WO2015164616A1

WO2015164616A1 - Biomarkers for detection of tuberculosis

Info

Publication number: WO2015164616A1
Application number: PCT/US2015/027316
Authority: WO
Inventors: Urs Ochsner; David Sterling; Nebojsa Janjic; Stephen Alaric Williams
Original assignee: Somalogic, Inc.
Priority date: 2014-04-24
Filing date: 2015-04-23
Publication date: 2015-10-29

Abstract

The present application relates generally to biomarkers for tuberculosis (TB) infection and disease and methods of detection thereof. In various embodiments, the invention relates to one or more biomarkers, biomarker panels, methods, devices, reagents, systems, and kits for detecting and/or characterizing TB infection and/or disease.

Description

BIOMARKERS FOR DETECTION OF TUBERCULOSIS

FIELD

BACKGROUND

Tuberculosis (TB) is caused by a bacterium called Mycobacterium tuberculosis. 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. As a result, two TB-related conditions exist: latent TB infection and TB disease. Both latent TB infection and TB disease can be treated. SUMMARY

In some embodiments, methods of detecting TB infection and/or disease in a subject or patient are provided. In some embodiments methods of detecting active TB infection (also referred to as TB disease) in a subject or patient are provided.

In some embodiments, 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 at least nine biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM in a sample (e.g., plasma, serum, urine, saliva, etc.) from the subject. In some embodiments, a method comprises detecting the presence or level of in a sample from the subject. In some embodiments, a method comprises detecting the level of Kallistatin and optionally one or more of Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM, in a sample from the subject. In some embodiments, a method comprises detecting the level of Gelsolin and optionally one or more of Kallistatin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM, in a sample from the subject. In some embodiments, a method comprises detecting the level of TSP4 and optionally one or more of Kallistatin, Gelsolin, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM, in a sample from the subject. In some embodiments, a method comprises detecting the level of Afamin and optionally one or more of Kallistatin, Gelsolin, TSP4, BGH3, C9, Testican-2, FCG3B, and DERM, in a sample from the subject. In some embodiments, a method comprises detecting the level of BGH3 and optionally one or more of Kallistatin, Gelsolin, TSP4, Afamin, C9, Testican-2, FCG3B, and DERM, in a sample from the subject. In some embodiments, a method comprises detecting the level of C9 and optionally one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, FCG3B, and DERM, in a sample from the subject. In some embodiments, a method comprises detecting the level of Testican-2 and optionally one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, FCG3B, and DERM, in a sample from the subject. In some embodiments, a method comprises detecting the level of FCG3B and optionally one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, and DERM, in a sample from the subject. In some embodiments, a method comprises detecting the level of DERM and optionally one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, and FCG3B, in a sample from the subject. In some embodiments, a method comprises detecting the level of Testican-2 and DERM and optionally one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, and FCG3B, in a sample from the subject. In some embodiments, detection of a particular level of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and/or DERM in a sample (e.g., plasma, serum, urine, saliva, etc.) from the subject (e.g., a level that is altered (e.g., increased and/or decreased) from a control level of the respective biomarker, a level above or below a threshold, etc.) is indicative or and/or diagnostic for TB infection and/or disease. In some embodiments, a level of at least one, at least two, at least three, at least four, at least five, at least six, or seven markers selected from

Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, and DERM that is higher in the sample from the subject than a control level indicates that the subject has TB. In some embodiments, a level of at least one or two markers selected from C9 and FCG3B that is lower in the sample from the subject than a control level indicates that the subject has TB.

In some embodiments, a method of detecting and/or diagnosing TB infection or disease in a subject comprises forming a biomarker panel having N biomarker proteins selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and/or DERM, and detecting the level of each of the N biomarker proteins of the panel in a sample from the subject. In some embodiments, 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 embodiments, 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, at least one of the N biomarker proteins is selected from Testican-2 and DERM. In some embodiments, two of the N biomarker proteins are Testican-2 and DERM.

In some embodiments, a method of detecting and/or diagnosing TB infection or disease in a subject comprises forming a biomarker panel having X biomarker proteins, wherein N biomarker proteins are selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and/or DERM, and detecting the level of each of the X biomarker proteins of the panel in a sample from the subject. In some embodiments, 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). 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 between 1 and 9 (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9). In some embodiments, at least one N biomarker is selected from Testican-2 and DERM. In some embodiments, two of the N biomarkers are Testican-2 and DERM.

In some embodiments, 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 Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and/or DERM. In some embodiments, 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 Testican-2 and/or DERM, and optionally at least one marker selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, and FCG3B.

In some embodiments, a method comprises detecting the presence or level of at least one, at least two, at least three, or at least four biomarkers selected from SAA, NPS-PLA2, IP- 10, and CA6 in a sample (e.g., plasma, serum, urine, saliva, etc.) from the subject. In some embodiments, a method comprises detecting the presence or level of in a sample from the subject. In some embodiments, a method comprises detecting the level of SAA and optionally one or more of NPS-PLA2, IP- 10, and CA6, in a sample from the subject. In some embodiments, a method comprises detecting the level of NPS-PLA2 and optionally one or more of SAA, IP- 10, and CA6, in a sample from the subject. In some embodiments, a method comprises detecting the level of IP- 10 and optionally one or more of SAA, NPS-PLA2, and CA6, in a sample from the subject. In some embodiments, a method comprises detecting the level of CA6 and optionally one or more of SAA, NPS-PLA2, and IP- 10, in a sample from the subject. In some embodiments, a method comprises detecting the level of SAA, NPS-PLA2, IP- 10 and CA6 in a sample from the subject. In some embodiments, detection of a particular level of SAA, NPS-PLA2, IP- 10 and/or CA6 in a sample (e.g., plasma, serum, urine, saliva, etc.) from the subject (e.g., a level that is altered (e.g., increased and/or decreased) from a control level of the respective biomarker, a level above or below a threshold, etc.) is indicative or and/or diagnostic for TB infection and/or disease. In some embodiments, a level of at least one, at least two, or three markers selected from SAA, NPS-PLA2, and IP-10 that is higher in the sample from the subject than a control level indicates that the subject has TB. In some embodiments, a level of CA6 that is lower in the sample from the subject than a control level indicates that the subject has TB.

In some embodiments, a method of detecting and/or diagnosing TB infection or disease in a subject comprises forming a biomarker panel having N biomarker proteins selected from SAA, NPS-PLA2, ΓΡ-10 and/or CA6, and detecting the level of each of the N biomarker proteins of the panel in a sample from the subject. In some embodiments, N is 1 to 4. In some embodiments, N is 2 to 4. In some embodiments, N is 3 to 4. In some embodiments, N is 4. In some

embodiments, N is 1 to 3. In some embodiments, N is 2 to 3. In some embodiments, N is 3. In some embodiments, N is 1 to 2. In some embodiments, N is 1. In some embodiments, N is 2.

In some embodiments, a method of detecting and/or diagnosing TB infection or disease in a subject comprises forming a biomarker panel having X biomarker proteins, wherein N biomarker proteins are selected from SAA, NPS-PLA2, IP- 10 and/or CA6, and detecting the level of each of the X biomarker proteins of the panel in a sample from the subject. In some embodiments, 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 5 or greater (e.g., >11 biomarkers, >12 biomarkers, >13 biomarkers, >14 biomarkers, >15 biomarkers, >20 biomarkers, >30 biomarkers, >40 biomarkers, >50 biomarkers). 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 between 1 and 4 (e.g., 1, 2, 3, 4). In some embodiments, the biomarker panel comprises X biomarker proteins, wherein four of the biomarker proteins are SAA, NPS-PLA2, IP- 10 and CA6.

In some embodiments, 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 SAA, NPS-PLA2, IP- 10 and/or CA6. In some embodiments, 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, comprises SAA, NPS-PLA2, IP- 10 and CA6.

In some embodiments, methods of monitoring progression of TB infection/disease in a patient are provided. In some embodiments, a method comprises 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 at least nine biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM in a sample from the patient at a first time point. In some embodiments, a method comprises detecting the level of Testican-2 and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, FCG3B, and DERM in a sample from the patient at a first time point. In some embodiments, a method comprises detecting the level of DERM and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, and FCG3B in a sample from the patient at a first time point. In some embodiments, the method further comprises 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 at least nine of the biomarkers at a second time point. In some embodiments, TB infection/disease is worsening if the levels of one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and/or DERM are further removed from a control value, control range, and/or threshold than at the first time point. In some embodiments, TB infection/disease is improving if the levels of one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and/or DERM are less removed from a control value, control range, and/or threshold than at the first time point. In some embodiments, a level of at least one, at least two, at least three, at least four, at least five, at least six, or seven markers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, and DERM that is higher in the sample from the second time point than in the sample from the first time point indicates that the TB infection has progressed. Similarly, in some embodiments, a level of at least one, at least two, at least three, at least four, at least five, at least six, or seven markers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, and DERM that is substantially the same or lower in the sample from the second time point than in the sample from the first time point indicates that the TB infection has not progressed or has regressed. In some embodiments, a level of at least one or two markers selected from C9 and FCG3B that is lower in the sample from the second time point than in the sample from the first time point indicates that the TB infection has progressed. Similarly, in some embodiments, a level of at least one or two markers selected from C9 and FCG3B that is higher in the sample from the second time point than in the sample from the first time point indicates that the TB infection has not progressed or has regressed. In some embodiments, first and second time points are separated by 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. In some embodiments, first and second time points are separated by no more than 1 week, 2 weeks, 1 month, 2 months, 3 months, 4 months, 6 months, or 1 year. In some embodiments, first and second time points are separated by 1 month to 1 year. In some embodiments, first and second time points are separated by 1 to 6 months. In some embodiments, first and second time points are separated by 1 to 4 months. In some embodiments, first and second time points are separated by 1 to 3 months. In some embodiments, methods of monitoring progression of TB infection/disease in a patient are provided. In some embodiments, a method comprises detecting the level of at least one, at least two, at least three, or at least four biomarkers selected from SAA, NPS-PLA2, IP- 10, and CA6 in a sample from the patient at a first time point. In some embodiments, a method comprises detecting the level of SAA, NPS-PLA2, IP- 10, and CA6 in a sample from the patient at a first time point. In some embodiments, the method further comprises measuring the level of the at least one, at least two, at least three, or at least four of the biomarkers at a second time point. In some embodiments, TB infection/disease is worsening if the levels of one or more of SAA, NPS-PLA2, IP-10, and/or CA6 are further removed from a control value, control range, and/or threshold than at the first time point. In some embodiments, TB infection/disease is improving if the levels of one or more of SAA, NPS-PLA2, IP-10, and/or CA6 are less removed from a control value, control range, and/or threshold than at the first time point. In some embodiments, a level of at least one, at least two, or three markers selected from SAA, NPS-PLA2, and IP-10 that is higher in the sample from the second time point than in the sample from the first time point indicates that the TB infection has progressed. Similarly, in some embodiments, a level of at least one, at least two, or three markers selected from SAA, NPS-PLA2, and IP-10 that is substantially the same or lower in the sample from the second time point than in the sample from the first time point indicates that the TB infection has not progressed or has regressed. In some embodiments, a level of CA6 that is lower in the sample from the second time point than in the sample from the first time point indicates that the TB infection has progressed. Similarly, in some embodiments, a level of CA6 that is substantially the same or higher in the sample from the second time point than in the sample from the first time point indicates that the TB infection not progressed or has regressed. In some embodiments, first and second time points are separated by 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. In some embodiments, first and second time points are separated by no more than 1 week, 2 weeks, 1 month, 2 months, 3 months, 4 months, 6 months, or 1 year. In some embodiments, first and second time points are separated by 1 month to 1 year. In some embodiments, first and second time points are separated by 1 to 6 months. In some embodiments, first and second time points are separated by 1 to 4 months. In some embodiments, first and second time points are separated by 1 to 3 months.

In some embodiments, methods of monitoring treatment of TB infection/disease in a patient and/or for monitoring patient compliance with a treatment regimen for TB are provided. In some embodiments, a method comprises: (a) 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 at least nine biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM in a sample from the patient at a first time point; (b) administering a treatment for TB infection/disease to the patient; and (c) 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 at least nine of the biomarkers at a second time point. In some embodiments, treatment is ineffective if the levels at the second timepoint of one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and/or DERM are unchanged or further removed from a control value, control range, and/or threshold than at the first time point. In some embodiments, treatment is effective if the levels of one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican- 2, FCG3B, and/or DERM are less removed from a control value, control range, and/or threshold than at the first time point. In some embodiments, a level of at least one, at least two, at least three, at least four, at least five, at least six, or seven markers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, and DERM that is lower in the sample from the second time point than in the sample from the first time point indicates that the treatment is effective or indicates good patient compliance with the treatment regimen. Similarly, in some embodiments, a level of at least one, at least two, at least three, at least four, at least five, at least six, or seven markers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, and DERM that is substantially the same or higher in the sample from the second time point than in the sample from the first time point indicates that the treatment is not effective or indicates poor patient compliance with the treatment regimen. In some embodiments, a level of at least one or two markers selected from C9 and FCG3B that is higher in the sample from the second time point than in the sample from the first time point indicates that the treatment is effective or indicates good patient compliance with the treatment regimen. Similarly, in some embodiments, a level of at least one or two markers selected from C9 and FCG3B that is substantially the same or lower in the sample from the second time point than in the sample from the first time point indicates that the treatment is not effective or indicates poor patient compliance with the treatment regimen. In some embodiments, first and second time points are separated by 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. In some

embodiments, first and second time points are separated by no more than 1 week, 2 weeks, 1 month, 2 months, 3 months, 4 months, 6 months, or 1 year. In some embodiments, first and second time points are separated by 1 month to 1 year. In some embodiments, first and second time points are separated by 1 to 6 months. In some embodiments, first and second time points are separated by 1 to 4 months. In some embodiments, first and second time points are separated by 1 to 3 months. In some embodiments, if treatment is determined to be ineffective, an alternative course of treatment is administered.

In some embodiments, methods of monitoring treatment of TB infection/disease in a patient and/or for monitoring patient compliance with a treatment regimen for TB are provided. In some embodiments, a method comprises: (a) detecting the level of at least one, at least two, at least three, or at least four biomarkers selected from SAA, NPS-PLA2, IP- 10, and CA6 in a sample from the patient at a first time point; (b) administering a treatment for TB

infection/disease to the patient; and (c) measuring the level of the at least one, at least two, at least three, or at least four of the biomarkers at a second time point. In some embodiments, treatment is ineffective if the levels at the second timepoint of one or more of SAA, NPS-PLA2, IP-10, and/or CA6 are unchanged or further removed from a control value, control range, and/or threshold than at the first time point. In some embodiments, treatment is effective if the levels of one or more of SAA, NPS-PLA2, IP-10, and/or CA6 are less removed from a control value, control range, and/or threshold than at the first time point. In some embodiments, a level of at least one, at least two, or three markers selected from SAA, NPS-PLA2, and IP-10 that is lower in the sample from the second time point than in the sample from the first time point indicates that the treatment is effective or indicates good patient compliance with the treatment regimen.

Similarly, in some embodiments, a level of at least one, at least two, or three markers selected from SAA, NPS-PLA2, and IP-10 that is substantially the same or higher in the sample from the second time point than in the sample from the first time point indicates that the treatment is not effective or indicates poor patient compliance with the treatment regimen. In some embodiments, a level of CA6 that is higher in the sample from the second time point than in the sample from the first time point indicates that the treatment is effective or indicates good patient compliance with the treatment regimen. Similarly, in some embodiments, a level of CA6 that is substantially the same or lower in the sample from the second time point than in the sample from the first time point indicates that the treatment is not effective or indicates poor patient compliance with the treatment regimen. In some embodiments, first and second time points are separated by 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. In some embodiments, first and second time points are separated by no more than 1 week, 2 weeks, 1 month, 2 months, 3 months, 4 months, 6 months, or 1 year. In some embodiments, first and second time points are separated by 1 month to 1 year. In some embodiments, first and second time points are separated by 1 to 6 months. In some embodiments, first and second time points are separated by 1 to 4 months. In some embodiments, first and second time points are separated by 1 to 3 months. In some embodiments, if treatment is determined to be ineffective, an alternative course of treatment is administered.

In some embodiments, one or more additional steps are taken upon identifying a subject as TB infected. In some embodiments, methods further comprise a subsequent step of treating said subject or patient for tuberculosis. In some embodiments, methods further comprise a subsequent step of additional TB-diagnostic steps. In some embodiments, said additional TB- diagnostic steps comprise a chest x-ray. In some embodiments, methods further comprise generating a report diagnosing said subject as having tuberculosis infection.

In some embodiments, methods for predicting whether a subject having latent

tuberculosis (TB) infection will develop TB disease are provided. In some embodiments, methods comprise 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 Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM in a sample from the subject, wherein the subject is predicted to develop TB disease if the level of the respective biomarker is altered relative to a control level of the respective biomarker. In some embodiments, a level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, and DERM that is higher than a control level of the respective biomarker, and/or a level of at least one or two biomarkers selected from C9 and FCG3B that is lower than a control level of the respective biomarker, indicates that the subject will develop TB disease. In some embodiments, methods predict that the subject will develop TB disease within about 180 days. In some embodiments, methods predict that the subject will develop TB disease in less than about 180 days. In some embodiments, methods predict that the subject will develop TB disease within about 90 days. In some embodiments, methods predict that the subject will develop TB disease in less than about 90 days. In some embodiments, methods predict that the subject will develop TB disease within about 45 days. In some embodiments, methods predict that the subject will develop TB disease in less than about 45 days. In some embodiments, methods predict that the subject will develop TB disease within about 30 days. In some embodiments, methods predict that the subject will develop TB disease in less than about 30 days.

In some embodiments, methods for determining whether a subject having TB disease is responding to treatment are provided. In some embodiments, methods comprise 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 Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM in a first sample from the subject taken at a first time point and in a second sample from the subject taken at a second time point, wherein the subject is determined to be responding to treatment for TB disease if the level of the respective biomarker is altered from the first time point to the second time point. In some embodiments, the first time point is within 2 weeks, within 1 week, within 3 days, within 1 day, or within 12 hours of beginning treatment for TB disease, and the second time point is at least 1 month, at least 6 weeks, at least 2 months, at least 3 months, at least 4 months, at least 5 months, or at least 6 months after the first time point. By "within" a certain time period is meant that time period before or after beginning treatment (i.e., "within 2 weeks" means within a time period beginning 2 weeks before treatment has begun and ending 2 weeks after treatment has begun). In some embodiments, the second time point is 2 months to 1 year, or 3 months to 1 year, or 2 to 6 months, or 3 to 6 months after the first time point. In some embodiments, a level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, and DERM that is lower at the second time point than at the first time point, and/or a level of at least one or two biomarkers selected from C9 and FCG3B that is higher at the second time point than at the first time point, indicates that the subject is responding to treatment for TB disease.

In any of the embodiments described herein, the each biomarker may be a protein biomarker. 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 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. In any of the embodiments described herein, each biomarker capture reagent may be an aptamer. In any of the embodiments described herein, at least one aptamer may be a slow off-rate aptamer. In any of the embodiments described herein, 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. In some embodiments, the modifications are hydrophobic modifications. In some embodiments, the modifications are hydrophobic base modifications. In some embodiments, one or more of the modifications may be selected from the modifications shown in Figure 17. In some

embodiments, each slow off-rate aptamer binds to its target protein with an off rate (t½) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes. In any of the embodiments described herein, the sample may be a blood sample. In some embodiments, the blood sample is selected from a serum sample and a plasma sample. In some embodiments, 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.

In any of the embodiments described herein, a method may further comprise treating the subject or patient for TB infection. In some embodiments, treating the subject or patient for TB infection 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.

In some embodiments, kits are provided. In some embodiments, 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, or at least nine aptamers, wherein each aptamer specifically binds to a different target protein selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM. In some embodiments, a kit comprises an aptamer that specifically binds Kallistatin and optionally one or more aptamers that specifically bind one or more of, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM. In some embodiments, a kit comprises an aptamer that specifically binds Gelsolin and optionally one or more aptamers that specifically bind one or more of, Kallistatin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM. In some embodiments, a kit comprises an aptamer that specifically binds TSP4 and optionally one or more aptamers that specifically bind one or more of, Kallistatin, Gelsolin, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM. In some embodiments, a kit comprises an aptamer that specifically binds Afamin and optionally one or more aptamers that specifically bind one or more of, Kallistatin, Gelsolin, TSP4, BGH3, C9, Testican-2, FCG3B, and DERM. In some embodiments, a kit comprises an aptamer that specifically binds BGH3 and optionally one or more aptamers that specifically bind one or more of, Kallistatin, Gelsolin, TSP4, Afamin, C9, Testican-2, FCG3B, and DERM. In some embodiments, a kit comprises an aptamer that specifically binds C9 and optionally one or more aptamers that specifically bind one or more of, Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, FCG3B, and DERM. In some embodiments, a kit comprises an aptamer that specifically binds Testican-2 and optionally one or more aptamers that specifically bind one or more of, Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, FCG3B, and DERM. In some embodiments, a kit comprises an aptamer that specifically binds FCG3B and optionally one or more aptamers that specifically bind one or more of, Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, and DERM. In some embodiments, a kit comprises an aptamer that specifically binds DERM and optionally one or more aptamers that specifically bind one or more of, Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, and FCG3B. In some embodiments, a kit comprises an aptamer that specifically binds Testican-2 and/or DERM and optionally one or more aptamers that specifically bind one or more of, Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, and FCG3B. In some embodiments, each aptamer binds to a different target protein.

In some embodiments, a kit comprises X aptamers, wherein N aptamers specifically bind to a biomarker protein selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM. In some embodiments, X is less than 100 (e.g., <90, <80, <70, <60, <50, <40, <30, <20, <15). In some embodiments, 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, at least one of the N biomarker proteins is selected from DERM and Testican-2. In some embodiments, two of the N biomarker proteins are DERM and Testican-2.

In some embodiments, kits are provided. In some embodiments, a kit comprises at least one, at least two, at least three, or at least four aptamers, wherein each aptamer specifically binds to a different target protein selected from SAA, NPS-PLA2, IP- 10, and CA6. In some embodiments, a kit comprises an aptamer that specifically binds SAA and optionally one or more aptamers that specifically bind one or more of NPS-PLA2, IP- 10, and CA6. In some

embodiments, a kit comprises an aptamer that specifically binds NPS-PLA2 and optionally one or more aptamers that specifically bind one or more of SAA, IP- 10, and CA6. In some

embodiments, a kit comprises an aptamer that specifically binds IP- 10 and optionally one or more aptamers that specifically bind one or more of SAA, NPS-PLA2, and CA6. In some

embodiments, a kit comprises an aptamer that specifically binds CA6 and optionally one or more aptamers that specifically bind one or more of SAA, NPS-PLA2, and IP- 10. In some

embodiments, a kit comprises aptamers that specifically bind one or more of SAA, NPS-PLA2, IP- 10, and CA6 in addition to aptamers that specifically bind other biomarkers (e.g., other TB biomarkers, non-TB biomarkers). In some embodiments, each aptamer binds to a different target protein.

In some embodiments, a kit comprises X aptamers, wherein N aptamers specifically bind to a biomarker protein selected from SAA, NPS-PLA2, IP- 10, and/or CA6. In some

embodiments, X is less than 100 (e.g., <90, <80, <70, <60, <50, <40, <30, <20, <15). In some embodiments, X is 5 or more (e.g., >5, >6, >7, >8, >9, >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 4 (1, 2, 3, 4). In some embodiments, N is 4.

In some embodiments, compositions are provided 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 Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds Kallistatin and optionally one or more aptamers that specifically bind one or more of Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds Gelsolin and optionally one or more aptamers that specifically bind one or more of Kallistatin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds TSP4 and optionally one or more aptamers that specifically bind one or more of Kallistatin, Gelsolin, Afamin, BGH3, C9,

Testican-2, FCG3B, and DERM. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds Afamin and optionally one or more aptamers that specifically bind one or more of Kallistatin, Gelsolin, TSP4, BGH3, C9, Testican-2, FCG3B, and DERM. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds BGH3 and optionally one or more aptamers that specifically bind one or more of Kallistatin, Gelsolin, TSP4, Afamin, C9, Testican-2, FCG3B, and DERM. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds C9 and optionally one or more aptamers that specifically bind one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, FCG3B, and DERM. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds Testican-2 and optionally one or more aptamers that specifically bind one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, FCG3B, and DERM. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds FCG3B and optionally one or more aptamers that specifically bind one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, and DERM. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds DERM and optionally one or more aptamers that specifically bind one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, and FCG3B. In some embodiments, a composition comprises proteins of a sample from a subject or patient and aptamers that specifically binds Testican-2 and DERM and optionally one or more aptamers that specifically bind one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, and FCG3B.

In some embodiments, compositions are provided comprising proteins of a sample from a subject or patient and at least one, at least two, at least three, or at least four aptamers, wherein each aptamer specifically binds to a different target protein selected from SAA, NPS-PLA2, ΓΡ- 10, and CA6. In some embodiments, compositions are provided comprising proteins of a sample from a subject or patient and four aptamers, wherein each aptamer specifically binds to a different target protein selected from SAA, NPS-PLA2, IP- 10, and CA6. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds SAA and optionally one or more aptamers that specifically bind one or more of NPS-PLA2, IP- 10, and CA6. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds NPS-PLA2and optionally one or more aptamers that specifically bind one or more of SAA, IP- 10, and CA6. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds IP- 10 and optionally one or more aptamers that specifically bind one or more of SAA, NPS-PLA2, and CA6. In some embodiments, a composition comprises proteins of a sample from a subject or patient and an aptamer that specifically binds CA6 and optionally one or more aptamers that specifically bind one or more of SAA, NPS-PLA2, and ΓΡ- 10.

In any of the embodiments described herein, a kit or composition may comprise at least one aptamer that is a slow off-rate aptamer. In any of the embodiments described herein, each aptamer of a kit or composition may be a slow off-rate aptamer. In some embodiments, 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. In some embodiments, at least one nucleotide with a modification is a nucleotide with a hydrophobic base modification. In some embodiments, each nucleotide with a

modification is a nucleotide with a hydrophobic base modification. In some embodiments, each hydrophobic base modification is independently selected from the modification in Figure 17. In some embodiments, each slow off-rate aptamer in a kit binds to its target protein with an off rate (t½) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes. BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 shows a dot plot depicting significance and fold-change of serum biomarkers distinguishing TB from non-TB.

Figure 2 shows stability paths for protein data augmented by co-variates. Stability paths are labeled in order of total area under the path rather than by maximum selection probability obtained.

Figure 3 shows a (Pearson) correlation matrix with proteins ordered to cluster correlations of similar magnitude using a seriation procedure. The column order is determined in the TB vs. Non-TB comparison and then fixed for the HIV negative and positive populations.

Figure 4 shows cross-validated model performance (Sensitivity + Specificity) as a function of model size for Naive Bayes model using cumulative features selected by stability selection (blue), or top ranked KS distance (magenta), with each feature labeled.

Figure 5 shows matrices of spearman correlation between top 20 candidate biomarkers ranked by KS distance (left) and stability selection using logistic regression (right).

Figure 6 shows a graph depicting margin differential between 5 (solid) and 9 (hollow) protein naive Bayes model.

Figure 7 shows graphs depicting robust parameter estimates for univariate Gaussian distribution of log RFU values for each marker.

Figure 8 shows graphs depicting ROC curves and the samples relative to the decision boundary for a naive Bayes model using 9 features for all samples of the 80% training set.

Figure 9 shows graphs depicting ROC curves and the samples relative to the decision boundary for a naive Bayes model using 9 features for the smear-negative TB samples of the 80%) training set.

Figure 10 shows graphs depicting samples relative to the decision boundary for a naive Bayes model using 9 features for the healthy normal population (left) and previous TB treatment study samples (right).

Figure 11 shows graphs of ROC curves (top) and decision boundaries (bottom) the of a verification set using the HR9 model.

Figure 12 shows graphs depicting concordance of TB marker performance between training and verification set.

Figure 13 shows graphs depicting differences in geographic origin of samples between training and verification set.

Figure 14 shows graphs attributing false-negatives (FN) and false-positives (FP) to country, HIV-status, smear-status, gender, and age. Figure 15 shows a volcano plot of serum proteins in TB vs. non-TB. Four markers with >2-fold median changes and significant KS distance are highlighted.

Figure 16 shows graphs depicting sensitivity and specificity of TB diagnostic models based on 1-4 markers in Training set (T) and Verification set (V). Cut-off values for

classification were optimized toward high sensitivity for a "rule-out" test (left) or high specificity for a "rule-in" test (right).

Figure 17 shows certain exemplary modified pyrimidines that may be incorporated into aptamers, such as slow off-rate aptamers.

Figure 18 illustrates a nonlimiting exemplary computer system for use with various computer-implemented methods described herein.

Figure 19 illustrates a nonlimiting exemplary aptamer assay that can be used to detect one or more biomarkers in a biological sample.

Figure 20 shows signed KS values for each biomarker in the 9-marker model (left), and a boxplot of time log-odds generated from the 9-marker model (right), as described in Example 11.

DETAILED DESCRIPTION

While the invention will be described in conjunction with certain representative embodiments, it will be understood that the invention is defined by the claims, and is not limited to those embodiments.

One skilled in the art will recognize many methods and materials similar or equivalent to those described herein may be used in the practice of the present invention. The present invention is in no way limited to the methods and materials described.

Unless defined otherwise, technical and scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art to which this invention belongs.

Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice of the invention, certain methods, devices, and materials are described herein.

All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as though each individual publication, published patent document, or patent application was specifically and individually indicated as being incorporated by reference.

As used in this application, including the appended claims, the singular forms "a," "an," and "the" include the plural, unless the context clearly dictates otherwise, and may be used interchangeably with "at least one" and "one or more." Thus, reference to "an aptamer" includes mixtures of aptamers, reference to "a probe" includes mixtures of probes, and the like. As used herein, 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.

The present application includes biomarkers, methods, devices, reagents, systems, and kits for detecting, characterizing, monitoring progression, and/or monitoring treatment of TB infection and/or TB disease.

In one aspect, one or more biomarkers are provided for use either alone or in various combinations to detect TB infection/disease, and/or to monitor progression or treatment of TB infection/disease. As described in detail herein, exemplary embodiments include one or more biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM; or one or more biomarkers selected from SAA, NPS-PLA2, IP- 10, and CA6.

Biomarkers and biomarker panels provided herein are useful for distinguishing samples obtained from individuals with TB infection/disease from samples from individuals without TB infection/disease.

While the described biomarkers are useful alone for providing a TB diagnosis, methods and kits are also described herein for grouping the biomarkers with additional biomarkers described herein and/or with additional biomarkers not listed herein. In some embodiments, 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 1 1 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.

In some embodiments, the number and identity of biomarkers in a panel are selected based on the sensitivity and specificity for the particular combination of biomarker values. The terms "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, as being infected with TB or not being infected with TB. "Sensitivity" indicates the performance of the biomarker(s) with respect to correctly classifying individuals as infected with TB or having TB disease. "Specificity" indicates the performance of the biomarker(s) with respect to correctly classifying individuals who are not infected with TB or do not have 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 uninfected individuals) and test samples (such as samples from TB- infected individuals) 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.

In some embodiments, overall performance of a panel of one or more biomarkers is represented by the area-under-the-curve (AUC) value. 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. The term "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. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest (e.g., TB-infected vs. non-infected individuals). 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 having TB and controls). Typically, the feature data across the entire population (e.g., all tested subject) are sorted in ascending order based on the value of a single feature. Then, for each value for that feature, 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. Although this definition refers to scenarios in which a feature is elevated in cases compared to controls, this definition also applies to scenarios in which a feature is lower in cases compared to the controls (in such a scenario, samples below the value for that feature would be counted). 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.

In some such embodiments, 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 whose levels are being detected. In some embodiments, the method comprises contacting the sample, or proteins from the sample, with at least one aptamer, wherein each aptamer specifically binds a biomarker whose levels are being detected.

In some embodiments, a method comprises detecting the level of at least one biomarker from a first panel of biomarkers, the first panel comprising biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM, and at least one biomarker from a second panel of biomarkers, the second panel comprising biomarkers selected from selected from SAA, NPS-PLA2, IP- 10, and CA6. In some embodiments, if the level of one or more biomarkers from the first panel and/or one or more biomarkers from the second panel are altered (e.g., higher or lower) from a control level, outside a control range, and/or beyond a threshold value, the subject is identified as a TB-infected individual or having TB disease.

The biomarkers identified herein provide a number of choices for subsets or panels of biomarkers that can be used to effectively identify TB infection and/or disease. 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. In some

embodiments, 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 Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM in a sample from the subject. In some embodiments, a method comprises detecting the level of any number and combination of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM. In some embodiments, a method comprises detecting Testican-2 and DERM. In some embodiments, a method comprises detecting the level of at least one biomarker, at least two biomarkers, at least three biomarkers, or four biomarkers selected SAA, NPS-PLA2, IP- 10, and CA6 in a sample from the subject. In some embodiments, a method comprises detecting the level of any number and combination of SAA, NPS-PLA2, IP- 10, and CA6.

"Biological 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. This also includes experimentally separated fractions of all of the preceding. For example, 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). In some embodiments, a sample can be a combination of samples from an individual, such as a combination of a tissue and fluid sample. The term "biological sample" also includes materials containing homogenized solid material, such as from a stool sample, a tissue sample, or a tissue biopsy, for example. The term "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. Exemplary tissues 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.

Further, in some embodiments, 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 TB infection is detected in the pooled sample, then each individual biological sample can be re-tested to identify the TB-infected individual(s).

"Target", "target molecule", and "analyte" are used interchangeably herein to refer 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. A "target molecule", "target", or "analyte" refers to a set of copies of one type or species of molecule or multi-molecular structure. "Target molecules", "targets", and "analytes" refer to more than one type or species of molecule or multi-molecular structure. Exemplary 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. In some embodiments, a target molecule is a protein, in which case the target molecule may be referred to as a "target protein."

As used herein, 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. In some embodiments, a capture reagent is selected from an aptamer and an antibody.

The term "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. The term "antibody" also refers to synthetically-derived antibodies, such as phage display-derived antibodies and fragments, affybodies, nanobodies, etc.

As used herein, "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.

As used herein, "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. The exact nature of the "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 have the disease or condition. 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. In some embodiments, a control level in a method described herein is the level that has been observed in one or more subjects without TB infection and/or TB disease. In some embodiments, a control level in a method described herein is the average or mean level, optionally plus or minus a statistical variation, that has been observed in a plurality of subjects without TB infection and/or TB disease.

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 disease or condition. A "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. In some embodiments, 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 TB infection and/or TB disease.

As used herein, "individual" and "subject" and "patient" are used interchangeably to refer to a test subject or patient. The individual 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 healthy or normal individual is an individual in which the disease or condition of interest (e.g., TB infection) is not detectable by conventional diagnostic methods.

"Diagnose", "diagnosing", "diagnosis", and variations thereof refer 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) or diagnosed as ill / abnormal (e.g., a diagnosis of the presence, or an assessment of the characteristics, of a disease or condition). The terms "diagnose", "diagnosing", "diagnosis", etc., encompass, with respect to a particular disease or condition, the initial detection of the disease; the characterization or classification of the disease; the detection of the progression, remission, or recurrence of the disease; and the detection of disease response after the

administration of a treatment or therapy to the individual.

"Prognose", "prognosing", "prognosis", and variations thereof refer 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), 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. Thus, "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; 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.

As used herein, "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. In various embodiments, the level is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman

spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.

As used herein, "tuberculosis infection" or "TB infection" refers to the infection of an individual with any of a variety of 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). "Active TB infection" may also be referred to as "TB disease." Observable signs of active TB infection include chronic cough with blood-tinged sputum, fever, night sweats, and weight loss. As used herein, "tuberculosis disease" refers to the condition of a subject with an active tuberculosis infection.

As used herein, a "TB-infected subject" refers to a subject that has a diagnosed or undiagnosed TB infection (e.g., active and/or latent).

As used herein, a "subject at risk of TB infection" refers to a subject with exposed to one or more risk factors for TB infection. Such risk factors include HIV infection, poverty, geographic location, chronic lung disease, poverty, diabetes, genetic susceptibility, imprisonment, etc.

As used herein "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). Detection and/or quantification of host biomarkers allows for diagnosis of pathogen infection.

As used herein "pathogen biomarkers" are molecules (e.g., proteins) that are not endogenous to an 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 diagnosis of pathogen infection.

The present application includes 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) infection (e.g., TB infection) or tuberculosis (TB).

"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, microfiuidics 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 micro fluidic 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 methacrylate, polytetrafluoroethylene, butyl rubber, styrenebutadiene rubber, natural rubber, polyethylene, polypropylene, (poly)tetrafluoroethylene, (poly)vinylidenefluoride, polycarbonate, and polymethylpentene. Suitable solid support particles that can be used include, e.g., encoded particles, such as Luminex®-type encoded particles, magnetic particles, and glass particles.

Exemplary Uses of Biomarkers

In various exemplary embodiments, methods are provided for determining whether a subject is infected with Mycobacterium tuberculosis (TB infection) and/or is suffering from Tuberculosis (TB). Methods are also provided for assessing the effectiveness of TB treatment. In some embodiments, biomarkers are indicative of co-infection with TB and human immunodeficiency virus (HIV). In some embodiments, biomarkers are indicative of infection with TB but not HIV. In some embodiments, methods comprise detecting the presence of one or more biomarkers. In some embodiments, methods comprise measuring the level or

concentrations of one or more biomarkers by any number of analytical methods, including any of the analytical methods described herein. These biomarkers are, for example, present at different levels in TB-positive and TB-negative subjects. In some embodiments, detection of the differential levels of a biomarker in an individual can be used, for example, to permit the determination of whether the individual has TB infection, active TB, etc. In some embodiments, detection of the presence of a biomarker in an individual can be used, for example, to permit the determination that the individual has TB infection and/or active TB, etc. In some embodiments, any of the biomarkers described herein may be used to monitor TB infection in an individual over time, and to permit the determination of whether treatment is effective.

In addition to testing 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 of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM; or one or more of SAA, NPS-PLA2, IP- 10, and CA6) as a stand-alone diagnostic test, in some embodiments, biomarker levels are tested in conjunction with other markers or assays indicative of TB (e.g., skin test, sputum culture, blood test, tissue culture, body fluid culture, chest x-ray, etc.). In addition to testing biomarker levels in conjunction with other TB diagnostic methods, information regarding the 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.

Detection and Determination of Biomarkers and Biomarker Levels

A biomarker level for the biomarkers described herein can be detected using any of a variety of known analytical methods. In one embodiment, a biomarker level is detected using a capture reagent. In various embodiments, 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. In other embodiments, 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. The 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.

In some embodiments, biomarker presence or level is detected using a biomarker/capture reagent complex.

In some embodiments, 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.

In some embodiments, biomarker presence or level is detected directly from the biomarker in a biological sample.

In some embodiments, biomarkers are detected using a multiplexed format that allows for the simultaneous detection of two or more biomarkers in a biological sample. In some embodiments of the multiplexed format, capture reagents are immobilized, directly or indirectly, covalently or non-covalently, in discrete locations on a solid support. In some embodiments, 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. In some embodiments, 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.

In one or more of the foregoing embodiments, a fluorescent tag can be used to label a component of the biomarker/capture reagent complex to enable the detection of the biomarker level. In various embodiments, 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.

In some embodiments, the fluorescent label is a fluorescent dye molecule. In some embodiments, 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. In some embodiments, the dye molecule includes an AlexFluor molecule, such as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700. In some embodiments, the dye molecule includes a first type and a second type of dye molecule, such as, e.g., two different AlexaFluor molecules. In some embodiments, 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. For example, 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.

In one or more embodiments, 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, peroxyoxalates, Aryl oxalates, Acridinium esters, dioxetanes, and others.

In some embodiments, the detection method includes an enzyme/substrate combination that generates a detectable signal that corresponds to the biomarker level. Generally, 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.

In some embodiments, the detection method can be a combination of fluorescence, chemiluminescence, radionuclide or enzyme/substrate combinations that generate a measurable signal. In some embodiments, multimodal signaling could have unique and advantageous characteristics in biomarker assay formats.

In some embodiments, 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". Once the microarray is contacted with a sample, 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.

As used herein, 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. Different 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. As further described below, 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. 5,580,737, see supra, describes highly specific aptamers containing one or more nucleotides modified with 2'-amino (2'-NH2), 2'-fiuoro (2'-F), and/or 2'- O-methyl (2'-OMe). See also, U.S. Patent Application Publication No. 2009/0098549, entitled "SELEX and PHOTOSELEX", which describes nucleic acid libraries having expanded physical and chemical properties and their use in SELEX and photoSELEX.

SELEX can also be used to identify aptamers that have desirable off-rate characteristics.

See U.S. Publication No. US 2009/0004667, entitled "Method for Generating Aptamers with Improved Off-Rates", which describes improved SELEX methods for generating aptamers that can bind to target molecules. Methods for producing aptamers and photoaptamers having slower rates of dissociation from their respective target molecules are described. The methods involve contacting the candidate mixture with the target molecule, allowing the formation of nucleic acid- target complexes to occur, and performing a slow off-rate enrichment process wherein nucleic acid-target complexes with fast dissociation rates will dissociate and not reform, while complexes with slow dissociation rates will remain intact. Additionally, the methods include the use of modified nucleotides in the production of candidate nucleic acid mixtures to generate aptamers with improved off-rate performance. Nonlimiting exemplary modified nucleotides include, for example, the modified pyrimidines shown in Figure 17. In some embodiments, an aptamer comprises at least one nucleotide with a modification, such as a base modification. In some embodiments, 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. Such 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 17. In some embodiments, 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. In some embodiments, 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 17.

In some embodiments, a slow off-rate aptamer (including an aptamers comprising at least one nucleotide with a hydrophobic modification) has an off-rate (t½) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.

In some embodiments, 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. 6,291,184, each of which is entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX"; see also, e.g., U.S. Patent No. 6,458,539, entitled "Photoselection of Nucleic Acid Ligands". After the microarray is contacted with the sample and the photoaptamers have had an opportunity to bind to their target molecules, the photoaptamers are photoactivated, and the solid support is washed to remove any non-specifically bound molecules. Harsh wash conditions may be used, since target molecules that are bound to the photoaptamers are generally not removed, due to the covalent bonds created by the

photoactivated functional group(s) on the photoaptamers. In this manner, the assay enables the detection of a biomarker level corresponding to a biomarker in the test sample.

In some assay formats, 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. Moreover, depending upon the method employed, detection of target molecules bound to their aptamers can be subject to imprecision, since the surface of the solid support may also be exposed to and affected by any labeling agents that are used. Finally, 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.

Aptamer assays that permit an aptamer to capture its target in solution and then employ separation steps that are designed to remove specific components of the aptamer-target mixture prior to detection have also been described (see U.S. Publication No. 2009/0042206, entitled "Multiplexed Analyses of Test Samples"). 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). 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. In one embodiment, these constructs can include a cleavable or releasable element within the aptamer sequence. In other embodiments, 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. For example, 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. In one embodiment, 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.

Homogenous assays, done with all assay components in solution, do not require separation of sample and reagents prior to the detection of signal. These methods are rapid and easy to use. These methods generate signal based on a molecular capture or binding reagent that reacts with its specific target. In some embodiments of the methods described herein, 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., Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and/or DERM; and/or SAA, NPS-PLA2, IP- 10, and/or CA6.

In some embodiments, 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. When 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. By monitoring the anisotropy change, 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-complexed aptamer from the aptamer affinity complex; (h) eluting the aptamer from the solid support; and (i) detecting the biomarker by detecting the aptamer component of the aptamer affinity complex.

A nonlimiting 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.

Determination of Biomarker Levels using Immunoassays

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. To improve specificity and sensitivity of an assay method based on immuno-reactivity, 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.

Numerous immunoassay formats have been designed. 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, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, and others (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

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. Examples of procedures for detecting 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 (where the detectable label is an enzyme; see above) can be, without limitation, fluorescent, luminescent, or radioactive or they may absorb visible or ultraviolet light. Examples of 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. Determination of Biomarker Levels using Gene Expression Profiling

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. Thus, in some embodiments, a biomarker or biomarker panel described herein can be detected by detecting the appropriate RNA.

In some embodiments, 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.

Detection of Biomarkers Using In Vivo Molecular Imaging Technologies

In some embodiments, a biomarker described herein may be used in molecular imaging tests. For example, 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.

The use of in vivo molecular imaging technologies is 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.

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. Such labels are well known in the art and could easily be selected by one of ordinary skill in the art.

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. For diagnostic in vivo imaging, 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. Also, when selecting a radionuclide for in vivo diagnosis, 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 sophistication of the instruments used to detect them, the two-dimensional distribution of radioactivity may be inferred from outside of the body.

Commonly used 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 bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.

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. Similarly, aptamers may be used for such in vivo imaging diagnostic methods. For example, 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.

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.

Other techniques are review, for example, in N. Blow, Nature Methods, 6, 465-469, 2009; herein incorporated by reference in its entirety.

Determination of Biomarkers using Histology/Cytology Methods

In some embodiments, the biomarkers described herein may be detected in a variety of tissue samples using histological or cytological methods. For example, 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.

In some embodiments, 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. In another embodiment, the cell sample is produced from a cell block.

In some embodiments, 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. In another embodiment, fixing and dehydrating are replaced with freezing.

In another embodiment, 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.

In one embodiment, 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. Determination of Biomarker Levels using Mass Spectrometry Methods

A variety of configurations of mass spectrometers can be used to detect biomarker levels. Several types of mass spectrometers are available or can be produced with various configurations. In general, 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. For example, 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. Common 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- flight 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/MS, and APPI-(MS)N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.

Sample preparation strategies are used to label and enrich samples before mass spectroscopic characterization of protein biomarkers and determination biomarker levels.

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. diabodies etc) imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acid, a hormone receptor, a cytokine receptor, and synthetic receptors, and modifications and fragments of these.

The foregoing assays enable the detection of 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. Thus, while some of the described biomarkers may be useful alone for detecting TB infection, methods are also described herein for the grouping of multiple biomarkers and subsets of the biomarkers to form panels of two or more biomarkers. In accordance with any of the methods 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.

Classification of Biomarkers and Calculation of Disease Scores

In some embodiments, 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, in some embodiments, may refer to the mean or average of the biomarker levels for the individuals in a particular group. In some embodiments, 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.) is known as classification, and the procedure used to accomplish this assignment is known as a classifier or a classification method. Classification methods may also be referred to as scoring methods. There are many classification methods that can be used to construct a diagnostic classifier from a set of biomarker levels. In some instances, 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.

Common approaches for developing 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 objective functions. For a review, 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. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009.

To produce a classifier using supervised learning techniques, a set of samples called training data are obtained. In the context of diagnostic tests, training data includes samples from the distinct groups (classes) to which unknown samples will later be assigned. For example, 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. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009). Training of a naive Bayesian classifier is described, e.g., in U.S. Publication Nos: 2012/0101002 and 2012/0077695. Since typically there are many more potential biomarker levels than samples in a training set, care must be used to avoid over-fitting. 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. The joint 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. Using the sensitivity plus specificity as a classifier score, 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.)

Another way to depict classifier performance is through a receiver operating

characteristic (ROC), or simply ROC curve or ROC plot. The ROC is a graphical plot of the sensitivity, or true positive rate, vs. false positive rate (1 - specificity or 1 - true negative rate), for a binary classifier system as its discrimination threshold is varied. The ROC can also be represented equivalently by plotting the fraction of true positives out of the positives (TPR = true positive rate) vs. the fraction of false positives out of the negatives (FPR = false positive rate). Also known as a Relative Operating Characteristic curve, because it is a comparison of two operating characteristics (TPR & FPR) as the criterion changes. 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. Pattern 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. For example, a classifier may comprise Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9,

Testican-2, FCG3B, and DERM; or it may comprise DERM and one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, and FCG3B; or it may comprise Testican-2 and one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, DERM, and FCG3B; or it may comprise DERM and Testican-2, and one or more of Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, and FCG3B; or any subcombinations thereof. Other example classifiers comprise SAA, NPS-PLA2, IP- 10, and CA6; or any subcombinations thereof. Other examplary classifiers comprise any suitable combinations of SAA, NPS-PLA2, IP- 10, CA6, Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM.

In some embodiments, once a panel is defined to include a particular set of biomarkers and a classifier is constructed from a set of training data, the diagnostic test parameters are complete. In some embodiments, 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.

In some embodiments, a biological sample is optionally diluted and run in a multiplexed aptamer assay, and data is assessed as follows. First, the data from the assay are optionally normalized and calibrated, and the resulting biomarker levels are used as input to a Bayes classification scheme. Second, 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 classification score can be reported. In some embodiments, the individual log-likelihood risk factors computed for each biomarker level can be reported as well. Kits

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. Furthermore, any kit can contain one or more detectable labels as described herein, such as a fluorescent moiety, etc.

In some embodiments, 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.

Alternatively, rather than one or more computer program products, one or more instructions for manually performing the above steps by a human can be provided.

In some embodiments, 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.

The kits can also contain one or more reagents (e.g., solubilization buffers, detergents, washes, or buffers) for processing a biological sample. 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.

In some embodiments, kits are provided for the analysis of TB infection, wherein the kits comprise PCR primers for one or more biomarkers described herein. In some embodiments, a kit may further include instructions for use and correlation of the biomarkers with TB infection. In some embodiments, 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.

For example, 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. In some embodiments, 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. Further, in some embodiments, an algorithm or computer program compares the total score with a predetermined score, and uses the comparison to determine likelihood of TB infection. Alternatively, rather than one or more algorithms or computer programs, one or more instructions for manually performing the above steps by a human can be provided.

Computer Methods and Software

Once a biomarker or biomarker panel is selected, a method for detecting TB infection in an individual 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. In some embodiments, the results of the biomarker levels are reported qualitatively rather than quantitatively, such as, for example, a proposed diagnosis ("TB infection", "latent TB infection," "active TB infection," etc.) or simply a positive / negative result where "positive" and "negative" are defined. In some embodiments, a method for detecting TB infection in an individual 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; 3) perform any data normalization or standardization; 4) calculate each biomarker level; and 5) report the results of the biomarker levels. In some embodiments, the biomarker levels are combined in some way and a single value for the combined biomarker levels is reported. In this approach, in some embodiments, 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. Or 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.

At least some embodiments of the methods described herein can be implemented with the use of a computer. An example of a computer system 100 is shown in Figure 18. With reference to Figure 18, 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. 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.

With respect to Figure 18, system 100 has extensive flexibility and configurability. Thus, for example, 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. However, it will be apparent to those skilled in the art that embodiments may well be utilized in accordance with more specific application requirements. For example, one or more 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.

In one aspect, 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.

In one aspect, the system further comprises one or more devices for providing input data to the one or more processors.

In some embodiments, the system further comprises a memory for storing a data set of ranked data elements.

In another aspect, 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. Preferably, 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. In one aspect, 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). In one aspect, 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 according to various embodiments 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. For example, in one embodiment, 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. Detecting TB in a subject 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. Some embodiments described herein can be implemented so as to include a computer program product. 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.

As used herein, 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. Furthermore, 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.

In one aspect, a computer program product is provided for indicating the TB-infection status 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.

While various embodiments have been described as methods or apparatuses, it should be understood that embodiments can be implemented through code coupled with a computer, e.g., code resident on a computer or accessible by the computer. For example, software and databases could be utilized to implement many of the methods discussed above. Thus, in addition to embodiments accomplished by hardware, it is also noted that these embodiments can be accomplished through the use of an article of manufacture comprised of a computer usable medium having a computer readable program code embodied therein, which causes the enablement of the functions disclosed in this description. Therefore, it is desired that

embodiments also be considered protected by this patent in their program code means as well. Furthermore, 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).

It is also envisioned that 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. Thus, 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. Methods of Treatment

In some embodiments, following a determination that a subject is infected with or suffers from TB, the subject is treated for TB infection. In some embodiments, medications used to treat latent TB infection include: isoniazid (INH), rifampin (RIF), and rifapentine (RPT). In some embodiments, TB disease is treated by taking several drugs for 6 to 9 months. There are 10 drugs currently approved by the U.S. Food and Drug Administration (FDA) for treating TB. Of the approved drugs, 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).

In some embodiments, methods of monitoring TB infection/disease and/or treatment of

TB infection/disease are provided. In some embodiments, the present methods of detecting TB infection are carried out at a time 0. In some embodiments, 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. In some embodiments, a treatment regimen is altered based upon the results of monitoring (e.g., upon determining that a first treatment is ineffective). EXAMPLES

Example 1: Subjects and Samples

Experiments were conducted to assess the ability of protein biomarkers to distinguish TB positive subjects from non-TB subjects in the presence and absence of HIV. The TB

classification was based on sputum-smear and culture. Non-TB subjects presented with symptoms consistent with TB, but were determined not to have TB infection. Serum samples were obtained from subjects in multiple sites in South Africa, Peru, and Vietnam, provided by Foundation for Innovative New Diagnostics (FIND). In total, 150 TB positive/HIV negative sample, 150 TB positive/HIV positive, 50 TB negative/HIV positive, and 50 TB negative/HIV negative samples were used. In addition 25 TB samples that were culture positive but smear negative were included as a "challenge" set to provide more difficult TB diagnosis cases.

Samples were collected in three countries, but within countries there were multiple study IDs and in some cases different case report form (CRF) IDs associated with a given study ID. Treating the combination of study ID and CRF ID as a proxy for collection site/time, a surrogate SitelD field was created.

Proteins with median (over all the samples in a given diagnostic category) values that differ between sitelDs are potential "site markers", though given the geographic differences in the collection sites the "non-TB" group is not expected to be a clinically homogeneous population. Proteins that distinguish the sites were identified using the non-parametric Kruskal-Wallis test and a Bonferroni corrected 5% significance level.

Since the non-TB population consists of individuals with a variety of non-TB pulmonary issues we might reasonably expect to see differences between South African subjects and Vietnamese subjects with different types of (regional) non-TB conditions. Therefore, such candidate site markers were not excluded from subsequent biomarker discovery analysis. Instead, it was required that the "effect size" for a candidate TB marker exceed the "site marker" effect size.

Samples were tested on SOMAscan v3, which detects 1129 proteins. Hemolyzed samples were excluded from analysis.

This full data set was randomly split into a "training set" containing 80% of the observations and a "test set" containing the remaining 20% of samples to use for independent evaluation of preliminary model performance. Only the training set was used to establish the models; after fixing the models to carry forward into the validation phase the test set will be used to benchmark the performance of the candidate model. During the validation phase, an independent blinded sample collection is run through the candidate models to predict the classification (TB or non-TB) of each sample. Once the true sample classes are un-blinded, the performance in the validation set is compared with that obtained with the test set to determine the extent to which the model performance estimates obtained in the test set can be expected to generalize to the broader TB population.

All data were log transformed to stabilize the variance. Non-parametric statistical tests were used for all comparisons - the KS test for cross-sectional comparisons and the Kruskal- Wallis test for inter-site comparisons within each diagnostic category. Stability selection using LI -regularized logistic regression was used to identify stable features in the presence of the available clinical covariates.

Individual samples were examined for evidence of hemolysis that was not apparent in the visual inspection performed at the time the samples were assayed, using scatter plots of

hemoglobin and haptoglobin signals (low Haptoglobin and high hemoglobin levels are indicative of hemolysis). Hemolyzed samples were removed from the preliminary analysis.

Example 2: Biomarker Discovery (All): TB vs. non-TB

At a 5% Bonferroni corrected significance level the KS test identified 364 proteins differentially expressed between TB and non-TB groups, regardless of HIV status. Of these -53% (192/364) are higher in non-TB subjects than in TB subjects. Table 1 shows the top 100 proteins distinguishing TB from non-TB ranked by KS distance. Positive values for the KS distance indicate proteins with higher signal in the non-TB subjects than in TB subjects.

Proteins with the greatest median fold-change between TB and non-TB were SAA, NPS- PLA2, IP10, 1-TAC, CA6, and CK-MB, as shown in the Volcano plot (Figure 1).

Table 1. Top 100 serum markers for distinguishing TB from non-TB ranked by KS distance.

Rank Target Swiss Prot signed KS pjemp} p-value q-value

1) Kallistatin P29622 0.596 1.60E-06 1.58E-33 6.63E-06

2) TSP4 P35443 0.577 1.60E-06 1.94E-31 6.63E-06

3) Gelsolin P06396 0.568 1.60E-06 1.85E-30 6.63E-06

4) LBP P18428 -0.552 1.60E-06 7.33E-29 6.63E-06

5) C9 P02748 -0.551 1.60E-06 9.82E-29 6.63E-06

6) NPS-PLA2 P14555 -0.542 1.60E-06 8.31E-28 6.63E-06

7) DERM Q07507 0.537 1.60E-06 2.88E-27 6.63E-06

8) ITI heavy chain H4 Q14624 -0.528 1.60E-06 2.14E-26 6.63E-06

9) Afamin P43652 0.526 1.60E-06 3.32E-26 6.63E-06

10) C9 P02748 -0.507 1.60E-06 2.50E-24 6.63E-06

11) IP-10 P02778 -0.506 1.60E-06 2.92E-24 6.63E-06

12) BGH3 Q15582 0.502 1.60E-06 6.55E-24 6.63E-06

13) suPAR Q03405 -0.497 1.60E-06 2.10E-23 6.63E-06

14) LRIG3 Q6UXM1 0.494 1.60E-06 4.05E-23 6.63E-06

15) MMP-2 P08253 0.489 1.60E-06 1.14E-22 6.63E-06

16) BMP-1 P13497 0.487 1.60E-06 1.53E-22 6.63E-06

17) Cathepsin V 060911 0.487 1.60E-06 1.69E-22 6.63E-06

18) TrkC Q16288 0.485 1.60E-06 2.56E-22 6.63E-06

19) Carbonic anhydrase 6 P23280 0.476 1.60E-06 1.43E-21 6.63E-06

20) IL-6 P05231 -0.472 1.60E-06 3.68E-21 6.63E-06

21) CDON Q4KMG0 0.467 1.60E-06 9.15E-21 6.63E-06

22) kallikrein 8 060259 0.465 1.60E-06 1.46E-20 6.63E-06

23) al-Antitrypsin P01009 -0.462 1.60E-06 2.84E-20 6.63E-06 ) CK-MB P12277 P06732 0.456 1.60E- -06 8.92E- -20 6, .63E- -06) Albumin P02768 0.454 1. 60E- -06 1. 24E- -19 6, .63E- -06) contactin-1 Q12860 0.449 1. 60E- -06 3. 13E- -19 6, .63E- -06) CD109 Q6YHK3 0.448 1. 60E- -06 3. 91E- -19 6, .63E- -06) M C2 Q9UBG0 0.441 1. 60E- -06 1. 46E- -18 6, .63E- -06) IL-1 R AcP Q9NPH3 0.441 1. 60E- -06 1. 57E- -18 6, .63E- -06) Macrophage mannose P22897 -0.439 1. 60E- -06 2. 24E- -18 6, .63E- -06) HAI-1 043278 0.438 1. 60E- -06 2. 66E- -18 6, .63E- -06) Fibrinogen g-chain P02679 -0.433 1. 60E- -06 7. 25E- -18 6, .63E- -06) EDA Q92838 0.432 1. 60E- -06 8. 16E- -18 6, .63E- -06) TGF-b3 P10600 -0.432 1. 60E- -06 8. 88E- -18 6, .63E- -06) complement factor H- Q9BXR6 -0.43 1. 60E- -06 1. 18E- -17 6, .63E- -06) CRP P02741 -0.429 1. 60E- -06 1. 32E- -17 6, .63E- -06) PCI P05154 0.429 1. 60E- -06 1. 38E- -17 6, .63E- -06) NCAM-L1 P32004 0.429 1. 60E- -06 1. 52E- -17 6, .63E- -06) D-dimer P02671 P02675 -0.426 1. 60E- -06 2. 56E- -17 6, .63E- -06) GFRa-2 000451 0.426 1. 60E- -06 2. 68E- -17 6, .63E- -06) Antithrombin III P01008 0.425 1. 60E- -06 2. 87E- -17 6, .63E- -06) FCG3B 075015 -0.425 1. 60E- -06 3. 05E- -17 6, .63E- -06) JAK2 060674 -0.425 1. 60E- -06 3. 16E- -17 6, .63E- -06) GDF-11 095390 0.422 1. 60E- -06 5. 05E- -17 6, .63E- -06) CK-MM P06732 0.421 1. 60E- -06 6. 69E- -17 6, .63E- -06) SCF sR P10721 0.42 1. 60E- -06 7. 26E- -17 6, .63E- -06) NovH P48745 0.42 1. 60E- -06 7. 69E- -17 6, .63E- -06) ARMEL Q49AH0 0.42 1. 60E- -06 7. 69E- -17 6, .63E- -06) IL-11 RA Q14626 0.419 1. 60E- -06 8. 15E- -17 6, .63E- -06) Mn SOD P04179 0.418 1. 60E- -06 1. 00E- -16 6, .63E- -06) TrkB Q16620 0.414 1. 60E- -06 2. 01E- -16 6, .63E- -06) Proteinase-3 P24158 -0.414 1. 60E- -06 2. 31E- -16 6, .63E- -06) Testican-2 Q92563 0.413 1. 60E- -06 2. 58E- -16 6, .63E- -06) a2-HS-Glycoprotein P02765 0.412 1. 60E- -06 3. 17E- -16 6, .63E- -06) TNF sR-l P19438 -0.411 1. 60E- -06 3. 44E- -16 6, .63E- -06) TIMP-1 P01033 -0.409 1. 60E- -06 5. 41E- -16 6, .63E- -06) PSA-ACT P07288, P01011 -0.405 1. 60E- -06 1. 03E- -15 6, .63E- -06) ATS 13 Q76LX8 0.401 1. 60E- -06 2. 23E- -15 6, .63E- -06) CNDP1 Q96KN2 0.396 1. 60E- -06 4. 67E- -15 6, .63E- -06) IDS P22304 0.396 1. 60E- -06 4. 82E- -15 6, .63E- -06) Myeloperoxidase P05164 -0.395 1. 60E- -06 5. 94E- -15 6, .63E- -06) CD36 ANTIGEN P16671 0.394 1. 60E- -06 6. 42E- -15 6, .63E- -06) RGM-C Q6ZVN8 0.391 1. 60E- -06 1. 08E- -14 6, .63E- -06) SAA P0DJI8 -0.39 1. 60E- -06 1. 37E- -14 6, .63E- -06) CAPG P40121 -0.388 1. 60E- -06 1. 76E- -14 6, .63E- -06) Apo A-l P02647 0.388 1. 60E- -06 1. 80E- -14 6, .63E- -06) AS AH 2 Q9NR71 0.387 1. 60E- -06 2. 28E- -14 6, .63E- -06) NCAM-120 P13591 0.386 1. 60E- -06 2. 76E- -14 6, .63E- -06) Activated Protein C P04070 0.383 1. 60E- -06 3. 97E- -14 6, .63E- -06) MFRP Q9BY79 -0.383 1. 60E- -06 4. 56E- -14 6, .63E- -06) ASAHL Q02083 0.382 1. 60E- -06 4. 85E- -14 6, .63E- -06 72) BOC Q9BWV1 0.382 1.60E- -06 4.85E- -14 6, .63E- -06

73) HGFA Q04756 0.381 1. 60E- -06 6. 26E- -14 6, .63E- -06

74) IL-19 Q9UHD0 0.38 1. 60E- -06 7. 02E- -14 6, .63E- -06

75) Contactin-4 Q8IWV2 0.379 1. 60E- -06 8. 84E- -14 6, .63E- -06

76) Lysozyme P61626 -0.378 1. 60E- -06 9. 52E- -14 6, .63E- -06

77) SEP Q12884 0.378 1. 60E- -06 9. 63E- -14 6, .63E- -06

78) TIMP-2 P16035 0.375 1. 60E- -06 1. 67E- -13 6, .63E- -06

79) Prekallikrein P03952 0.374 1. 60E- -06 1. 76E- -13 6, .63E- -06

80) PGRP-S 075594 -0.373 1. 60E- -06 2. 02E- -13 6, .63E- -06

81) Nr-CAM Q92823 0.371 1. 60E- -06 3. 20E- -13 6, .63E- -06

82) MPIF-1 P55773 -0.37 1. 60E- -06 3. 79E- -13 6, .63E- -06

83) BMP-7 P18075 0.368 1. 60E- -06 4. 79E- -13 6, .63E- -06

84) OMD Q99983 0.368 1. 60E- -06 4. 82E- -13 6, .63E- -06

85) SET Q01105 0.368 1. 60E- -06 5. 04E- -13 6, .63E- -06

86) WIF-1 Q9Y5W5 0.365 1. 60E- -06 8. 03E- -13 6, .63E- -06

87) B7-H2 075144 -0.365 1. 60E- -06 8. 03E- -13 6, .63E- -06

88) CHL1 000533 0.364 1. 60E- -06 8. 53E- -13 6, .63E- -06

89) ERBB1 P00533 0.364 1. 60E- -06 9. 73E- -13 6, .63E- -06

90) Collectin Kidney 1 Q9BWP8 0.363 1. 60E- -06 1. 09E- -12 6, .63E- -06

91) PAK6 Q9NQU5 -0.362 1. 60E- -06 1. 17E- -12 6, .63E- -06

92) sLeptin R P48357 0.359 1. 60E- -06 2. OOE- -12 6, .63E- -06

93) PSA2 P25787 -0.358 1. 60E- -06 2. 15E- -12 6, .63E- -06

94) SLIK5 094991 0.358 1. 60E- -06 2. 34E- -12 6, .63E- -06

95) CYTF 076096 -0.356 1. 60E- -06 3. 19E- -12 6, .63E- -06

96) bFGF-R P11362 0.355 1. 60E- -06 3. 77E- -12 6, .63E- -06

97) l-TAC 014625 -0.354 1. 60E- -06 4. 12E- -12 6, .63E- -06

98) AN32B Q92688 -0.354 1. 60E- -06 4. 15E- -12 6, .63E- -06

99) DR6 075509 0.354 1. 60E- -06 4. 45E- -12 6, .63E- -06

100) FABP P05413 0.353 1. 60E- -06 4. 67E- -12 6, .63E- -06

Stability selection was performed using an LI -regularized logistic regression model including human and TB proteins along with GENDER, SITE ID, CASE ID and

BIRTH_PLACE. Figure 2 shows stability paths for protein data augmented by co-variates.

Stability paths are labeled in order of total area under the path rather than by maximum selection probability obtained.

Table 2 shows the proteins (and Age) with selection probabilities that exceed 0.5 for at least one value of the regularization parameter. Entries in the table are sorted by decreasing area under the stability path listed in the last column. CCL28 was removed since it was a strong site marker.

Table 2 Top markers distinguishing TB from non-TB ranked by area under the stability selection curve.

Rank Target Feature Name Max Selection Prob Area

3) Kallistatin SERPINA4.3449.58.2 0.920 0.24

1) Gelsolin GSN.4775.34.3 0.978 0.23 6) TSP4 TH BS4.3340.53.1 0.835 0.17

7) Afamin AFM.4763.31.3 0.813 0.16

12) BGH3 TGFBI.3283.21.1 0.703 0.11

10) C9 C9.2292.17.4 0.740 0.10

2) Testican-2 SPOCK2.5491.12.3 0.960 0.09

8) FCG3B FCG R3B.3311.27.1 0.810 0.06

9) CCL28 CCL28.2890.59.2 0.802 0.06

17) DERM DPT.4979.34.2 0.598 0.05

11) GFRa-2 GFRA2.2515.14.3 0.723 0.05

5) Age Age 0.843 0.05

18) CN DP1 CN DPl.3604.6.4 0.563 0.05

13) IM DH2 I MPDH2.5250.53.3 0.667 0.05

14) C3b C3.4480.59.2 0.630 0.04

16) IP-10 CXCLIO.4141.79.1 0.598 0.04

21) CRP CRP.4337.49.2 0.517 0.03

15) Coagulation Factor IX F9.4876.32.1 0.623 0.03

4) M MP-12 MM P12.4496.60.2 0.868 0.03

20) Fibronectin FN 1.4131.72.2 0.540 0.02

19) TCPTP PTPN2.3401.8.2 0.547 0.02

Example 3: Biomarker Discovery (HIV Negative): TB vs. non-TB

At a 5% Bonferroni corrected significance level 304 proteins distinguish TB from non-TB subjects in the HIV negative stratum. Of these -59% (180/304) have positive KS distances indicating they are high in non-TB than in TB. Table 3 shows the top 25 markers ranked by KS distance.

Table 3. Top 25 serum markers in HIV-negative population for distinguishing TB from non-TB ranked by KS distance.

Rank Target UniProt ID signed KS P iemp} p-value q-value

1) Kail istatin P29622 0.651 1.6e-06 1.96e-27 7.40e-06

2) C9 P02748 -0.642 1.6e-06 9.98e-27 7.40e-06

3) C9 P02748 -0.613 1.6e-06 2.26e-24 7.40e-06

4) LBP P18428 -0.589 1.6e-06 1.59e-22 7.40e-06

5) IP-10 P02778 -0.583 1.6e-06 4.41e-22 7.40e-06

6) TrkC Q16288 0.576 1.6e-06 1.30e-21 7.40e-06

7) Afamin P43652 0.571 1.6e-06 3.44e-21 7.40e-06

8) Albumin P02768 0.568 1.6e-06 5.11e-21 7.40e-06

9) DERM Q07507 0.567 1.6e-06 6.37e-21 7.40e-06

10) TSP4 P35443 0.565 1.6e-06 8.15e-21 7.40e-06

11) BGH3 Q15582 0.561 1.6e-06 1.58e-20 7.40e-06

12) LRIG3 Q6UXM 1 0.556 1.6e-06 3.60e-20 7.40e-06

13) N PS-PLA2 P14555 -0.552 1.6e-06 7.07e-20 7.40e-06

14) Gelsolin P06396 0.549 1.6e-06 1.17e-19 7.40e-06

15) ITI heavy chain H4 Q14624 -0.548 1.6e-06 1.45e-19 7.40e-06

16) MM P-2 P08253 0.531 1.6e-06 1.91e-18 7.40e-06

17) I L-6 P05231 -0.513 1.6e-06 3.33e-17 7.40e-06

18) complement factor H-related 5 Q9BXR6 -0.512 1.6e-06 4.07e-17 7.40e-06

19) GFRa-2 000451 0.507 1.6e-06 7.72e-17 7.40e-06

20) CRP P02741 -0.503 1.6e-06 1.43e-16 7.40e-06

21) al-Antitrypsin P01009 -0.503 1.6e-06 1.56e-16 7.40e-06

22) PSA-ACT P07288, P01011 -0.502 1.6e-06 1.78e-16 7.40e-06

23) CD 109 Q6YH K3 0.499 1.6e-06 2.74e-16 7.40e-06 24) kallikrein 8 060259 0.490 1.6e-06 1.02e-15 7.40e-06

25) Proteinase-3 P24158 -0.487 1.6e-06 1.40e-15 7.40e-06

Example 4: Biomarker Discovery (HIV Positive): TB vs. non-TB

At a 5% Bonferroni corrected significance level 150 proteins distinguish TB from non-TB subjects in the HIV positive population. Of these, -49% (73/150) are higher in non-TB than TB subjects. Table 4 shows the top 25 markers ranked by KS distance.

Table 4. Top 25 serum markers in HIV-positive population for distinguishing TB from non-TB ranked by KS distance.

Rank Target Uniprot ID signed KS pjemp} p-value q-value

1) Gelsolin P06396 0.660 1.6e-06 1.17e-13 2.17e-05

2) Kallistatin P29622 0.656 1.6e-06 1.59e-13 2.17e-05

3) FCG3B 075015 -0.654 1.6e-06 1.91e-13 2.17e-05

4) CK-MB P12277 P06732 0.653 1.6e-06 2.20e-13 2.17e-05

5) LKHA4 P09960 -0.633 1.6e-06 1.27e-12 2.17e-05

6) TSP4 P35443 0.623 1.6e-06 3.20e-12 2.17e-05

7) TGF-b3 P 10600 -0.609 1.6e-06 1.08e-ll 2.17e-05

8) suPAR Q03405 -0.607 1.6e-06 1.23e-ll 2.17e-05

9) Cathepsin V 060911 0.605 1.6e-06 1.46e-ll 2.17e-05

10) IP- 10 P02778 -0.602 1.6e-06 1.98e-ll 2.17e-05

11) C3b P01024 -0.595 1.6e-06 3.53e-ll 2.17e-05

12) TIMP-1 P01033 -0.593 1.6e-06 4.02e-ll 2.17e-05

13) BMP-1 P13497 0.588 1.6e-06 6.27e-ll 2.17e-05

14) Endothel in-converting P42892 0.586 1.6e-06 7.26e-ll 2.17e-05

enzyme 1

15) Afamin P43652 0.579 1.6e-06 1.30e-10 2.17e-05

16) CK-MM P06732 0.572 1.6e-06 2.27e-10 2.17e-05

17) IL-1 R AcP Q9NPH3 0.570 1.6e-06 2.67e-10 2.17e-05

18) sE-Selectin P16581 -0.569 1.6e-06 2.97e-10 2.17e-05

19) LBP P18428 -0.568 1.6e-06 3.03e-10 2.17e-05

20) Macrophage mannose P22897 -0.556 1.6e-06 7.97e-10 2.17e-05

receptor

21) phosphoglycerate P00558 0.547 1.6e-06 1.56e-09 2.17e-05

kinase 1

22) Midkine P21741 -0.545 1.6e-06 1.82e-09 2.17e-05

23) TSG-6 P98066 -0.542 1.6e-06 2.35e-09 2.17e-05

24) CSF-1 P09603 -0.542 1.6e-06 2.39e-09 2.17e-05

25) Fibrinogen g-chain P02679 -0.540 1.6e-06 2.69e-09 2.17e-05

dimer

Example 5: Biomarker Discovery (HIV-positive): Smear Negative vs. non-TB

At a 5% Bonferroni corrected significance level 36 proteins distinguish smear negative TB from non-TB subjects in the HIV positive population. Of these, 64% (23/36) are higher in non-TB than TB subjects. Table 5 shows the top 25 markers ranked by KS distance. Table 5. Top 25 serum markers in HIV-positive population for distinguishing smear-negative TB from non-TB ranked by KS distance.

Rank Target Uniprot ID signed KS p_{emp} p-value q-value

1) Kallistatin P29622 0.778 1.6e-06 2.50e-09 1.41e-04

2) suPAR Q03405 -0.698 1.6e-06 1.32e-07 1.41e-04

3) FCG3B 075015 -0.698 1.6e-06 1.32e-07 1.41e-04

4) SCF sR P10721 0.683 1.6e-06 2.78e-07 1.41e-04

5) LKHA4 P09960 -0.683 1.6e-06 2.78e-07 1.41e-04

6) TSP4 P35443 0.683 1.6e-06 2.78e-07 1.41e-04

7) C3b P01024 -0.683 1.6e-06 2.78e-07 1.41e-04

8) Cytochrome c P99999 0.667 1.6e-06 5.75e-07 1.41e-04

9) BMP-1 P13497 0.667 1.6e-06 5.75e-07 1.41e-04

10) PSA2 P25787 -0.667 1.6e-06 5.75e-07 1.41e-04

11) PPase Q15181 0.651 1.6e-06 1.17e-06 1.41e-04

12) Layilin Q6UX15 -0.635 1.6e-06 2.33e-06 1.41e-04

13) RGM-C Q6ZVN8 0.635 1.6e-06 2.33e-06 1.41e-04

14) HAI-1 043278 0.619 4.9e-06 4.58e-06 3.44e-04

15) CK-MB P12277 P06732 0.619 4.9e-06 4.58e-06 3.44e-04

16) Afamin P43652 0.619 4.9e-06 4.58e-06 3.44e-04

17) Cathepsin D P07339 -0.603 6.6e-06 8.84e-06 4.07e-04

18) MK13 015264 0.603 6.6e-06 8.84e-06 4.07e-04

19) ROR1 Q01973 0.587 l.le-05 1.68e-05 6.11e-04

20) al-Antitrypsin P01009 -0.587 l.le-05 1.68e-05 6.11e-04

21) TrkB Q16620 0.587 l.le-05 1.68e-05 6.11e-04

22) BMP-6 P22004 -0.587 1.5e-05 1.68e-05 6.60e-04

23) Prekallikrein P03952 0.587 1.5e-05 1.68e-05 6.60e-04

24) Gelsolin P06396 0.587 1.5e-05 1.68e-05 6.60e-04

25) PERL P22079 0.587 1.5e-05 1.68e-05 6.60e-04

Example 6: Correlation analysis

Correlations between biomarkers were analyzed to identify the most independent markers. Looking at the Spearman correlations between the top 50 proteins, two large blocks emerge, distinguished by the sign of the correlation. Figure 3 shows the (Pearson) correlation matrix with proteins ordered to cluster correlations of similar magnitude using a sedation procedure.

Example 7: Model Building

The data was randomly split into a training set containing 80% of the observations and a "test" set containing the remaining 20%. Ten- fold stratified cross-validation was used to select the number of proteins in a model to optimize the balance of complexity (number of proteins) and performance. Models with increasing numbers of proteins were trained and tested using 10 fold stratified cross-validation in the "training set" and the average (over the 10 folds) performance was monitored as a function of model size. This process was repeated 20 times resulting in empirical 95% confidence intervals associated with the performance estimates. The optimal model was the smallest model with performance similar to that achieved by the best performing model.

As shown in Figure 4, the na^'ive Bayes model (and logistic regression too) generally performs better with the features generated using stability selection (and ranked by area under selection probability curve) than with features ranked by KS distance (magenta).

The correlation matrices in Figure 5 show that the Spearman correlation is stronger between the top KS markers than those chosen by stability selection.

Figure 6 shows the margin "differential" between 5 and 9 protein naive Bayes models with grossly misclassified samples labeled. With few exceptions, increasing the model size from 5 to 9 proteins increases the margin (distance from the decision boundary) reflecting an increased confidence in the classification. Though confidence goes up, increasing the model has very little effect on the extreme mis-classifications and in some cases moves the misclassified samples even farther from the decision boundary.

The 9 protein model performs slightly better (based on AUC and also specificity) and was selected for additional performance characterization. The 9 markers for the model, referred to as HR9, for Host Response 9 marker model, are summarized below in Table 6. A positive signed KS indicates that the level of the marker is higher in serum of TB-infected subjects than in serum of non-TB-infected subjects. Similarly, a negative signed KS indicates that the level of the marker is lower in serum of TB-infected subjects than in serum of non-TB-infected subjects. Table 6. HR9 model markers that distinguish TB from non-TB in serum.

9-Marker Model Signal distribution (KS distance) Stability selection (logistic regression)

(H 9) signed KS KS Rank p-value Max Stability Area under for TB vs. non-TB selection Rank selection

Kail istatin 0.596 1 1.58e-33 0.920 1 0.24

Gelsolin 0.568 3 1.85e-30 0.978 2 0.23

TSP4 0.577 2 1.94e-31 0.835 3 0.17

Afamin 0.526 9 3.32e-26 0.813 4 0.16

BGH3 0.502 12 6.55e-24 0.703 5 0.11

C9 -0.551 5 9.82e-29 0.740 6 0.10

Testican-2 0.413 53 2.58e-16 0.960 7 0.09

FCG3B -0.425 42 3.05e-17 0.810 8 0.06

DERM 0.537 7 2.88e-27 0.598 10 0.05

Figure 7 shows (log) normal models (with robust parameter estimates) to the cumulative distribution function (CDF), for each of the HR9 markers. A single Gaussian with robust parameter estimates provides a reasonable approximation to the underlying density. Example 8: Model Performance

The performance of the HR9 model on the entire 80% training set is shown in Figure 8 and gave 90% sensitivity / 85%) specificity. Within the smear-negative TB population, the model had 100%) sensitivity / 90%> specificity (Figure 9). In a "healthy normal" population (n=168 samples from 5 sites in the US), 100% accurate diagnosis of non-TB was achieved (Figure 10, left panel). In samples from a recent TB treatment study that compared baseline to week-8 measurements using older SOMAscan v2 and only 6 of the 9 markers, sensitivity was 95%o

(Figure 10, right panel). Example 9: TB-Blinded Sample Analysis

The TB host response model was frozen at the time the blinded verification samples were assayed. The verification sample set was then calibrated to the training set and the model was used to predict the diagnosis (TB or non-TB) for the blinded samples. The predicted class labels were recorded and the "true" diagnosis was unblended for the verification samples.

A blinded set consisting of 150 TB and 150 non-TB samples was tested, along with 36 bridging samples from the training and challenge sets. Again, the "true" classification was based on sputum-smear (S; S- is sputum-smear negative, S+ is sputum-smear positive) and culture (C; C- is culture negative, C+ is culture positive), and non-TB samples were from TB suspects that were ruled-out for TB in follow-up visits.

Table 7. Subject classification

Subject Class Number of subjects

TB (S-C+) 30

TB (S-C+/HIV+) 20

TB (S+C+) 50

TB (S+C+/HIV+) 50

NonTB 100

NonTB/HIV+ 50

Removing 12 hemolyzed samples as well as 14 samples that were not truly blinded since they overlapped with challenge set samples of known diagnosis, and 4 samples that failed in the assay (low cy3 reading), left 270 samples for analysis and classification. Based on the fixed classifier, 124 samples were predicted as TB, 126 samples predicted as non-TB, and 20 were indeterminate (-1.2 < LogOdds < +1.3). These classifications and the logOdds were sent to Foundation for Innovative New Diagnostics (FIND), who then unblinded the diagnosis. The HR9 model performed well in the verification set, with 80% sensitivity / 84% specificity within the 250 samples for which a prediction was made, excluding indeterminate samples (See Figure 1 1 and Table 8). All markers of the HR9 model were confirmed (Figure 12).

Table S. Perfor manes of

Sample Set K TB rton-TB sens/spec¹ sens/spec¹ sens/spec³ AUC

Training, SQ% 173 ISO 90» 85% 95% / 81% 55% / 95% 0.94

227 117 110 95% / 84% 95 % / 84 % 61% /95% 0.94

HtV-pos 106 55 50 88% / 30% 95% / 80% 55% / 35% 0.34 smear-nej I 67 17 50 100% / 90% 95% / 93% 53% / 95% 0.35

Test 20% 86 45 41 89% / 88% 95% / 80% 36% / 95% 0.92

Verification 250 132 118 SQ% / 84% 95 / S% 56% / 95% 0.8S

¹ calculated at opti at operating pomt (point with ROC curve slope =# rtort TB / #TB^'t

² point on the ROC curve at 95% sensitivity

5 point on the ROC curve at 95% specificity The slight drop in performance was due to: (1) differences in geographic origin, in particular samples from S. Africa, mostly from site 18 in training, all from site 41 in verification (Figure 13), and no smear-negative TB / HIV-negative in training, but 28 such samples in verification (Figure 13). Attribution of false-negatives (FN) and false-positives (FP) to country, HlV-status, smear-status, gender, and age is shown in Figure 14. The false-negatives were mostly older males with smear-negative TB. Some misclassifications, among both false- negatives and false-positives, are contestable based on available clinical interpretations or lack thereof: some patients had missing metadata or were lost to follow-up.

Example 10: 4-Marker Model

In addition to the HR9 model described above, alternate markers SAA, NPS-PLA2, ΓΡ-

10, and CA6 were used and calculated sensitivity and specificity separately for two distinct Target Product Profiles (TPPs) of a potential TB diagnostic test. TPP#1 : "Rule-out" test, which is useful as a triage/referral screening test and has high sensitivity with at least moderate specificity. TPP#2: "Rule-in" test, which is useful for diagnosis and therapy initiation and has high specificity with at least moderate sensitivity. The markers were chosen based on the largest fold-change of the medians of TB vs. non-TB samples in the Training set. SAA, NPS-PLA2, ΓΡ- 10, and CA6 had >2-fold median changes (2-fold or greater differences can easily be measured with most simple platforms) and were highly significant (p<10^~10) in the KS test (Figure 15). Levels of SAA, NPS-PLA2, and IP- 10 are higher in subjects with TB infection than in non- infected subjects, while the level of CA6 is lower in subjects with TB infection than in non- infected subjects. One to four of the markers and different cut-offs were used for a "rule-out" test to optimize for high sensitivity or for a "rule-in" test to optimize specificity. Performance of these models in the training set and verification set is shown in Figure 16. For a screening test useful for triage and referral, a 2-marker model with SAA and NPS-PLA2 resulted in 95-97% sensitivity and ~40% specificity. A 4-marker model that also included IP- 10 and CA6 provided 99% sensitivity. For a diagnostic test useful to warrant therapy initiation, the 2-marker model with SAA and NPS-PLA2 showed 90-92% specificity and the 4-marker model reached 98% specificity. Example 11: Prediction of transition from latent TB infection to active TB disease

Experiments were conducted to determine the effectiveness of using the 9-marker model to identify subjects with latent TB infections that will transition into active TB disease (e.g., within 360 days, within 270 days, within 180 days, within 90 days, within 45 days, within 30 days, etc.). Quantitative measurements were made of the level of the biomarkers in the 9-marker model in samples obtained from TB-infected subjects prior to onset of active TB disease and/or following initiation of treatment. The non-parametric Kolmogorov-Smirnov (KS) test was used to first compare TB Case (latent TB-infected subjects that transitioned to active TB disease) and non-TB Control (latent TB-infected subjects that did not transition to active TB disease) samples across all time points, then TB Case samples were compared within the intervals >306 days, 270- 360 days, 180-270 days, 90-180 days, and 0-90 days before the date of clinical diagnosis, as well as 0-90 days, 90-180, and >180 post-treatment (treatment beginning upon diagnosis). Figure 20 shows increasing odds ratios for active TB disease as the cases move closer to the time of clinical diagnosis, followed by a subsequent reduction in odds ratios following the beginning of treatment. The experiments demonstrate that the 9-marker model predicts the likelihood and/or imminence of the transition from latent TB infection to active TB disease.

The foregoing embodiments and examples are intended only as examples. No particular embodiment, example, or element of a particular embodiment or example is to be construed as a critical, required, or essential element or feature of any of the claims. Various alterations, modifications, substitutions, and other variations can be made to the disclosed embodiments without departing from the scope of the present application, which is defined by the appended claims. The specification, including the figures and examples, is to be regarded in an illustrative manner, rather than a restrictive one, and all such modifications and substitutions are intended to be included within the scope of the application. Steps recited in any of the method or process claims may be executed in any feasible order and are not limited to an order presented in any of the embodiments, the examples, or the claims. Further, in any of the aforementioned methods, one or more specifically listed biomarkers can be specifically excluded either as an individual biomarker or as a biomarker from any panel.

REFERENCES

The following references are herein incorporated by reference in their entireties.

De Groote MA, Nahid P, Jarlsberg L, Johnson JL, Weiner M, Muzanyi G, et al.

Elucidating novel serum biomarkers associated with pulmonary tuberculosis treatment. PLoS One 2013;8(4):e61002.

Giri PK, Kruh NA, Dobos KM, Schorey JS. Proteomic analysis identifies highly antigenic proteins in exosomes from M. tuberculosis-infected and culture filtrate protein-treated macrophages. Proteomics 2010;10(17):3190-202.

Walzl G, Ronacher K, Hanekom W, Scriba TJ, Zumla A. Immunological biomarkers of tuberculosis. Nat Rev Immunol;l l(5):343-54.

Wallis PvS, Doherty TM, Onyebujoh P, Vahedi M, Laang H, Olesen O, et al. Biomarkers for tuberculosis disease activity, cure, and relapse. Lancet Infect Dis 2009;9(3): 162-72.

Claims

1. A method of detecting tuberculosis (TB) infection in a subject, 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 Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM in a sample from the subject, wherein the subject is identified as TB-infected if the level of the respective biomarker is altered relative to a control level of the respective biomarker.

2. The method of claim 1, comprising detecting the level of Testican-2.

3. The method of claim 2, further comprising detecting the level of at least one biomarker selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, FCG3B, and DERM.

4. The method of claim 1, comprising detecting the level of DERM.

5. The method of claim 4, further comprising detecting the level of at least one biomarker selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, and FCG3B.

6. The method of claim 1, comprising detecting the level of DERM and Testican-2.

7. The method of claims 6, further comprising detecting the level of at least one biomarker selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, and FCG3B.

8. The method of any one of the preceding claims, wherein a level of at least one, at least two, at least three, at least four, at least five, at least six, or seven biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, and DERM that is higher than a control level of the respective biomarker, and/or a level of at least one or two biomarkers selected from C9 and FCG3B that is lower than a control level of the respective biomarker, indicates that the subject has TB infection.

9. The method of any one of the preceding claims, comprising 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.

10. A method of detecting tuberculosis (TB) infection in a subject, comprising detecting the level of at least one, at least two, at least three, or four biomarkers selected from SAA, NPS-PLA2, IP-10, and CA6 in a sample from the subject, wherein the subject is identified as TB-infected if the level of the respective biomarker is altered relative to a control level of the respective biomarker.

11. The method of claim 10, wherein a level of at least one, at least two, or three biomarkers selected from SAA, NPS-PLA2, and IP-10 that is higher than a control level of the respective biomarker, and/or a level of CA6 that is lower than a control level of the respective biomarker, indicates that the subject has TB infection.

12. The method of claim 10 or claim 11, comprising detecting the level of SAA and NPS-PLA2.

13. The method of claim 12, further comprising detecting the level of IP-10.

14. The method of claim 10 or 11, comprising detecting the level of SAA, NPS- PLA2, IP-10, and CA6.

15. The method of any one of claims 10 to 13, comprising detecting the levels of 2 to 10 biomarkers, or 2 to 8 biomarkers, or 2 to 6 biomarkers, or 3 to 10 biomarkers, or 3 to 8 biomarkers, or 3 to 6 biomarkers.

16. The method of any one of claims 10 to 13, comprising detecting the levels of 2, 3, or 4 biomarkers.

17. The method of any one of the preceding claims, wherein the TB infection is active TB infection.

18. A method of monitoring progression of TB infection in a patient, 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 Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM 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, wherein TB infection is progressing if the level of at least one, at least two, at least three, at least four, at least five, at least six, or seven biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, and DERM is higher at the second time point compared to the first time point, and/or a level of at least one or two biomarkers selected from C9 and FCG3B is lower at the second time point compared to the first time point.

19. The method of claim 18, comprising detecting the level of Testican-2.

20. The method of claim 19, further comprising detecting the level of at least one biomarker selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, FCG3B, and DERM.

21. The method of claim 18, comprising detecting the level of DERM.

22. The method of claim 21, further comprising detecting the level of at least one biomarker selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, and FCG3B.

23. The method of claim 18, comprising detecting the level of DERM and Testican-2.

24. The method of claims 23, further comprising detecting the level of at least one biomarker selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, and FCG3B.

25. The method of any one of claims 18 to 24, comprising 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.

26. A method of monitoring progression of TB infection in a patient, comprising detecting the level of at least one, at least two, at least three, or four biomarkers selected from SAA, NPS-PLA2, IP- 10, and CA6 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, or four biomarkers at a second time point, wherein TB infection is progressing if the level of at least one, at least two, or three biomarkers selected from SAA, NPS-PLA2, and IP- 10 is higher at the second time point compared to the first time point, and/or if the level of CA6 is lower at the second time point compared to the first time point.

27. The method of claim 26, comprising detecting the level of SAA and NPS-PLA2.

28. The method of claim 27, further comprising detecting the level of IP-10.

29. The method of claim 26, comprising detecting the level of SAA, NPS-PLA2, IP-

10, and CA6.

30. The method of any one of claims 26 to 29, comprising detecting the levels of 2 to 10 biomarkers, or 2 to 8 biomarkers, or 2 to 6 biomarkers, or 3 to 10 biomarkers, or 3 to 8 biomarkers, or 3 to 6 biomarkers.

31. The method of any one of claims 26 to 29, comprising detecting the levels of 2, 3, or 4 biomarkers.

32. The method of any one of claims 18 to 31 , wherein the patient has active TB infection.

33. A method of monitoring treatment of TB infection and/or monitoring patient compliance with a treatment regimen for TB infection in a patient, 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 Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM in a sample from the patient at a first time point, administering at least one treatment for TB infection to the patient, 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, wherein the treatment is effective and/or wherein the patient is complying with the treatment regimen if the level of the at least one, at least two, at least three, at least four, at least five, at least six, or seven biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, and DERM is lower at the second time point compared to the first time point, and/or a level of at least one or two biomarkers selected from C9 and FCG3B is higher at the second time point compared to the first time point.

34. The method of claim 33, comprising detecting the level of Testican-2.

35. The method of claim 34, further comprising detecting the level of at least one biomarker selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, FCG3B, and DERM.

36. The method of claim 33, comprising detecting the level of DERM.

37. The method of claim 36, further comprising detecting the level of at least one biomarker selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, and FCG3B.

38. The method of claim 33, comprising detecting the level of DERM and Testican-2.

39. The method of claims 38, further comprising detecting the level of at least one biomarker selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, and FCG3B.

40. The method of any one of claims 33 to 39, comprising 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.

41. A method of monitoring treatment of TB infection and/ or monitoring patient compliance with a treatment regimen for TB infection in a patient, comprising detecting the level of at least one, at least two, at least three, or four biomarkers selected from SAA, NPS-PLA2, ΓΡ- 10, and CA6 in a sample from the patient at a first time point, administering at least one treatment for TB infection to the patient, and measuring the level of the at least one, at least two, at least three, or four biomarkers at a second time point, wherein the treatment is effective and/or the patient is complying with the treatment regimen if the level of at least one, at least two, or three biomarkers selected from SAA, NPS-PLA2, and IP- 10 is lower at the second time point compared to the first time point, and/or if the level of CA6 is higher at the second time point compared to the first time point.

42. The method of claim 41 , comprising detecting the level of SAA and NPS-PLA2.

43. The method of claim 42, further comprising detecting the level of IP- 10.

44. The method of claim 41 , comprising detecting the level of SAA, NPS-PLA2, IP- 10, and CA6.

45. The method of any one of claims 41 to 44, comprising detecting the levels of 2 to 10 biomarkers, or 2 to 8 biomarkers, or 2 to 6 biomarkers, or 3 to 10 biomarkers, or 3 to 8 biomarkers, or 3 to 6 biomarkers.

46. The method of any one of claims 41 to 45, comprising detecting the levels of 2, 3, or 4 biomarkers.

47. The method of any one of claims 33 to 46, wherein the patient is being treated for active TB infection.

48. The method of any one of the preceding claims, wherein the method further comprises treating the subject or patient for tuberculosis infection.

49. The method of claim 48, comprising treating the subject or patient for latent tuberculosis infection.

50. The method of claim 48, comprising treating the subject or patient for active tuberculosis infection.

51. The method of any one of claims 48 to 50, comprising administering isoniazid (INH), rifampin (RTF), rifapentine (RPT), ethambutol (EMB), pyrazinamide (PZA), and/or another approved TB therapeutic to the subject.

52. A method for predicting whether a subject having latent tuberculosis (TB) infection will develop TB disease, the method 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 Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM in a sample from the subject, wherein the subject is predicted to develop TB disease if the level of the respective biomarker is altered relative to a control level of the respective biomarker.

53. The method of claim 52, wherein a level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven biomarkers selected from Kallistatin,

Gelsolin, TSP4, Afamin, BGH3, Testican-2, and DERM that is higher than a control level of the respective biomarker, and/or a level of at least one or two biomarkers selected from C9 and FCG3B that is lower than a control level of the respective biomarker, indicates that the subject will develop TB disease.

54. The method of claim 52 or 53, wherein the method predicts that the subject will develop TB disease within about 180 days, within about 90 days, within about 45 days, or within about 30 days.

55. The method of claim 52 or 53, wherein the method predicts that the subject will develop TB disease in less than about 180 days, less than about 90 days, less than about 45 days, or less than about 30 days.

56. A method for determining whether a subject having TB disease is responding to treatment, the method 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 Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM in a first sample from the subject taken at a first time point and in a second sample from the subject taken at a second time point, wherein the subject is determined to be responding to treatment for TB disease if the level of the respective biomarker is altered in the second sample relative to the first sample.

57. The method of claim 56, wherein a level of at least one, at least two, at least three, at least four, at least five, at least six, at least seven biomarkers selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, Testican-2, and DERM that is lower in the second sample than in the first sample, and/or a level of at least one or two biomarkers selected from C9 and FCG3B that is higher in the second sample than in the first sample, indicates that the subject is responding to treatment for TB disease.

58. The method of claim 56 or claim 57, wherein the first time point is within 2 weeks, within 1 week, within 3 days, within 1 day, or within 12 hours of beginning treatment for TB disease.

59. The method of any one of claims 56 to 58, wherein the second time point is at least 1 month, at least 6 weeks, at least 2 months, at least 3 months, at least 4 months, at least 5 months, or at least 6 months after the first time point.

60. The method of any one of the preceding claims, wherein the method further comprises performing one or more additional tests for TB infection.

61. The method of claim 60, wherein said one or more additional tests for TB infection comprises chest x-ray.

62. The method of any one of the preceding claims, wherein each biomarker is a protein biomarker.

63. The method of any one of the preceding claims, wherein the method comprises 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.

64. The method of claim 63, wherein each biomarker capture reagent is an antibody or an aptamer.

65. The method of claim 64, wherein each biomarker capture reagent is an aptamer.

66. The method of claim 65, wherein at least one aptamer is a slow off-rate aptamer.

67. The method of claim 66, wherein 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.

68. The method of claim 66 or claim 67, wherein each slow off-rate aptamer binds to its target protein with an off rate (t½) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.

69. The method of any one of the preceding claims, wherein the sample is a blood sample.

70. The method of claim 69, wherein the sample is a serum sample.

71. A kit comprising 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 Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican- 2, FCG3B, and DERM.

72. The kit of claim 71, wherein at the least one aptamer specifically binds DERM.

73. The kit of claim 71 or claim 72, wherein at the least one aptamer specifically binds Testican-2.

74. The kit of any one of claims 71 to 73, wherein 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.

75. A kit comprising at least one, at least two, at least three, or four aptamers, wherein each aptamer specifically binds to a target protein selected from SAA, NPS-PLA2, IP- 10, and CA6.

76. The kit of claim 75, wherein the kit comprises a total of 2 to 10 aptamers, or 2 to 8 aptamers, or 2 to 6 aptamers, or 3 to 10 aptamers, or 3 to 8 aptamers, or 3 to 6 aptamers.

77. The kit of claim 75, wherein the kit comprises a total of 2, 3, or 4 aptamers.

78. A composition comprising proteins of a sample from a subject 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 target protein selected from Kallistatin, Gelsolin, TSP4, Afamin, BGH3, C9, Testican-2, FCG3B, and DERM.

79. The composition of claim 78, comprising at least one aptamer that specifically binds DERM.

80. The composition of claim 78 or 79, comprising at least one aptamer that specifically binds Testican-2.

81. The composition of any one of claims 78 to 80, wherein the composition 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.

82. A composition comprising proteins of a sample from a subject and at least one, at least two, at least three, or four aptamers, wherein each aptamer specifically binds to a target protein selected from SAA, NPS-PLA2, IP- 10, and CA6.

83. The composition of claim 82, wherein the composition comprises a total of 2 to 10 aptamers, or 2 to 8 aptamers, or 2 to 6 aptamers, or 3 to 10 aptamers, or 3 to 8 aptamers, or 3 to 6 aptamers.

84. The composition of claim 82, wherein the composition comprises a total of 2, 3, or 4 aptamers.

85. The composition of any one of claims 78 to 84, where each aptamer specifically binds to a different target protein.

86. The composition of any one of claims 78 to 85, wherein the sample is a blood sample.

87. The composition of claim 86, wherein the sample is a serum sample.

88. The kit or composition of any one of claims 71 to 87, wherein at least one aptamer is a slow off-rate aptamer.

89. The kit or composition of claim 88, wherein each aptamer is a slow off-rate aptamer.

90. The kit or composition of claim 88 or claim 89, wherein 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 hydrophobic modifications.

91. The kit or composition of any one of claims 88 to 90, wherein each slow off-rate aptamer binds to its target protein with an off rate (t½) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.