual that does not exhibit the characteristic being assayed for (e.g., TB infection, risk of transition from latent TB infection to active TB disease, etc.).
  • a “control level” of a target molecule need not be determined each time the present methods are carried out, and may be a previously determined level that is used as a reference or threshold to determine whether the level in a particular sample is higher or lower than a normal level.
  • a control level in a method described herein is the level that has been observed in one or more subjects whose latent TB infection did not advance to active TB disease within a particular time period, such as within 540 days or 2 years of sample collection.
  • a control level in a method described herein is the average or mean level, optionally plus or minus a statistical variation, which has been observed in a plurality of subjects with latent TB infection that did not advance to active TB disease within the particular time period.
  • a control level in a method described herein is a level that is indicative of chronic latent TB infection.
  • a “threshold level" of a target molecule refers to the level beyond which (e.g., above or below, depending upon the biomarker) is indicative of or diagnostic for a particular infection, disease, condition, or characteristic thereof.
  • a threshold level of for the likelihood of latent TB infection transitioning into active TB disease is a level of a target molecule beyond which (e.g., above or below, depending upon the biomarker) is indicative of a latnet TB infection that is likely to transition into active TB disease within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days.
  • threshold level of a target molecule need not be determined each time the present methods are carried out, and may be a previously determined level that is used as a reference or threshold to determine whether the level in a particular sample is higher or lower than a normal level.
  • a subject with a biomarker level beyond (e.g., above or below, depending upon the biomarker) a threshold level has a statistically significant likelihood (e.g., 80% confidence, 85% confidence, 90% confidence, 95% confidence, 98% confidence, 99% confidence, 99.9% confidence, etc.) of having a latent TB infection transition into active TB disease.
  • Diagnose refers to the detection, determination, or recognition of a health status or condition of an individual on the basis of one or more signs, symptoms, data, or other information pertaining to that individual.
  • the health status of an individual can be diagnosed as healthy/normal (e.g., a diagnosis of the absence of a disease or condition), diagnosed as ill/abnormal (e.g., a diagnosis of the presence, or an assessment of the characteristics, of a disease or condition), and/or high- risk/low-risk (e.g., of developing a disease or condition, of transitioning from a latent infection to an active disease state).
  • diagnosis encompass, with respect to a particular disease or condition: the initial detection of the disease; the characterization or classification of the disease; the characterization of likelihood of advancement of the disease (e.g., from latent to active); the detection of the progression, remission, or recurrence of the disease; and/or the detection of disease response after the administration of a treatment or therapy to the individual.
  • Prognose refers to the prediction of a future course of a disease or condition in an individual who has the disease or condition (e.g., predicting patient survival, predicting likelihood of transition from latent infection to active disease, etc.), and such terms encompass the evaluation of disease response after the administration of a treatment or therapy to the individual.
  • “Evaluate”, “evaluating”, “evaluation”, and variations thereof encompass both “diagnose” and “prognose” and also encompass determinations or predictions about the future course of a disease or condition in an individual who does not have the disease as well as determinations or predictions regarding the likelihood that a disease or condition will recur in an individual who apparently has been cured of the disease.
  • the term “evaluate” also encompasses assessing an individual's response to a therapy, such as, for example, predicting whether an individual is likely to respond favorably to a therapeutic agent or is unlikely to respond to a therapeutic agent (or will experience toxic or other undesirable side effects, for example), selecting a therapeutic agent for administration to an individual, or monitoring or determining an individual's response to a therapy that has been administered to the individual.
  • "evaluating" TB can include, for example, any of the following: diagnosing a subject with TB infection, diagnosing a subject as suffering from TB disease, determining a subject should undergo further testing (e.g., chest x-ray for TB); prognosing the future course of TB infection/disease in an individual; prognosing a the likelihood of TB transitioning from latent to active; determining whether a TB treatment being administered is effective in the individual; or determining or predicting an individual's response to a TB treatment; or selecting a TB treatment to administer to an individual based upon a determination of the biomarker levels derived from the individual's biological sample.
  • detecting or “determining” with respect to a biomarker level includes the use of both the instrument used to observe and record a signal corresponding to a biomarker level and the material/s required to generate that signal.
  • the level is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared
  • host biomarkers are biological molecules (e.g., proteins) that are endogenous to an individual, the expression or level of which is altered (e.g., increased or decreased) upon infection by a pathogenic agent (e.g., Mycobacterium tuberculosis).
  • a pathogenic agent e.g., Mycobacterium tuberculosis
  • Detection and/or quantification of host biomarkers allows for characterization of a pathogenic infection.
  • pathogen biomarkers are molecules (e.g., proteins) that are not endogenous to an infected individual, but produced by a pathogen (e.g., Mycobacterium tuberculosis) that has infected the individual. Detection and/or quantification of pathogen biomarkers (e.g., Mtb biomarkers) allows for characterization of pathogenic infection.
  • pathogen biomarkers e.g., Mtb biomarkers
  • Embodiments described herein include biomarkers, panels of biomarkers, methods, devices, reagents, systems, and kits for detecting, identifying, characterizing, and/or diagnosing infection of a subject (e.g., human subject) with Mycobacterium tuberculosis (Mtb).
  • a subject e.g., human subject
  • Mtb Mycobacterium tuberculosis
  • embodiments relate to characterizing a latent TB infection: (1) as one is likely to advancing or transition into active TB disease within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days; or (2) as one that is unlikely to advance or transition into active TB disease within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days.
  • Such embodiments involve the quantification of one or more biomarkers selected from AMBN, C5, MMP-1, D- dimer, SG1C1, 2DMA, IP- 10, and CXCL16 (soluble); or from AMBN, C5, MMP-1, D- dimer, SG1C1, 2DMA, IP-10, CXCL16 (soluble), and C9; or from: AMBN, C5, MMP-1, D- dimer, SG1C1, 2DMA, KCNE2, and CXCL16 (soluble); or from AMBN, C5, MMP-1, C9, SG1C1, 2DMA, KCNE2, and CXCL16 (soluble).
  • Solid support refers herein to any substrate having a surface to which molecules may be attached, directly or indirectly, through either covalent or non-covalent bonds.
  • a “solid support” can have a variety of physical formats, which can include, for example, a membrane; a chip (e.g., a protein chip); a slide (e.g., a glass slide or coverslip); a column; a hollow, solid, semi-solid, pore- or cavity- containing particle, such as, for example, a bead; a gel; a fiber, including a fiber optic material; a matrix; and a sample receptacle.
  • Exemplary sample receptacles include sample wells, tubes, capillaries, vials, and any other vessel, groove or indentation capable of holding a sample.
  • a sample receptacle can be contained on a multi-sample platform, such as a microtiter plate, slide, microfluidics device, and the like.
  • a support can be composed of a natural or synthetic material, an organic or inorganic material. The composition of the solid support on which capture reagents are attached generally depends on the method of attachment (e.g., covalent attachment).
  • Other exemplary receptacles include microdroplets and microfluidic controlled or bulk oil/aqueous emulsions within which assays and related manipulations can occur.
  • Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glasses, membranes, nylon, natural fibers (such as, for example, silk, wool and cotton), polymers, and the like.
  • the material composing the solid support can include reactive groups such as, for example, carboxy, amino, or hydroxyl groups, which are used for attachment of the capture reagents.
  • Polymeric solid supports can include, e.g., polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone, polyacrylonitrile, polymethyl
  • Suitable solid support particles include, e.g., encoded particles, such as Luminex®-type encoded particles, magnetic particles, and glass particles.
  • methods are provided for determining the likelihood or risk of a subject infected with Mycobacterium tuberculosis (e.g., a subject with latent TB infection) transitioning into active TB disease within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days.
  • Mycobacterium tuberculosis e.g., a subject with latent TB infection
  • a finding that a TB-infected subject is unlikely to transition into active TB disease indicates that the subject is not presently at significant risk of active TB disease.
  • methods are provided for determining the likelihood or risk that a non-infected subject would transition from latent infection to active TB disease within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days, should they become infected by Mycobacterium tuberculosis (or another agent causative of TB).
  • methods comprise testing a subject for TB infection, for example, by skin test, sputum culture, blood test, tissue culture, body fluid culture, chest x- ray, and/or using the methods described in U.S. Prov. Pat. App. 61/987,888, which is herein incorporated by reference in its entirety.
  • a subject is infected with TB (e.g. latent infection), and a determination (e.g., by monitoring symptoms, by chest x-ray, etc.) that a subject does not have active TB disease
  • methods described herein are employed to determine the likelihood that such an infection may progress into active TB disease.
  • biomarker levels e.g., one or more of the TB biomarkers identified in experiments conducted during development of embodiments of the present invention (e.g., one or more biomarkers selected from AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP- 10, KCNE2, and CXCL16 (soluble); or from AMBN, C5, MMP-1, D- dimer, SG1C1, 2DMA, IP-10, KCNE2, CXCL16 (soluble), and C9) as a stand-alone diagnostic test, in some embodiments, biomarker levels are tested in conjunction with other markers or assays for characterizing TB (e.g., skin test, sputum culture, blood test, tissue culture, body fluid culture, chest x-ray, methods described in U.S.
  • biomarker levels are tested in conjunction with other markers or assays for characterizing TB (e.g., skin test, sputum culture, blood test, tissue culture, body fluid culture, chest x-ray,
  • biomarkers can also be evaluated in conjunction with other types of data, particularly data that indicates an individual's risk for TB (e.g., lifestyle, location, age, etc.). These various data can be assessed by automated methods, such as a computer program/software, which can be embodied in a computer or other apparatus/device.
  • a biomarker level for the biomarkers described herein can be detected using any of a variety of known analytical methods.
  • a biomarker level is detected using a capture reagent.
  • the capture reagent can be exposed to the biomarker in solution or can be exposed to the biomarker while the capture reagent is immobilized on a solid support.
  • the capture reagent contains a feature that is reactive with a secondary feature on a solid support. In these embodiments, the capture reagent can be exposed to the biomarker in solution, and then the feature on the capture reagent can be used in conjunction with the secondary feature on the solid support to immobilize the biomarker on the solid support.
  • Capture reagent is selected based on the type of analysis to be conducted.
  • Capture reagents include but are not limited to aptamers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, F(ab')2 fragments, single chain antibody fragments, Fv fragments, single chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, affybodies, nanobodies, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, and synthetic receptors, and modifications and fragments of these.
  • biomarker presence or level is detected using a biomarker/capture reagent complex.
  • the biomarker presence or level is derived from the biomarker/capture reagent complex and is detected indirectly, such as, for example, as a result of a reaction that is subsequent to the biomarker/capture reagent interaction, but is dependent on the formation of the biomarker/capture reagent complex.
  • biomarker presence or level is detected directly from the biomarker in a biological sample.
  • biomarkers are detected using a multiplexed format that allows for the simultaneous detection of two or more biomarkers in a biological sample.
  • capture reagents are immobilized, directly or indirectly, covalently or non-covalently, in discrete locations on a solid support.
  • a multiplexed format uses discrete solid supports where each solid support has a unique capture reagent associated with that solid support, such as, for example quantum dots.
  • an individual device is used for the detection of each one of multiple biomarkers to be detected in a biological sample. Individual devices can be configured to permit each biomarker in the biological sample to be processed simultaneously. For example, a microtiter plate can be used such that each well in the plate is used to analyze one or more of multiple biomarkers to be detected in a biological sample.
  • a fluorescent tag can be used to label a component of the biomarker/capture reagent complex to enable the detection of the biomarker level.
  • the fluorescent label can be conjugated to a capture reagent specific to any of the biomarkers described herein using known techniques, and the fluorescent label can then be used to detect the corresponding biomarker level.
  • Suitable fluorescent labels include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas Red, and other such compounds.
  • the fluorescent label is a fluorescent dye molecule.
  • the fluorescent dye molecule includes at least one substituted indolium ring system in which the substituent on the 3-carbon of the indolium ring contains a chemically reactive group or a conjugated substance.
  • the dye molecule includes an AlexFluor molecule, such as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700.
  • the dye molecule includes a first type and a second type of dye molecule, such as, e.g., two different AlexaFluor molecules.
  • the dye molecule includes a first type and a second type of dye molecule, and the two dye molecules have different emission spectra.
  • Fluorescence can be measured with a variety of instrumentation compatible with a wide range of assay formats.
  • spectrofluorimeters have been designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. See Principles of Fluorescence Spectroscopy, by J.R. Lakowicz, Springer Science + Business Media, Inc., 2004. See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002.
  • a chemiluminescence tag can optionally be used to label a component of the biomarker/capture complex to enable the detection of a biomarker level.
  • Suitable chemiluminescent materials include any of oxalyl chloride, Rodamin 6G,
  • Ru(bipy)32+ TMAE (tetrakis(dimethylamino)ethylene), Pyrogallol (1,2,3- trihydroxibenzene), Lucigenin, peroxy oxalates, Aryl oxalates, Acridinium esters, dioxetanes, and others.
  • the detection method includes an enzyme/substrate combination that generates a detectable signal that corresponds to the biomarker level.
  • the enzyme catalyzes a chemical alteration of the chromogenic substrate which can be measured using various techniques, including spectrophotometry, fluorescence, and chemiluminescence.
  • Suitable enzymes include, for example, luciferases, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, beta- galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6- phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, and the like.
  • the detection method can be a combination of fluorescence, chemiluminescence, radionuclide or enzyme/substrate combinations that generate a measurable signal.
  • multimodal signaling could have unique and advantageous characteristics in biomarker assay formats.
  • the biomarker levels for the biomarkers described herein can be detected using any analytical methods including, singleplex aptamer assays, multiplexed aptamer assays, singleplex or multiplexed immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometric analysis, histological/cytological methods, etc. as discussed below. Determination of Biomarker Levels using Aptamer-Based Assays
  • Assays directed to the detection and quantification of physiologically significant molecules in biological samples and other samples are important tools in scientific research and in the health care field.
  • One class of such assays involves the use of a microarray that includes one or more aptamers immobilized on a solid support.
  • the aptamers are each capable of binding to a target molecule in a highly specific manner and with very high affinity. See, e.g., U.S. Patent No. 5,475,096 entitled “Nucleic Acid Ligands”; see also, e.g., U.S. Patent No. 6,242,246, U.S. Patent No. 6,458,543, and U.S. Patent No. 6,503,715, each of which is entitled "Nucleic Acid Ligand Diagnostic Biochip".
  • the aptamers bind to their respective target molecules present in the sample and thereby enable a determination of a biomarker level corresponding to a biomarker.
  • an "aptamer” refers to a nucleic acid that has a specific binding affinity for a target molecule. It is recognized that affinity interactions are a matter of degree; however, in this context, the "specific binding affinity" of an aptamer for its target means that the aptamer binds to its target generally with a much higher degree of affinity than it binds to other components in a test sample.
  • An “aptamer” is a set of copies of one type or species of nucleic acid molecule that has a particular nucleotide sequence.
  • An aptamer can include any suitable number of nucleotides, including any number of chemically modified nucleotides. "Aptamers" refers to more than one such set of molecules.
  • aptamers can have either the same or different numbers of nucleotides.
  • Aptamers can be DNA or RNA or chemically modified nucleic acids and can be single stranded, double stranded, or contain double stranded regions, and can include higher ordered structures.
  • An aptamer can also be a photoaptamer, where a photoreactive or chemically reactive functional group is included in the aptamer to allow it to be covalently linked to its corresponding target. Any of the aptamer methods disclosed herein can include the use of two or more aptamers that specifically bind the same target molecule.
  • an aptamer may include a tag. If an aptamer includes a tag, all copies of the aptamer need not have the same tag. Moreover, if different aptamers each include a tag, these different aptamers can have either the same tag or a different tag.
  • An aptamer can be identified using any known method, including the SELEX process. Once identified, an aptamer can be prepared or synthesized in accordance with any known method, including chemical synthetic methods and enzymatic synthetic methods.
  • the terms "SELEX” and “SELEX process” are used interchangeably herein to refer generally to a combination of (1) the selection of aptamers that interact with a target molecule in a desirable manner, for example binding with high affinity to a protein, with (2) the amplification of those selected nucleic acids.
  • the SELEX process can be used to identify aptamers with high affinity to a specific target or biomarker.
  • SELEX generally includes preparing a candidate mixture of nucleic acids, binding of the candidate mixture to the desired target molecule to form an affinity complex, separating the affinity complexes from the unbound candidate nucleic acids, separating and isolating the nucleic acid from the affinity complex, purifying the nucleic acid, and identifying a specific aptamer sequence.
  • the process may include multiple rounds to further refine the affinity of the selected aptamer.
  • the process can include amplification steps at one or more points in the process. See, e.g., U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands".
  • the SELEX process can be used to generate an aptamer that covalently binds its target as well as an aptamer that non-covalently binds its target. See, e.g., U.S. Patent No. 5,705,337 entitled “Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi- SELEX.”
  • the SELEX process can be used to identify high-affinity aptamers containing modified nucleotides that confer improved characteristics on the aptamer, such as, for example, improved in vivo stability or improved delivery characteristics. Examples of such modifications include chemical substitutions at the ribose and/or phosphate and/or base positions. SELEX process-identified aptamers containing modified nucleotides are described in U.S. Patent No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides", which describes oligonucleotides containing nucleotide derivatives chemically modified at the 5'- and 2'-positions of pyrimidines. U.S. Patent No.
  • SELEX can also be used to identify aptamers that have desirable off-rate
  • an aptamer comprises at least one nucleotide with a modification, such as a base modification.
  • an aptamer comprises at least one nucleotide with a hydrophobic modification, such as a hydrophobic base modification, allowing for hydrophobic contacts with a target protein.
  • hydrophobic contacts in some embodiments, contribute to greater affinity and/or slower off- rate binding by the aptamer.
  • Nonlimiting exemplary nucleotides with hydrophobic modifications are shown in Figure 52.
  • an aptamer comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least 10 nucleotides with hydrophobic modifications, where each hydrophobic modification may be the same or different from the others.
  • At least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least 10 hydrophobic modifications in an aptamer may be independently selected from the hydrophobic modifications shown in Figure 52.
  • a slow off-rate aptamer (including an aptamers comprising at least one nucleotide with a hydrophobic modification) has an off-rate (t1 ⁇ 2) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.
  • a "SOMAmer” or “Slow Off-Rate Aptamer” refers to an aptamer having improved off-rate characteristics.
  • Slow off-rate aptamers can be generated using the modified SELEX methods described in U.S. Publication No. 20090004667; herein incorporated by reference in its entirety. The methods disclosed herein are in no way limited to slow off-rate aptamers, however, use of the slow off-rate process described in U.S. Pat. No. 7,964,356 and U.S. Publication No. 2012/0115752 (herein incorporated by reference in their entireties), may provide improved results.
  • an assay employs aptamers that include photoreactive functional groups that enable the aptamers to covalently bind or "photocrosslink" their target molecules. See, e.g., U.S. Patent No. 6,544,776 entitled “Nucleic Acid Ligand Diagnostic Biochip”. These photoreactive aptamers are also referred to as photoaptamers. See, e.g., U.S. Patent No. 5,763,177, U.S. Patent No. 6,001,577, and U.S. Patent No.
  • the aptamers are immobilized on the solid support prior to being contacted with the sample. Under certain circumstances, however, immobilization of the aptamers prior to contact with the sample may not provide an optimal assay. For example, pre-immobilization of the aptamers may result in inefficient mixing of the aptamers with the target molecules on the surface of the solid support, perhaps leading to lengthy reaction times and, therefore, extended incubation periods to permit efficient binding of the aptamers to their target molecules. Further, when photoaptamers are employed in the assay and depending upon the material utilized as a solid support, the solid support may tend to scatter or absorb the light used to effect the formation of covalent bonds between the photoaptamers and their target molecules.
  • immobilization of the aptamers on the solid support generally involves an aptamer-preparation step (i.e., the immobilization) prior to exposure of the aptamers to the sample, and this preparation step may affect the activity or functionality of the aptamers.
  • the described aptamer assay methods enable the detection and quantification of a non-nucleic acid target (e.g., a protein target) in a test sample by detecting and quantifying a nucleic acid (i.e., an aptamer).
  • a nucleic acid i.e., an aptamer
  • the described methods create a nucleic acid surrogate (i.e., the aptamer) for detecting and quantifying a non-nucleic acid target, thus allowing the wide variety of nucleic acid technologies, including amplification, to be applied to a broader range of desired targets, including protein targets.
  • Aptamers can be constructed to facilitate the separation of the assay components from an aptamer biomarker complex (or photoaptamer biomarker covalent complex) and permit isolation of the aptamer for detection and/or quantification.
  • these constructs can include a cleavable or releasable element within the aptamer sequence.
  • additional functionality can be introduced into the aptamer, for example, a labeled or detectable component, a spacer component, or a specific binding tag or immobilization element.
  • the aptamer can include a tag connected to the aptamer via a cleavable moiety, a label, a spacer component separating the label, and the cleavable moiety.
  • a cleavable element is a photocleavable linker.
  • the photocleavable linker can be attached to a biotin moiety and a spacer section, can include an NHS group for derivatization of amines, and can be used to introduce a biotin group to an aptamer, thereby allowing for the release of the aptamer later in an assay method.
  • the molecular capture reagents comprise an aptamer or an antibody or the like and the specific target may be a biomarker described herein (e.g., AMBN, C5, MMP-1, D-dimer, SG1C 1, 2DMA, IP-10, KCNE2, CXCL16 (soluble), C9, etc.).
  • a method for signal generation takes advantage of anisotropy signal change due to the interaction of a fluorophore-labeled capture reagent with its specific biomarker target.
  • the labeled capture reacts with its target, the increased molecular weight causes the rotational motion of the fluorophore attached to the complex to become much slower changing the anisotropy value.
  • binding events may be used to quantitatively measure the biomarkers in solutions.
  • Other methods include fluorescence polarization assays, molecular beacon methods, time resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, and the like.
  • An exemplary solution-based aptamer assay that can be used to detect a biomarker level in a biological sample includes the following: (a) preparing a mixture by contacting the biological sample with an aptamer that includes a first tag and has a specific affinity for the biomarker, wherein an aptamer affinity complex is formed when the biomarker is present in the sample; (b) exposing the mixture to a first solid support including a first capture element, and allowing the first tag to associate with the first capture element; (c) removing any components of the mixture not associated with the first solid support; (d) attaching a second tag to the biomarker component of the aptamer affinity complex; (e) releasing the aptamer affinity complex from the first solid support; (f) exposing the released aptamer affinity complex to a second solid support that includes a second capture element and allowing the second tag to associate with the second capture element; (g) removing any non-complexed aptamer from the mixture by partitioning the non-complex
  • a non-limiting exemplary method of detecting biomarkers in a biological sample using aptamers is described, for example, in Kraemer et al, 2011, PLoS One 6(10): e26332; herein incorporated by reference in its entirety.
  • Immunoassay methods are based on the reaction of an antibody to its corresponding target or analyte and can detect the analyte in a sample depending on the specific assay format.
  • monoclonal antibodies and fragments thereof are often used because of their specific epitope recognition.
  • Polyclonal antibodies have also been successfully used in various immunoassays because of their increased affinity for the target as compared to monoclonal antibodies.
  • Immunoassays have been designed for use with a wide range of biological sample matrices. Immunoassay formats have been designed to provide qualitative, semi-quantitative, and quantitative results.
  • Quantitative results are generated through the use of a standard curve created with known concentrations of the specific analyte to be detected.
  • the response or signal from an unknown sample is plotted onto the standard curve, and a quantity or level corresponding to the target in the unknown sample is established.
  • ELISA or EIA can be quantitative for the detection of an analyte. This method relies on attachment of a label to either the analyte or the antibody and the label component includes, either directly or indirectly, an enzyme. ELISA tests may be formatted for direct, indirect, competitive, or sandwich detection of the analyte. Other methods rely on labels such as, for example, radioisotopes (1125) or fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation,
  • Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescent, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time resolved-FRET (TR-FRET) immunoassays.
  • ELISA enzyme-linked immunosorbent assay
  • FRET fluorescence resonance energy transfer
  • TR-FRET time resolved-FRET
  • biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level discrimination, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.
  • Methods of detecting and/or for quantifying a detectable label or signal generating material depend on the nature of the label.
  • the products of reactions catalyzed by appropriate enzymes can be, without limitation, fluorescent, luminescent, or radioactive or they may absorb visible or ultraviolet light.
  • detectors suitable for detecting such detectable labels include, without limitation, x-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and densitometers.
  • Any of the methods for detection can be performed in any format that allows for any suitable preparation, processing, and analysis of the reactions. This can be, for example, in multi-well assay plates (e.g., 96 wells or 386 wells) or using any suitable array or microarray. Stock solutions for various agents can be made manually or robotically, and all subsequent pipetting, diluting, mixing, distribution, washing, incubating, sample readout, data collection and analysis can be done robotically using commercially available analysis software, robotics, and detection instrumentation capable of detecting a detectable label.
  • Measuring mRNA in a biological sample may, in some embodiments, be used as a surrogate for detection of the level of the corresponding protein in the biological sample.
  • a biomarker or biomarker panel described herein can be detected by detecting the appropriate RNA.
  • mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed with qPCR).
  • RT-PCR is used to create a cDNA from the mRNA.
  • the cDNA may be used in a qPCR assay to produce fluorescence as the DNA amplification process progresses. By comparison to a standard curve, qPCR can produce an absolute measurement such as number of copies of mRNA per cell.
  • Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure expression levels of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.
  • a biomarker described herein may be used in molecular imaging tests.
  • an imaging agent can be coupled to a capture reagent, which can be used to detect the biomarker in vivo.
  • In vivo imaging technologies provide non-invasive methods for determining the state of a particular disease in the body of an individual. For example, entire portions of the body, or even the entire body, may be viewed as a three dimensional image, thereby providing valuable information concerning morphology and structures in the body. Such technologies may be combined with the detection of the biomarkers described herein to provide information concerning the biomarker in vivo.
  • in vivo molecular imaging technologies are expanding due to various advances in technology. These advances include the development of new contrast agents or labels, such as radiolabels and/or fluorescent labels, which can provide strong signals within the body; and the development of powerful new imaging technology, which can detect and analyze these signals from outside the body, with sufficient sensitivity and accuracy to provide useful information.
  • the contrast agent can be visualized in an appropriate imaging system, thereby providing an image of the portion or portions of the body in which the contrast agent is located.
  • the contrast agent may be bound to or associated with a capture reagent, such as an aptamer or an antibody, for example, and/or with a peptide or protein, or an oligonucleotide (for example, for the detection of gene expression), or a complex containing any of these with one or more macromolecules and/or other particulate forms.
  • a capture reagent such as an aptamer or an antibody, for example, and/or with a peptide or protein, or an oligonucleotide (for example, for the detection of gene expression), or a complex containing any of these with one or more macromolecules and/or other particulate forms.
  • the contrast agent may also feature a radioactive atom that is useful in imaging.
  • Suitable radioactive atoms include technetium-99m or iodine-123 for scintigraphic studies.
  • Other readily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI) such as, for example, iodine-123 again, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese or iron.
  • MRI magnetic resonance imaging
  • Standard imaging techniques include but are not limited to magnetic resonance imaging, computed tomography scanning, positron emission tomography (PET), single photon emission computed tomography (SPECT), and the like.
  • the type of detection instrument available is a major factor in selecting a given contrast agent, such as a given radionuclide and the particular biomarker that it is used to target (protein, mRNA, and the like).
  • the radionuclide chosen typically has a type of decay that is detectable by a given type of instrument.
  • its half-life should be long enough to enable detection at the time of maximum uptake by the target tissue but short enough that deleterious radiation of the host is minimized.
  • Exemplary imaging techniques include but are not limited to PET and SPECT, which are imaging techniques in which a radionuclide is synthetically or locally administered to an individual. The subsequent uptake of the radiotracer is measured over time and used to obtain information about the targeted tissue and the biomarker. Because of the high-energy (gamma-ray) emissions of the specific isotopes employed and the sensitivity and
  • the two-dimensional distribution of radioactivity may be inferred from outside of the body.
  • positron-emitting nuclides in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18.
  • Isotopes that decay by electron capture and/or gamma-emission are used in SPECT and include, for example iodine-123 and technetium- 99m.
  • An exemplary method for labeling amino acids with technetium-99m is the reduction of pertechnetate ion in the presence of a chelating precursor to form the labile technetium- 99m-precursor complex, which, in turn, reacts with the metal binding group of a
  • Antibodies are frequently used for such in vivo imaging diagnostic methods.
  • the preparation and use of antibodies for in vivo diagnosis is well known in the art.
  • aptamers may be used for such in vivo imaging diagnostic methods.
  • an aptamer that was used to identify a particular biomarker described herein may be appropriately labeled and injected into an individual to detect the biomarker in vivo.
  • the label used will be selected in accordance with the imaging modality to be used, as previously described.
  • Aptamer-directed imaging agents could have unique and advantageous characteristics relating to tissue penetration, tissue distribution, kinetics, elimination, potency, and selectivity as compared to other imaging agents.
  • Such techniques may also optionally be performed with labeled oligonucleotides, for example, for detection of gene expression through imaging with antisense oligonucleotides. These methods are used for in situ hybridization, for example, with fluorescent molecules or radionuclides as the label. Other methods for detection of gene expression include, for example, detection of the activity of a reporter gene.
  • optical imaging Another general type of imaging technology is optical imaging, in which fluorescent signals within the subject are detected by an optical device that is external to the subject. These signals may be due to actual fluorescence and/or to bioluminescence. Improvements in the sensitivity of optical detection devices have increased the usefulness of optical imaging for in vivo diagnostic assays.
  • the biomarkers described herein may be detected in a variety of tissue samples using histological or cytological methods.
  • endo- and trans- bronchial biopsies, fine needle aspirates, cutting needles, and core biopsies can be used for histology.
  • Bronchial washing and brushing, pleural aspiration, and sputum, can be used for cyotology.
  • Any of the biomarkers identified herein can be used to stain a specimen as an indication of disease.
  • one or more capture reagent/s specific to the corresponding biomarker/s are used in a cytological evaluation of a sample and may include one or more of the following: collecting a cell sample, fixing the cell sample, dehydrating, clearing, immobilizing the cell sample on a microscope slide, permeabilizing the cell sample, treating for analyte retrieval, staining, destaining, washing, blocking, and reacting with one or more capture reagent/s in a buffered solution.
  • the cell sample is produced from a cell block.
  • one or more capture reagent/s specific to the corresponding biomarkers are used in a histological evaluation of a tissue sample and may include one or more of the following: collecting a tissue specimen, fixing the tissue sample, dehydrating, clearing, immobilizing the tissue sample on a microscope slide, permeabilizing the tissue sample, treating for analyte retrieval, staining, destaining, washing, blocking, rehydrating, and reacting with capture reagent/s in a buffered solution.
  • fixing and dehydrating are replaced with freezing.
  • the one or more aptamer/s specific to the corresponding biomarker/s are reacted with the histological or cytological sample and can serve as the nucleic acid target in a nucleic acid amplification method.
  • Suitable nucleic acid amplification methods include, for example, PCR, q-beta replicase, rolling circle amplification, strand displacement, helicase dependent amplification, loop mediated isothermal amplification, ligase chain reaction, and restriction and circularization aided rolling circle amplification.
  • the one or more capture reagent/s specific to the corresponding biomarkers for use in the histological or cytological evaluation are mixed in a buffered solution that can include any of the following: blocking materials, competitors, detergents, stabilizers, carrier nucleic acid, polyanionic materials, etc.
  • a “cytology protocol” generally includes sample collection, sample fixation, sample immobilization, and staining.
  • Cell preparation can include several processing steps after sample collection, including the use of one or more aptamers for the staining of the prepared cells.
  • mass spectrometers can be used to detect biomarker levels.
  • a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and instrument- control system, and a data system. Difference in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities.
  • an inlet can be a capillary-column liquid chromatography source or can be a direct probe or stage such as used in matrix-assisted laser desorption.
  • Common ion sources are, for example, electrospray, including nanospray and microspray or matrix-assisted laser desorption.
  • Mass analyzers include a quadrupole mass filter, ion trap mass analyzer and time- of-flight mass analyzer. Additional mass spectrometry methods are well known in the art (see Burlingame et al. Anal. Chem. 70:647 R-716R (1998); Kinter and Sherman, New York (2000)).
  • Protein biomarkers and biomarker levels can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI- MS/(MS)n, matrix-assisted laser desorption ionization time-of-fiight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-fiight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF/TOF) technology, called ultraflex III TOF/TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS)N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS/
  • Labeling methods include but are not limited to isobaric tag for relative and absolute quantitation (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC).
  • Capture reagents used to selectively enrich samples for candidate biomarker proteins prior to mass spectroscopic analysis include but are not limited to aptamers, antibodies, nucleic acid probes, chimeras, small molecules, an F(ab')2 fragment, a single chain antibody fragment, an Fv fragment, a single chain Fv fragment, a nucleic acid, a lectin, a ligand-binding receptor, affybodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g.
  • biomarker levels that are useful in the methods described herein, where the methods comprise detecting, in a biological sample from an individual, at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or at least nine biomarkers selected from the described herein.
  • the methods comprise detecting, in a biological sample from an individual, at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or at least nine biomarkers selected from the described herein.
  • biomarker levels can be detected and classified individually or they can be detected and classified collectively, as for example in a multiplex assay format.
  • a biomarker "signature" for a given diagnostic test contains a set of markers, each marker having characteristic levels in the populations of interest.
  • Characteristic levels may refer to the mean or average of the biomarker levels for the individuals in a particular group.
  • a diagnostic method described herein can be used to assign an unknown sample from an individual into one of two groups: TB infected or non-infected, active TB or no active TB, latent TB or no TB infection, etc.
  • the assignment of a sample into one of two or more groups e.g.,, TB infection, latent infection, active infection, non-infected, etc.
  • classification e.g., TB infection, latent infection, active infection, non-infected, etc.
  • Classification methods may also be referred to as scoring methods.
  • classification methods can be used to construct a diagnostic classifier from a set of biomarker levels.
  • classification methods are performed using supervised learning techniques in which a data set is collected using samples obtained from individuals within two (or more, for multiple classification states) distinct groups one wishes to distinguish. Since the class (group or population) to which each sample belongs is known in advance for each sample, the classification method can be trained to give the desired classification response. It is also possible to use unsupervised learning techniques to produce a diagnostic classifier.
  • diagnostic classifiers include decision trees; bagging + boosting + forests; rule inference based learning; Parzen Windows; linear models; logistic; neural network methods; unsupervised clustering; K-means; hierarchical ascending/ descending; semi-supervised learning; prototype methods; nearest neighbor; kernel density estimation; support vector machines; hidden Markov models; Boltzmann Learning; and classifiers may be combined either simply or in ways which minimize particular obj ective functions.
  • Pattern Classification R.O. Duda, et al, editors, John Wiley & Sons, 2nd edition, 2001 ; see also, The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al, editors, Springer Science+Business Media, LLC, 2nd edition, 2009.
  • training data includes samples from the distinct groups (classes) to which unknown samples will later be assigned.
  • samples collected from individuals in a control population and individuals in a particular disease population can constitute training data to develop a classifier that can classify unknown samples (or, more particularly, the individuals from whom the samples were obtained) as either having the disease or being free from the disease.
  • the development of the classifier from the training data is known as training the classifier.
  • Specific details on classifier training depend on the nature of the supervised learning technique. Training a naive Bayesian classifier is an example of such a supervised learning technique (see, e.g., Pattern Classification, R.O. Duda, et al, editors, John Wiley & Sons, 2nd edition, 2001 ; see also, The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T.
  • Over-fitting occurs when a statistical model describes random error or noise instead of the underlying relationship. Over-fitting can be avoided in a variety of way, including, for example, by limiting the number of markers used in developing the classifier, by assuming that the marker responses are independent of one another, by limiting the complexity of the underlying statistical model employed, and by ensuring that the underlying statistical model conforms to the data.
  • An illustrative example of the development of a diagnostic test using a set of biomarkers includes the application of a naive Bayes classifier, a simple probabilistic classifier based on Bayes theorem with strict independent treatment of the biomarkers.
  • Each biomarker is described by a class-dependent probability density function (PDF) for the measured RFU values or log RFU (relative fluorescence units) values in each class.
  • PDFs for the set of markers in one class is assumed to be the product of the individual class- dependent PDFs for each biomarker.
  • Training a naive Bayes classifier in this context amounts to assigning parameters ("parameterization") to characterize the class dependent PDFs. Any underlying model for the class-dependent PDFs may be used, but the model should generally conform to the data observed in the training set.
  • the performance of the naive Bayes classifier is dependent upon the number and quality of the biomarkers used to construct and train the classifier.
  • a single biomarker will perform in accordance with its KS-distance (Kolmogorov-Smirnov).
  • the addition of subsequent markers with good KS distances (>0.3, for example) will, in general, improve the classification performance if the subsequently added markers are independent of the first marker.
  • KS-distance Kolmogorov-Smirnov
  • KS distances >0.3, for example
  • many high scoring classifiers can be generated with a variation of a greedy algorithm. (A greedy algorithm is any algorithm that follows the problem solving metaheuristic of making the locally optimal choice at each stage with the hope of finding the global optimum.)
  • ROC receiver operating characteristic
  • TPR true positive rate
  • FPR false positive rate
  • the area under the ROC curve (AUC) is commonly used as a summary measure of diagnostic accuracy. It can take values from 0.0 to 1.0.
  • the AUC has an important statistical property: the AUC of a classifier is equivalent to the probability that the classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance (Fawcett T, 2006. An introduction to ROC analysis. Partem Recognition Letters .27: 861-874). This is equivalent to the Wilcoxon test of ranks (Hanley, J.A., McNeil, B.J., 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29- 36.).
  • Exemplary embodiments use any number of the biomarkers provided herein in various combinations to produce diagnostic tests for detecting TB infection in a sample from an individual.
  • the markers provided herein can be combined in many ways to produce classifiers.
  • a classifier may comprise AMBN, C5, MMP-1, D-dimer, SG1C1, 2DMA, IP- 10, KCNE2, CXCL16 (soluble), and/or C9; or any suitable combinations or subcombinations thereof.
  • a biological sample is run in one or more assays to produce the relevant quantitative biomarker levels used for classification.
  • the measured biomarker levels are used as input for the classification method that outputs a classification and an optional score for the sample that reflects the confidence of the class assignment.
  • a biological sample is optionally diluted and run in a multiplexed aptamer assay, and data is assessed as follows.
  • the data from the assay are optionally normalized and calibrated, and the resulting biomarker levels are used as input to a Bayes classification scheme.
  • the log-likelihood ratio is computed for each measured biomarker individually and then summed to produce a final classification score, which is also referred to as a diagnostic score. The resulting assignment as well as the overall
  • any combination of the biomarkers described herein can be detected using a suitable kit, such as for use in performing the methods disclosed herein.
  • the biomarkers described herein may be combined in any suitable combination, or may be combined with other markers not described herein.
  • any kit can contain one or more detectable labels as described herein, such as a fluorescent moiety, etc.
  • a kit includes (a) one or more capture reagents (such as, for example, at least one aptamer or antibody) for detecting one or more biomarkers in a biological sample, and optionally (b) one or more software or computer program products for predicting whether the individual from whom the biological sample was obtained is TB infected.
  • capture reagents such as, for example, at least one aptamer or antibody
  • software or computer program products for predicting whether the individual from whom the biological sample was obtained is TB infected.
  • one or more instructions for manually performing the above steps by a human can be provided.
  • a kit comprises a solid support, a capture reagent, and a signal generating material.
  • the kit can also include instructions for using the devices and reagents, handling the sample, and analyzing the data. Further the kit may be used with a computer system or software to analyze and report the result of the analysis of the biological sample.
  • kits can also contain one or more reagents (e.g., solubilization buffers, detergents, washes, or buffers) for processing a biological sample.
  • reagents e.g., solubilization buffers, detergents, washes, or buffers
  • Any of the kits described herein can also include, e.g., buffers, blocking agents, mass spectrometry matrix materials, antibody capture agents, positive control samples, negative control samples, software and information such as protocols, guidance and reference data.
  • kits are provided for the analysis of TB infection, wherein the kits comprise PCR primers for one or more biomarkers described herein.
  • a kit may further include instructions for use and correlation of the biomarkers with TB infection.
  • a kit may include a DNA array containing the complement of one or more of the biomarkers described herein, reagents, and/or enzymes for amplifying or isolating sample DNA.
  • the kits may include reagents for real-time PCR, for example, TaqMan probes and/or primers, and enzymes.
  • a kit can comprise (a) reagents comprising at least one capture reagent for determining the level of one or more biomarkers in a test sample, and optionally (b) one or more algorithms or computer programs for performing the steps of comparing the amount of each biomarker quantified in the test sample to one or more predetermined cutoffs.
  • an algorithm or computer program assigns a score for each biomarker quantified based on said comparison and, in some embodiments, combines the assigned scores for each biomarker quantified to obtain a total score.
  • an algorithm or computer program compares the total score with a predetermined score, and uses the comparison to determine, for example, likelihood of latent TB infection advancing into active TB disease.
  • one or more instructions for manually performing the above steps by a human can be provided.
  • a method may comprise the following: 1) collect or otherwise obtain a biological sample; 2) perform an analytical method to detect and measure the biomarker or biomarkers in the panel in the biological sample; and 3) report the results of the biomarker levels.
  • the results of the biomarker levels are reported qualitatively rather than quantitatively, such as, for example, a proposed diagnosis or numeric result indicating the percent likelihood (e.g., within a margin of error) of a latent infection transitioning to active TB.
  • a qualitative or quantitative risk of developing active TB disease within a particular time period is provided (e.g., within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days).
  • a method comprises the following: 1) collect or otherwise obtain a biological sample; 2) perform an analytical method to detect and measure the biomarker or biomarkers in the panel in the biological sample; 3) perform any data normalization or standardization; 4) calculate each biomarker level; and 5) report the results of the biomarker levels.
  • the biomarker levels are combined in some way and a single value for the combined biomarker levels is reported.
  • the reported value may be a single number determined from the sum of all the marker calculations that is compared to a pre - set threshold value that is an indication of the presence or absence of disease.
  • the diagnostic score may be a series of bars that each represent a biomarker value and the pattern of the responses may be compared to a pre-set pattern for determination of the presence or absence of disease.
  • FIG. 53 An example of a computer system 100 is shown in Figure 53.
  • system 100 is shown comprised of hardware elements that are electrically coupled via bus 108, including a processor 101 , input device 102, output device 103, storage device 104, computer-readable storage media reader 105a, communications system 106 processing acceleration (e.g., DSP or special-purpose processors) 107 and memory 109.
  • Computer-readable storage media reader 105a is further coupled to computer- readable storage media 105b, the combination comprehensively representing remote, local, fixed and/or removable storage devices plus storage media, memory, etc.
  • System 100 for temporarily and/or more permanently containing computer-readable information, which can include storage device 104, memory 109 and/or any other such accessible system 100 resource.
  • System 100 also comprises software elements (shown as being currently located within working memory 191) including an operating system 192 and other code 193, such as programs, data and the like.
  • system 100 has extensive flexibility and configurability.
  • a single architecture might be utilized to implement one or more servers that can be further configured in accordance with currently desirable protocols, protocol variations, extensions, etc.
  • embodiments may well be utilized in accordance with more specific application
  • system elements might be implemented as sub- elements within a system 100 component (e.g., within communications system 106).
  • Customized hardware might also be utilized and/or particular elements might be implemented in hardware, software or both. Further, while connection to other computing devices such as network input/output devices (not shown) may be employed, it is to be understood that wired, wireless, modem, and/or other connection or connections to other computing devices might also be utilized.
  • network input/output devices not shown
  • wired, wireless, modem, and/or other connection or connections to other computing devices might also be utilized.
  • the system can comprise a database containing features of biomarkers characteristic of TB infection.
  • the biomarker data (or biomarker information) can be utilized as an input to the computer for use as part of a computer implemented method.
  • the biomarker data can include the data as described herein.
  • system further comprises one or more devices for providing input data to the one or more processors.
  • system further comprises a memory for storing a data set of ranked data elements.
  • the device for providing input data comprises a detector for detecting the characteristic of the data element, e.g., such as a mass spectrometer or gene chip reader.
  • the system additionally may comprise a database management system. User requests or queries can be formatted in an appropriate language understood by the database management system that processes the query to extract the relevant information from the database of training sets.
  • the system may be connectable to a network to which a network server and one or more clients are connected.
  • the network may be a local area network (LAN) or a wide area network (WAN), as is known in the art.
  • the server includes the hardware necessary for running computer program products (e.g., software) to access database data for processing user requests.
  • the system may include an operating system (e.g., UNIX® or Linux) for executing instructions from a database management system.
  • the operating system can operate on a global communications network, such as the internet, and utilize a global communications network server to connect to such a network.
  • the system may include one or more devices that comprise a graphical display interface comprising interface elements such as buttons, pull down menus, scroll bars, fields for entering text, and the like as are routinely found in graphical user interfaces known in the art.
  • Requests entered on a user interface can be transmitted to an application program in the system for formatting to search for relevant information in one or more of the system databases.
  • Requests or queries entered by a user may be constructed in any suitable database language.
  • the graphical user interface may be generated by a graphical user interface code as part of the operating system and can be used to input data and/or to display inputted data.
  • the result of processed data can be displayed in the interface, printed on a printer in communication with the system, saved in a memory device, and/or transmitted over the network or can be provided in the form of the computer readable medium.
  • the system can be in communication with an input device for providing data regarding data elements to the system (e.g., expression values).
  • the input device can include a gene expression profiling system including, e.g., a mass spectrometer, gene chip or array reader, and the like.
  • the methods and apparatus for analyzing biomarker information may be implemented in any suitable manner, for example, using a computer program operating on a computer system.
  • a conventional computer system comprising a processor and a random access memory, such as a remotely-accessible application server, network server, personal computer or workstation may be used.
  • Additional computer system components may include memory devices or information storage systems, such as a mass storage system and a user interface, for example a conventional monitor, keyboard and tracking device.
  • the computer system may be a stand-alone system or part of a network of computers including a server and one or more databases.
  • the biomarker analysis system can provide functions and operations to complete data analysis, such as data gathering, processing, analysis, reporting and/or diagnosis.
  • the computer system can execute the computer program that may receive, store, search, analyze, and report information relating to the biomarkers.
  • the computer program may comprise multiple modules performing various functions or operations, such as a processing module for processing raw data and generating supplemental data and an analysis module for analyzing raw data and supplemental data to generate a disease status and/or diagnosis.
  • Methods may comprise generating or collecting any other information, including additional biomedical information, regarding the condition of the individual relative to the disease, identifying whether further tests may be desirable, or otherwise evaluating the health status of the individual.
  • a computer program product may include a computer readable medium having computer readable program code embodied in the medium for causing an application program to execute on a computer with a database.
  • a "computer program product” refers to an organized set of instructions in the form of natural or programming language statements that are contained on a physical media of any nature (e.g., written, electronic, magnetic, optical or otherwise) and that may be used with a computer or other automated data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to act in accordance with the particular content of the statements.
  • Computer program products include without limitation: programs in source and object code and/or test or data libraries embedded in a computer readable medium.
  • the computer program product that enables a computer system or data processing equipment device to act in pre-selected ways may be provided in a number of forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or compressed versions of the foregoing and any and all equivalents.
  • a computer program product for characterizing the TB- infection status (e.g., likelihood of advancement to active TB) of a subject.
  • the computer program product includes a computer readable medium embodying program code executable by a processor of a computing device or system, the program code comprising: code that retrieves data attributed to a biological sample from an individual, wherein the data comprises biomarker levels that correspond to one or more of the biomarkers described herein, and code that executes a classification method that indicates the TB-infection status of the individual as a function of the biomarker levels.
  • the embodiments may be embodied as code stored in a computer-readable memory of virtually any kind including, without limitation, RAM, ROM, magnetic media, optical media, or magneto-optical media. Even more generally, the embodiments could be implemented in software, or in hardware, or any combination thereof including, but not limited to, software running on a general purpose processor, microcode, programmable logic arrays (PLAs), or application-specific integrated circuits (ASICs).
  • a general purpose processor microcode, programmable logic arrays (PLAs), or application-specific integrated circuits (ASICs).
  • PLAs programmable logic arrays
  • ASICs application-specific integrated circuits
  • embodiments could be accomplished as computer signals embodied in a carrier wave, as well as signals (e.g., electrical and optical) propagated through a transmission medium.
  • signals e.g., electrical and optical
  • the various types of information discussed above could be formatted in a structure, such as a data structure, and transmitted as an electrical signal through a transmission medium or stored on a computer readable medium.
  • a subject's TB status e.g., no infection; latent infection not likely to advance to active TB; latent infection - likely to advance to active TB within 30 days, 45 days, 60 days, 90 days, 120 days, 180 days, 270 days, 360 days, 450 days, or 540 days; active TB disease; etc.
  • medications used to treat latent TB infection include:
  • TB disease is treated by taking several drugs for 6 to 9 months.
  • drugs currently approved by the U.S. Food and Drug Administration (FDA) for treating TB.
  • the first-line anti-TB agents that form the core of treatment regimens include: isoniazid (INH), rifampin (RIF), ethambutol (EMB), and pyrazinamide (PZA).
  • Regimens for treating TB disease have an initial phase of 2 months, followed by a choice of several options for the continuation phase of either 4 or 7 months (total of 6 to 9 months for treatment).
  • methods of monitoring TB infection/disease and/or treatment of TB infection/disease are provided.
  • the present methods of detecting TB infection are carried out at a time 0.
  • the method is carried out again at a time 1, and optionally, a time 2, and optionally, a time 3, etc., in order to monitor the progression of TB infection or to monitor the effectiveness of one or more treatments of TB.
  • Time points for detection may be separated by, for example at least 1 day, at least 2 days, at least 4 days, at least 1 week, at least 2 weeks, at least 1 month, at least 2 months, at least 3 months, at least 4 months, at least 6 months, or by 1 year or more.
  • a treatment regimen is altered based upon the results of monitoring (e.g., upon determining that a first treatment is ineffective).
  • Example 1 Samples and subjects
  • Samples were obtained from a study of TB risk conducted by the South African Tuberculosis Vaccine Initiative (SATVI) in collaboration with the University of Cape Town (UCT).
  • SATVI South African Tuberculosis Vaccine Initiative
  • UCT University of Cape Town
  • the TB Risk study enrolled 6,363 adolescents (12-18 years of age) prospectively at several high schools in an area -lOOkm from Cape Town with a high burden of TB. Blood was collected at mobile collection centers from participants at 6 month intervals between 2006 and 2008 and during this time some participants developed active TB.
  • TB diagnosis was determined by bacteriological testing, though subjects had positive Quantiferon Gold In- Tube (QFT) and tuberculin skin tests (TST) at time of enrollment as immunological evidence of Mtb infection.
  • QFT Quantiferon Gold In- Tube
  • TST tuberculin skin tests
  • Table 1 Participant demographics for the case cohort in discovery (training) and verification (test) sets, as well as a p-value for the two group comparison using a t-test
  • Table 2 Number of study participants, samples, and sample locations for the TB case and non-TB control cohorts. * discovery samples only listed (verification samples are blinded)
  • Hybridization normalization was performed using elution probes and is performed on a per sample basis.
  • Hybridization scale factors are expected to be within the range 0.4-2.5, and all samples passed. As shown in Figure 3, the median hybridization scale factor in each run is within 10% of unity except for plate B, which is slightly brighter compared to the other plates.
  • mice were selected by matching bin number and study day. All samples were put into bins selected to control for various factors such as age, location, Mycobacterium tuberculosis exposure, etc. Stability selection using LI -regularized logistic regression was used to identify stable features in the presence of the available clinical covariates.
  • Figure 5 shows demographic information for 57 TB Case samples (pre-treatment) and 197 Control samples used in the analysis. Comparing all 197 Control samples to all 57 TB case samples, 500 proteins were found to be significant at a 5% Benjamini-Hochberg False
  • Table 3 shows the KS statistics for the top 25 ranked proteins.
  • Table 3 Top 25 ranked proteins differentiating all TB Cases from all non-TB Controls. Proteins with positive KS distances are lower in TB cases.
  • Figure 6 shows the cumulative distribution functions (CDFs) for the top 9 proteins listed in the table above. P-values were calculated using a standard distribution (p-value) as well as an empirical null distribution created through class scrambling (Pemp).
  • Figure 7 shows the KS distances with class randomization statistics for the top 150 ranked proteins, which corresponded to a p-value cutoff of 1.35e-4.
  • the total height of each bar represents the KS distance for the TB Case vs. Control comparison, with the top being green for proteins that are higher in TB Cases and red if they are lower.
  • the height of the orange portion of each bar represents the median KS distance achieved through class randomization for that feature, and the error bars represent 95% confidence intervals.
  • Figure 8 shows a volcano plot of the negative loglO-transformed p-values versus the log2 of the median TB RFU value over the median Control RFU value. A value of 1 on the horizontal axis corresponds to a 2-fold change in RFU.
  • Figure 9 shows the longitudinal RFU measurements for the 16 TB subjects with >1 time points overlaid on to a 'control band' created by interpolating the median, inter-quartile range and range of the control data.
  • the control band is analogous to an interpolated boxplot of the RFU values of the Controls between days in study.
  • the top axis (Days in Study) corresponds to the controls and the bottom axis (Days to Rx) corresponds to the TB cases. Time moves to the right in both groups.
  • Hierarchical clustering arranges proteins according to similarities in expression.
  • each row corresponds to a bin and each column a protein. Therefore, the coloring of each column represents the magnitude of the t-statistics for a particular protein across the 7 bins.
  • Dendrograms ( Figurea 11 -15, right) show the hierarchical grouping structure for regions marked A-E, with the height of each branch corresponding to the similarity between the underlying groups.
  • a generalized linear mixed effects model was used to determine the ability of each protein to classify subjects based on diagnosis while controlling for the bin number, as well as to determine which proteins have differences between the two groups which are dependent on the bin itself.
  • Table 4 shows statistics for the top 50 ranked proteins.
  • a single p- value was generated for each protein (pfixed) and was corrected for 3040 multiple comparisons (qfixed).
  • 21 random effects p-value (p ra ndom) were generate (one for each bin), and the minimum value is shown in the table.
  • p ra ndom random effects p-value
  • Table 4 Generalized linear mixed model statistics comparing all 57 TB cases to 197 Controls.
  • Table 5 Linear regression statistics for all TB Case samples ⁇ 300 days before treatment initiation.
  • Figure 16 shows scatter plots for the top 9 ranked proteins from a linear regression. Linear fits for all TB cases are shown as a function of time to treatment, with time moving to the left. To provide a notion for how well these proteins distinguish the TB from non-TB cohorts, this information is overlaid onto data representing a boxplot for all control RFU data.
  • the dark band corresponds to the interquartile range (IQR), while the lighter shaded region corresponds to the whiskers, or the nearest data point that's within the upper/lower quartile + 1.5*IQR.
  • IQR interquartile range
  • Time bins were created based on the distribution of non-repeated subjects within each time interval.
  • Figure 17 shows sample times for all TB subjects as a function of time to the beginning of treatment.
  • Figure 18 shows demographics for TB Cases 0 to 180 days pre-treatment and matched controls.
  • bhFDR Benjamini-Hochberg False Discovery Rate
  • Table 6 shows KS statistics for the top 25 ranked proteins.
  • Table 6 Top 25 ranked proteins differentiating TB Cases 0-180 days pre-Rx from matched non-TB Controls. Proteins with positive KS distances are lower in TB cases.
  • the second ranked protein CLFB STAAE is a Staphylococcus aureus protein.
  • Figure 19 shows CDFs for the top 9 ranked proteins, including CLFB STAAE, and
  • Figure 20 shows KS distances with class randomization statistics for the top 100 ranked proteins, which corresponded to a p-value cutoff of 1.12e-3.
  • Figure 21 shows a volcano plot of the negative loglO-transformed p-values versus the log2 of the median TB RFU value over the median Control RFU value. A value of 1 on the horizontal axis corresponds to a 2-fold change in RFU.
  • Figure 22 shows control band plots for the top 6 ranked proteins.
  • Figure 23 shows demographics for the controls at all time-points as well as the TB cases 180 to 360 days pre-treatment.
  • a KS test identified 0 proteins to be differentially expressed between TB and non-TB subjects at 5% FDR. 16 proteins were found to be significant at a 20% FDR with 14 having higher expression i TB group. None of the known TB-specific were observed to have an FDR ⁇ 40%.
  • Table 7 Top 25 ranked proteins differentiating TB Cases 180-360 days pre-Rx from matched non-TB Controls. Proteins with positive KS distances are lower in TB cases.
  • Figure 24 shows CDFs for the top 9 ranked proteins.
  • Figures 29-32 show CDFs of the top 9, the top 100 features with class randomization statistics, a volcano plot and control band plots for the top 6, respectively.
  • Figure 33 shows demographics for TB cases 540 to 700 days pre-treatment, as well as their matched controls. Comparing 8 TB Cases with 33 matched non-TB Controls, 0.92 was the smallest FDR attained. However, the KS distances were relatively large with an absolute range of [0.842 0.699] for the top 10 ranked proteins. Table 9 below shows KS statistics for the top 25 proteins. Plasminogen(#6), I-TAC (#17), Fibronectin (#22), D-dimer(#24). IgG (#22) and D-dimer (#7) were also found to be a top 20 markers in the 0-180 time point. Also, 2DMA (#5) is a major histo-compatibility antigen which has implications in infection.
  • Figures 34-37 shows biomarker data for 540 to 700 days pre-treatment.
  • Table 9 Top 25 ranked proteins differentiating TB Cases 540-700 days pre-Rx from matched non-TB Controls. Proteins with positive KS distances are lower in TB cases.
  • Metadata for GENDER, SITE ID, BMI, and AGE were included along with all 3040 human, non-human, and TB-specific proteins when performing stability selection using an LI -regularized logistic regression model. As with the univariate KS analysis this was done using all TB cases and matched controls, then within each time point.
  • Table 3 Stability selection statistics for proteins with a maximum selection probability >50%.
  • AMBN, C5, MMP-1, DR3, SIRT2, and C9 were also found to be in the top 25 proteins ranked by KS distance comparing all TB Case to all Control samples.
  • Figure 38 shows the stability paths for all proteins in the upper panel and the regularization paths in the bottom. Stability paths are labeled by total area under the path in each figure, while tables are ranked by maximum selection probability.
  • Figure 39 shows CDFs for the top 6 proteins ranked by selection probability.
  • Logistic regression was performed on standardized RFU values
  • >4 were replaced with simulated values from the 90th percentile of a simulated distribution, the CDF plots show the actual standardized scores without replacement. No proteins in the top 11 were observed to have values
  • a subset of proteins for model building was first selected using the ranked lists from stability selection and univariate KS tests. From the stability selection list only proteins with a maximum selection probability >50% were included, and a KS distance of 0.4 was used as a threshold for the univariate KS list. These proteins were then selected against poor analytical performance by investigating the % coefficient of variation (CV) in a healthy normal population and the overall signal quality/strength. The CDFs for each protein were also checked for abnormalities such as bimodal distributions or multiple outliers which have a lower probability of reproducing in the general population. Table 11 and Table 12 below show the ranked protein lists from stability selection and univariate KS with relevant performance measures.
  • Table 11 Ranked proteins by maximum probability of selection by stability selection with Ll- regularized logistic regression with measures of signal strength and assay performance.
  • Table 12 Ranked proteins by KS distance with measures of signal strength and assay performance.
  • Protein SAP was excluded due to its implications in general inflammatory processes, making it a risk for false positives.
  • a KS threshold of 0.4 was used; however, since several proteins with high biological significance and good analytical performance were found to have a KS distance within 0.015 of 0.4, ranked proteins 17-21 as well as 24 were included. This resulted in a final list of 17 proteins for model building, which are listed in Table 13 below. For each of these proteins a single model cross-validation was performed. Figure 40 shows their univariate CDFs.
  • Figure 41 shows boxplots of model performance as a function of model size for forward and backward selection using one of the same seed. The dark dot indicates the highest median performance achieved, and the light dot indicates the lowest complexity model with equal performance within error, which would be the optimal model using this seed.
  • Figure 42 is a bar graph showing the frequencies each protein was included in the model from the 10 different seeds using forward and backward selection. Although the selection frequencies differ slightly, both methods repeatedly selected the same 8 protein model.
  • FIG. 43 shows the overall performance of the model on the training and single class test set.
  • the left panel shows the ROC with the operating point and bootstrap 95% confidence bounds.
  • the model correctly classified 42/52 of the 52 hold out controls that were not used for model fitting (AUC from the data set without these controls was 0.88).
  • the right panel shows the log-odds for all subjects colored by diagnosis; the blue dots are control samples and the red dots are TB samples. Hollow data points indicate a misclassification based on the operating point.
  • Figure 44 shows a control band plot of the odds of developing active TB as a function of time to treatment initiation. Responsiveness was quantified by first fitting a locally weighted least squares (LWLS) estimate to the log odds of all TB cases as a function of time. These functions fit the estimated mean of the data and are represented as the bold dotted line in Figure 44 left. The area between the dotted line and the top of the light gray region of the control data in was used as a responsiveness index. The area within 200 days of diagnosis was weighted twice to select for models which show large increases in log- odds of the TB cases within 6 months of diagnosis. The responsiveness indices were normalized to the maximum area observed for any model.
  • LWLS locally weighted least squares
  • Figure 45 shows a boxplot of the log-odds from another TB-diagnostic 9-marker model ("HR9") re-fit to the training data, and the 8 marker model described herein, on samples binned by intervals of 180 day intervals. Time moves to the right. Although the median log odds for the control population is higher for the 8-marker model compared to HR9, the range of the scores is smaller. The HR9 log-odds only became positive within 180 days of treatment. In contrast, the log-odds for the 8-marker model are positive 540+ days from treatment.
  • HR9 TB-diagnostic 9-marker model
  • the correlation matrix in the left panel of Figure 46 shows Spearman correlations of all samples for the 17 proteins that entered model building with the proteins in the 8-marker model highlighted.
  • the ordering is based on a greedy seriation procedure to identify correlation structure. Two clusters are evident with Spearman p>0.6; a 3 protein cluster composed of C5, C9 and Factor I, as well as a 6 protein cluster of 2DMA, DR3, DIAC, SG1C1, AMBN and GPHB5 (scatter plot is shown in Figure 47).
  • the only HR9 marker selected for model building was C9. Although it was not selected for the 8 protein model, linear regression and a Cox Model found it to have a high level of responsiveness to time. C9 was added to the 8-marker model and the resulting model's performance is shown in Figure 48. Although it does not seem to affect overall performance, adding C9 decreases the log-odds of developing TB in the control samples and increases the log odds in the TB samples.
  • Figure 49 shows performance plots for the 9-protein predictive model.
  • the 9-protein predictive model misclassifying 11/52 compared to 10/52 for the 8-protein model without C9.
  • Figure 50 shows responsiveness plots for the 9-protein predictive model.
  • the addition of C9 to the 8-protein model increased responsiveness by -10%.
  • a further subset of proteins for model building was selected using the ranked lists from stability selection and univariate KS tests. From the stability selection list only proteins with a maximum selection probability >50% were included, and a KS distance of 0.4 was used as a threshold for the univariate KS list. These proteins were then selected against poor analytical performance by investigating the % coefficient of variation (CV) in a healthy normal population and the overall signal quality/strength. The CDFs for each protein were also checked for abnormalities such as bimodal distributions or multiple outliers which have a lower probability of reproducing in the general population. Table 15 and Table 16 below show the ranked protein lists from stability selection and univariate KS with relevant performance measures.
  • Table 15 Ranked proteins by maximum probability of selection by stability selection with Ll- regularized logistic along with shift in median signal level between groups and sample dilution for associated SOMAmer.
  • Table 16 Ranked proteins by KS distance with measures of signal strength and assay performance.
  • Table 57 Table of proteins used for model building
  • Figure 56 shows boxplots of model performance as a function of model size for forward and backward selection using one of the same seed. The dark dot indicates the highest median performance achieved, and the light dot indicates the lowest complexity model with equal performance within error, which would be the optimal model using this seed.
  • Figure 57 is a bar graph showing the frequencies each protein was included in the model from the 10 different seeds using forward and backward selection. Although the selection frequencies differ slightly, both methods repeatedly selected the same 8 protein model.
  • FIG. 58 shows the overall performance of the model on the training and single class test set.
  • the left panel shows the ROC with the operating point and bootstrap 95% confidence bounds.
  • the model correctly classified 42/52 of the 52 hold out controls that were not used for model fitting (AUC from the data set without these controls was 0.88).
  • the right panel shows the log-odds for all subjects colored by diagnosis; the blue dots are control samples and the red dots are TB samples. Hollow data points indicate a misclassifi cation based on the operating point.
  • model performance as a function of time is an important factor.
  • the odds of developing active TB should increase as the time of diagnosis (here start of treatment) draws nearer.
  • To quantify the "responsiveness" to proximity of treatment initiation the area under the "average” response as a function of time was estimated using an increased weight for the ⁇ 6 month interval immediately preceeding the time of treatment initiation.
  • Figure 59 shows a control band plot of the odds of developing active TB as a function of time to treatment initiation.
  • the bold dotted line is a locally weighted least squares (LWLS) estimate of logOdds as function of time for TB cases.
  • the area between between the dotted line and the top of the light gray region of the control data in was used as a responsiveness index with the area within 200 days of treatment activation weighted twice to select for models which show large increases in log-odds of the TB cases within 6 months of diagnosis.
  • the subsequent weighted area was normalized to the maximum area observed for any model to create a "responsiveness index”.
  • Figure 59 (right frame) shows the responsive index of each model versus performance (AUC) for models of increasing complexity. Model performance saturates at D-dimer, but substantial gains in responsiveness are observed with the additions of IP-10, MMP-1 and SG1C 1.
  • Figure 60 shows a boxplot of the log-odds from another TB-diagnostic 9-marker model ("HR9") re-fit to the training data, and the 8 marker model shown in Figure 59, on samples binned by intervals of 180 day intervals. Time moves to the right. Although the median log odds for the control population is higher for TBR8 compared to HR9, the robust range (region between the whiskers) of the scores is smaller. The HR9 log-odds only become positive within 180 days of treatment and then decrease again after treatment in response to changes in "host response" associated with the development and resolution of active disease.
  • HR9 TB-diagnostic 9-marker model
  • the log-odds for the TBR9 model are positive 540+ days from treatment, although they appear to oscillate somewhat with a marked decrease at the 180-360 day time point.
  • a Kruskall-Wallis test with Tukey's HSD (honest significant difference) for individual comparisons was used to determine which time points were different in the context of the overall error generated by the model predictions. Only the controls were found to be significantly different from the others time points for the 8 protein model (p ⁇ le-4), with all
  • the correlation matrix in the left panel of Figure 61 shows Spearman correlations of all samples for the 18 proteins that entered model building with the proteins in the 8-marker model highlighted.
  • the ordering is based on a greedy seriation procedure to identify
  • Figure 64 shows performance plots for the 7 protein model + C9.
  • the AUC is the same compared to TBR8 with mildly larger confidence intervals.
  • the performance estimates from the ROC curve in Figure 64 were obtained by bootstrapping, or repeatedly sampling the training population and estimating the class labels without re-fitting the model. A more conservative estimate of the performance of the model is to take bias in the model fit into account through cross-validation. A cross-validated performance estimate was generated by fitting the model to n-1 folds and predicting on the nth fold.
  • this bias may be overcome by adding an external cross validation loop to the feature selection/model building process, where the non-biased performance of the model can be quantified on the hold-out fold of the data. Given n folds, the feature selection/model build are run n times giving n models. None of these models are used in the end; the final model is obtained by running the entire feature selection/build on all of the data (as shown, e.g., in Example 4), using the double cross validated performance estimates.
  • Cross validation may be considered to assess the performance of a procedure for fitting a model, rather than the final model itself. If the feature set varies greatly from one fold of the cross validation to another, it is an indication that the selection strategy is unstable.
  • Figure 65 shows the frequency that each protein was included in the optimal model, with TBR8 proteins highlighted in blue. Seven of the TBR8 proteins are within the top 10 most selected features, with SG1C1 being selected less frequently. Within the double cross validation folds, these 7 TBR8 proteins were chosen in >30% of the optimal models, indicating very reasonable stability in the model building process.
  • Figure 66 shows a histogram of AUC estimates on the test set for each of the 90 optimal models generated.
  • the average performance estimate was 0.8 with a standard deviation of 0.08 and a 95% confidence interval of [0.63 0.94].
  • DG Ochsner UA. Elucidating novel serum biomarkers associated with pulmonary tuberculosistreatment. PLoS One 8, e61002 (2013).

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Abstract

La présente invention concerne en général des biomarqueurs d'infection et de maladie de la tuberculose (TB), et des méthodes de détection de ceux-ci. Dans divers modes de réalisation, l'invention concerne un ou plusieurs biomarqueurs, des panels de biomarqueurs, des méthodes, des dispositifs, des réactifs, des systèmes et des trousses permettant de détecter et/ou de caractériser une infection et/ou une maladie de TB.
PCT/US2016/014840 2015-01-27 2016-01-26 Biomarqueurs pour la détection du risque de tuberculose WO2016123058A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108845129A (zh) * 2018-06-01 2018-11-20 广东医科大学 一种活动性结核类疾病的生物标志物的应用
WO2020167899A1 (fr) * 2019-02-12 2020-08-20 Baylor Research Institute Système et méthode de thérapie de la tuberculose
US20210134453A1 (en) * 2019-10-30 2021-05-06 Samsung Electronics Co., Ltd. Apparatus and method for estimating bio-information

Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5475096A (en) 1990-06-11 1995-12-12 University Research Corporation Nucleic acid ligands
US5580737A (en) 1990-06-11 1996-12-03 Nexstar Pharmaceuticals, Inc. High-affinity nucleic acid ligands that discriminate between theophylline and caffeine
US5660985A (en) 1990-06-11 1997-08-26 Nexstar Pharmaceuticals, Inc. High affinity nucleic acid ligands containing modified nucleotides
US5705337A (en) 1990-06-11 1998-01-06 Nexstar Pharmaceuticals, Inc. Systematic evolution of ligands by exponential enrichment: chemi-SELEX
US5763177A (en) 1990-06-11 1998-06-09 Nexstar Pharmaceuticals, Inc. Systematic evolution of ligands by exponential enrichment: photoselection of nucleic acid ligands and solution selex
US6001577A (en) 1998-06-08 1999-12-14 Nexstar Pharmaceuticals, Inc. Systematic evolution of ligands by exponential enrichment: photoselection of nucleic acid ligands and solution selex
US6242246B1 (en) 1997-12-15 2001-06-05 Somalogic, Inc. Nucleic acid ligand diagnostic Biochip
US6458539B1 (en) 1993-09-17 2002-10-01 Somalogic, Inc. Photoselection of nucleic acid ligands
US20090004667A1 (en) 2007-01-16 2009-01-01 Somalogic, Inc. Method for generating aptamers with improved off-rates
US20090042206A1 (en) 2007-01-16 2009-02-12 Somalogic, Inc. Multiplexed Analyses of Test Samples
US20090098549A1 (en) 2007-07-17 2009-04-16 Somalogic, Inc. Selex and photoselex
US7964356B2 (en) 2007-01-16 2011-06-21 Somalogic, Inc. Method for generating aptamers with improved off-rates
US20120077695A1 (en) 2010-09-27 2012-03-29 Somalogic, Inc. Mesothelioma Biomarkers and Uses Thereof
US20120101002A1 (en) 2008-09-09 2012-04-26 Somalogic, Inc. Lung Cancer Biomarkers and Uses Thereof
US20120115752A1 (en) 2007-01-16 2012-05-10 Somalogic, Inc. Method for Generating Aptamers with Improved Off-Rates

Patent Citations (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6291184B1 (en) 1990-06-11 2001-09-18 Somalogic, Inc. Systematic evolution of ligands by exponential enrichment: photoselection of nucleic acid ligands and solution selex
US5580737A (en) 1990-06-11 1996-12-03 Nexstar Pharmaceuticals, Inc. High-affinity nucleic acid ligands that discriminate between theophylline and caffeine
US5660985A (en) 1990-06-11 1997-08-26 Nexstar Pharmaceuticals, Inc. High affinity nucleic acid ligands containing modified nucleotides
US5705337A (en) 1990-06-11 1998-01-06 Nexstar Pharmaceuticals, Inc. Systematic evolution of ligands by exponential enrichment: chemi-SELEX
US5763177A (en) 1990-06-11 1998-06-09 Nexstar Pharmaceuticals, Inc. Systematic evolution of ligands by exponential enrichment: photoselection of nucleic acid ligands and solution selex
US5475096A (en) 1990-06-11 1995-12-12 University Research Corporation Nucleic acid ligands
US6458539B1 (en) 1993-09-17 2002-10-01 Somalogic, Inc. Photoselection of nucleic acid ligands
US6242246B1 (en) 1997-12-15 2001-06-05 Somalogic, Inc. Nucleic acid ligand diagnostic Biochip
US6458543B1 (en) 1997-12-15 2002-10-01 Somalogic, Incorporated Nucleic acid ligand diagnostic biochip
US6503715B1 (en) 1997-12-15 2003-01-07 Somalogic, Inc. Nucleic acid ligand diagnostic biochip
US6544776B1 (en) 1997-12-15 2003-04-08 Somalogic, Inc. Nucleic acid ligand diagnostic biochip
US6001577A (en) 1998-06-08 1999-12-14 Nexstar Pharmaceuticals, Inc. Systematic evolution of ligands by exponential enrichment: photoselection of nucleic acid ligands and solution selex
US20090004667A1 (en) 2007-01-16 2009-01-01 Somalogic, Inc. Method for generating aptamers with improved off-rates
US20090042206A1 (en) 2007-01-16 2009-02-12 Somalogic, Inc. Multiplexed Analyses of Test Samples
US7964356B2 (en) 2007-01-16 2011-06-21 Somalogic, Inc. Method for generating aptamers with improved off-rates
US20120115752A1 (en) 2007-01-16 2012-05-10 Somalogic, Inc. Method for Generating Aptamers with Improved Off-Rates
US20090098549A1 (en) 2007-07-17 2009-04-16 Somalogic, Inc. Selex and photoselex
US20120101002A1 (en) 2008-09-09 2012-04-26 Somalogic, Inc. Lung Cancer Biomarkers and Uses Thereof
US20120077695A1 (en) 2010-09-27 2012-03-29 Somalogic, Inc. Mesothelioma Biomarkers and Uses Thereof

Non-Patent Citations (19)

* Cited by examiner, † Cited by third party
Title
"Bioluminescence & Chemiluminescence: Progress & Current Applications", January 2002, SCIENTIFIC PUBLISHING COMPANY
"Gene Expression Profiling: Methods and Protocols", 2004, HUMANA PRESS
"ImmunoAssay: A Practical Guide", 2005, TAYLOR & FRANCIS, LTD.
"Pattern Classification", 2001, JOHN WILEY & SONS
"The Elements of Statistical Learning - Data Mining, Inference, and Prediction", 2009, SPRINGER SCIENCE+BUSINESS MEDIA, LLC
BRIDGET LOLLO: "SOMAscan TM : A Quantitative Multiplex Proteomic Platform that Measures 1129 Analytes in Complex Matrices", 1 January 2012 (2012-01-01), XP055259693, Retrieved from the Internet <URL:http://www.somalogic.com/somalogic/media/Assets/Posters/Lollo-et-al-HUPO-poster-v6.pdf> [retrieved on 20160318] *
BURLINGAME ET AL., ANAL. CHEM., vol. 70, 1998, pages 647 R - 716R
DE GROOTE MA; NAHID P; JARLSBERG L; JOHNSON JL; WEINER M; MUZANYI G; JANJIC N; STERLING DG; OCHSNER UA: "Elucidating novel serum biomarkers associated with pulmonary tuberculosistreatment", PLOS ONE, vol. 8, 2013, pages E61002
FAWCETT T: "An introduction to ROC analysis", PATTERN RECOGNITION LETTERS, vol. 27, 2006, pages 861 - 874
GOLD L. ET AL.: "Aptamer-based multiplexed proteomic technology for biomarker discovery", PLOS ONE, vol. 5, 2010, pages E15004
GOLD L.; WALKER JJ; WILCOX SK; WILLIAMS S: "Advances in human proteomics at high scale with the SOMAscan proteomics platform", N BIOTECHNOL, vol. 29, 2012, pages 543 - 9
HANEKOM WA; HAWKRIDGE A; MAHOMED H; SCRIBA TJ; TAMERIS M; HUGHES J; HATHERILL M; DAY CL; HUSSEY GD: "SATVI - after 10 years closing in on a new and better vaccine to prevent tuberculosis", S AFR MED J, vol. 102, 2012, pages 438 - 41
HANLEY, J.A.; MCNEIL, B.J.: "The meaning and use of the area under a receiver operating characteristic (ROC) curve", RADIOLOGY, vol. 143, 1982, pages 29 - 36
J.R. LAKOWICZ: "Principles of Fluorescence Spectroscopy", 2004, SPRINGER SCIENCE + BUSINESS MEDIA, INC.
KRAEMER ET AL., PLOS ONE, vol. 6, no. 10, 2011, pages E26332
MAITOURNAM A; SIMON R: "On the efficiency of targeted clinical trials", STAT MED, vol. 24, 2005, pages 329 - 39
N. BLOW, NATURE METHODS, vol. 6, 2009, pages 465 - 469
ROHIT MISTRY ET AL: "Gene-Expression Patterns in Whole Blood Identify Subjects at Risk for Recurrent Tuberculosis", JID, vol. 195, 1 February 2007 (2007-02-01), pages 357 - 365, XP055259542 *
T. HASTIE, ET AL.,: "The Elements of Statistical Learning - Data Mining, Inference, and Prediction, 2nd edition,", 2009, SPRINGER SCIENCE+BUSINESS MEDIA

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108845129A (zh) * 2018-06-01 2018-11-20 广东医科大学 一种活动性结核类疾病的生物标志物的应用
CN108845129B (zh) * 2018-06-01 2021-03-30 广东医科大学 一种活动性结核类疾病的生物标志物的应用
WO2020167899A1 (fr) * 2019-02-12 2020-08-20 Baylor Research Institute Système et méthode de thérapie de la tuberculose
US20210134453A1 (en) * 2019-10-30 2021-05-06 Samsung Electronics Co., Ltd. Apparatus and method for estimating bio-information

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