U.S. patent application number 14/968276 was filed with the patent office on 2016-04-21 for tuberculosis biomarkers and uses thereof.
The applicant listed for this patent is SomaLogic, Inc.. Invention is credited to Nebojsa Janjic, Urs Ochsner, David G. Sterling.
Application Number | 20160109462 14/968276 |
Document ID | / |
Family ID | 49328218 |
Filed Date | 2016-04-21 |
United States Patent
Application |
20160109462 |
Kind Code |
A1 |
Ochsner; Urs ; et
al. |
April 21, 2016 |
Tuberculosis Biomarkers and Uses Thereof
Abstract
The present application includes biomarkers, methods, devices,
reagents, systems, and kits for the detection, treatment and
diagnosis of tuberculosis (TB). In one aspect, the present
application includes the identification of biomarkers that can be
used alone or in various combinations for the detection of TB,
including those set forth in Tables 1, 2, 4, 5, and 8 to 12. In
another aspect, the application provides biomarkers that can be
used alone or in various combinations to diagnose or prognose TB or
follow treatment response. In another aspect, methods are provided
for diagnosing TB in an individual, where the methods include
detecting, in a biological sample from an individual, at least one
biomarker value corresponding to at least one biomarker selected
from the group of biomarkers provided in Tables 1, 2, 4, 5, and 8
to 12, wherein the individual is classified as having TB, or the
likelihood of the individual having TB is determined, based on the
at least one biomarker value.
Inventors: |
Ochsner; Urs; (Denver,
CO) ; Sterling; David G.; (Boulder, CO) ;
Janjic; Nebojsa; (Boulder, CO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SomaLogic, Inc. |
Boulder |
CO |
US |
|
|
Family ID: |
49328218 |
Appl. No.: |
14/968276 |
Filed: |
December 14, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13862177 |
Apr 12, 2013 |
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14968276 |
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61623732 |
Apr 13, 2012 |
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Current U.S.
Class: |
506/9 ; 435/7.9;
436/501 |
Current CPC
Class: |
G01N 2800/52 20130101;
C12Q 2600/158 20130101; A61K 31/496 20130101; G01N 33/5695
20130101; C12Q 2600/106 20130101; G01N 2800/60 20130101; G01N
2570/00 20130101; G01N 33/6893 20130101; C12Q 1/6883 20130101 |
International
Class: |
G01N 33/68 20060101
G01N033/68 |
Claims
1. A method comprising detecting the level of at least one
biomarker selected from the group consisting of PSME1, HSP 70,
.alpha.2-antiplasmin, IFN-.lamda., MMP-12, IL-11 Ra, Galectin-8,
MMP-13, iC3b and APRIL in a sample from an individual; predicting
whether the individual has an increased likelihood of being culture
negative for TB after a treatment for TB, wherein the predicting is
based on a classification method that uses the level of the at
least one biomarker as input for the classification method; wherein
the classification method was developed by comparing samples from
at least a first reference group and a second reference group,
wherein the first reference group was culture negative for TB after
treatment for TB, and the second reference group was culture
positive for TB after treatment for TB; wherein the level of each
biomarker protein is detected with a capture reagent-based assay,
and wherein the capture reagents is selected from the group
consisting of an aptamer and an antibody; and wherein, the
individual is infected with TB.
Description
RELATED APPLICATIONS
[0001] This application is a continuation application of U.S.
application Ser. No. 13/862,177, filed Apr. 12, 2013. U.S.
application Ser. No. 13/862,177 claims the benefit of U.S.
Provisional Application Ser. No. 61/623,732, filed Apr. 13, 2012,
which is incorporated herein by reference in its entirety.
FIELD OF THE INVENTION
[0002] The present invention relates generally to the
identification and detection of biomarkers for Tuberculosis (TB).
More specifically, the invention relates to one or more biomarkers,
methods, devices, reagents, mathematical modeling, systems, and
kits for the evaluation of TB infection and disease; wherein the
evaluation may comprise diagnosis, prognosis, treatment, treatment
response and treatment effects and treatment toxicity,
determination of recurrence, reinfection, relapse or predicting
reactivation from the latent infection to active disease of TB in
an individual.
BACKGROUND
[0003] The following description provides a summary of information
relevant to the present disclosure and is not an admission that any
of the information provided or publications referenced herein is
prior art to the present disclosure.
[0004] Tuberculosis (TB) causes 1.4 million deaths annually and is
associated with substantial personal, social, public health, and
economic costs, particularly in those individuals co-infected with
HIV and other chronic diseases. Proper, accurate, and timely
diagnosis of TB is essential to rapidly identify patients for
treatment and targeted public health intervention to prevent spread
of disease and minimize the emergence of drug resistant strains.
Worldwide, most cases of TB are diagnosed using a sputum smear,
clinical symptoms, and/or radiographs. There is a clear imperative
for improved diagnostics, because the current diagnosis of
mycobacterial disease by microscopic stain for acid-fast bacilli
(AFB, e.g. Ziehl-Neelsen method) in sputum fails to detect
mycobacteria in approximately 50% of cases of TB. This method of
diagnosis performs poorly in HIV co-infected individuals and is
particularly problematic when an individual is unable to produce a
specimen (e.g. an infant unable to produce sputum) or has disease
outside of the lung (extrapulmonary). According to the World Health
Organization (WHO) estimates, the global case detection rate is
just 63%, and only half of the TB cases in Africa are detected and
notified (McNerney and Daley (2011) Nat Rev Microbiol
9(3):204-213). The under-diagnosis of TB is critically important in
AIDS due to the high mortality associated with TB-HIV co-infection.
Diagnosing TB in HIV negative patients with contagious disease is a
priority intervention to continue progress in decreasing the
worldwide TB incidence. An estimated 400,000 people died of
HIV-related TB in 2009, which makes TB responsible for one in four
AIDS deaths.
[0005] Undiagnosed patients are a major reservoir for spread of
disease including drug resistant TB. Microbiological techniques
required for specific identification and drug susceptibility can
take days to weeks and are often not available in resource poor and
remote areas. A rapid, accurate, and inexpensive TB test used by
personnel in the clinic or local hospital would add tremendous
value to public health in areas with limited resources by
identifying those in need of treatment rapidly and hence decrease
the spread of disease to others. The lack of a point-of-care (POC)
test has been identified as a major gap in the existing pipeline of
TB diagnostics (Pal et al. (2010) Curr Opin Pulm Med
16(3):271-284).
[0006] Clinical response to treatment for tuberculosis is manifest
by improvement in constitutional symptoms, decreased microbial
burden, lessening risk of spread to others and fairly rapid return
to well-being in patients treated with multiple drug therapy, but
predicting who will ultimately relapse requires long-term clinical
follow up. With new regimens that may significantly shorten TB
treatment duration, more rapid surrogate markers for sterilizing
regimens are needed. (Spigelman et al. (2010) The international
journal of tuberculosis and lung disease: the official journal of
the International Union against Tuberculosis and Lung Disease
14:663-664; Wallis et al. (2009) The Lancet infectious diseases
9:162-172). There is no perfect surrogate endpoint of cure (defined
as the absence of relapse after 1 to 2 years of close clinical
follow-up); however, the 8-week sputum culture status is the most
widely accepted endpoint (Chakera et al. (2011) Biomarkers in
medicine 5:131-148; Nemeth et al. (2011) Clinical immunology
138:50-59; Wallis et al. (2009) The Lancet infectious diseases
9:162-172).
[0007] The discovery of robust protein biomarkers for treatment
response that could be used earlier in treatment assessments is
expected to have implications for clinical trials and potentially
be helpful for clinical care of patients. Due to the rarity of
relapses (<5% of patients relapse in studies of drug susceptible
disease using standard therapy) large sample sizes need to be
included in clinical trials, typically 75-300 subjects per arm or
comparison group. It has been suggested that in TB trials serial
measurement of surrogate markers such as multiple serum proteins
with large dynamic range could reduce patient sample sizes by
50-90% and decrease the time and monetary investment in desperately
needed human trials. (Burman (2003) American journal of respiratory
and critical care medicine 167:1299-1301; Nahid et al. (2011)
American journal of respiratory and critical care medicine 184
(8):972-979e). Accordingly, a need exists for biomarkers, methods,
devices, reagents, systems, and kits that enable the diagnosis,
prognosis, treatment response markers and determination of
recurrence or prediction of reactivation of TB.
[0008] Biomarker selection for a specific disease state involves
first the identification of markers that have a measurable and
statistically significant difference in a disease population
compared to a control population for a specific medical
application. Biomarkers can include secreted or shed molecules that
parallel disease development or progression and readily diffuse
into the blood stream or other body fluids from TB tissue or from
surrounding tissues and circulating cells in response to a TB. The
biomarker or set of biomarkers identified are generally clinically
validated or shown to be a reliable indicator for the original
intended use for which it was selected. Biomarkers can include
small molecules, peptides, proteins, and nucleic acids. Some of the
key issues that affect the identification of biomarkers include
over-fitting of the available data and bias in the data.
[0009] A variety of methods have been used in an attempt to
identify biomarkers for evaluation, diagnosis, prognosis and
determination of recurrence or reactivation of disease. For
protein-based markers, these include two-dimensional
electrophoresis, mass spectrometry, and immunoassay methods. For
nucleic acid markers, these include mRNA expression profiles,
microRNA profiles, fluorescence in situ hybridization (FISH),
serial analysis of gene expression (SAGE), methylation profiles,
and large-scale gene expression arrays.
[0010] The utility of two-dimensional electrophoresis is limited by
low detection sensitivity; issues regarding protein solubility,
charge, and hydrophobicity; gel reproducibility; and the
possibility of a single spot representing multiple proteins. For
mass spectrometry, depending on the format used, limitations
revolve around the sample processing and separation, sensitivity to
low abundance proteins, signal to noise considerations, and
inability to immediately identify the detected protein, lipid or
small molecule. Limitations in immunoassay approaches to biomarker
discovery are centered on the inability of antibody-based multiplex
assays to measure a large number of analytes. One might simply
print an array of antibodies and, without sandwiches, measure the
analytes bound to those antibodies. This would be the formal
equivalent of using a whole genome of nucleic acid sequences to
measure by hybridization all DNA or RNA sequences in an organism or
a cell. The hybridization experiment works because hybridization
can be a stringent test for identity. Even high-affinity antibodies
are not stringent enough in selecting their binding partners to
work in the context of blood or even cell extracts because the
protein ensemble in those matrices have extremely different
abundances. Thus, one must use a different approach with
immunoassay-based approaches to biomarker discovery--one would need
to use multiplexed ELISA assays (that is, sandwiches) to get
sufficient stringency to measure many analytes simultaneously to
decide which analytes are indeed biomarkers. Sandwich immunoassays
do not scale to high content, and thus biomarker discovery using
stringent sandwich immunoassays is not possible using standard
array formats. Lastly, antibody reagents are subject to substantial
lot variability and reagent instability. The instant platform for
protein biomarker discovery overcomes these problems.
[0011] Many of these methods rely on or require some type of sample
fractionation prior to the analysis. Thus the sample preparation
required to run a sufficiently powered study designed to identify
and discover statistically relevant biomarkers in a series of
well-defined sample populations is extremely difficult, costly, and
time consuming. During fractionation, a wide range of variability
can be introduced into the various samples. For example, a
potential marker could be unstable to the process, the
concentration of the marker could be changed, inappropriate
aggregation or disaggregation could occur, and inadvertent sample
contamination could occur and thus obscure the subtle changes
anticipated in early disease.
[0012] It is widely accepted that biomarker discovery and detection
methods using these technologies have serious limitations for the
identification of diagnostic biomarkers. These limitations include
an inability to detect low-abundance biomarkers, an inability to
consistently cover the entire dynamic range of the proteome,
irreproducibility in sample processing and fractionation, and
overall irreproducibility and lack of robustness of the method.
Further, these methods have introduced biases into the data and not
adequately addressed the complexity of the sample populations,
including appropriate controls, in terms of the distribution and
randomization required to identify and validate biomarkers within a
target disease population.
[0013] Although efforts aimed at the discovery of new and effective
TB biomarkers have gone on for several decades, the efforts have
been largely unsuccessful. Biomarkers for various diseases
typically have been identified in academic laboratories, usually
through an accidental discovery while doing basic research on some
disease process. Based on the discovery and with small amounts of
clinical data, papers were published that suggested the
identification of a new biomarker. Most of these proposed
biomarkers, however, have not been confirmed as real or useful
biomarkers, primarily because the small number of clinical samples
tested, and have provided only weak statistical proof that an
effective biomarker has in fact been found. That is, the initial
identification was not rigorous with respect to the basic elements
of statistics. In each of the years 1994 through 2003, a search of
the scientific literature shows that thousands of references
directed to biomarkers were published. During that same timeframe,
however, the FDA approved for diagnostic use, at most, three new
protein biomarkers in a year, and in several years, no new protein
biomarkers were approved.
[0014] Based on the history of failed biomarker discovery efforts,
mathematical theories have been proposed that further promote the
general understanding that biomarkers for disease are rare and
difficult to find. Biomarker research based on 2D gels or mass
spectrometry supports these notions. Very few useful biomarkers
have been identified through these approaches. However, it is
usually overlooked that 2D gel and mass spectrometry measure
proteins that are present in blood at approximately 1 nM
concentrations and higher, and that this ensemble of proteins may
well be the least likely to change with disease. Other than the
instant biomarker discovery platform, proteomic biomarker discovery
platforms that are able to accurately measure protein expression
levels at much lower concentrations do not exist.
[0015] Much is known about biochemical pathways for complex human
biology. Many biochemical pathways culminate in or are started by
secreted proteins that work locally within the pathology, for
example, growth factors are secreted to stimulate the replication
of other cells in the pathology, and other factors are secreted to
ward off the immune system, and so on. While many of these secreted
proteins work in a paracrine fashion, some operate distally in the
body. One skilled in the art with a basic understanding of
biochemical pathways would understand that many pathology-specific
proteins ought to exist in blood at concentrations below (even far
below) the detection limits of 2D gels and mass spectrometry. What
must precede the identification of this relatively abundant number
of disease biomarkers is a proteomic platform that can analyze
proteins at concentrations below those detectable by 2D gels or
mass spectrometry.
SUMMARY
[0016] The present disclosure includes biomarkers, methods,
reagents, devices, systems, mathematical modeling and kits for the
evaluation of TB and subsequent TB treatment responses. The
biomarkers of the present disclosure were identified using a
multiplex aptamer-based assay, which is described in Example 1. By
using the aptamer-based biomarker identification method described
herein, this application describes a surprisingly large number of
TB biomarkers that are useful for the evaluation and treatment of
TB. In identifying these biomarkers, approximately 1030 proteins
from individual participant samples were measured, some of which
were at concentrations in the low femtomolar range. This is about
four orders of magnitude lower than biomarker discovery experiments
done with 2D gels or mass spectrometry.
[0017] While certain of the described TB biomarkers may be useful
alone for detecting host responses to TB treatment and diagnosing
TB, methods are described herein for the grouping of multiple
subsets of the TB biomarkers that are useful as a panel of
biomarkers to diagnose TB, detect sub-clinical or overt drug
toxicity and predict successful response to therapy. Once an
individual biomarker or subset of biomarkers is identified, the
detection or diagnosis of TB and following TB treatment and
response to treatment can be accomplished using any assay platform
or format that is capable of measuring differences in the levels of
the selected biomarker or biomarkers in a biological sample.
[0018] However, it was only by using the multiplex aptamer-based
biomarker identification method described herein, wherein about
1030 separate potential biomarker values were individually screened
from individuals diagnosed with TB and monitoring biomarkers that
change with treatment that it was possible to identify the TB
biomarkers disclosed herein. This discovery approach is in stark
contrast to biomarker discovery using animal models or in vitro
systems as it queries a more patient-relevant system and measures
proteins and not RNA expression (Berry (August 2010) Nature 466,
doi:10.1038/nature09247); an interferon-inducible neutrophil-driven
blood transcriptional signature in human tuberculosis.
[0019] Thus, in one aspect of the instant application, one or more
biomarkers are provided for use either alone or in various
combinations to diagnose and treat TB. Exemplary embodiments
include the biomarkers provided in Tables 1, 2, 4, 5 and 8 to 12,
which as noted above were identified using a multiplex
aptamer-based assay, as described in Examples 1 and 2. The markers
could also be reflective of the effect of treatment agents on
biomarkers and could prove to be markers of treatment toxicity or
effects.
[0020] While certain of the described TB biomarkers are useful
alone the evaluation of TB, methods are also described herein for
the grouping of multiple subsets of the TB biomarkers that are each
useful as a panel of two or more biomarkers. Thus, various
embodiments of the instant application provide combinations
comprising N biomarkers, wherein N is at least two; wherein N is at
least 3; wherein N is at least 4; wherein N is at least 5; wherein
N is at least 6; wherein N is at least 7; wherein N is at least 8;
wherein N is at least 9; wherein N is at least 10; and so on;
wherein N can be any number from 1 biomarker to 239 biomarkers.
[0021] As used herein, evaluation of TB refers to evaluating
whether an individual has a first evaluation of no evidence of
disease (NED) when at least one biomarker of one or more of Tables
1, 2, 4, 5 or 8 to 12 is not detected as differentially expressed
from the control distribution, or has a second evaluation of
evidence of disease (EVD) when at least one biomarker of one or
more of Tables 1, 2, 4, 5 or 8 to 12 is detected as differentially
expressed from the control distribution.
[0022] In another aspect, a method is provided for evaluating an
individual for TB, wherein the method comprises diagnosing the
individual as having or not having TB, prognosing a future course
of the TB and its treatment, determining recurrence or predicting
reactivation of TB in an individual who had been apparently cured
of the active TB, has only the latent form of the disease and
characterizing the response of the person to treatment of TB, or
any combination thereof.
[0023] In another embodiment, the method of evaluating an
individual for TB comprises diagnosing an individual by determining
a detectable value corresponding to a biomarker of one or more of
Tables 1, 2, 4, 5 or 8 to 12 in a biological sample of the
individual, wherein the diagnosing comprises a first diagnosis of
no evidence of disease (NED) and no TB when there is substantially
no differential expression of the biomarker value of the individual
relative to a biomarker value of the control population; or a
second diagnosis of evidence of disease (EVD) and TB when there is
differential expression of the biomarker value of the individual
relative to the biomarker value of the control population. As noted
above, various embodiments provide combinations comprising N
biomarkers, wherein N is at least two; wherein N is at least 3;
wherein N is at least 4; wherein N is at least 5; wherein N is at
least 6; wherein N is at least 7; wherein N is at least 8; wherein
N is at least 9; wherein N is at least 10; and so on; wherein N can
be any number from 1 biomarker to 239 biomarkers.
[0024] In one aspect, the method of diagnosing comprises assaying a
biological sample of an individual to determine a biomarker value
corresponding to at least one biomarker of one or more of Tables 1,
2, 4, 5 or 8 to 12, comparing the biomarker value of the individual
to a biomarker value of a control population to determine whether
there is a differential expression; and classifying the individual
as having the first diagnosis where there no differential
expression relative to the control population, or with the second
diagnosis where there is a differential expression relative to the
control population. Various embodiments provide combinations
comprising N biomarkers, wherein N is at least two; wherein N is at
least 3; wherein N is at least 4; wherein N is at least 5; wherein
N is at least 6; wherein N is at least 7; wherein N is at least 8;
wherein N is at least 9; wherein N is at least 10; and so on;
wherein N can be any number from 1 biomarker to 239 biomarkers.
[0025] In another aspect, the evaluating of TB comprises prognosing
a first prognosis of no evidence of disease (NED) and a favorable
prognosis, or a second prognosis of evidence of disease (EVD) and
an unfavorable prognosis.
[0026] In one aspect, the prognosing method comprises assaying a
biological sample of an individual to determine a biomarker value
corresponding to at least one biomarker of one or more of Tables 1,
2, 4, 5 or 8 to 12, comparing the biomarker value of the individual
to a biomarker value of a control population to determine if there
is a differential expression; and classifying the individual as
having the first prognosis for a negative TB diagnosis when there
is no differential expression, or the second prognosis for a
positive TB diagnosis when there is differential expression. In
various embodiments, combinations comprising N biomarkers are
provided, wherein N is at least two; wherein N is at least 3;
wherein N is at least 4; wherein N is at least 5; wherein N is at
least 6; wherein N is at least 7; wherein N is at least 8; wherein
N is at least 9; wherein N is at least 10; and so on; wherein N can
be any number from 1 biomarker to 239 biomarkers.
[0027] In one aspect, a method of evaluating response to treatment
is provided that comprises: assaying a biological sample of an
individual being treated to determine a value corresponding to at
least one biomarker from one or more of Tables 8 to 12; comparing
the biomarker value of the individual to a biomarker value of a
control population to determine the extent of differential
expression; and correlating the extent of differential expression
of said biomarker value with an increased likelihood of positive
response to treatment. In various embodiments, combinations
comprising N biomarkers are provided, wherein N is at least two;
wherein N is at least 3; wherein N is at least 4; wherein N is at
least 5; wherein N is at least 6; wherein N is at least 7; wherein
N is at least 8; wherein N is at least 9; wherein N is at least 10;
and so on; wherein N can be any number from 1 biomarker to 122
biomarkers.
[0028] In another aspect, a method of evaluating response to
treatment is provided that comprises assaying a biological sample
of an individual being treated to determine a value corresponding
to at least one biomarker from one or more of Tables 8 to 12;
initiating treatment comprising antimicrobial therapies directed at
the tubercle bacillus; and comparing the biomarker value before
treatment with the biomarker value after some period of treatment,
whereby a change in the biomarker value is indicative of response
to treatment. In various embodiments, combinations comprising N
biomarkers are provided, wherein N is at least two; wherein N is at
least 3; wherein N is at least 4; wherein N is at least 5; wherein
N is at least 6; wherein N is at least 7; wherein N is at least 8;
wherein N is at least 9; wherein N is at least 10; and so on;
wherein N can be any number from 1 biomarker to 122 biomarkers.
[0029] In another aspect, a method of evaluating is provided that
comprises: determining the recurrence or reactivation of TB in an
individual who had apparently been cured of active TB or had latent
form of infection, wherein the determining of recurrence or
reactivation comprises a first determination of no evidence of
disease (NED) or a second determination of evidence of disease
(EVD). The first determination of NED indicates no recurrence or
reactivation of TB, and the second determination of EVD indicates
treatment failure, recurrence or reactivation of the TB.
[0030] One method of determining recurrence or reactivation
comprises: assaying a biological sample of an individual to
determine a value corresponding to an at least one biomarker
selected from one or more of Tables 1, 2, 4, 5 or 8 to 12;
comparing the biomarker value of the individual to a biomarker
value of a control population to determine if there is differential
expression; and classifying the individual as having said first
determination of no TB recurrence or reactivation when there is no
differential expression relative to the control population, or said
second determination of TB recurrence or reactivation when there is
differential expression relative to the control value. In various
embodiments, combinations comprising N biomarkers are provided,
wherein N is at least two; wherein N is at least 3; wherein N is at
least 4; wherein N is at least 5; wherein N is at least 6; wherein
N is at least 7; wherein N is at least 8; wherein N is at least 9;
wherein N is at least 10; and so on; wherein N can be any number
from 1 biomarker to 239 biomarkers.
[0031] In another aspect, a method for treating tuberculosis
infection in an individual is provided, comprising: diagnosing
tuberculosis infection by causing to be determined a value
corresponding to at least one biomarker selected from one or more
of Tables 1, 2, 4, 5 or 8 to 12 in a biological sample of the
individual, wherein said determination comprises comparing the
biomarker value of the individual to the biomarker value in a
control population for differential expression of the biomarker,
wherein differential expression of the biomarker indicates the
presence of tuberculosis infection in the individual; and
administering treatment for tuberculosis infection to the
individual thereby diagnosed with tuberculosis infection. In
various embodiments, combinations comprising N biomarkers are
provided, wherein N is at least two; wherein N is at least 3;
wherein N is at least 4; wherein N is at least 5; wherein N is at
least 6; wherein N is at least 7; wherein N is at least 8; wherein
N is at least 9; wherein N is at least 10; and so on; wherein N can
be any number from 1 biomarker to 239 biomarkers.
[0032] In yet another aspect, an assay for diagnosing tuberculosis
infection in an individual, comprising: causing to be determined a
value corresponding to at least one biomarker selected from one or
more of Tables 1, 2, 4, 5 or 8 to 12 in a biological sample of the
individual, wherein said determining comprises comparing the
biomarker value of the individual to the biomarker value in a
control population for differential expression of the biomarker,
wherein differential expression of the biomarker indicates the
presence of tuberculosis infection in the individual; and
diagnosing an individual to have tuberculosis infection based on
said differential expression of the biomarker. In various
embodiments, combinations comprising N biomarkers are provided,
wherein N is at least two; wherein N is at least 3; wherein N is at
least 4; wherein N is at least 5; wherein N is at least 6; wherein
N is at least 7; wherein N is at least 8; wherein N is at least 9;
wherein N is at least 10; and so on; wherein N can be any number
from 1 biomarker to 239 biomarkers.
[0033] In another embodiment, a method for diagnosing that an
individual does or does not have tuberculosis is provided, the
method comprising: contacting a biological sample from an
individual with at least N aptamers wherein each aptamer has
specific affinity for a biomarker corresponding to one of N
biomarkers selected from one or more of Tables 1, 2, 4, 5 or 8 to
12; detecting the levels of the biomarkers, in the biological
sample; and determining a biomarker value for each of said N
biomarkers, wherein said individual is diagnosed as having or not
having tuberculosis based on said biomarker values, and wherein N
is any integer from 2 to 239.
[0034] In one embodiment, at least one biomarker is selected from
Table 1 and/or Table 4 and/or Table 5. In one embodiment, at least
two biomarkers are selected from Table 1 and/or Table 4 and/or
Table 5. In one embodiment, at least three biomarkers are selected
from Table 1 and/or Table 4 and/or Table 5. In one embodiment, at
least four biomarkers are selected from Table 1 and/or Table 4
and/or Table 5. In one embodiment, at least five biomarkers are
selected from Table 1 and/or Table 4 and/or Table 5. In one
embodiment, at least six biomarkers are selected from Table 1
and/or Table 4 and/or Table 5. In one embodiment, at least seven
biomarkers are selected from Table 1 and/or Table 4 and/or Table 5.
In one embodiment, at least eight biomarkers are selected from
Table 1 and/or Table 4 and/or Table 5. In one embodiment, at least
nine biomarkers are selected from Table 1 and/or Table 4 and/or
Table 5. In one embodiment, at least ten biomarkers are selected
from Table 1 and/or Table 4 and/or Table 5, etc.
[0035] In one embodiment, at least one biomarker is selected from
the group consisting of TSP4, TIMP-2, SEPR, MRC-2, Antithrombin
III, SAA, CRP, NPS-PLA2, LEAP-1, and LBP. In one embodiment, at
least two biomarkers are selected from the group consisting of
TSP4, TIMP-2, SEPR, MRC-2, Antithrombin III, SAA, CRP, NPS-PLA2,
LEAP-1, and LBP. In one embodiment, at least three biomarkers are
selected from the group consisting of TSP4, TIMP-2, SEPR, MRC-2,
Antithrombin III, SAA, CRP, NPS-PLA2, LEAP-1, and LBP. In one
embodiment, at least four biomarkers are selected from the group
consisting of TSP4, TIMP-2, SEPR, MRC-2, Antithrombin III, SAA,
CRP, NPS-PLA2, LEAP-1, and LBP. In yet another embodiment, at least
five biomarkers are selected from the group consisting of TSP4,
TIMP-2, SEPR, MRC-2, Antithrombin III, SAA, CRP, NPS-PLA2, LEAP-1,
and LBP, etc.
[0036] In one embodiment N is any integer from 3 to 239 or from 4
to 239 or from 5 to 239 or from 6 to 239 or from 7 to 239 or from 8
to 239 or from 9 to 239 or from 10 to 239 or from 15 to 239 or from
20 to 239, etc.
[0037] In another aspect, a method for determining whether
treatment for tuberculosis infection should be administered to a
patient is provided, comprising: a) causing an assay to be
conducted for determining a biomarker value corresponding to at
least one biomarker of 1, 2, 4 or 5 and Tables 8 to 12 in a
biological sample of the individual, wherein said determining
comprises comparing the biomarker value of the individual to the
biomarker value in a control population for differential expression
of the biomarker, wherein differential expression of the biomarker
indicates the presence of tuberculosis infection in the individual;
b) diagnosing an individual to have tuberculosis infection based on
said differential expression of the biomarker; and c) administering
treatment for tuberculosis infection. In one embodiment, at least
one biomarker is selected from Table 1 and/or Table 4 and/or Table
5 and/or Table 9 and/or Table 10 and/or Table 11 and/or Table 12.
In one embodiment, at least one biomarker is selected from the
group consisting of TSP4, TIMP-2, SEPR, MRC-2, Antithrombin III,
SAA, CRP, NPS-PLA2, LEAP-1, LBP, Coagulation factor V, XPNPEP1,
PSME1, IL-11 R.alpha., HSP70, Galectin-8, .alpha.2-Antiplasmin,
ECM1, YES, IGFBP-1, CATZ, BGN, LYNB, and IL-7.
[0038] In another embodiment, a method for determining whether
treatment for tuberculosis should be administered is provided, the
method comprising: a) contacting a biological sample from an
individual with at least N aptamers wherein each aptamer has
specific affinity for a biomarker corresponding to one of N
biomarkers selected from Table 1, 2, 4, 5 or Tables 8 to 12; b)
detecting the levels of the biomarkers, in the biological sample;
and determining a biomarker value for each of said N biomarkers, c)
diagnosing an individual to have tuberculosis infection based on
said biomarker values, and administering treatment for tuberculosis
infection; wherein N is any integer from 2 to 239.
[0039] In one embodiment, at least one biomarker is selected from
Table 1 and/or Table 4 and/or Table 5 and/or Table 9 and/or Table
10 and/or Table 11 and/or Table 12. In one embodiment, at least one
biomarker is selected from the group consisting of TSP4, TIMP-2,
SEPR, MRC-2, Antithrombin III, SAA, CRP, NPS-PLA2, LEAP-1, LBP,
Coagulation factor V, XPNPEP1, PSME1, IL-11 R.alpha., HSP70,
Galectin-8, .alpha.2-Antiplasmin, ECM1, YES, IGFBP-1, CATZ, BGN,
LYNB, and IL-7.
[0040] In one embodiment N is any integer from 3 to 239 or from 4
to 239 or from 5 to 239 or from 6 to 239 or from 7 to 239 or from 8
to 239 or from 9 to 239 or from 10 to 239 or from 15 to 239 or from
20 to 239 etc.
[0041] In another aspect, a method for evaluating the drug toxicity
resulting from the administration of drugs for the treatment of
tuberculosis in and individual is provided comprising: a)
administering treatment for tuberculosis infection to the
individual, wherein a value corresponding to at least one biomarker
of one or more of Tables 1, 2, 4, 5 or 8 to 12 in a biological
sample of the individual has been determined; and b) evaluating the
toxicity of the drug used in treatment by comparing said biomarker
value(s) subsequent to treatment.
[0042] In another aspect, a method for evaluating the effectiveness
of treatment for tuberculosis in and individual is provided
comprising: a) administering treatment for tuberculosis infection
to the individual, wherein a value corresponding to at least one
biomarker of Tables 8 to 12 in a biological sample of the
individual has been determined; and b) evaluating the effectiveness
of treatment by comparing said biomarker value(s) subsequent to
treatment. In another aspect, at least one of said biomarkers is
selected from Table 9 and/or Table 10 and/or Table 11 and/or Table
12. In another aspect, at least two of said biomarkers are selected
from Table 9 and/or Table 10 and/or Table 11 and/or Table 12. In
another aspect, at least three of said biomarkers are selected from
Table 9 and/or Table 10 and/or Table 11 and/or Table 12. In another
aspect, at least four of said biomarkers are selected from Table 9
and/or Table 10 and/or Table 11 and/or Table 12. In another aspect,
at least five of said biomarkers are selected from Table 9 and/or
Table 10 and/or Table 11 and/or Table 12, etc.
[0043] In yet another, aspect at least one biomarker is selected
from the group consisting of Coagulation factor V, XPNPEP1, PSME1,
IL-11 R.alpha., HSP70, Galectin-8, .alpha.2-Antiplasmin, SAA, ECM1,
YES, IGFBP-1, CATZ, BGN, LYNB and IL-7. In yet another aspect, at
least two biomarkers are selected from the group consisting of
Coagulation factor V, XPNPEP1, PSME1, IL-11 R.alpha., HSP70,
Galectin-8, .alpha.2-Antiplasmin, SAA, ECM1, YES, IGFBP-1, CATZ,
BGN, LYNB and IL-7. In yet another, aspect at least three
biomarkers are selected from the group consisting of Coagulation
factor V, XPNPEP1, PSME1, IL-11 R.alpha., HSP70, Galectin-8,
.alpha.2-Antiplasmin, SAA, ECM1, YES, IGFBP-1, CATZ, BGN, LYNB and
IL-7. In yet another aspect, at least four biomarkers are selected
from the group consisting of Coagulation factor V, XPNPEP1, PSME1,
IL-11 R.alpha., HSP70, Galectin-8, .alpha.2-Antiplasmin, SAA, ECM1,
YES, IGFBP-1, CATZ, BGN, LYNB and IL-7. In yet another, aspect at
least five biomarkers are selected from the group consisting of
Coagulation factor V, XPNPEP1, PSME1, IL-11 R.alpha., HSP70,
Galectin-8, .alpha.2-Antiplasmin, SAA, ECM1, YES, IGFBP-1, CATZ,
BGN, LYNB and IL-7, etc.
[0044] In another embodiment, a method for evaluating the
effectiveness of treatment for tuberculosis is provided comprising:
a) contacting a biological sample from an individual with at least
N aptamers wherein each aptamer has specific affinity for a
biomarker corresponding to one of N biomarkers selected from Tables
8 to 12; b) detecting the levels of the biomarkers, in the
biological sample; and determining a biomarker value for each of
said N biomarkers; c) diagnosing an individual to have tuberculosis
infection based on said biomarker values; d) administering
treatment for tuberculosis infection; wherein N is any integer from
2 to 23; and e) evaluating the effectiveness of treatment by
comparing said biomarker value(s) subsequent to treatment.
[0045] In another aspect, at least two of said biomarkers are
selected from Table 9 and/or Table 10 and/or Table 11 and/or Table
12. In another aspect, at least three of said biomarkers are
selected from Table 9 and/or Table 10 and/or Table 11 and/or Table
12. In another aspect, at least four of said biomarkers are
selected from Table 9 and/or Table 10 and/or Table 11 and/or Table
12. In another aspect, at least five of said biomarkers are
selected from Table 9 and/or Table 10 and/or Table 11 and/or Table
12, etc.
[0046] In yet another, aspect, at least one biomarker is selected
from the group consisting of Coagulation factor V, XPNPEP1, PSME1,
IL-11 R.alpha., HSP70, Galectin-8, .alpha.2-Antiplasmin, SAA, ECM1,
YES, IGFBP-1, CATZ, BGN, LYNB and IL-7. In yet another aspect, at
least two biomarkers are selected from the group consisting of
Coagulation factor V, XPNPEP1, PSME1, IL-11 R.alpha., HSP70,
Galectin-8, .alpha.2-Antiplasmin, SAA, ECM1, YES, IGFBP-1, CATZ,
BGN, LYNB and IL-7. In yet another aspect, at least three
biomarkers are selected from the group consisting of Coagulation
factor V, XPNPEP1, PSME1, IL-11 R.alpha., HSP70, Galectin-8,
.alpha.2-Antiplasmin, SAA, ECM1, YES, IGFBP-1, CATZ, BGN, LYNB and
IL-7. In yet another aspect, at least four biomarkers are selected
from the group consisting of Coagulation factor V, XPNPEP1, PSME1,
IL-11 R.alpha., HSP70, Galectin-8, .alpha.2-Antiplasmin, SAA, ECM1,
YES, IGFBP-1, CATZ, BGN, LYNB and IL-7. In yet another aspect, at
least five biomarkers are selected from the group consisting of
Coagulation factor V, XPNPEP1, PSME1, IL-11 R.alpha., HSP70,
Galectin-8, .alpha.2-Antiplasmin, SAA, ECM1, YES, IGFBP-1, CATZ,
BGN, LYNB and IL-7, etc.
[0047] In one embodiment N is any integer from 3 to 239 or from 4
to 239 or from 5 to 239 or from 6 to 239 or from 7 to 239 or from 8
to 239 or from 9 to 239 or from 10 to 239 or from 15 to 239 or from
20 to 239, etc.
[0048] In another aspect, a method for treating recurrence or
reactivation of tuberculosis infection in an individual is
provided, said method comprising: diagnosing recurrence or
reactivation of tuberculosis infection by causing to be determined
a biomarker value corresponding to a biomarker of Tables 1, 2, 4, 5
or 8 to 12 in a biological sample of the individual, wherein said
determination comprises comparing the biomarker value of the
individual to the biomarker value in a control population for
differential expression of the biomarker, wherein differential
expression of the biomarker indicates the recurrence or
reactivation of tuberculosis infection in the individual; and
administering treatment for recurrence or reactivation of
tuberculosis infection to the individual thereby diagnosed with
recurrent or reactivated tuberculosis infection.
[0049] In one embodiment, at least one biomarker is selected from
Table 1 and/or Table 4 and/or Table 5 and/or Table 9 and/or Table
10 and/or Table 11 and/or Table 12. In one embodiment, at least one
biomarker is selected from the group consisting of TSP4, TIMP-2,
SEPR, MRC-2, Antithrombin III, SAA, CRP, NPS-PLA2, LEAP-1, LBP,
Coagulation factor V, XPNPEP1, PSME1, IL-11 R.alpha., HSP70,
Galectin-8, .alpha.2-Antiplasmin, ECM1, YES, IGFBP-1, CATZ, BGN,
LYNB, and IL-7.
[0050] In one embodiment N is any integer from 3 to 239 or from 4
to 239 or from 5 to 239 or from 6 to 239 or from 7 to 239 or from 8
to 239 or from 9 to 239 or from 10 to 239 or from 15 to 239 or from
20 to 239 etc.
[0051] In yet another aspect, a method is provided for modifying
treatment of tuberculosis infection in an individual, comprising:
evaluating the status of tuberculosis infection by causing to be
determined a biomarker value corresponding to a biomarker of Tables
1, 2, 4, 5 or 8 to 12 in a biological sample of the individual,
wherein said evaluation comprises comparing the biomarker value of
the individual to the biomarker value in a control population for
differential expression of the biomarker, wherein differential
expression of the biomarker indicates the need to intensify
treatment of tuberculosis infection in the individual, and
intensifying treatment for tuberculosis infection to the individual
thereby evaluated as in need of intensified treatment of
tuberculosis infection.
[0052] In one embodiment, at least one biomarker is selected from
Table 1 and/or Table 4 and/or Table 5 and/or Table 9 and/or Table
10 and/or Table 11 and/or Table 12. In one embodiment, at least two
biomarkers are selected from Table 1 and/or Table 4 and/or Table 5
and/or Table 9 and/or Table 10 and/or Table 11 and/or Table 12,
etc. In one embodiment, at least one biomarker is selected from the
group consisting of TSP4, TIMP-2, SEPR, MRC-2, Antithrombin III,
SAA, CRP, NPS-PLA2, LEAP-1, LBP, Coagulation factor V, XPNPEP1,
PSME1, IL-11 R.alpha., HSP70, Galectin-8, .alpha.2-Antiplasmin,
ECM1, YES, IGFBP-1, CATZ, BGN, LYNB and IL-7. In one embodiment, at
least two biomarkers are selected from the group consisting of
TSP4, TIMP-2, SEPR, MRC-2, Antithrombin III, SAA, CRP, NPS-PLA2,
LEAP-1, LBP, Coagulation factor V, XPNPEP1, PSME1, IL-11 R.alpha.,
HSP70, Galectin-8, .alpha.2-Antiplasmin, ECM1, YES, IGFBP-1, CATZ,
BGN, LYNB and IL-7, etc.
[0053] In another aspect, a computer-implemented method is provided
for classifying an individual as either having a first evaluation
of NED, or as having a second evaluation of EVD. The method
comprises: a) retrieving on a computer biomarker information for an
individual, wherein the biomarker information comprises a biomarker
value that corresponds to the at least one biomarker of Tables 1,
2, 4, 5 or 8 to 12; b) comparing said biomarker value of step a) to
a biomarker value of a control population to determine if there is
differential expression, and c) classifying the individual as
having a first evaluation of NED when there is no differential
expression of the biomarker value of the individual relative to the
control population, or as having a second evaluation of EVD when
there is differential expression of the biomarker value of the
individual relative to the control population.
[0054] In the computer-implemented method, the evaluation can
comprise a diagnosis, treatment, prognosis, determination of
recurrence or reactivation of TB, and/or a combination thereof. The
evaluation of NED can be indicative of a diagnosis of no TB, a
prognosis of an outcome of no TB at a selected future time point, a
determination of no recurrence or reactivation of TB, and/or a
combination thereof. The evaluation of EVD can be indicative of a
diagnosis of the presence of TB, a prognosis of an outcome of TB at
a selected future time point, a determination of recurrence or
reactivation of TB, and/or a combination thereof.
[0055] In another aspect, a 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
values that correspond to at least one of the biomarkers provided
in Tables 1, 2, 4, 5 or 8 to 12; code for comparing the biomarker
value of the individual to a biomarker value of a control
population; and code that executes a classification method that
indicates a first evaluation of NED when there is no differential
expression of the individual's biomarker value relative to the
control population, or a second evaluation of EVD when there is
differential expression of the individual's biomarker value
relative to the control population.
[0056] In another aspect, the computer-implemented classification
of TB status of an individual by the computer program product or
the computer readable medium can reflect a diagnosis, treatment
plan, prognosis, determination of recurrence or reactivation of TB,
and/or a combination thereof. The evaluation of NED can be
indicative of a diagnosis classification of no TB, a prognosis
classification of an outcome of no TB at a selected future time
point, a determination classification of no recurrence or
reactivation of TB, and/or a combination thereof. The evaluation of
EVD can be indicative of a diagnosis classification of TB, a
prognosis classification of an outcome of TB at a selected future
time point, a determination classification of recurrence or
reactivation of TB, and/or a combination thereof.
[0057] In certain aspects of the disclosure, the biomarker panel or
the number of biomarker values considered are selected from at
least two biomarkers, at least three biomarkers, at least 4
biomarkers, at least 5 biomarkers, at least 6 biomarkers, at least
7 biomarkers, at least 8 biomarkers, at least 9 biomarkers, at
least 10 biomarkers, and so on; wherein N can be any number of
biomarkers from 1 biomarker to 239 biomarkers.
[0058] In other aspects of the disclosure, at least one biomarker
is selected from Table 1, and/or Table 4, and/or Table 5, and/or
Table 9, and/or Table 10, and/or Table 11, and/or Table 12. In
other aspects of the disclosure, at least two biomarkers are
selected from Table 1, and/or Table 4, and/or Table 5, and/or Table
9, and/or Table 10, and/or Table 11, and/or Table 12. In other
aspects of the disclosure, at least three biomarkers are selected
from Table 1, and/or Table 4, and/or Table 5, and/or Table 9,
and/or Table 10, and/or Table 11, and/or Table 12. In other aspects
of the disclosure, at least four biomarkers are selected from Table
1, and/or Table 4, and/or Table 5, and/or Table 9, and/or Table 10,
and/or Table 11, and/or Table 12. In other aspects of the
disclosure, at least five biomarkers are selected from Table 1,
and/or Table 4, and/or Table 5, and/or Table 9, and/or Table 10,
and/or Table 11, and/or Table 12. In other aspects of the
disclosure, at least six biomarkers are selected from Table 1,
and/or Table 4, and/or Table 5, and/or Table 9, and/or Table 10,
and/or Table 11, and/or Table 12. In other aspects of the
disclosure, at least seven biomarkers are selected from Table 1,
and/or Table 4, and/or Table 5, and/or Table 9, and/or Table 10,
and/or Table 11, and/or Table 12. In other aspects of the
disclosure, at least eight biomarkers are selected from Table 1,
and/or Table 4, and/or Table 5, and/or Table 9, and/or Table 10,
and/or Table 11, and/or Table 12. In other aspects of the
disclosure, at least nine biomarkers are selected from Table 1,
and/or Table 4, and/or Table 5, and/or Table 9, and/or Table 10,
and/or Table 11, and/or Table 12. In other aspects of the
disclosure, at least ten biomarkers are selected from Table 1,
and/or Table 4, and/or Table 5, and/or Table 9, and/or Table 10,
and/or Table 11, and/or Table 12, and so on.
[0059] In other aspects, at least one biomarker is selected from
the group consisting of TSP4, TIMP-2, SEPR, MRC-2, Antithrombin
III, SAA, CRP, NPS-PLA2, LEAP-1, LBP, Coagulation factor V,
XPNPEP1, PSME1, IL-11 R.alpha., HSP70, Galectin-8,
.alpha.2-Antiplasmin, ECM1, YES, IGFBP-1, CATZ, BGN, LYNB and IL-7.
In other aspects, at least two biomarker are selected from the
group consisting of TSP4, TIMP-2, SEPR, MRC-2, Antithrombin III,
SAA, CRP, NPS-PLA2, LEAP-1, LBP, Coagulation factor V, XPNPEP1,
PSME1, IL-11 R.alpha., HSP70, Galectin-8, .alpha.2-Antiplasmin,
ECM1, YES, IGFBP-1, CATZ, BGN, LYNB and IL-7. In other aspects, at
least three biomarker are selected from the group consisting of
TSP4, TIMP-2, SEPR, MRC-2, Antithrombin III, SAA, CRP, NPS-PLA2,
LEAP-1, LBP, Coagulation factor V, XPNPEP1, PSME1, IL-11 R.alpha.,
HSP70, Galectin-8, .alpha.2-Antiplasmin, ECM1, YES, IGFBP-1, CATZ,
BGN, LYNB and IL-7. In other aspects, at least four biomarker are
selected from the group consisting of TSP4, TIMP-2, SEPR, MRC-2,
Antithrombin III, SAA, CRP, NPS-PLA2, LEAP-1, LBP, Coagulation
factor V, XPNPEP1, PSME1, IL-11 R.alpha., HSP70, Galectin-8,
.alpha.2-Antiplasmin, ECM1, YES, IGFBP-1, CATZ, BGN, LYNB and IL-7.
In other aspects, at least five biomarker are selected from the
group consisting of TSP4, TIMP-2, SEPR, MRC-2, Antithrombin III,
SAA, CRP, NPS-PLA2, LEAP-1, LBP, Coagulation factor V, XPNPEP1,
PSME1, IL-11 R.alpha., HSP70, Galectin-8, .alpha.2-Antiplasmin,
ECM1, YES, IGFBP-1, CATZ, BGN, LYNB and IL-7, etc.
BRIEF DESCRIPTION OF THE DRAWINGS
[0060] FIG. 1A is a flowchart for an exemplary method for detecting
TB in a biological sample.
[0061] FIG. 1B is a flowchart for an exemplary method for detecting
TB in a biological sample using a naive Bayes classification
method.
[0062] FIG. 2 illustrates an exemplary aptamer assay that can be
used to detect one or more TB biomarkers in a biological
sample.
[0063] FIG. 3 illustrates an exemplary computer system for use with
various computer-implemented methods described herein. System 100
is 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.
[0064] FIG. 4 is a flowchart 3000 for a method of indicating the
likelihood that an individual has TB in accordance with one
embodiment. In block 3004, biomarker information can be retrieved
for an individual. In block 3008, a computer can be utilized to
classify each of the biomarker values. In block 3012, an evaluation
can be made regarding TB status based upon a plurality of
classifications.
[0065] FIG. 5 is a flowchart 3200 for a method of indicating the
likelihood that an individual has TB in accordance with one
embodiment. In block 3204, a computer can be utilized to retrieve
biomarker information for an individual. In block 3208, a
classification of the biomarker value can be performed with the
computer. In block 3212, an indication can be made as to the TB
status of the individual based upon the classification.
[0066] FIGS. 6A-6C illustrate changes in expression of non-specific
markers for active TB, including acute phase reactants SAA (FIG.
6A) and CRP (FIG. 6B), and albumin (FIG. 6C), between baseline and
week 8 of therapy. Each line represents an individual TB
patient.
[0067] FIGS. 7A-7D illustrate association of clinical parameters
(BMI, time to detection) and serum protein levels (plasminogen,
thrombospondin-2) with radiographic classification of cavitation.
Each circle represents an individual TB patient at baseline. CXR
class 1=no cavitation; class 2=cavitation<4 cm in size; class
3=cavitation>4 cm in size.
[0068] FIGS. 8A-8I depict correlation of serum protein markers with
TB disease severity. Top markers showed the largest differential
expression in mild disease (n=13) compared to severe disease
(n=13), based on medians at baseline. Baseline (log) RFU was used
as the response in a linear model and a 5% false discovery rate.
FIGS. 8A-8D illustrate markers of disease severity identified via
linear regression analysis of the protein concentration correlated
with disease severity score at baseline. FIGS. 8E-8F illustrate
levels of .alpha.2-antiplasmin and fibrinogen (respectively)
relative to disease severity score at 8 weeks. FIGS. 8G-8I
illustrate markers of disease severity based on a regression
analysis of the expression shift from baseline to week 8. Each dot
represents an individual TB patient, showing the measurement of the
indicated protein at the indicated time (8A-8F) or the ratio of the
protein measurements at the two time-points (8G-8I).
[0069] FIG. 9 illustrates paired analysis of markers with largest
change between baseline and week 8 of TB therapy in n=39 patients,
ranked by the median intra-subject fold-change.
[0070] FIG. 10 illustrates feature separation by KS distance for
1,030 measured proteins, with corresponding significance levels
shown as q-values.
[0071] FIGS. 11A-11H illustrate empirical cumulative distribution
functions showing Logio RFU for the eight proteins with largest KS
distances between baseline and week 8.
[0072] FIGS. 12A-G illustrates empirical cumulative distribution
functions for the top 59 proteins from an unpaired analysis of
baseline and week 8 measurements in samples from n=39 TB
patients.
[0073] FIG. 13 illustrates a scatter plot of the separation of
baseline and week 8 samples using the two markers with the largest
KS distances, TSP4 and SEPR. Black dots represent samples from TB
patients at baseline and open circles represent samples from the
same TB patients after week 8 of treatment.
[0074] FIG. 14 illustrates normalized disease severity scores of
samples from 39 patients with pulmonary TB.
[0075] FIGS. 15A-15D depict box plots of patient demographic
parameters (age (FIG. 15A), BMI (FIG. 15B), days to detection (FIG.
15C) and CXR extent (FIG. 15D)) in responders and slow-responders,
depicting quartiles, medians, and outliers.
[0076] FIG. 16A depicts KS distance plots of 1030 proteins measured
in baseline samples from responders versus slow-responders. Squares
mark the top ten proteins that are higher in responders (open
squares) or slow-responders (solid squares). The dashed line
indicates a Bonferroni-corrected 30% significance level. FIG. 16B
depicts cumulative distribution function (CDF) plots of the most
differentially expressed proteins in responders (R) versus
slow-responders (S) at baseline. Axis labels and scales for RFU
(x-axis) and for cumulative fraction of all samples within each
group (y-axis) were omitted for clarity.
[0077] FIGS. 17A and 17B depict protein markers at 8 weeks based on
treatment response. FIG. 17A shows KS distance plots of all 1030
proteins measured in samples from responders versus slow-responders
after 8 weeks of treatment. Squares mark the top ten proteins that
are higher in responders (open squares) or slow-responders (solid
squares). The dashed line indicates a Bonferroni-corrected 30%
significance level. FIG. 17B shows cumulative distribution function
(CDF) plots of the most differentially expressed proteins in
responders (R) versus slow-responders (S) at 8 weeks of TB
treatment. Axis labels and scales for RFU (x-axis) and for
cumulative fraction of all samples within each group (y-axis) were
omitted for clarity.
[0078] FIG. 18 depicts box plots for the log 2 ratio of week 8 to
baseline signal in responders (R) and slow-responders (S).
[0079] FIGS. 19A and 19B show a Naive Bayes model using five
markers (coagulation factor V, XPNPEP1, soluble gp130, TIMP-2 and
ECM1) as a classifier to "predict" treatment response at week 8.
FIG. 19A depicts an ROC curve, showing AUC=0.88 and 95% confidence
interval (0.75, 0.98). FIG. 19B depicts training sample
classification. Solid squares represent true positive
classifications, solid circles are true negative classifications;
open squares are false positive and open circles are false negative
results. Two samples were from subjects with drug-resistant (dr) TB
strain.
[0080] FIGS. 20A-20B shows a matrix plot of the KS-distances of
slow-responders (week 8) to baseline (all) versus responders (week
8) to slow-responders (week 8). Potential treatment response
markers fall into the lower right area (large dots). FIG. 20B shows
CDF plots of representative candidate treatment response markers
identified via this KS distance metric. This figure illustrates
that week eight responder samples (R) track distinctly different
from week eight slow-responder samples (S) and all baseline samples
(A).
[0081] FIGS. 21A-21D illustrate models and algorithms to "predict"
treatment response at week 8. FIG. 21A depicts stability paths for
Li-regularized logistic regression using randomized lasso
(weakness=0.25, W=0.9) applied to combination of baseline
measurements and clinical covariates to classify responders from
non-responders. Dashed lines indicate the expected number of false
positive (FP) discoveries at different selection probability
thresholds computed from class-randomized observations. FIG. 21B
depicts stability paths for Li-regularized logistic regression of
8-week measurements and clinical covariates to classify responders
from non-responders. FIG. 21C depicts training sample
classification based on (log) odds ratio produced by logistic
regression model using five markers (IL-11 R.alpha.,
.alpha.2-Antiplasmin, PSME1, SAA, and subject age) measured at
baseline. Solid squares represent true positive classifications
(responders), solid circles are true negative classifications
(slow-responders); open squares are false positive and open circles
are false negative results. Two samples from subjects with
drug-resistant TB are marked (dr). FIG. 21D depicts the
corresponding ROC curve and point wise 95% CI for this analysis of
the training samples, showing AUC=0.96 and bootstrapped 95% CI
(0.88, 0.99).
[0082] FIGS. 22A-22D depict association of serum protein levels
with TTCC. FIG. 22A depicts regression of baseline protein data
(log 10 RFU) on TTCC. FIG. 22B depicts regression of week-8 protein
data (log RFU) on TTCC. FIG. 22C depicts differential expression of
proteins based on the medians of the responder groups at baseline
(top) and at 8 weeks (bottom). FIG. 22D depicts regression of SAA
data (log 10 RFU) on TTCC at baseline (left) and at 8 weeks
(right).
DETAILED DESCRIPTION
[0083] Reference will now be made in detail to representative
embodiments of the invention. While the invention will be described
in conjunction with the enumerated embodiments, it will be
understood that the invention is not intended to be limited to
those embodiments. On the contrary, the invention is intended to
cover all alternatives, modifications, and equivalents that may be
included within the scope of the present invention as defined by
the claims.
[0084] One skilled in the art will recognize many methods and
materials similar or equivalent to those described herein, which
could be used in and are within the scope of the practice of the
present invention. The present invention is in no way limited to
the methods and materials described.
[0085] Unless defined otherwise, technical and scientific terms
used herein have the same meaning as 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 or testing of the
invention, the preferred methods, devices and materials are now
described.
[0086] All publications, published patent documents, and patent
applications cited in this application are indicative of the level
of skill in the art(s) to which the application pertains. 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.
[0087] As used in this application, including the appended claims,
the singular forms "a," "an," and "the" include plural references,
unless the content clearly dictates otherwise, and are 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.
[0088] As used herein, the term "about" represents an insignificant
modification or variation of the numerical value such that the
basic function of the item to which the numerical value relates is
unchanged.
[0089] 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 does not include only those elements
but may include other elements not expressly listed or inherent to
such process, method, product-by-process, or composition of
matter.
[0090] The present disclosure includes biomarkers, methods,
devices, reagents, systems, and kits for the evaluation and
treatment of TB, as well as the response to treatment in an
individual. The specific intended uses and clinical applications
for the subject invention include: 1) diagnosis of the presence or
absence of TB; 2) treatment of TB; 3) prognosis during treatment
and of the outcome of treatment for TB in an individual at a
selected future time point; and 4) monitoring of recurrence or
reactivation of TB in an individual that has apparently been cured
of TB.
[0091] In one aspect, one or more biomarkers are provided for use
either alone or in various combinations to evaluate TB, including
the diagnosis of TB in an individual, treat TB upon diagnosis, the
prognosis of the outcome of treatment for TB, the monitoring of
recurrence or reactivation of TB, or the addressing other clinical
indications. As described in detail below, exemplary embodiments
include the biomarkers provided in Tables 1, 2, 4, 5, or 8 to 12,
identified using a multiplex aptamer-based assay, as described
generally in Example 1 and according to the method of Gold et al.
(2010) PLoS ONE 5(12):e15004 doi:10.1371/journal.pone.0015004.
[0092] Tables 1, 2, 4, 5, or 8 to 12 set forth the findings
obtained from analyzing serum samples from 39 individuals diagnosed
with TB. The training group was designed to match the population
with which a prognostic TB diagnostic test can have significant
benefit. (These cases and controls were obtained from serum samples
from 39 patients with pulmonary TB from Kampala, Uganda enrolled in
the U.S. Center for Disease Control's TB Trials Consortium (CDC
TBTC) Study 29). The potential biomarkers were measured in
individual samples rather than pooling the disease samples; this
allowed a better understanding of the individual and group
variations in the phenotypes associated with the presence of
disease at baseline and after 8 weeks of study therapy (in this
case TB and its treatment). Since about 1030 protein measurements
were made on each sample, and a total of 39 samples from the
disease population were individually measured in all samples,
Tables 1, 2, 4, 5, or 8 to 12 resulted from an analysis of a
relatively large set of data. The measurements were analyzed using
the methods described in the section, "Classification of Biomarkers
and Calculation of TB Prognosis Scores" herein.
[0093] While certain of the described TB biomarkers are useful
alone for diagnosing, treating, prognosing, and/or determining the
recurrence or reactivation of TB, methods are also described herein
for the grouping of multiple subsets of the biomarkers, where each
grouping or subset selection is useful as a panel of two or more
biomarkers, interchangeably referred to herein as a "biomarker
panel" and a "panel." Thus, various embodiments of the instant
application provide combinations comprising N biomarkers, wherein N
is selected from at least two, at least 3; at least 4, at least 5,
at least 6, at least 7, at least 8, at least 9, at least 10,
etc.
[0094] In one embodiment, the number of biomarkers useful for a
biomarker subset or panel is based on the sensitivity and
specificity value for the particular combination of biomarker
values. The terms "sensitivity" and "specificity" are used herein
with respect to the ability to correctly classify the TB diagnosis,
TB prognosis and TB recurrence after apparent cure for an
individual, based on one or more biomarker values detected in their
biological sample. "Sensitivity" indicates the performance of the
biomarker(s) with respect to correctly classifying individuals that
have a positive TB diagnosis, a positive TB prognosis (EVD), or a
positive TB recurrence after apparent cure, i.e., evidence of
disease (EVD). "Specificity" indicates the performance of the
biomarker(s) with respect to correctly classifying individuals who
have a negative TB diagnosis, a negative TB prognosis (NED), or a
negative TB recurrence following apparent cure of TB. For example,
85% specificity and 90% sensitivity for a panel of markers used to
test a set of control samples and TB diagnosis samples indicates
that 85% of the control samples were correctly classified as NED
samples by the panel, and 90% of the positive samples were
correctly classified as EVD samples by the panel.
[0095] The TB biomarkers identified herein represent a considerable
number of choices for subsets or panels of biomarkers that can be
used to effectively evaluate an individual for TB. Selection of the
desired number of such biomarkers depends on the specific
combination of biomarkers chosen. It is important to remember that
panels of biomarkers for evaluation of TB in an individual may also
include biomarkers not found in Tables 2 and 8 to 12, and that the
inclusion of additional biomarkers not found in Tables 2 and 8 to
12 may reduce the number of biomarkers in the particular subset or
panel that is selected from Tables 2 and 8 to 12. The number of
biomarkers from Tables 2 and 8 to 12 used in a subset or panel may
also be reduced if additional biomedical information is used in
conjunction with the biomarker values to establish acceptable
sensitivity and specificity values for a given assay.
[0096] Another factor that can affect the number of biomarkers to
be used in a subset or panel of biomarkers is the procedures used
to obtain biological samples from individuals who are being
evaluated for TB. In a carefully controlled sample procurement
environment, the number of biomarkers necessary to meet desired
sensitivity and specificity values will be lower than in a
situation where there can be more variation in sample collection,
handling and storage.
[0097] In one embodiment, the subject invention comprises obtaining
a biological sample from an individual or individuals of interest.
The biological sample is then assayed to detect the presence of one
or more (N) biomarkers of interest and to determine a biomarker
value for each of said N biomarkers (typically measured as marker
RFU (relative fluorescence units)). Once a biomarker has been
detected and a biomarker value assigned, each marker is scored or
classified as described in detail herein. The marker scores are
then combined to provide a total evaluation score, which reflects
whether the individual has evidence of disease, i.e., current TB
diagnosis, prognosis of a future TB outcome, or current evidence of
the recurrence of TB after an apparent cure.
[0098] "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, cyst fluid, meningeal fluid, amniotic fluid, glandular
fluid, lymph fluid, nipple aspirate, bronchial aspirate, pleural
fluid, peritoneal fluid, synovial fluid, joint aspirate, ascites,
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 or into fractions containing particular types of blood cells,
such as red blood cells or white blood cells (leukocytes). If
desired, 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 may be employed; exemplary methods include, e.g.,
phlebotomy, urine collection, sputum collection, swab (e.g., buccal
swab), lavage, and a fine needle aspirate biopsy procedure. Samples
can also be collected, e.g., by micro dissection (e.g., laser
capture micro dissection (LCM) or laser micro dissection (LMD),
sputum, bronchoalveolar fluid, lung, fluid, lymph node or other
relevant tissues. 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.
[0099] Further, it should be realized that a biological sample can
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 can be treated as
a sample from a single individual and if the TB evaluation
indicates evidence of disease (EVD) in the pooled sample, then each
individual biological sample can be re-tested to determine which
individuals have EVD.
[0100] For purposes of this specification, the phrase "data
attributed to a biological sample from an individual" is intended
to mean that the data in some form derived from, or were generated
using, the biological sample of the individual. The data may have
been reformatted, revised, or mathematically altered to some degree
after having been generated, such as by conversion from units in
one measurement system to units in another measurement system; but,
the data are understood to have been derived from, or were
generated using, the biological sample.
[0101] "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" is 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 such
set of molecules. Exemplary target molecules include proteins,
polypeptides, nucleic acids, carbohydrates, lipids,
polysaccharides, glycoproteins, hormones, receptors, methylated
nucleic acid, 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.
[0102] As used herein, "polypeptide," "peptide," and "protein" are
used interchangeably to refer to polymers of amino acids of any
length. The polymer may be linear or branched, it may comprise
modified amino acids, and it may be interrupted by non-amino acids.
The terms also encompass an amino acid polymer that has been
modified naturally or by intervention; for example, disulfide bond
formation, glycosylation, lipidation, acetylation, phosphorylation,
or any other manipulation or modification, such as conjugation with
a labeling component. Also included within the definition are, for
example, polypeptides containing one or more analogs of an amino
acid (including, for example, unnatural amino acids, etc.), as well
as other modifications known in the art. Polypeptides can be single
chains or associated chains. Also included within the definition
are preproteins and intact mature proteins; peptides or
polypeptides derived from a mature protein; fragments of a protein;
splice variants; recombinant forms of a protein; protein variants
with amino acid modifications, deletions, or substitutions;
digests; and post-translational modifications, such as
glycosylation, acetylation, phosphorylation, and the like.
[0103] 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. When a biomarker is a protein, it is also possible
to use the expression of the corresponding gene as a surrogate
measure of the amount or presence or absence of the corresponding
protein biomarker in a biological sample or methylation state of
the gene encoding the biomarker or proteins that control expression
of the biomarker.
[0104] As used herein, "biomarker value", "value", "biomarker
level", and "level" are used interchangeably to 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
"value" or "level" depends on the specific design and components of
the particular analytical method employed to detect the
biomarker.
[0105] When a biomarker indicates or is a sign of an abnormal
process or a disease or other condition in an individual, that
biomarker is generally described as being either over-expressed or
under-expressed as compared to an expression level or value of the
biomarker that indicates or is a sign of a normal process or an
absence of a disease or other condition in an individual.
"Up-regulation", "up-regulated", "over-expression",
"over-expressed", and any variations thereof are used
interchangeably to refer to a value or level of a biomarker in a
biological sample that is greater than a value or level (or range
of values or levels) of the biomarker that is typically detected in
similar biological samples from healthy or normal individuals. The
terms may also refer to a value or level of a biomarker in a
biological sample that is greater than a value or level (or range
of values or levels) of the biomarker that may be detected at a
different stage of a particular disease.
[0106] "Down-regulation", "down-regulated", "under-expression",
"under-expressed", and any variations thereof are used
interchangeably to refer to a value or level of a biomarker in a
biological sample that is less than a value or level (or range of
values or levels) of the biomarker that is typically detected in
similar biological samples from healthy or normal individuals. The
terms may also refer to a value or level of a biomarker in a
biological sample that is less than a value or level (or range of
values or levels) of the biomarker that may be detected at a
different stage of a particular disease.
[0107] Further, a biomarker that is either over-expressed or
under-expressed can also be referred to as being "differentially
expressed" or as having a "differential level" or "differential
value" as compared to a "normal" or "control" expression level or
value of the biomarker that indicates or is a sign of a normal or a
control process or an absence of a disease or other condition in an
individual. Thus, "differential expression" of a biomarker can also
be referred to as a variation from a "normal" or "control"
expression level of the biomarker.
[0108] The term "differential gene expression" and "differential
expression" are used interchangeably to refer to a gene (or its
corresponding protein expression product) whose expression is
activated to a higher or lower level in a subject suffering from a
specific disease, relative to its expression in a normal or control
subject. The terms also include genes (or the corresponding protein
expression products) whose expression is activated to a higher or
lower level at different stages of the same disease. It is also
understood that a differentially expressed gene may be either
activated or inhibited at the nucleic acid level or protein level,
or may be subject to alternative splicing to result in a different
polypeptide product. Such differences may be evidenced by a variety
of changes including mRNA levels, surface expression, secretion or
other partitioning of a polypeptide. Differential gene expression
may include a comparison of expression between two or more genes or
their gene products; or a comparison of the ratios of the
expression between two or more genes or their gene products; or
even a comparison of two differently processed products of the same
gene, which differ between normal subjects and subjects suffering
from a disease; or between various stages of the same disease.
Differential expression includes both quantitative, as well as
qualitative, differences in the temporal or cellular expression
pattern in a gene or its expression products among, for example,
normal and diseased cells, or among cells which have undergone
different disease events or disease stages.
[0109] As used herein, "individual" refers 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 is not detectable by
conventional diagnostic methods.
[0110] "Diagnose", "diagnosing", "diagnosis", and variations
thereof refer to the detection, determination, or recognition of a
health status or condition of an individual based on 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 (i.e., a diagnosis of the absence of a disease or
condition) or diagnosed as ill/abnormal (i.e., 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 or reactivation of the disease; and the
detection of disease response after the administration of a
treatment or therapy to the individual. The diagnosis of TB
includes distinguishing individuals who have TB from individuals
who do not.
[0111] "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 to the administration of a treatment
or therapy to the individual. "Prognosing" and variants thereof can
also mean predicting evidence of disease (EVD) or no evidence of
disease (NED) in the individual at a future preselected time point.
The date of prognosing can be referred to as time point 1 (TP1),
and the preselected future time point may be referred to as time
point 2 (TP2) and can include a specific future date or range of
dates, for example post-treatment follow-up.
[0112] "Evaluate", "evaluating", "evaluation", and variations
thereof encompass "diagnosing," "treating," "prognosing" and
monitoring of recurrence in a treated individual. "Evaluating" TB
can include any of the following: 1) diagnosing TB, i.e., initially
detecting the presence or absence of TB; 2) prognosing at time
point 1 (TP1), the future outcome of TB treatment at time point 2
(TP2), i.e., where TP2 may follow TB therapy; 3) detecting or
monitoring TB progression or recurrence after apparent cure of TB,
i.e., wherein "monitoring after apparent cure of TB" means testing
an individual a time point after he or she has received successful
treatment for TB, and/or 4) detecting progression from latent
infection to active disease.
[0113] "Treatment," as used herein refers to an intervention
performed with the intention of preventing the development or
altering the pathology of a disorder. Accordingly, "treatment"
refers to both therapeutic and prophylactic or preventative
measures. Those in need of treatment include those already with the
disorder as well as those in which the disorder is to be prevented.
Standard treatment for TB currently comprises a standard regimen of
rifampin in combination with isoniazid, ethambutol and pyrazinamide
for the treatment of drug-susceptible TB, or second line
antibiotics for drug-resistant TB.
[0114] "Therapy" as used herein refers to an intervention performed
with the intention of preventing the development or altering the
pathology of a disorder. "Therapy" refers to various methods that
target particular diseases with particular disease fighting agents.
For example, a targeted TB therapy might involve antimicrobial or
immunomodulatory therapy directed at eradication the tubercle
bacillus.
[0115] As used herein, "additional biomedical information" refers
to one or more evaluations of an individual, other than using any
of the biomarkers described herein, that are associated with TB
risk. "Additional biomedical information" includes any of the
following: signs and symptoms of disease, physical descriptors of
an individual; the height and/or weight (BMI) of an individual;
change in weight; the gender and ethnicity of an individual;
relevant secondary diagnosis (e.g. extrapulmonary or pleural
involvement); additional comorbid disease information (e.g. HIV,
malaria, malignancies, hepatic disorders, diabetes, etc.); current
prescription and non-prescription drug use (e.g. TB regimen and how
many days of treatment received before sample taken, other
medications); tobacco, alcohol, recreational drug use; any lab
results (relevant culture results, i.e. bacillary burden in the
sample, time to culture positivity or time to culture conversion to
the negative if available, sputum smear results; CXR cavitation,
extent if available); occupational history; family history; the
presence of a genetic marker(s) correlating with a higher risk of
TB in the individual or a family member; other clinical symptoms
such as persistent cough, fever, weight loss. Additional biomedical
information can be obtained from an individual using routine
techniques known in the art, such as from the individual themselves
by use of a routine patient questionnaire or health history
questionnaire, etc., or from a medical practitioner, etc.
Alternately, additional biomedical information can be obtained from
study investigators, data collection forms, interviews etc.
[0116] Testing of biomarker levels in combination with an
evaluation of any additional biomedical information, including
other laboratory tests, may, for example, improve sensitivity,
specificity, and/or AUC for detecting TB (or other TB-related uses)
as compared to biomarker testing alone or evaluating any particular
item of additional biomedical information alone.
[0117] 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 samples and normal or control samples or TB samples from
patients with different forms of the TB disease (e.g. severe vs.
mild, pulmonary vs. extrapulmonary disease). 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 such as responders to treatment vs.
non-responders) in distinguishing between two populations (e.g.,
cases having TB and controls without TB or those who fail treatment
or are likely to be slowly responding to TB treatment). Typically,
the feature data across the entire population (e.g., the cases and
controls) 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. These
combinations of features may comprise a test. 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.
[0118] As used herein, "detecting" or "determining" with respect to
a biomarker value includes the use of both the instrument required
to observe and record a signal corresponding to a biomarker value
and the material/s required to generate that signal. In various
embodiments, the biomarker value 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. "Detecting" and
"determining," used interchangeably herein, both refer to the
identification or observation of the presence of a biomarker in a
biological sample, and/or to the measurement of the biomarker
value.
[0119] "Solid support" refers herein to any substrate having a
surface to which molecules may be attached, directly or indirectly,
through either covalent or non-covalent bonds. A "solid support"
can have a variety of physical formats, which can include, for
example, a membrane; a chip (e.g., a protein chip); a slide (e.g.,
a glass slide or coverslip); a column; a hollow, solid, semi-solid,
pore- or cavity-containing particle, such as, for example, a bead;
a gel; a fiber, including a fiber optic material; a matrix; and a
sample receptacle. Exemplary sample receptacles include sample
wells, tubes, capillaries, vials, and any other vessel, groove or
indentation capable of holding a sample. A sample receptacle can be
contained on a multi-sample platform, such as a microtiter plate,
slide, microfluidics device, and the like. A support can be
composed of a natural or synthetic material, an organic or
inorganic material. The composition of the solid support on which
capture reagents are attached generally depends on the method of
attachment (e.g., covalent attachment). Other exemplary receptacles
include microdroplets and microfluidic controlled or bulk
oil/aqueous emulsions within which assays and related manipulations
can occur. Suitable solid supports include, for example, plastics,
resins, polysaccharides, silica or silica-based materials,
functionalized glass, modified silicon, carbon, metals, inorganic
glasses, membranes, nylon, natural fibers (such as, for example,
silk, wool and cotton), polymers, and the like. The material
composing the solid support can include reactive groups such as,
for example, carboxy, amino, or hydroxyl groups, which are used for
attachment of the capture reagents. Polymeric solid supports can
include, e.g., polystyrene, polyethylene glycol tetraphthalate,
polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone,
polyacrylonitrile, polymethyl 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 Luminee-type encoded
particles, magnetic particles, and glass particles.
Exemplary Uses of Biomarkers
[0120] In various exemplary embodiments, methods are provided for
diagnosing TB in an individual by detecting one or more biomarker
values corresponding to one or more biomarkers that are present in
the circulation of an individual, such as in serum or plasma or
secreted or excreted in the urine or present in the sputum, by any
number of analytical methods, including any of the analytical
methods described herein. These biomarkers are, for example,
differentially expressed in individuals with TB as compared to
individuals without TB. Detection of the differential expression of
a biomarker in an individual can be used, for example, to permit
the early diagnosis of TB, to treat TB once detected, to prognose
future outcome of TB in an individual following therapy and/or to
monitor TB recurrence after therapy, predicting who might go on
from latent infection to active disease or for other clinical
indications.
[0121] Any of the biomarkers described herein may be used in a
variety of clinical indications for TB, including, but not limited
to detection of TB (such as in a high-risk or symptomatic
individual or population); treating TB, determining TB prognosis;
monitoring TB progression or monitoring for TB recurrence;
monitoring treatment selection; monitoring response to a
therapeutic agent or other treatment; combining biomarker testing
with additional biomedical information, as detailed above; and
facilitating decisions regarding clinical follow-up. Furthermore,
the described biomarkers may also be useful in permitting certain
of these uses before indications of TB are detected by imaging
modalities or other clinical correlates, or before symptoms
appear.
[0122] As an example of the manner in which any of the biomarkers
described herein can be used to diagnose TB, differential
expression of one or more of the described biomarkers in an
individual who is not known to have TB may indicate that the
individual has TB, thereby enabling detection of TB at an early
stage of the disease when treatment is most effective, perhaps
before the TB is detected by other means or before symptoms appear.
Increased differential expression from "normal" (since some
biomarkers may be down-regulated with disease) of one or more of
the biomarkers during the course of TB may be indicative of TB
progression (and thus indicate a poor prognosis), whereas a
decrease in the degree to which one or more of the biomarkers is
differentially expressed (i.e., in subsequent biomarker tests, the
expression level in the individual is moving toward or approaching
a "normal" expression level) may be indicative of TB resolution
(and thus indicate a good or better prognosis and indicate a
positive response to therapy). Similarly, an increase in the degree
to which one or more of the biomarkers is differentially expressed
(i.e., in subsequent biomarker tests, the expression level in the
individual is moving further away from a "normal" expression level)
during the course of TB treatment may indicate that the TB is
progressing or that drug resistant TB is present and therefore
indicate that the treatment is ineffective or be an early indicator
that the TB bacillus is resistant to one or more of the drugs the
patient is being treated with, whereas a decrease in differential
expression of one or more of the biomarkers during the course of TB
treatment may be indicative of TB response to treatment and
therefore indicate that the treatment is working successfully.
Additionally, an increase or decrease in the differential
expression of one or more of the biomarkers after an individual has
apparently been cured of TB may be indicative of TB recurrence. In
a situation such as this, for example, the individual can be
restarted on therapy. Furthermore, a differential expression level
of one or more of the biomarkers in an individual may be predictive
of the individual's response to a particular therapeutic agent. In
monitoring for TB recurrence or progression, changes in the
biomarker expression levels may indicate the need for repetitive
biomarker assays or repeat imaging, such as to determine TB
infection status or burden or to determine the need for changes in
treatment or to inform clinical trial investigators of the
superiority of one test regimen over another. Measuring biomarker
changes longitudinally within an individual establishes a personal
baseline and provides a sensitive method to detect changes that may
be evident prior to clinical emergence of altered disease
state.
[0123] Detection of any of the biomarkers described herein may be
particularly useful following, or in conjunction with, TB
treatment, such as to evaluate the success of the treatment or to
monitor TB, recurrence, reactivation, and/or progression following
treatment. TB treatment may include, for example, administration of
one or more therapeutic agent to the individual or any other type
of TB treatment used in the art, and any combination of these
treatments. For example, any of the biomarkers may be detected at
least once after treatment or may be detected multiple times after
treatment (such as at periodic intervals), or may be detected both
before and after treatment. Differential expression levels of any
of the biomarkers in an individual over time may be indicative of
TB progression, recurrence or reactivation examples of which
include any of the following: an increase or decrease in the
expression level of the biomarkers after treatment compared with
the expression level of the biomarker before treatment; an increase
or decrease in the expression level of the biomarker at a later
time point after treatment compared with the expression level of
the biomarker at an earlier time point after treatment; and a
differential expression level of the biomarker at a single time
point after treatment compared with normal levels of the
biomarker.
[0124] In addition to testing biomarker levels in conjunction with
relevant symptoms, physical signs, microbiological data or imaging
data, 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., body fluid, tissue
culture for the causative agent of TB, patient clinical history,
radiographic severity of disease, symptoms, family history of TB,
risk factors such as the presence of a genetic marker(s), and/or
status of other biomarkers, clinical symptoms, 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.
[0125] Any of the described biomarkers may also be used in imaging
tests. For example, an imaging agent can be coupled to any of the
described biomarkers, which can be used to aid in TB diagnosis, to
prognose outcome following treatment, to monitor disease
progression/remission, to monitor for disease recurrence,
reactivation or to monitor response to therapy, among other
uses.
Detection and Determination of Biomarkers and Biomarker Values
[0126] A biomarker value for the biomarkers described herein can be
detected using any of a variety of known analytical methods. In one
embodiment, a biomarker value is detected using a capture reagent.
As used herein, a "capture agent" or "capture reagent" refers to a
molecule that is capable of binding specifically to a biomarker. 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, an
F(ab').sub.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, imprinted
polymers, avimers, peptidomimetics, a hormone receptor, a cytokine
receptor, and synthetic receptors, and modifications and fragments
of these.
[0127] In some embodiments, a biomarker value is detected using a
biomarker/capture reagent complex.
[0128] In other embodiments, the biomarker value 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.
[0129] In some embodiments, the biomarker value is detected
directly from the biomarker in a biological sample.
[0130] In one embodiment, the biomarkers are detected using a
multiplexed format that allows for the simultaneous detection of
two or more biomarkers in a biological sample. In one embodiment of
the multiplexed format, capture reagents are immobilized, directly
or indirectly, covalently or non-covalently, in discrete locations
on a solid support. In another embodiment, 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 another embodiment, 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 uniquely analyze
one of multiple biomarkers to be detected in a biological
sample.
[0131] In one or more of the foregoing embodiments, a fluorescent
tag can be used to label a component of the biomarker/capture
complex to enable the detection of the biomarker value. 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 value. 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.
[0132] In one embodiment, 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 AlexaFluor molecule, such
as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647,
AlexaFluor 680, or AlexaFluor 700. In other embodiments, the dye
molecule includes a first type and a second type of dye molecule,
such as, e.g., two different AlexaFluor molecules. In other
embodiments, the dye molecule includes a first type and a second
type of dye molecule, and the two dye molecules have different
emission spectra.
[0133] 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. (Principles of Fluorescence Spectroscopy, by J. R. Lakowicz,
Springer Science+Business Media, Inc., (2004); Bioluminescence
& Chemiluminescence: Progress & Current Applications;
Philip E. Stanley and Larry J. Kricka editors, World Scientific
Publishing Company, January (2002)).
[0134] In one or more of the foregoing embodiments, a
chemiluminescence tag can optionally be used to label a component
of the biomarker/capture complex to enable the detection of a
biomarker value. Suitable chemiluminescent materials include any of
oxalyl chloride, Rodamin 6G, Ru(bipy).sub.3.sup.2+, TMAE
(tetrakis(dimethylamino)ethylene), Pyrogallol
(1,2,3-trihydroxibenzene), Lucigenin, peroxyoxalates, Aryl
oxalates, Acridinium esters, dioxetanes, and others.
[0135] In yet other embodiments, the detection method includes an
enzyme/substrate combination that generates a detectable signal
that corresponds to the biomarker value. 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.
[0136] In yet other embodiments, the detection method can be a
combination of fluorescence, chemiluminescence, radionuclide or
enzyme/substrate combinations that generate a measurable signal.
Multimodal signaling could have unique and advantageous
characteristics in biomarker assay formats.
[0137] More specifically, the biomarker values for the biomarkers
described herein can be detected using known 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 detailed below.
Determination of Biomarker Values Using Aptamer-Based Assays
[0138] 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. Pat. No. 5,475,096 entitled "Nucleic Acid Ligands,"
see also, e.g., U.S. Pat. No. 6,242,246, U.S. Pat. No. 6,458,543,
and U.S. Pat. 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 value corresponding to a biomarker.
[0139] 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 a
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 may be a slow off
rate modified aptamer or SOMAmer.TM. aptamer. (See, e.g., U.S. Pat.
No. 7,947,447, entitled "A Method for Generating Aptamers with
Improved Off-Rates.") 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.
[0140] 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.
[0141] 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.
[0142] 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. Pat. 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. Pat. No. 5,705,337
entitled "Systematic Evolution of Nucleic Acid Ligands by
Exponential Enrichment: Chemi-SELEX."
[0143] 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. Pat.
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. Pat. No. 5,580,737, see
supra, describes highly specific aptamers containing one or more
nucleotides modified with 2'-amino (2'-NH2), 2'-fluoro (2'-F),
and/or 2'-O-methyl (2'-OMe). See also, U.S. Patent Application
Publication 2009/0098549, entitled "SELEX and PHOTOSELEX", which
describes nucleic acid libraries having expanded physical and
chemical properties and their use in SELEX and photoSELEX.
[0144] A variation of this 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. Pat. No. 6,544,776 entitled "Nucleic Acid Ligand
Diagnostic Biochip". These photoreactive aptamers are also referred
to as photoaptamers. See, e.g., U.S. Pat. No. 5,763,177, U.S. Pat.
No. 6,001,577, and U.S. Pat. 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. Pat. 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 value
corresponding to a biomarker in the test sample.
[0145] In both of these 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.
[0146] 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. Pat. No. 7,947,447,
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.
[0147] 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.
[0148] 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. For TB, the
molecular capture reagents would be an aptamer or an antibody or
the like and the specific target would be a TB biomarker of Tables
2 or 8 to 12.
[0149] In one embodiment, 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.
[0150] An exemplary solution-based aptamer assay that can be used
to detect a biomarker value corresponding to a biomarker 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.
[0151] SELEX can also be used to identify aptamers that have
desirable off-rate characteristics. See U.S. Pat. No. 7,947,447,
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 (also referred to herein as SOMAmer.TM.
aptamers) 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.
[0152] Regarding their use in diagnostic applications, aptamers,
including slow off-rate aptamers have several advantages over
antibodies, including lower molecular weight, higher multiplexing
capabilities (low cross-reactivity, universally-applicable assay
conditions), chemical stability (to heat, drying, and solvents,
reversible renaturation), ease of reagent manufacturing, consistent
lot-to-lot performance and lower cost (fully synthetic). They are
stable at room temperature and over a range of physiochemical
conditions.
[0153] Any means known in the art can be used to detect a biomarker
value by detecting the aptamer component of an aptamer affinity
complex. A number of different detection methods can be used to
detect the aptamer component of an affinity complex, such as, for
example, hybridization assays, mass spectroscopy, or QPCR. In some
embodiments, nucleic acid sequencing methods can be used to detect
the aptamer component of an aptamer affinity complex and thereby
detect a biomarker value. Briefly, a test sample can be subjected
to any kind of nucleic acid sequencing method to identify and
quantify the sequence or sequences of one or more aptamers present
in the test sample.
[0154] In some embodiments, the sequence includes the entire
aptamer molecule or any portion of the molecule that may be used to
uniquely identify the molecule. In other embodiments, the
identifying sequencing is a specific sequence added to the aptamer;
such sequences are often referred to as "tags," "barcodes," or
"zipcodes."
[0155] In some embodiments, the sequencing method includes
enzymatic steps to amplify the aptamer sequence or to convert any
kind of nucleic acid, including RNA and DNA that contain chemical
modifications to any position, to any other kind of nucleic acid
appropriate for sequencing.
[0156] In some embodiments, the sequencing method includes one or
more cloning steps. In other embodiments the sequencing method
includes a direct sequencing method without cloning.
[0157] In some embodiments, the sequencing method includes a
directed approach with specific primers that target one or more
aptamers in the test sample. In other embodiments, the sequencing
method includes a shotgun approach that targets all aptamers in the
test sample.
[0158] In some embodiments, the sequencing method includes
enzymatic steps to amplify the molecule targeted for sequencing. In
other embodiments, the sequencing method directly sequences single
molecules.
[0159] An exemplary nucleic acid sequencing-based method that can
be used to detect a biomarker value corresponding to a biomarker in
a biological sample includes the following: (a) converting a
mixture of aptamers that contain chemically modified nucleotides to
unmodified nucleic acids with an enzymatic step; (b) shotgun
sequencing the resulting unmodified nucleic acids with a massively
parallel sequencing platform such as, for example, the 454
Sequencing System (454 Life Sciences/Roche), the Illumina
Sequencing System (Illumina), the ABI SOLiD SequencingSystem
(Applied Biosystems), the HeliScope Single Molecule Sequencer
(Helicos Biosciences), or the Pacific Biosciences Real Time
Single-Molecule Sequencing System (Pacific BioSciences) or the
Polonator G Sequencing System (Dover Systems); and (c) identifying
and quantifying the aptamers present in the mixture by specific
sequence and sequence count.
Determination of Biomarker Values Using Immunoassays
[0160] 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 are often used because of
their specific epitope recognition. Polyclonal antibodies have also
been successfully used in various immunoassays due to 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.
[0161] 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 value
corresponding to the target in the unknown sample is
established.
[0162] 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
(I.sup.125) 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).
[0163] 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.
[0164] Methods of detecting and/or 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.
[0165] 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 384 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 Values Using Gene Expression
Profiling
[0166] Measuring mRNA in a biological sample may be used as a
surrogate for detection of the level of the corresponding protein
in the biological sample. Thus, any of the biomarkers or biomarker
panels described herein can also be detected by detecting the
appropriate RNA.
[0167] 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.
[0168] miRNA molecules are small RNAs that are non-coding but may
regulate gene expression. Any of the methods suited to the
measurement of mRNA expression levels can also be used for the
corresponding miRNA. Recently many laboratories have investigated
the use of miRNAs as biomarkers for disease. Many diseases involve
wide spread transcriptional regulation, and it is not surprising
that miRNAs might find a role as biomarkers. The connection between
miRNA concentrations and disease is often even less clear than the
connections between protein levels and disease, yet the value of
miRNA biomarkers might be substantial. Of course, as with any RNA
expressed differentially during disease, the problems facing the
development of an in vitro diagnostic product will include the
requirement that the miRNAs survive in the diseased cell and are
easily extracted for analysis, or that the miRNAs are released into
blood or other matrices where they must survive long enough to be
measured. Protein biomarkers have similar requirements, although
many potential protein biomarkers are secreted intentionally at the
site of pathology and function, during disease, in a paracrine
fashion. Many potential protein biomarkers are designed to function
outside the cells within which those proteins are synthesized.
Detection of Biomarkers Using In Vivo Molecular Imaging
Technologies
[0169] Any of the described biomarkers (see Tables 1, 2, 4, 5 and 8
to 12 may also be used in molecular imaging tests. For example, an
imaging agent can be coupled to any of the described biomarkers,
which can be used to aid in TB diagnosis, prognosis, to monitor
disease progression, to monitor for disease recurrence, or to
monitor response to therapy, among other uses.
[0170] 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 TB status, in particular the TB status, of an
individual.
[0171] 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.
[0172] 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.
[0173] Standard imaging techniques include but are not limited to
magnetic resonance imaging, contrast-enhanced abdominal or
transvaginal ultrasound, computed tomography (CT) 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. In
addition, 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.
[0174] 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.
[0175] 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.
[0176] 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. Labeled antibodies which
specifically bind any of the biomarkers in Tables 2 and 8 to 12 can
be injected into an individual suspected of having a certain type
of disease (e.g., TB), detectable according to the particular
biomarker used, for the purpose of diagnosing or evaluating the
disease status of the individual. The label used will be selected
in accordance with the imaging modality to be used, as previously
described. The amount of label within an organ or tissue also
allows determination of the presence or absence of disease in that
organ or tissue.
[0177] 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 in Tables 2 and 8 to 12
(and therefore binds specifically to that particular biomarker) may
be appropriately labeled and injected into an individual suspected
of having TB, detectable according to the particular biomarker, for
the purpose of diagnosing or evaluating the TB status of the
individual. The label used will be selected in accordance with the
imaging modality to be used, as previously described. The amount of
label within an organ or tissue also allows determination of the
presence or absence of TB in that organ or tissue. 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.
[0178] 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.
[0179] 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.
[0180] The use of in vivo molecular biomarker imaging is
increasing, including for clinical trials, for example, to more
rapidly measure clinical efficacy in trials for new disease
therapies and/or to avoid prolonged treatment with a placebo for
those diseases, such as multiple sclerosis, in which such prolonged
treatment may be considered to be ethically questionable.
[0181] For a review of other techniques, see N. Blow, Nature
Methods, 6, 465-469, 2009.
Determination of Biomarker Values Using Histology or Cytology
Methods
[0182] For evaluation of TB, a variety of tissue and fluid body
samples may be used in histological or cytological methods. Sample
selection depends on the location of the disease and sample
availability. For example, sputum collection, fine needle
aspirates, cutting needles, core biopsies and resected, sampled or
biopsied infected fluids or tissue can be used for histology. Any
of the biomarkers identified herein that were shown to be
up-regulated in the individuals with TB EVD can be used to stain a
histological specimen as an indication of disease.
[0183] In one embodiment, one or more capture reagents specific to
the corresponding biomarker is used in a cytological evaluation of
a TB sample and may include one or more of the following:
collecting a sputum or other body fluid or tissue 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.
[0184] In another embodiment, one or more capture reagents specific
to the corresponding biomarker is used in a histological evaluation
of a TB 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.
[0185] In another embodiment, the one or more aptamers specific to
the corresponding biomarker is 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.
[0186] 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.
[0187] 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 slow off-rate aptamers
for the staining of the prepared cells.
[0188] Sample collection can include directly placing the sample in
an untreated transport container, placing the sample in a transport
container containing some type of media, or placing the sample
directly onto a slide (immobilization) without any treatment or
fixation.
[0189] Sample immobilization can be improved by applying a portion
of the collected specimen to a glass slide that is treated with
polylysine, gelatin, or a silane. Slides can be prepared by
smearing a thin and even layer of cells across the slide. Care is
generally taken to minimize mechanical distortion and drying
artifacts. Liquid specimens can be processed in a cell block
method. Alternatively, liquid specimens can be mixed 1:1 with the
fixative solution for about ten minutes at room temperature.
[0190] Cell blocks can be prepared from residual effusions, sputum,
urine sediments, gastrointestinal fluids, cell scraping, ascites,
or fine needle aspirates. Cells are concentrated or packed by
centrifugation or membrane filtration. A number of methods for cell
block preparation have been developed. Representative procedures
include the fixed sediment, bacterial agar, or membrane filtration
methods. In the fixed sediment method, the cell sediment is mixed
with a fixative like Bouins, picric acid, or buffered formalin and
then the mixture is centrifuged to pellet the fixed cells. The
supernatant is removed, drying the cell pellet as completely as
possible. The pellet is collected and wrapped in lens paper and
then placed in a tissue cassette. The tissue cassette is placed in
a jar with additional fixative and processed as a tissue sample.
Agar method is very similar but the pellet is removed and dried on
paper towel and then cut in half. The cut side is placed in a drop
of melted agar on a glass slide and then the pellet is covered with
agar making sure that no bubbles form in the agar. The agar is
allowed to harden and then any excess agar is trimmed away. This is
placed in a tissue cassette and the tissue process completed.
Alternatively, the pellet may be directly suspended in 2% liquid
agar at 65.degree. C. and the sample centrifuged. The agar cell
pellet is allowed to solidify for an hour at 4.degree. C. The solid
agar may be removed from the centrifuge tube and sliced in half.
The agar is wrapped in filter paper and then the tissue cassette.
Processing from this point forward is as described above.
Centrifugation can be replaced in any these procedures with
membrane filtration. Any of these processes may be used to generate
a "cell block sample."
[0191] Cell blocks can be prepared using specialized resin
including Lowicryl resins, LR White, LR Gold, Unicryl, and
MonoStep. These resins have low viscosity and can be polymerized at
low temperatures and with ultra violet (UV) light. The embedding
process relies on progressively cooling the sample during
dehydration, transferring the sample to the resin, and polymerizing
a block at the final low temperature at the appropriate UV
wavelength.
[0192] Cell block sections can be stained with hematoxylin-eosin
for cytomorphological examination while additional sections are
used for examination for specific markers.
[0193] Whether the process is cytological or histological, the
sample may be fixed prior to additional processing to prevent
sample degradation. This process is called "fixation" and describes
a wide range of materials and procedures that may be used
interchangeably. The sample fixation protocol and reagents are best
selected empirically based on the targets to be detected and the
specific cell/tissue type to be analyzed. Sample fixation relies on
reagents such as ethanol, polyethylene glycol, methanol, formalin,
or isopropanol. The samples should be fixed as soon after
collection and affixation to the slide as possible. However, the
fixative selected can introduce structural changes into various
molecular targets making their subsequent detection more difficult.
The fixation and immobilization processes and their sequence can
modify the appearance of the cell and these changes must be
anticipated and recognized by the cytotechnologist. Fixatives can
cause shrinkage of certain cell types and cause the cytoplasm to
appear granular or reticular. Many fixatives function by
crosslinking cellular components. This can damage or modify
specific epitopes, generate new epitopes, cause molecular
associations, and reduce membrane permeability. Formalin fixation
is one of the most common cytological and histological approaches.
Formalin forms methyl bridges between neighboring proteins or
within proteins. Precipitation or coagulation is also used for
fixation and ethanol is frequently used in this type of fixation. A
combination of crosslinking and precipitation can also be used for
fixation. A strong fixation process is best at preserving
morphological information while a weaker fixation process is best
for the preservation of molecular. However, given that virulent
Mycobacterium tuberculosis is a biosafety level 3 organism any
processing that has risk of aerosol generation should be assured of
decontamination efficiency to prevent inadvertent exposures to
laboratory personnel.
[0194] A representative fixative is 50% absolute ethanol, 2 mM
polyethylene glycol (PEG), 1.85% formaldehyde. Variations on this
formulation include ethanol (50% to 95%), methanol (20%-50%), and
formalin (formaldehyde) only. Another common fixative is 2% PEG
1500, 50% ethanol, and 3% methanol. Slides are placed in the
fixative for about 10 to 15 minutes at room temperature and then
removed and allowed to dry. Once slides are fixed they can be
rinsed with a buffered solution like PBS.
[0195] A wide range of dyes can be used to differentially highlight
and contrast or "stain" cellular, sub-cellular, and tissue features
or morphological structures. Hematoylin is used to stain nuclei a
blue or black color. Orange G-6 and Eosin Azure both stain the
cell's cytoplasm. Orange G stains keratin and glycogen containing
cells yellow. Eosin Y is used to stain nucleoli, cilia, red blood
cells, and superficial epithelial squamous cells. Romanowsky stains
are used for air dried slides and are useful in enhancing
pleomorphism and distinguishing extracellular from intracytoplasmic
material.
[0196] The staining process can include a treatment to increase the
permeability of the cells to the stain. Treatment of the cells with
a detergent can be used to increase permeability. To increase cell
and tissue permeability, fixed samples can be further treated with
solvents, saponins, or non-ionic detergents. Enzymatic digestion
can also improve the accessibility of specific targets in a tissue
sample.
[0197] After staining, the sample is dehydrated using a succession
of alcohol rinses with increasing alcohol concentration. The final
wash is done with xylene or a xylene substitute, such as a citrus
terpene, that has a refractive index close to that of the coverslip
to be applied to the slide. This final step is referred to as
clearing. Once the sample is dehydrated and cleared, a mounting
medium is applied. The mounting medium is selected to have a
refractive index close to the glass and is capable of bonding the
coverslip to the slide. It will also inhibit the additional drying,
shrinking, or fading of the cell sample.
[0198] Regardless of the stains or processing used, the final
evaluation of the renal cytological specimen is made by some type
of microscopy to permit a visual inspection of the morphology and a
determination of the marker's presence or absence. Exemplary
microscopic methods include brightfield, phase contrast,
fluorescence, and differential interference contrast.
[0199] If secondary tests are required on the sample after
examination, the coverslip may be removed and the slide destained.
Destaining involves using the original solvent systems used in
staining the slide originally without the added dye and in a
reverse order to the original staining procedure. Destaining may
also be completed by soaking the slide in an acid alcohol until the
cells are colorless. Once colorless the slides are rinsed well in a
water bath and the second staining procedure applied.
[0200] In addition, specific molecular differentiation may be
possible in conjunction with the cellular morphological analysis
through the use of specific molecular reagents such as antibodies
or nucleic acid probes or aptamers. This improves the accuracy of
diagnostic cytology. Micro-dissection can be used to isolate a
subset of cells for additional evaluation, in particular, for
genetic evaluation of abnormal chromosomes, gene expression, or
mutations.
[0201] Preparation of a tissue sample for histological evaluation
involves disinfection, fixation, dehydration, infiltration,
embedding, and sectioning. The fixation reagents used in histology
are very similar or identical to those used in cytology and have
the same issues of preserving morphological features at the expense
of molecular ones such as individual proteins. Time can be saved if
the tissue sample is not fixed and dehydrated but instead is frozen
and then sectioned while frozen. This is a more gentle processing
procedure and can preserve more individual markers. However,
freezing is not acceptable for long term storage of a tissue sample
as subcellular information is lost due to the introduction of ice
crystals. Ice in the frozen tissue sample also prevents the
sectioning process from producing a very thin slice and thus some
microscopic resolution and imaging of subcellular structures can be
lost. In addition to formalin fixation, osmium tetroxide is used to
fix and stain phospholipids (membranes).
[0202] Dehydration of tissues or sputum or other fluid is
accomplished with successive washes of increasing alcohol
concentration. Clearing employs a material that is miscible with
alcohol and the embedding material and involves a stepwise process
starting at 50:50 alcohol clearing reagent and then 100% clearing
agent (xylene or xylene substitute). Infiltration involves
incubating the tissue with a liquid form of the embedding agent
(warm wax, nitrocellulose solution) first at 50:50 embedding agent:
clearing agent and the 100% embedding agent. Embedding is completed
by placing the tissue in a mold or cassette and filling with melted
embedding agent such as wax, agar, or gelatin. The embedding agent
is allowed to harden. The hardened tissue sample may then be sliced
into thin section for staining and subsequent examination.
[0203] Prior to staining, the tissue section is dewaxed and
rehydrated. Xylene is used to dewax the section, one or more
changes of xylene may be used, and the tissue is rehydrated by
successive washes in alcohol of decreasing concentration. Prior to
dewax, the tissue section may be heat immobilized to a glass slide
at about 80.degree. C. for about 20 minutes.
[0204] Laser capture micro-dissection allows the isolation of a
subset of cells for further analysis from a tissue section.
[0205] As in cytology, to enhance the visualization of the
microscopic features, the tissue section or block or slice can be
stained with a variety of stains. A large menu of commercially
available stains can be used to enhance or identify specific
features.
[0206] To further increase the interaction of molecular reagents
with cytological or histological samples, a number of techniques
for "analyte retrieval" have been developed. The first such
technique uses high temperature heating of a fixed sample. This
method is also referred to as heat-induced epitope retrieval or
HIER. A variety of heating techniques have been used, including
steam heating, microwaving, autoclaving, water baths, and pressure
cooking or a combination of these methods of heating. Analyte
retrieval solutions include, for example, water, citrate, and
normal saline buffers. The key to analyte retrieval is the time at
high temperature but lower temperatures for longer times have also
been successfully used. Another key to analyte retrieval is the pH
of the heating solution. Low pH has been found to provide the best
immunostaining but also gives rise to backgrounds that frequently
require the use of a second tissue section as a negative control.
The most consistent benefit (increased immunostaining without
increase in background) is generally obtained with a high pH
solution regardless of the buffer composition. The analyte
retrieval process for a specific target is empirically optimized
for the target using heat, time, pH, and buffer composition as
variables for process optimization. Using the microwave analyte
retrieval method allows for sequential staining of different
targets with antibody reagents. However, the time required to
achieve antibody and enzyme complexes between staining steps has
also been shown to degrade cell membrane analytes. Microwave
heating methods have improved in situ hybridization methods as
well.
[0207] To initiate the analyte retrieval process, the section is
first dewaxed and hydrated. The slide is then placed in 10 mM
sodium citrate buffer pH 6.0 in a dish or jar. A representative
procedure uses an 1100 W microwave and microwaves the slide at 100%
power for 2 minutes followed by microwaving the slides using 20%
power for 18 minutes after checking to be sure the slide remains
covered in liquid. The slide is then allowed to cool in the
uncovered container and then rinsed with distilled water. HIER may
be used in combination with an enzymatic digestion to improve the
reactivity of the target to immunochemical reagents.
[0208] One such enzymatic digestion protocol uses proteinase K. A
20 .mu.g/ml concentration of proteinase K is prepared in 50 mM Tris
Base, 1 mM EDTA, 0.5% Triton X-100, pH 8.0 buffer. The process
first involves dewaxing sections in 2 changes of xylene, 5 minutes
each. Then the sample is hydrated in 2 changes of 100% ethanol for
3 minutes each, 95% and 80% ethanol for 1 minute each, and then
rinsed in distilled water. Sections are covered with Proteinase K
working solution and incubated 10-20 minutes at 37.degree. C. in
humidified chamber (optimal incubation time may vary depending on
tissue type and degree of fixation). The sections are cooled at
room temperature for 10 minutes and then rinsed in PBS Tween 20 for
2.times.2 min. If desired, sections can be blocked to eliminate
potential interference from endogenous compounds and enzymes. The
section is then incubated with primary antibody at appropriate
dilution in primary antibody dilution buffer for 1 hour at room
temperature or overnight at 4.degree. C. The section is then rinsed
with PBS Tween 20 for 2.times.2 min. Additional blocking can be
performed, if required for the specific application, followed by
additional rinsing with PBS Tween 20 for 3.times.2 min and then
finally the immunostaining protocol completed.
[0209] A simple treatment with 1% SDS at room temperature has also
been demonstrated to improve immunohistochemical staining. Analyte
retrieval methods have been applied to slide mounted sections as
well as free floating sections. Another treatment option is to
place the slide in a jar containing citric acid and 0.1 Nonident
P40 at pH 6.0 and heating to 95.degree. C. The slide is then washed
with a buffer solution like PBS.
[0210] For immunological staining of tissues it may be useful to
block non-specific association of the antibody with tissue proteins
by soaking the section in a protein solution like serum or non-fat
dry milk.
[0211] Blocking reactions may include the need to do any of the
following, either alone or in combination: reduce the level of
endogenous biotin; eliminate endogenous charge effects; inactivate
endogenous nucleases; and inactivate endogenous enzymes like
peroxidase and alkaline phosphatase. Endogenous nucleases may be
inactivated by degradation with proteinase K, by heat treatment,
use of a chelating agent such as EDTA or EGTA, the introduction of
carrier DNA or RNA, treatment with a chaotrope such as urea,
thiourea, guanidine hydrochloride, guanidine thiocyanate, lithium
perchlorate, etc., or diethyl pyrocarbonate. Alkaline phosphatase
may be inactivated by treatment with 0.1N HCl for 5 minutes at room
temperature or treatment with 1 mM levamisole. Peroxidase activity
may be eliminated by treatment with 0.03% hydrogen peroxide.
Endogenous biotin may be blocked by soaking the slide or section in
an avidin (streptavidin, neutravidin may be substituted) solution
for at least 15 minutes at room temperature. The slide or section
is then washed for at least 10 minutes in buffer. This may be
repeated at least three times. Then the slide or section is soaked
in a biotin solution for 10 minutes. This may be repeated at least
three times with a fresh biotin solution each time. The buffer wash
procedure is repeated. Blocking protocols should be minimized to
prevent damaging either the cell or tissue structure or the target
or targets of interest but one or more of these protocols could be
combined to "block" a slide or section prior to reaction with one
or more slow off-rate aptamers. See Basic Medical Histology: the
Biology of Cells, Tissues and Organs, authored by Richard G.
Kessel, Oxford University Press, 1998.
Determination of Biomarker Values Using Mass Spectrometry
Methods
[0212] A variety of configurations of mass spectrometers can be
used to detect biomarker values. 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. Differences 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. (1998) Anal. Chem. 70:647 R-716R;
Kinter and Sherman, New York (2000)).
[0213] Protein biomarkers and biomarker values 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-flight 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).sup.N, atmospheric pressure
photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and
APPI-(MS).sup.N, quadrupole mass spectrometry, Fourier transform
mass spectrometry (FTMS), quantitative mass spectrometry, and ion
trap mass spectrometry.
[0214] Sample preparation strategies are used to label and enrich
samples before mass spectroscopic characterization of protein
biomarkers and determination biomarker values. 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').sub.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.
Determination of Biomarker Values Using a Proximity Ligation
Assay
[0215] A proximity ligation assay can be used to determine
biomarker values. Briefly, a test sample is contacted with a pair
of affinity probes that may be a pair of antibodies or a pair of
aptamers, with each member of the pair extended with an
oligonucleotide. The targets for the pair of affinity probes may be
two distinct determinates on one protein or one determinate on each
of two different proteins, which may exist as homo- or
hetero-multimeric complexes. When probes bind to the target
determinates, the free ends of the oligonucleotide extensions are
brought into sufficiently close proximity to hybridize together.
The hybridization of the oligonucleotide extensions is facilitated
by a common connector oligonucleotide, which serves to bridge
together the oligonucleotide extensions when they are positioned in
sufficient proximity. Once the oligonucleotide extensions of the
probes are hybridized, the ends of the extensions are joined
together by enzymatic DNA ligation.
[0216] Each oligonucleotide extension comprises a primer site for
PCR amplification. Once the oligonucleotide extensions are ligated
together, the oligonucleotides form a continuous DNA sequence,
which, through PCR amplification, reveals information regarding the
identity and amount of the target protein as well as information
regarding protein-protein interactions where the target
determinates are on two different proteins. Proximity ligation can
provide a highly sensitive and specific assay for real-time protein
concentration and interaction information through use of real-time
PCR. Probes that do not bind the determinates of interest do not
have the corresponding oligonucleotide extensions brought into
proximity and no ligation or PCR amplification can proceed,
resulting in no signal being produced.
Aptamer Based Assay
[0217] The SomaLogic proteomics technology is an aptamer based
assay, which may include the use of aptamers with improved binding
properties (also referred to as slow off-rate aptamers), due to
long dissociation rates (>30 minutes) and the incorporation of
modified nucleotides that lead to unparalleled affinity of these
reagents compared to standard RNA or DNA aptamers. Slow off-rate
aptamers are typically made from single-stranded DNA (ssDNA) that
may contain pyrimidine residues modified at their 5-position to
mimic amino acid side-chains. The affinity of these aptamers for
their targets is a consequence of the three-dimensional shape and
these side chain modifications rather than the nucleotide
sequence.
[0218] Slow off-rate aptamers are selected in vitro by the SELEX
process (as described in detail above). To date, there are
>1,000 slow off-rate aptamers developed to bind to human
proteins. Regarding their use in diagnostic applications, aptamers,
including slow off-rate aptamers, have several advantages over
antibodies used for ELISAs, including better performance (affinity,
accuracy), higher multiplexing capabilities (low cross-reactivity,
identical assay conditions), chemical stability (heat, drying,
denaturation), ease and reproducibility of reagent manufacturing,
and lower cost (fully synthetic).
[0219] The current version of the aptamer based assay described
herein measures quantitatively approximately 1,030 human proteins
simultaneously using only 8 .mu.L of sample (serum, plasma, CSF,
blood, urine, sputum, lavage or tissue lysate). The small sample
volume reflects the high sensitivity of the assay (hybridization to
an array and fluorescence scanning), however, the actual sample
volume at the front end is not limited, and larger samples (e.g.,
0.5 ml serum) can be used. The assay can include an affinity column
to enrich for pathogen analytes and pathogens themselves. Such
enrichment procedures have been successfully performed with
bead-immobilized, target-specific aptamers to concentrate samples
(e.g. from 0.5 ml to <50 .mu.L) to detect very low abundance
biomarkers.
[0220] Using this assay, serum is currently tested at three
different concentrations (5%, 0.3%, 0.01%). These serum dilutions
are used simply for practical reasons to obtain accurate
measurements for low-, medium-, and high-abundant proteins,
respectively. For the quantitative determination of analytes in
serum the data points should lay well within the linear range of
the assay and between the lower limit of quantitation (LLOQ) and
upper limit of quantitation (ULOQ). Dilution of serum may be
advisable for the most abundant proteins where the linear range of
the assay is typically between 0.003% and 0.1% of serum, and a
plateau of the signal is reached at serum concentrations of 1% or
above.
[0221] In contrast, low abundance proteins are measured more
accurately and are easier to detect in less diluted serum. While
the assay works perfectly to measure nearly all low abundance human
proteins tested to date in 5% serum, such a 20-fold dilution of
serum is not required for the assay. In fact, the assay performs
well in 50% serum, resulting in a several-fold increased signal
compared to the signal in 5% serum. Together, the use of larger
sample sizes and less diluted serum will further improve the
sensitivity of detection of extremely low abundance proteins such
as may be the case for Mycobacterium tuberculosis (Mtb)
(pathogen-derived) markers.
[0222] Slow off-rate aptamers have an average sensitivity of 100
fM, and the aptamer based assay has a dynamic range of >5 logs,
and a median coefficient of variation (% CV) of 5% for individual
proteins measured repeatedly in replicate runs of serum and plasma
sample. Performance data are available for all of the more than
1,000 slow off-rate aptamers, including measured signal in standard
curves obtained by spiking purified protein into buffer and
precision profiles showing the % CV over the concentration range
between the LLOQ and ULOQ.
[0223] The sensitivity of the aptamers is excellent and not
adversely affected by multiplexing for over a thousand analytes in
a single assay. The median LLOQ is 300 fM, which is 6 pg/mL for a
small, 20 kDa protein. The limit of detection (LOD) for each
aptamer correlates well with its affinity (K.sub.d) for its target.
Over the past several years, the affinities (K.sub.d's) of aptamers
have improved by several orders of magnitude. Compared to standard
DNA or RNA aptamers with K.sub.d's in the high nanomolar range,
slow off-rate aptamers have typical K.sub.d's in the picomolar
range. Such high affinity aptamers result in low femtomolar LODs in
the assay. The concentration of Mtb pathogen markers in blood is
largely unknown; antigen 85 levels in sputum are in the 100 pg/ml
range, which is 3 pM and thus well within the sensitivity of the
assay. Moreover, the affinity of the reagents can often be further
increased by incorporating of alternate 5-position modified
nucleotides into the aptamers. The utility of this assay has been
demonstrated in oncology, cardiovascular, kidney, neurological and
infectious diseases (Ostroff et al. (2010) PloS one 5:e15003).
[0224] This aptamer based assay is a powerful tool to identify and
then validate serum proteomic signatures typical for the medical
condition of interest. Once a subset of biomarkers has been
identified, a corresponding "small-plex" aptamer panel can be
assembled (that is, a panel consisting of a few analytes). The
aptamer assay is easily adapted to multiple formats and platforms
besides hybridization to a slide-array, including standard clinical
laboratory assays (e.g., Luminex), but also very simple
sandwich-type assays with beads, plates, or membranes, and a
variety of signal enhancing and detection methods can be used. The
latter methods are well suited for point-of-care applications. The
envisioned aptamer-based TB test will not require staining, or
expensive equipment typically needed for molecular amplification
assays. It also has the potential for independence from electricity
for refrigeration of reagents or the need for powering detection
devices. These comparative advantages of the diagnostic reagents
and proteomics platform are encouraging toward the development of a
simple, rapid, and inexpensive TB test that can have a very
favorable impact on this global health problem.
[0225] The foregoing assays enable the detection of biomarker
values that are useful in methods for evaluating or diagnosing TB,
where the methods comprise detecting, in a biological sample from
an individual, at least N biomarker values that each correspond to
a biomarker selected from the group consisting of the biomarkers
provided in Tables 2 and 8 to 12, wherein a classification, as
described in detail below, using the biomarker values indicates
whether the individual has TB EVD. While certain of the described
TB biomarkers are useful alone for detecting, evaluating and
diagnosing TB, methods are also described herein for the grouping
of multiple subsets of the TB biomarkers that are each useful as a
panel of two, three, four or more biomarkers. In accordance with
any of the methods described herein, biomarker values can be
detected and classified individually or they can be detected and
classified collectively, as for example in a multiplex assay
format.
[0226] In another aspect, methods are provided for detecting an
absence of TB, the methods comprising detecting, in a biological
sample from an individual, at least N biomarker values that each
correspond to a biomarker selected from the group consisting of the
biomarkers provided in Tables 2 and 8 to 12, wherein a
classification, as described in detail below, of the biomarker
values indicates an absence of TB in the individual. In accordance
with any of the methods described herein, biomarker values 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 TB Prognosis
Scores
[0227] A biomarker "signature" for a given evaluation test contains
a set of markers, each marker having different levels in the
populations of interest. Different levels, in this context, may
refer to different means of the marker levels for the individuals
in two or more groups, or different variances in the two or more
groups, or a combination of both. For the simplest form of an
evaluation test, these markers can be used to assign an unknown
sample from an individual into one of two groups, either diseased
or not diseased. The assignment of a sample into one of two or more
groups 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 an evaluation classifier from a set of
biomarker values. In general, classification methods are most
easily performed using supervised learning techniques where 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 prognostic classifier.
[0228] Common approaches for developing evaluation 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; each of which is incorporated by reference in its
entirety.
[0229] To produce a classifier using supervised learning
techniques, a set of samples called training data are obtained. In
the context of prognostic 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. For purposes of
illustration, an example of training a random forest classifier
will be described below (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).
[0230] Since typically there are many more potential biomarker
values 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 ways,
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.
[0231] An illustrative example of the development of an evaluation
test using a set of biomarkers includes the application of a random
forest classifier (Shi and Horvath (2006) Unsupervised Learning
with Random Forest Predictors. (March 2006) Journal of
Computational and Graphical Statistics 15(1):118-138). A RF
predictor is an ensemble of individual classification tree
predictors (Breiman (2001) Machine Learning 45(1):5-32). For each
observation, each individual tree votes for one class and the
forest predicts the class that has the plurality of votes. The user
has to specify the number of randomly selected variables to be
searched through for the best split at each node. The Gini index
(Breiman et al. (1984), Classification and Regression Trees,
Chapman and Hall, New York.) is used as the splitting criterion.
The largest tree possible is grown and is not pruned. The root node
of each tree in the forest contains a bootstrap sample from the
original data as the training set. The observations that are not in
the training set, roughly 1=3 of the original data set, are
referred to as out-of-bag (OOB) observations. One can arrive at OOB
predictions as follows: for a case in the original data, predict
the outcome by plurality vote involving only those trees that did
not contain the case in their corresponding bootstrap sample. By
contrasting these OOB predictions with the training set outcomes,
one can arrive at an estimate of the prediction error rate, which
is referred to as the OOB error rate.
[0232] 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. 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.
[0233] The performance of the random forest 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) and its PCA value as
exemplified herein. If a classifier performance metric is defined
as the sum of the sensitivity (fraction of true positives,
f.sub.TP) and specificity (one minus the fraction of false
positives, 1-f.sub.FP), a perfect classifier will have a score of
two and a random classifier, on average, will have a score of one.
Using the definition of the KS-distance, that value x* which
maximizes the difference in the cdf functions can be found by
solving
.differential. KS .differential. x = .differential. ( cdf c ( x ) -
cdf d ( x ) ) .differential. x = 0 ##EQU00001##
for x, which leads to p(x*|c)=p(x*|d), i.e., the KS distance occurs
where the class-dependent pdfs cross. Substituting this value of x*
into the expression for the KS-distance yields the following
definition for KS
KS = cdf c ( x * ) - cdf d ( x * ) = .intg. - .infin. x * p ( x | c
) x - .intg. - .infin. x * p ( x | d ) x = 1 - .intg. x * .infin. p
( x | c ) x - .intg. - .infin. x * p ( x | d ) x = 1 - f FP - f FN
, ##EQU00002##
the KS distance is one minus the total fraction of errors using a
test with a cut-off at x*, essentially a single analyte Bayesian
classifier. Since we define a score of
sensitivity+specificity=2-f.sub.FP-f.sub.FN, combining the above
definition of the KS-distance we see that
sensitivity+specificity=1+KS. We select biomarkers with a statistic
that is inherently suited for building classifiers.
[0234] 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, it is straightforward to generate many high
scoring classifiers.
[0235] Another way to identify relevant biomarkers is through
Principal Components Analysis (PCA). PCA is a method that reduces
data dimensionality by performing a covariance analysis between
factors. As such, it is suitable for data sets in multiple
dimensions, such as a large experiment in protein or gene
expression. PCA uses an orthogonal transformation to convert a set
of observations of possibly correlated variables into a set of
values of uncorrelated variables called principal components. It is
used as a tool in exploratory data analysis and for making
predictive models. The central idea of principal component analysis
(PCA) is to reduce the dimensionality of a data set consisting of a
large number of interrelated variables, while retaining as much as
possible of the variation present in the data set. This is achieved
by transforming to a new set of variables, the principal components
(PCs), which are uncorrelated, and which are ordered so that the
first few retain most of the variation present in all of the
original variables (Joliffe IT. (2002) Principal Component
Analysis, 2.sup.nd Edition. Springer).
[0236] Another way to depict classifier performance is through a
receiver operating characteristic (ROC), or simply ROC curve. 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). This is 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) Pattern
Recognition Letters 27:861-874). This is equivalent to the Wilcoxon
test of ranks (Hanley and McNeil (1982) Radiology 143:29-36).
[0237] The algorithm approach used here is exemplified herein.
Briefly, all single analyte classifiers are generated from a table
of potential biomarkers and added to a list. Next, all possible
additions of a second analyte to each of the stored single analyte
classifiers is then performed, saving a predetermined number of the
best scoring pairs, say, for example, a thousand, on a new list.
All possible three-marker classifiers are explored using this new
list of the best two-marker classifiers, again saving the best
thousand of these. This process continues until the score either
plateaus or begins to deteriorate as additional markers are added.
Those high scoring classifiers that remain after convergence can be
evaluated for the desired performance for an intended use. For
example, in one prognostic application, classifiers with a high
sensitivity and modest specificity may be more desirable than
modest sensitivity and high specificity. In another prognostic
application, classifiers with a high specificity and a modest
sensitivity may be more desirable. The desired level of performance
is generally selected based upon a trade-off that must be made
between the number of false positives and false negatives that can
each be tolerated for the particular prognostic application. Such
trade-offs generally depend on the medical consequences of an
error, either false positive or false negative.
[0238] Various other techniques are known in the art and may be
employed to generate many potential classifiers from a list of
biomarkers using a random forest classifier. In one embodiment,
what is referred to as a genetic algorithm can be used to combine
different markers using the fitness score as defined above. Genetic
algorithms are particularly well suited to exploring a large
diverse population of potential classifiers. In another embodiment,
so-called ant colony optimization can be used to generate sets of
classifiers. Other strategies that are known in the art can also be
employed, including, for example, other evolutionary strategies as
well as simulated annealing and other stochastic search methods.
Metaheuristic methods, such as, for example, harmony search may
also be employed.
[0239] The markers listed in Tables 1, 2, 4, 5, and 8 to 12 can be
combined in many ways to produce classifiers for evaluating and
diagnosing TB. In some embodiments, panels of biomarkers are
comprised of different numbers of analytes depending on a specific
diagnostic performance criterion that is selected. For example,
certain combinations of biomarkers will produce tests that are more
sensitive (or more specific) than other combinations.
[0240] Once a panel is defined to include a particular set of
biomarkers from Tables 2 and 8 to 12 and a classifier is
constructed from a set of training data, the definition of the
diagnostic test is complete. The biological sample is appropriately
diluted and then 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.
[0241] Tables 8 to 12 identify 122 biomarkers that are useful for
evaluating TB treatment effect. We put forth any combination of
these markers (with the TB specific markers) as putative diagnostic
signatures of TB disease. This is a surprisingly larger number than
expected when compared to what is typically found during biomarker
discovery efforts and may be attributable to the scale of the
described study, which encompassed over 1030 proteins measured in
hundreds of individual samples, in some cases at concentrations in
the low femtomolar range. Presumably, the large number of
discovered biomarkers reflects the diverse biochemical pathways
implicated in both TB biology and the body's response to the
presence of TB and its subsequent treatment or response of the body
to the drugs; each pathway and process involves many proteins. The
results show that no single protein of a small group of proteins is
uniquely informative about such complex processes; rather, that
multiple proteins are involved in relevant processes, such as
inflammation, coagulation cascade, tissue remodeling, antimicrobial
protein functions, apoptosis or extracellular matrix turn over and
repair or fibrosis scarring and healing for example.
[0242] Given the number of biomarkers identified during the
described study, one would expect to be able to derive ample
numbers of high-performing classifiers that can be used in various
diagnostic methods. It was found that many subsets of the
biomarkers presented in Tables 2 and 8 to 12 can be combined with
each other or the TB specific markers to generate useful
classifiers.
[0243] The results of classifier evaluation tests suggest certain
possible conclusions: First, the identification of a large number
of biomarkers enables their aggregation into a vast number of
classifiers that offer similarly high performance. Second,
classifiers can be constructed such that particular biomarkers may
be substituted for other biomarkers in a manner that reflects the
redundancies that undoubtedly pervade the complexities of the
underlying disease processes. That is to say, the information about
the disease contributed by any individual biomarker identified in
Tables 2 and 8 to 12 overlaps with the information contributed by
other biomarkers, such that it may be that no particular biomarker
or small group of biomarkers in Tables 2 and 8 to 12 must be
included in any classifier.
[0244] Exemplary embodiments use random forest classifiers
constructed from the data in Tables 2 and 8 to 12 to classify an
unknown sample. In one embodiment, the biological sample is
optionally diluted and run in a multiplexed aptamer assay. The data
from the assay are normalized and calibrated, and the resulting
biomarker levels are used as input to a random forest
classification scheme.
[0245] The general method for performing the aptamer based
proteomic assay is set forth in Example 1. Example 2 describes the
targeted proteomic assay employed to identify and quantify protein
markers that were associated with active TB and that changed in
response to four-drug treatment. With reference to Example 2, it
can be seen that the healing process that accompanies effective
anti-TB therapy appears to be highly associated with a fibrotic
healing process and is in keeping with radiographic changes known
to occur with TB therapy. Among the diverse pathways identified,
the most predominant were those representing the core biological
themes of antimicrobial defenses and tissue remodeling/healing
functions.
Biomarkers of Inflammation and Anti-Microbial Defense
[0246] Differentially expressed proteins involved in innate and
adaptive immunity to which antimicrobial function has been
attributed were identified. Some of the proteins identified
include, but are not limited to, complement cascade components,
CRP, .alpha.-1 antitrypsin (AAT), hepcidin (LEAP), bactericidal
permeability increasing protein (BPI), lipopolysaccharide binding
protein (LBP) and phospholipase A2 (NPS-PLA2), all of which
decreased over time in the majority of patients. C9 and C3
breakdown products (C3b and C3d) decreased with therapy in the
majority of the patients. Components of the MTB bacillus are known
to induce the antimicrobial molecule hepcidin (LEAP) which has been
reported to have both iron handling and antibacterial properties.
The finding of NPS-PLA2 as a top marker distinguishing week 8
samples from baseline samples highlights the importance of this
protein, which has both antibacterial and lipolytic functions in
the host. NPS-PLA2 is an innate immune antibacterial molecule
involved in arachidonic acid and fatty acid generation and may be
involved in the lipoid pneumonia seen with pulmonary TB. Cathepsin
G, an antimicrobial molecule and serine protease found in hypoxic
TB granulomas decreased on therapy in the majority of patients.
Mannose receptor C type 2 (MRC-2), another pattern recognition
molecule known to be involved in TB and activating the innate
immune system was among the top 3 markers that were differentially
expressed between baseline and 8 weeks.
Biomarkers of Tissue Remodeling
[0247] A number of proteins involved in tissue healing including
proteases and anti-proteases, fibrotic process proteins, remodeling
of collagen and extracellular matrix (ECM), as well as, members of
the coagulation cascade were also identified. Plasminogen, a marker
found in all analyses, has been reported to be co-opted by MTB and
other respiratory pathogens in order to evade immune responses.
Once activated, plasminogen is converted to plasmin, a serine
protease that can degrade fibrin and activate complement. Plasmin
has also been reported to increase the activity of many proteins
including matrix metalloproteinases (MMP) and TGF-.beta. which can
alter host pathology and allow the tubercle bacillus to disseminate
more readily. Thrombospondin-4 (TSP4) prominently appears in both
the paired and unpaired analysis. The thrombospondins are a family
of extracellular matrix glycoproteins that mediate cell-to-cell and
cell-to-matrix interactions. They are reportedly involved in lung
adhesion, fibrosis, neovascularization and cardiac tissue
re-modeling, but to date it is believed that they have not been
associated with active TB. Fibroblast activation protein (SEPR) is
also involved in collagen and extracellular matrix degradation.
Additional MMPs and their endogenous inhibitors, tissue inhibitors
of metalloproteinases (TIMPs) are both classes of enzymes involved
in fibrosis and the proper formation of granulomatous inflammation,
tissue remodeling and turnover of extracellular matrix material in
normal and pathological conditions. The differential expression of
these proteins may relate to drug toxicity, underlying cavitary
disease, resolution of liquefaction and ultimate healing with
fibrosis.
Biomarkers of Angiogenesis and Coagulation
[0248] Angiogenesis is a complex biological phenomenon controlled
by both positive and negative signals. The finding of three forms
of vascular endothelial growth factor (VEGF) or its receptor among
the top markers changing over time supports an intriguing role for
angiogenesis and vascular remodeling and has recently been shown by
others to be associated with TB. Significant changes in levels of
proteins members (e.g. antithrombin III) of the coagulation
cascades were found, highlighting the importance of such cascades
in the course of TB. There is a pro-coagulant state in TB and
others have shown that hematologic/coagulation factors can be
biomarkers of TB infection. Indeed careful study of lung
histopathology of human and experimental MTB infection reveals
areas of vasculitis and microthrombi within vessels contributing to
lesion formation.
Biomarkers Associated with Disease Severity
[0249] When considering markers associated with the severity of
disease a few proteins are worthy of specific comment. The levels
of thrombospondin-2 (TSP-2), a protein regulating a variety of
cell-matrix interactions was found to be higher in those with more
cavitary manifestations of disease. Three additional markers stood
out in a logistic regression analysis of disease severity: DKK-1,
serum amyloid P and adiponectin. DKK-1 an inhibitor of wnt
signaling has also been shown to alter fibrosis and expression is
up-regulated in a Chlamydia infection model. Serum amyloid P is a
member (as is CRP) of the pentraxin family of proteins involved in
pattern recognition and complement activation and levels have been
shown to correlate with disease severity and rapidity of burn wound
healing. Adiponectin is associated with metabolic syndrome and
insulin resistance and is known to be increased with decreased body
fat so the finding of low levels with severe disease would argue
against the association being attributed exclusively to low BMI in
our patients. Adiponectin may be important in the lung, as
receptors for this adipokine have been shown in the lung and low
levels of adiponectin are associated with development of other
serious pulmonary diseases (e.g. asthma). Some neurological and
other markers identified may be related to drug toxicity.
[0250] With reference to Example 4, it can be seen that a number of
proteins were identified that were differentially present between
treatment of "slow-responders" (subjects that had yet to respond by
week 8) and responders at baseline or after 8 weeks of TB treatment
(Table 8). A "responder" as used herein refers to one who has
negative sputum culture results on both solid and liquid culture
media at week 8 and a "slow responder" refers to one who has a
positive on one or both media types at week 8.
[0251] Serum amyloid A (SAA) protein was a strong predictor of
treatment response in multiple analyses performed. Not
unexpectedly, many proteins involved in innate and adaptive
immunity were differentially expressed, including gp-130, TNF
pathway molecules, complement components, catalase, IgG,
IFN-.lamda., PSME1, PSD7. At baseline, the strongest marker
predicting treatment response was PSME1, an IFN-.gamma.-inducible
component of the immunoproteasome. The levels of this protein are
known to be increased under the conditions of intensified immune
response and are important for efficient antigen processing. IL-11
R.alpha. is a receptor for IL-11 and uses the high affinity gp130
transducing domain, which also appeared in both the baseline and
week 8 data, and both are acute phase response proteins. APRIL is a
TNF family ligand, can be involved in TGF-.beta. signaling, and has
been shown to have a role in the response to pathogens. Both
TGF-.beta. and TNF are important cytokines in TB. APRIL has also
been shown to be involved in promoting T-cell proliferation and
survival. MMP-12 and MMP-13 were differentially expressed at
baseline. The major substrate for MMP-12 is elastin, a major
protein of the lung connective tissue and it has been detected in
lung disease. Matrix proteoglycan (BGN) and BGH3 may also be
involved in extracellular matrix and tissue remodeling. The finding
of elevated ECM1 in responders is intriguing but the strength of
the association weakens somewhat when adjusted for age (data not
shown). Proteins involved in amyloids/fibrils (BGH3) and
potentially HSP70 deserve greater attention and may have to do with
the makeup of the TB lesions and can change with therapy. XPNPEP1
is a metalloaminopeptidase involved in the degradation of
neuropeptides and the finding of VIP in these analyses is
intriguing for the role of neuropeptides in treatment of TB. The
finding of coagulation factor V as the strongest marker at 8 weeks
could suggest either better protein calorie nutrition in responders
or that tissue remodeling, changes in fibrinolysis and resolution
of pulmonary TB lesions has heretofore undescribed connections with
the coagulation cascade.
[0252] We have built several mathematical models for the prediction
of the 8 week culture status. One example is a logistic regression
model using four features obtained during measurements of serum
protein levels at baseline together with subject age. The model
performed fairly accurately in sample classification and resulted
in an ROC curve with AUC=0.96 (see FIG. 21D). Similar performance
was observed for a model containing the top five serum protein
markers at baseline based on KS distances (see FIG. 21).
Separately, we also selected the top markers at 8 weeks based on
large KS distances (.gtoreq.0.5), and combined a five-feature
signature (coagulation factor V, XPNPEP1, gp130, TIMP-2 and ECM1)
in a naive Bayes classifier to "predict" treatment response, which
revealed an ROC curve with an AUC=0.88 (see FIG. 19A).
[0253] Correlation of serum protein measurements with time to
culture corroborated some of the markers found in previous analyses
and several other proteins linked to neutrophil function.
Kits
[0254] Any combination of the biomarkers of Tables 1, 2, 4, 5, and
8 to 12 (as well as additional biomedical information) can be
detected using a suitable kit, such as for use in performing the
methods disclosed herein. Furthermore, any kit can contain one or
more detectable labels as described herein, such as a fluorescent
moiety, etc.
[0255] In one embodiment, 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,
wherein the biomarkers include any of the biomarkers set forth in
Tables 1, 2, 4, 5, and 8 to 12, and optionally (b) one or more
software or computer program products for classifying the
individual from whom the biological sample was obtained, for
evaluation of TB status. 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.
[0256] The combination of a solid support with a corresponding
capture reagent and a signal generating material is referred to
herein as a "detection device" or "kit". 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.
[0257] 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.
[0258] In one aspect, the invention provides kits for the analysis
of TB status. The kits include PCR primers for one or more
biomarkers selected from Tables 1, 2, 4, 5, and 8 to 12. The kit
may further include instructions for use and correlation of the
biomarkers with TB. The kit may also include any of the following,
either alone or in combination: a DNA array containing the
complement of one or more of the biomarkers selected from Tables 1,
2, 4, 5, and 8 to 12, reagents, and enzymes for amplifying or
isolating sample DNA. The kits may include reagents for real-time
PCR, such as, for example, TaqMan probes and/or primers, and
enzymes.
[0259] For example, a kit can comprise: (a) reagents comprising at
least capture reagents for quantifying one or more biomarkers in a
test sample, wherein said biomarkers comprise the biomarkers set
forth in Tables 1, 2, 4, 5, and 8 to 12, or any other biomarkers or
biomarkers panels described herein; 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 and assigning a score
for each biomarker quantified based on said comparison, combining
the assigned scores for each biomarker quantified to obtain a total
score, comparing the total score with a predetermined score, and
using said comparison to evaluate TB status in an individual.
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
[0260] Once a biomarker or biomarker panel is selected, a method
for evaluating an individual for TB status can 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 required for the method used
to collect biomarker values; 4) calculate the marker score; 5)
combine the marker scores to obtain a total diagnostic score; and
6) report the individual's diagnostic score. In this approach, the
diagnostic score may be a single number determined from the sum of
all the marker calculations that is compared to a preset 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.
[0261] 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 FIG. 3. With reference to FIG. 3,
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.
[0262] With respect to FIG. 3, 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.
[0263] In one aspect, the system can comprise a database containing
features of biomarkers characteristic of TB. 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.
[0264] In one aspect, the system further comprises one or more
devices for providing input data to the one or more processors.
[0265] The system further comprises a memory for storing a data set
of ranked data elements.
[0266] 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.
[0267] 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.
[0268] 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.
[0269] 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.
[0270] 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.
[0271] 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.
[0272] 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.
[0273] The methods and apparatus for analyzing TB 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.
[0274] The TB 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 TB 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 TB status and/or
diagnosis. Evaluating TB status may comprise generating or
collecting any other information, including additional biomedical
information, regarding the condition of the individual relative to
TB, identifying whether further tests may be desirable, or
otherwise evaluating the health status of the individual.
[0275] Referring now to FIG. 4, an example of a method of utilizing
a computer in accordance with principles of a disclosed embodiment
can be seen. In FIG. 4, a flowchart 3000 is shown. In block 3004,
biomarker information can be retrieved for an individual. The
biomarker information can be retrieved from a computer database,
for example, after testing of the individual's biological sample is
performed. The biomarker information can comprise biomarker values
that each correspond to one of at least N biomarkers selected from
a group consisting of the biomarkers provided in the biomarkers
provided in Tables 1, 2, 4, 5, and 8 to 12. In block 3008, a
computer can be utilized to classify each of the biomarker values.
In block 3012, an evaluation can be made regarding TB status based
upon a plurality of classifications. The indication can be output
to a display or other indicating device so that it is viewable by a
person. Thus, for example, it can be displayed on a display screen
of a computer or other output device.
[0276] Referring now to FIG. 5, an alternative method of utilizing
a computer in accordance with another embodiment can be illustrated
via flowchart 3200. In block 3204, a computer can be utilized to
retrieve biomarker information for an individual. The biomarker
information comprises a biomarker value corresponding to a
biomarker selected from the group of biomarkers provided in Tables
1, 2, 4, 5, and 8 to 12. In block 3208, a classification of the
biomarker value can be performed with the computer. In block 3212,
an indication can be made as to the TB status of the individual
based upon the classification. The indication can be output to a
display or other indicating device so that it is viewable by a
person. Thus, for example, it can be displayed on a display screen
of a computer or other output device.
[0277] 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.
[0278] 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.
[0279] In one aspect, a computer program product is provided for
evaluating TB status of an individual. 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 values that each correspond to one of at least N
biomarkers in the biological sample selected from the group of
biomarkers provided in Tables 1, 2, 4, 5, and 8 to 12; and code
that executes a classification method that indicates TB status of
the individual as a function of the biomarker values.
[0280] In still another aspect, a computer program product is
provided for evaluating TB status. 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 a
biomarker value corresponding to a biomarker in the biological
sample selected from the group of biomarkers provided in Tables 1,
2, 4, 5, and 8 to 12; and code that executes a classification
method that indicates a TB disease status of the individual as a
function of the biomarker value.
[0281] 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, PLAs,
or ASICs.
[0282] 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.
[0283] With reference to Example 2, it can be seen that a number of
proteins were identified that were differentially present between
treatment slow-responders and responders at baseline or after 8
weeks of TB treatment (Tables 1, 2, 4, 5, and 8 to 12). Certain
themes revolving around inflammation, immunity, coagulation, tissue
remodeling, liquefaction, fibrinolysis and tissue repair emerge.
Not unexpectedly, many proteins involved in innate and adaptive
immunity to which antimicrobial function has been attributed are
differentially expressed (including but not limited to gp-130, TNF
pathway molecules, complement components, catalase, IgG,
IFN-.lamda., PSME, PSD7 etc.). At baseline, the strongest marker
predicting treatment response is PSME, an IFN-.gamma.-inducible
component of the immunoproteosome. The levels of this protein are
known to be increased under the conditions of intensified immune
response and are important for efficient antigen processing (Kohda
et al. (1998) Journal of immunology 160:4923-4935).
[0284] It is also noted that many of the structures, materials, and
acts recited herein can be recited as means for performing a
function or step for performing a function. Therefore, it should be
understood that such language is entitled to cover all such
structures, materials, or acts disclosed within this specification
and their equivalents, including the matter incorporated by
reference.
EXAMPLES
[0285] The following examples are provided for illustrative
purposes only and are not intended to limit the scope of the
application as defined by the appended claims. All examples
described herein were carried out using standard techniques, which
are well known and routine to those of skill in the art. Routine
molecular biology techniques described in the following examples
can be carried out as described in standard laboratory manuals,
such as Sambrook et al., Molecular Cloning: A Laboratory Manual,
3rd. ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor,
N.Y., (2001).
Example 1
Arrays and Methods for Generating Results
[0286] The SomaLogic aptamer based proteomics discovery platform
used in the studies presented herein quantitatively measures
approximately 1030 proteins simultaneously using serum, plasma, CSF
tissue lysate or blood from small sample volume that reflects the
high sensitivity of the assay (hybridization to an array and
fluorescence scanning) (Gold et al. (2010) PloS One 5:e15004). Over
all 1030 proteins the median lower limit of quantitation is 0.3 pM,
with a dynamic range of >5 logs, and a median coefficient of
variation (% CV) of 5% (Ostroff et al. (2010) Journal of proteomics
73:649-666).
[0287] The SomaLogic proteomics discovery platform is a multiplex
proteomics assay, which measures proteins by transforming the
quantity of a specific protein into an equivalent, or proportional,
quantity of its cognate aptamer, which is captured in the assay and
quantified by hybridization to a custom Agilent microarray (any DNA
chip could be used). A full description of the processes and
performance of slow off-rate aptamer reagents and the SomaLogic
multiplex proteomics assay is described in Gold et al. (2010) PLoS
One 5:e15004 (see FIG. 2).
Example 2
Study Design and Data Analysis
Study Population
[0288] Participants were enrolled from United States Centers for
Disease Control Tuberculosis (TBTC) Study 29, a prospective,
multicenter, open-label Phase 2B clinical trial (clinicaltrials.gov
NCT00694629) comparing efficacy and safety of standard TB therapy
comprised of rifampin, isoniazid, pyrazinamide and ethambutol with
rifapentine replacing rifampin (Dorman et al. (2012) J Infect Dis
206:1030-40). The 39 participants included in this pilot project
were all from the TBTC site based in Kampala, Uganda, were sputum
smear positive and HIV-uninfected. Patient samples to be included
in this study were selected to be free of significant
co-morbidities reported at enrollment (December 2008 to July 2009),
and had reasonably normal renal, hepatic and hematologic function.
Ages of the 39 study participants ranged from 19 to 53 years, 28
were males, the average body mass index (BMI) was 19.3 kg/m.sup.2,
and 22 (56%) had cavitary disease, three of which had bilateral
cavities.
TABLE-US-00001 Participant Characteristics Value (range) # patients
39 Rifampin treated (%) 14 (36) Age years, median 28.5 (19-53) Male
(%) 28 (72) BMI median, kg/m.sup.2 19 (15.2-26.7) Cavitary lesions
(%) 56 Smoker (%) 20 Alcohol use (%) 2
[0289] Participants completed between 6 and 24 months of anti-TB
treatment. Follow-up through the end of treatment did not reveal
any treatment failures. One participant had INH and RIF resistant
tuberculosis; one participant had mono-drug resistance to INH and
one had dual resistance (to streptomycin and rifampin), all were
detected after the participants completed intensive phase
treatment. Four participants received between 3-5 days of 4 drug,
standard chemotherapy prior to enrollment.
[0290] Serum was collected, processed and stored at baseline (time
of enrollment), and after 8 weeks (40 doses) of intensive phase
therapy. Efficacy of the regimens was assessed through
determination of sputum culture status on both Lowenstein-Jensen
(LJ) solid media and BACTEC Mycobacterial Growth Indicator Tube
(MGIT, Becton Dickinson and Co., Franklin Lakes, N.J.) liquid media
with the MGIT 960 system. For this analysis, patients who were
culture negative at completion of 8 weeks of treatment on both
media types were classified as "responders", whereas those patients
who remained culture positive on either (or both) of the culture
media, were deemed "slow responders". IRB approval for TBTC Study
29 was obtained from all participating institutions and from the
Centers for Disease Control and Prevention (CDC). Additionally,
this pilot project was also approved by the Committee on Human
Research of the University of California, San Francisco
(H45279-34102-02A). Of the 39 participants, 25 patients had been
randomly assigned to the rifapentine arm and 14 to the rifampin
arm. Four patients received between 3-5 days of therapy prior to
enrollment. By design half of the subjects selected for this
analysis had responded to the treatment after eight weeks (8 week),
as defined by culture status on LJ and MGIT liquid media. All 39
pairs of baseline and end of intensive phase treatment (8-week)
serum samples, respectively, were included in the proteomic assay
to measure 1030 proteins. Clinical, radiographic and microbiologic
data were not received until after all proteomic measurements were
completed and results had been submitted to the CDC. Additional
information including total duration of treatment and end of
treatment cure status was retrieved from patient charts by GM in
Kampala, Uganda.
Proteomic Methods
[0291] Aptamers were selected in vitro by the SELEX process, which
consists of multiple rounds of selection, partitioning, and
amplification (Brody and Gold (2000) Journal of biotechnology
74:5-13; Gold, L. (1995) The Journal of biological chemistry
270:13581-13584). As detailed above, the success of SELEX and the
affinity of the aptamers obtained has been greatly improved through
the use of modified nucleotides to expand the chemistry of DNA for
in vitro selection (Vaught et al. (2010) Journal of the American
Chemical Society 132:4141-4151). To date, aptamers with excellent
affinities (sub-nanomolar K.sub.d's) to >1,000 human serum
proteins have been generated and are used in the highly multiplexed
aptamer based assay described herein. Serum was tested at three
different concentrations (5%, 0.3%, 0.01%) to obtain accurate
measurements within the dynamic range of the assay for low-,
medium-, and high-abundant proteins, respectively. The integrity of
serum samples provided for testing was monitored for known sample
handling artifacts (Ostroff et al. (2010) Journal of proteomics
73:649-666). The aptamer based proteomics assay is described in
detail in Gold et al. ((2010) PloS one 5:e15004). In brief, the
assay consists of equilibrium binding of fluorophore-tagged
aptamers and proteins in plasma or serum in solution and automated
partitioning steps to capture only the aptamers that are in
complexes with their cognate proteins. In essence, the assay
transforms the measurement of proteins into the measurement of the
corresponding aptamers (DNA), via hybridization to an antisense
probe array using a hybridization gasket slide with eight
microarrays per slide (Agilent Technologies). The liquid handling
steps of the assay (protein binding) were performed by a Biomek
robot, and the fluorescent signal generated in the hybridization
step is captured using a fluorescence slide scanner (Agilent
Technologies). Protein concentrations are reported in relative
fluorescence units (RFU).
Statistical Analysis
[0292] Matlab.TM. and the R environment for statistical computing
were used for statistical analysis. Fisher's exact test was used to
compare the proportions of radiographic findings in responders and
non-responders. Linear regression analyses were used to assess the
association between protein levels measured in log RFU and culture
conversion times. The non-parametric Kolmogorov-Smirnov (KS) test
was applied for unpaired comparisons of the protein distributions
in rapid and slow-responders. The KS statistic is an unsigned
quantity, though a "signed" value is reported to convey the
directionality in the differential expression, that is, positive or
negative KS distances for increased or decreased protein levels,
respectively, in a given comparison of interest.
[0293] The Wilcoxon rank sum test was used to identify proteins
with paired (within-subject) differential response between baseline
and week 8 in the responders and slower responders respectively.
Multiple comparison corrections were performed using the false
discovery rate (FDR) methodology (Storey (2002) J Royal Stat Soc,
Series B 64:479-498). For each statistic both p-values and the
associated FDR corrected "q-values" computed with the R package
q-value (Dabney et al. qvalue: Q-value estimation for false
discovery rate control. R package version 1.26.0 2011 reported. The
q-value is analogous to a p-value--it measures the statistical
significance of a particular observation, but with respect to the
expected number of false positives rather than the probability of a
false positive. The database for annotation, visualization and
integrated discovery (DAVID) analysis was used for functional
clustering and annotation (http://david.abcc.ncifcrf.gov/) (Huang
et al. (2009) Nucleic acids research 37:1-13; Huang et al. (2009)
Nature protocols 4:44-57; Jiao et al. (2012) Bioinformatics
28:1805-1806).
[0294] A regularized logistic regression model was used to select
variables that distinguish responders from slow-responders.
Stability selection using the randomized lasso (Meinshausen and
Buhlmann (2006) The Annals of Statistics 34:1436-1462) was used to
identify proteins with high selection probabilities over a wide
range of regularization parameters. Selection probabilities were
computed from 500 random partitions of the observations using the
randomized lasso. One hundred random permutations of the
"responder" and "slow-responder" labels were used to estimate the
lower bound on the false discovery rate for stability selection. A
final logistic regression model was re-fit (without regularization)
to the markers with highest selection probability, and the
resulting sensitivity and specificity when classifying subjects by
treatment response was estimated using stratified
cross-validation.
Sample Handling
[0295] SomaLogic Inc., (Boulder, Colo.) performed all proteomic
assessments and was blinded to the clinical characteristics of
participating patients from whom sera was obtained for this study.
Affinity-based proteomic technology successfully measured 1,030
proteins from serum per subject, per time point. Samples were
analyzed in a single assay run and data validation was performed
using internal assay controls without a priori knowledge of time
point, clinical or microbiological details. A small systematic
difference (4%) in the overall protein concentrations between the
baseline and 8 week sample sets was removed during normalization.
Three samples had elevated hemoglobin levels and correspondingly
low haptoglobin levels when compared both to other subjects and
internal assay calibrators suggesting some degree of hemolysis. No
other evidence of sample processing errors (Ostroff et al. (2010)
Journal of Proteomics 73: 649-666) was observed so all samples were
considered fit for inclusion in the subsequent data analysis.
Nonspecific Markers of Active TB
[0296] Serum protein concentrations in TB patients at baseline were
compared to those measured in the same patients after 8 weeks of
therapy. The acute phase reactants C-reactive protein (CRP) and
serum amyloid A protein (SAA) decreased from baseline to week 8 in
all but one subject (FIG. 6). Serum albumin increased between
baseline and week 8 in all but one subject. Other known important
acute phase reactants including haptoglobin, alpha-1 antitrypsin
(AAT) and serum amyloid A protein declined from baseline to week 8
consistent with a reduction in the disease burden.
Correlations with Severity of Disease
[0297] Microbiological and radiographic parameters are used to
assess the severity of TB disease. At baseline, total cavitary
volume and time-to-detection in MGIT culture are markers of
severity of disease and bacillary burden in the sputum,
respectively. Among the data available for the patients involved in
this study, CXR class (as defined by presence cavitation and size
on baseline CXR, class 1=no cavitation; class 2=cavitation present,
<4 cm in size; class 3=cavitation present, >4 cm in size)
showed the strongest association with other parameters such as body
mass index (BMI, FIG. 7A) and time-to-detection in MGIT culture
(TTD, FIG. 7B), and as expected, both were lower in the 13 patients
with large and/or bilateral cavities (CXR class 3) compared to the
17 patients without cavitary disease (CXR class 1), but there was
substantial overlap between groups. Plasminogen (FIG. 7C), which
was lower in CXR class 3 compared to class 1 and thrombospondin-2
(TSP-2), which was higher in CXR class 3 compared to class 1 (FIG.
7D) best discriminate CXR class 1 from class 3 patients, though
these effects had an 18% false discovery rate.
[0298] The top markers at baseline distinguishing the thirteen
patients with more severe disease (score>0.60) and the thirteen
patients with less severe disease (score<0.40) were CRP, SAA,
and NPS-PLA2 with roughly two-fold increased levels of the
median.
[0299] Regression analysis was used to identify proteins that
changed with increasing disease severity measured. Using baseline
(log) RFU as the response in a linear model and a 5% false
discovery rate, Heparin cofactor 2, platelet factor-4 (PF-4),
G-protein coupled receptor associated sorting protein-2 (GASP-2),
and .alpha.2-antiplasmin had baseline concentrations that were
correlated with our disease severity score (FIGS. 8A-D). Low levels
of heparin cofactor 2 and GASP2 were both associated with more
severe disease. Interestingly, .alpha.2-antiplasmin levels at 8
weeks also decreased with increasing severity scores (FIG. 8E). In
contrast, fibrinogen levels measured at 8 weeks were higher in
patients with severe disease (FIG. 8F). Regression analysis using
the (log 2) ratio of week 8 to baseline concentration and a 5%
false discovery rate uncovered additional proteins whose rate of
change was associated with disease severity. The top markers were
DKK-1, adiponectin and serum amyloid P component (SAP) (FIGS.
3G-I). DKK-1 levels decreased from baseline to 8 weeks in patients
with mild disease, but remained high or even increased in patients
with more severe disease. Adiponectin increased in the majority of
patients with mild disease but remained unchanged in those with the
highest disease severity score (data not shown).
Paired Analysis of Serum Proteins at Baseline Versus Week 8 of TB
Therapy
[0300] At a 0.01% false discovery rate (q<0.0001), 239 of the
1,030 proteins measured were identified as differentially expressed
between baseline and 8 weeks of treatment using the Wilcoxon Signed
Rank test. Sixteen proteins shifted in the same direction from
baseline to week 8 in all 39 patients, and many other proteins
showed a consistent shift from baseline to week 8 in at least three
quarters (30 of 39) of the patients. The intra-subject shifts
indicate the number of patients showing up or down-regulation. The
top intra-subject markers (q<10.sup.-6) are shown in Table 1.
The complete list of 239 proteins with a 0.01% false discovery rate
(q<10.sup.-4) is shown in Table 2. Also shown are the raw
p-value and resulting FDR corrected "q-value" reflecting the
multiple testing correction.
[0301] Since the signed rank test is based on the ranks of the
measurements, it is difficult to appreciate the magnitude of the
changes for some of these proteins. In FIG. 9, the top ranked
proteins using the median fold-change between baseline and week 8
are shown. SAA (Serum Amyloid A Protein), NPS-PLA2 (Phospholipase
A2), and CRP (C-reactive Protein) showed median within-subject
decreases of 6.8-fold, 5.9-fold, and 4.7-fold, respectively, from
baseline to week 8. Sixteen additional proteins dropped at least
1.5-fold (median fold change), and a few markers increased between
baseline and week 8, including TSP-4, antithrombin III, mannose
receptor 2 (MRC-2), fetuin-like protein (FETUB) and plasma serine
protease inhibitor (PCI).
[0302] The paired data was then analyzed using the database for
annotation, visualization and integrated discovery (DAVID). The 239
proteins shown in Table 2 were input into DAVID, which contains
structure function information for whole proteins and regions
within proteins. The DAVID analysis clustered the 239 proteins
based on functional similarity. Those clusters with an enrichment
score of >1.3 with a Bonferroni corrected p-value of <0.05
were considered significant. The clusters (which are annotated with
gene ontology codes) indicated that proteins in pathways involved
in response to wounding, inflammatory response, defense response
and coagulation were prevalent among the 239 proteins identified. A
full list of annotated clusters is provided below in Table 3.
Unpaired Analysis
[0303] For prognostic applications, the objective is to be able to
predict treatment response using only a single sample. With an
unpaired analysis to identify proteins with baseline measurement
distributions that differed from the week 8 distributions was
performed. At a 0.1% false discovery rate (q<0.001) the KS test
identified 116 of 1,030 proteins as differentially expressed
between baseline and week 8. KS distances for all proteins are set
forth in FIG. 10. The top 60 markers (q<10) are listed in Table
4 and all 116 markers (q<10.sup.-3) are listed in Table 5. A
total of 55/116 features were up-regulated and 61/116 were
down-regulated over time. The most significant changes were noted
for TSP-4, fibroblast activation protein .alpha. (SEPR), MRC-2,
antithrombin III, PCI, LPS-binding protein (LBP),
.alpha.2-HS-glycoprotein, and phospholipase A2 (NPS-PLA2). The
empirical cumulative distribution functions for the baseline and
week-8 protein measurements (along with an indication of the KS
distance between the distributions) for the top eight markers are
shown in FIG. 11, and the plots for 51 additional proteins are
available in FIG. 12. A scatter plot using the top two markers that
were differentially expressed between baseline and 8 week samples
can be seen in FIG. 13. Using measurements of one or more of these
proteins it is possible to classify each sample within this data
set as belonging to either the baseline group (TB) or the week 8
group (treated TB) with sensitivity and specificity exceeding
90%.
Example 3
Calculation of Disease Severity Score
[0304] After analysis of serum and receipt of the clinical data, a
custom disease severity score was calculated based on chest
radiograph (CXR) extent and cavitary disease (absent, diameter of
all cavities summed less than or greater than 4 cm) (Falk et al.
(1969) Classification of pulmonary tuberculosis, p. 68-76. In Falk
et al. (ed.), Diagnostic standards and classification of
tuberculosis, vol. 12, National Tuberculosis and Respiratory
Disease Association, New York, N.Y.); sputum smear grade (Kent and
Kubica GP (1985) Public Health Mycobacteriology: A Guide for the
Level III Laboratory. Atlanta, Ga.: Centers for Disease Control);
days to detection of a positive culture in liquid media after
inoculation and body mass index [weight in kg/(height in m)].sup.2.
A clinical spectrum of severity was determined to see if further
refinement of disease severity could elucidate markers associated
with more severe disease. Given the clinical importance of BMI
(Zechariah et al. (2002) Transactions of the Royal Society of
Tropical Medicine and Hygiene 96:291-294) and bilateral cavitation
(Pakasi et al. (2009) European journal of clinical nutrition
63:1130-1135) these factors were weighted by a factor of two.
[0305] The following eight parameters were combined into a single
disease severity score:
TABLE-US-00002 CXRCLASS Chest X-ray cavitation classification; 1-3,
higher more severe; absent (1), <4 cm (2), >4 cm (3) CXREXTNT
Chest X-ray extent of disease; A to C, converted to 1-3: (limited
(A), moderate (B), extensive (C)) dtd_base Days to detection at
baseline; the lower the worse (quicker detection if microbial
burden is high) smearb 1-4, the higher the worse (bacillus count in
stained sputum sample via microscopy) bmi Body mass index at
enrollment; lower more severe (weight loss is a known effect of TB
disease) anycav 0 or 1 (1 = any cavitation reported at enrollment)
bilatcav 0 or 1 (1 = bilateral cavities reported at enrollment)
bilatabn 0 or 1 (1 = bilateral abnormalities - adenopathy, pleural
disease, infiltrates or cavities)
The parameters were normalized and weighed, and combined into a
total score, which was then scaled to values from 0-1.
Custom Score=(Total score+10)/16 (FIG. 14, Table 6).
Correlations with Severity of Disease
[0306] Microbiological and radiographic parameters were used to
assess the degree or severity of TB infection. Total cavitary
volume and time-to-detection in MGIT culture are markers of
severity of disease and bacillary burden in the sputum,
respectively. From the data available for the patients involved in
the study CXR class showed the strongest association with other
parameters such as body mass index (BMI, FIG. 14A) and
time-to-detection in MGIT culture (TTD, FIG. 14B). As expected,
both were lower in the 13 patients with large and/or bilateral
cavities (CXR class 3) compared to the 17 patients without cavitary
disease (CXR class 1). Plasminogen (FIG. 14C), which was lower in
CXR class 3 compared to class 1, and thromospondin-2 (TSP-2), which
was higher in CXR class 3 compared to class 1 (FIG. 14D) best
discriminate CXR class 1 from class 3 patients, though these
effects had an 18% false discovery rate so on average roughly 1 in
6 such effects are expected to be "false" discoveries.
[0307] Toward a more comprehensive association of serum protein
levels and disease severity, eight clinical parameters (CXR class,
CXR extent, time-to-detection, smear status, presence of any
cavity, presence of bilateral cavities or abnormalities, BMI) were
combined into a continuous disease severity score as described
above. The top markers at baseline distinguishing the thirteen
patients with more severe disease (score>0.60) and the thirteen
patients with less severe disease (score<0.40) were CRP, SAA,
and PLA2 with roughly two-fold increased levels of the median.
[0308] Regression analysis was used to identify proteins that
increased/decreased with increasing severity as measured by our
composite score. Using baseline (log) RFU as the response in a
linear model and a 5% false discovery rate, Heparin cofactor 2,
platelet factor-4 (PF-4), G-protein coupled receptor associated
sorting protein-2 (GASP-2), and .alpha.2-antiplasmin had baseline
concentrations that were correlated with our disease severity score
(FIGS. 15A-D). Low levels of heparin cofactor 2 and GASP2 were both
associated with more severe disease. Interestingly,
.alpha.2-antiplasmin levels at 8 weeks also decreased with
increasing severity scores (FIG. 15 E). In contrast, fibrinogen
levels measured at 8 weeks were higher in patients with severe
disease (FIG. 15 F). Regression analysis using the (log 2) ratio of
week 8 to baseline concentration and a 5% false discovery rate
uncovered additional proteins whose rate of change was associated
with disease severity. The top markers were DKK-1, adiponectin and
serum amyloid P component (SAP) (FIGS. 15 G-I). DKK-1 levels
decreased from baseline to 8 weeks in patients with mild disease,
but remained high or even increased in patients with more severe
disease. Adiponectin increased in the majority of patients with
mild disease, but remained unchanged in those with the highest
disease severity score.
Example 4
Proteins Predictive of Treatment Response
[0309] Basic Demographic Parameters and their Correlation with
Treatment Response
[0310] Age was an important predictor of TB treatment response
(FIG. 15A). Responders were younger than slow-responders (median
age 25.7 yrs vs. 31.8 yrs, p=0.01). Though responders had slightly
higher BMI than slow-responders (BMI of 19.7 vs 18.8 (FIG. 15B)),
the difference was not significant (p>0.1), nor was the
difference in quantitative culture analysis at baseline, determined
as number of days to detection in liquid culture (p>0.1) (FIG.
15C). Among radiographic parameters used to assess the degree or
severity of TB infection, chest x-ray (CXR) extent differed between
responders and slow-responders (p=0.02), with twice as many
responders having CXR extent B (moderate) and twice as many
slow-responders having CXR extent C (extensive), as shown in FIG.
15D.
Protein Markers at Baseline Based on Treatment Response
[0311] In one embodiment, proteins that distinguish responders from
slow-responders at baseline were identified using the
Kolmogorov-Smirnov (KS) test. FIG. 16A and Table 8 show the KS
distance for the comparison between responders and slow-responders
in each of the 1030 proteins. FIG. 16B shows plots of the empirical
cumulative distribution functions of the relative fluorescence
units (RFU) for the top 20 proteins with the largest KS distances
between responders (R) and slow-responders (S). Table 9 (top
section) shows the top ten proteins with the largest KS distances
for the comparison between responders and slow-responders among all
1030 proteins. The top five proteins have a 29% false discovery
rate indicating two of these five may be expected to false
discoveries. Responders had higher levels than slow-responders of
proteasome activator complex subunit 1 (PSME1), heat shock 70 kDa
cognate protein 8 (HSP 70), .alpha.2-antiplasmin, interferon lambda
2 (IFN-.lamda.), and matrix metalloproteinase 12 (MMP-12). In turn,
slow responders had higher levels of interleukin 11 receptor
antagonist (IL-11 R.alpha.), Galectin-8, matrix metalloproteinase
13 (MMP-13), iC3b, and a proliferation inducing ligand of the TNF
ligand family (APRIL) as compared to rapid responders.
Protein Markers at 8 Weeks Based on Treatment Response
[0312] The KS distances between responders and slow-responders were
determined for all proteins measured in the 8-week samples (FIG.
17A), and cumulative distribution function plots (FIG. 17B) are
shown for the top 20 proteins with the largest KS distances between
responders (R) and slow-responders (S). Table 9 (midsection) shows
the proteins that exhibited the largest differential expression
between responders and slow-responders at week 8 based on the KS
distances. Coagulation Factor V showed the most significant
difference and was elevated in responders compared to
slow-responders. Xaa-Pro aminopeptidase 1 (XPNPEP1), soluble gp130,
transforming growth factor-beta-induced protein ig-h3 (BGH3),
metalloproteinase inhibitor 2 (TIMP-2), extracellular matrix
protein 1 (ECM-1), vasoactive intestinal peptide (VIP), interferon
alpha-2 (IFN-.alpha.A), IL-11 were also elevated in responders
compared to slow-responders. Of the other proteins listed in this
section of Table 2, only tumor necrosis factor ligand superfamily
member 13 (APRIL) was lower in responders than slow-responders.
[0313] A "paired" analysis of responders and slow-responders was
conducted using the log-ratio of within-subject baseline to 8-week
response as a "fold change" metric (FIG. 18). This analysis targets
proteins that exhibit differential change between the two time
points in the responders (R) compared to slow-responders (S). The
ranking of proteins by the KS distance associated with
distributions of log-ratios in the two groups is illustrated in
Table 9 (bottom section). The top two features were EphA1 (Ephrin
type-A receptor 1) and gp130; both previously identified in the
unpaired analysis. Also appearing are TIMP-1, MMP-8
(metalloproteinase-8, also known as neutrophil collagenase), AMPM2
(methionine aminopeptidase 2), and MMP-12 (macrophage
metalloelastase).
[0314] Proteins were subsequently ranked using the Wilcoxon rank
sum test to identify those with different median fold changes in
the responders and slow-responders. The ten proteins with the most
differential change are listed in Table 10. For the first nine of
these proteins, the fold-change in signal from baseline to week 8
was larger in responders than in slow responders. These features
were nectin-like protein 1, EphA1 (Ephrin type-A receptor 1),
gp130, CATZ, CNDP1, TGF-b RIII, MRC2, ADAMS, and CDON. IL-2
sR.alpha. was the only protein that decreased in both groups, but
decreased to a greater extent in responders compared to
slow-responders.
[0315] Table 11 sets forth some additional markers that changed to
a greater extent between week-8 responders and slow-responders than
between week 8 slow-responders and all baseline samples. The levels
of these proteins track closely together in baseline and week-8
slow-responders, but are clearly different in week-8
responders.
Predicting Treatment Response with a Combination of Markers
[0316] Several strategies to identify signatures of treatment
response, specifically to use serum protein measurements for the
prediction of the culture status at 8 weeks were explored. In a
first approach, at 8 weeks the top markers distinguishing
responders vs. slow-responders were selected based on large KS
distances (.gtoreq.0.5) after excluding markers with low signal
strength (<500 RFU). A five-feature signature including
coagulation factor V, XPNPEP1, gp130, TIMP-2 and ECM1 were combined
in a naive Bayes classifier to "predict" treatment response using
8-week data. The procedure is outlined schematically in FIGS. 1A
and B. In one embodiment, the biological sample is optionally
diluted and run in a multiplexed aptamer assay. This five feature
classifier revealed an ROC curve with an AUC value of 0.89 and
confidence interval of 0.75-0.98 (FIG. 19A). The corresponding
classification of the training samples is depicted in FIG. 19B. The
model was fit to log RFU values using robust parameter estimation.
No covariate adjustment was performed, though subject age and BMI
are both strong predictors of treatment response. This pilot study
was too small to withhold an independent test set to assess the
classifier performance so 5-fold stratified cross-validation was
used to generate performance estimates. Sensitivity and specificity
were 0.85+/-0.1 and 0.95+/-0.05 with an AUC of 0.88+/-0.07.
[0317] In a second approach, markers that changed to a greater
extent between week-8 responders and slow-responders than between
week 8 slow-responders and all baseline samples were identified.
FIG. 20A shows a scatter plot of the KS distances for the
comparison of baseline samples to week 8 slow-responders versus the
KS distances for the comparison of week-8 responders and
slow-responders. Markers associated with treatment response
congregate in the lower right area since there is greater
differential expression (larger KS distance) at week 8 than between
week 8 slow-responders and baseline. Many of the proteins
identified above appear in the lower right corner, in addition, BGN
(matrix proteoglycan), YES and LYNB (tyrosine kinases) and IL-7 are
located in that area. FIG. 20B shows the empirical cumulative
distribution functions (CDFs) for four representative markers (BGN,
IL-7, coagulation Factor V, and LYNB) illustrating that the levels
of these proteins track closely together in baseline and week-8
slow-responders, but are clearly different in week-8
responders.
[0318] In yet another embodiment, stability selection was used to
identify a subset of covariates from the set of 1030 protein
measurements combined with age, gender, BMI, smear status, CXR
Class, CXR extent and time to detection. The most stable predictive
markers at baseline were IL-11 R.alpha., .alpha.2-Antiplasmin,
PSME1, SAA, and subject age (FIG. 21A). Three of the proteins are
among those with large KS distances between responders and
slow-responders as mentioned above, and the associated q-values
suggested on average at least one of these was falsely discovered.
This assessment is consistent with the average number of false
discoveries expected by stability selection at different selection
probabilities (FIG. 21A). At eight weeks, the most predictive
stable markers for treatment response were ECM1, YES, IGFBP1, CATZ,
Coagulation Factor V, and SAA (FIG. 21B).
[0319] As an example of a 5-marker signature to "predict" treatment
response, baseline measurements of IL-11 R.alpha.,
.alpha.2-Antiplasmin, PSME1, and SAA were combined with subject age
in a logistic regression model. The corresponding sample
classification (FIG. 21C) and resulting ROC curve (FIG. 21D) show
the performance of this model on the training data. Since there
were too few samples in this study to withhold an independent "test
set", a 5-fold stratified cross-validation was used to estimate
model performance. Under cross-validation, the estimated AUC was
0.9.+-.0.05 with sensitivity 0.9.+-.0.06 and specificity
0.95.+-.0.05. Similar performance was observed from a naive Bayes
model constructed using baseline measurements of the first 5
proteins in Table 9 with the best KS distances (PSME1, IL-11
R.alpha., HSP70, Galectin-8, and .alpha.2-Antiplasmin).
Markers Associated with Time-to-Culture-Conversion
[0320] Toward the identification of surrogate markers for treatment
response, meta data and serum protein data were analyzed with
regard to time-to-culture-conversion (TTCC), defined as the first
of at least two consecutive time-points where negative cultures
(solid and liquid) were obtained. Among the 39 patients there were
six responder groups, with TTCC of 4 wk (n=3), 6 wk (n=4), 8 wk
(n=12), 12 wk (n=10), 16 wk (n=9), and 20 wk (n=1). TTCC did not
correlate with clinical data obtained at baseline, such as
smear/culture results, chest X-ray classifications, presence of
cavities, or BMI (data not shown). Univariate regression analysis
of baseline and week-8 protein measurements (log RFU) on TTCC was
performed and sorted by statistical significance (Table 12). At
baseline, lower levels of ERP29, peroxiredoxin-5, HSP-70, and
.alpha.2-antiplasmin were associated with longer TTCC (FIG. 22A).
At 8 weeks, NKG2D (KLRK1) and CDK8 showed increased levels in
samples from patients with longer TTCC, while XPNPEP1, and BGH3
(TGFBI) levels were lower (FIG. 22B). Comparison of the medians of
all 8-week measurements within the different responder groups
showed a large number of proteins associated with neutrophil
function (FIG. 22C). BPI (bactericidal permeability-increasing
factor) and IL-1 R4 were higher in fast-responders compared to
slow-responders, and cathepsin D was lower in fast-responders
compared to slow-responders. The largest differences between the
responder groups were for SAA measured at 8 weeks, and a more
detailed regression analysis using baseline data and week-8 data
showed that signals dropped from baseline to week 8 by almost
10-fold, but a much sharper decrease was observed in samples from
the fast-responders (FIG. 22D)
TABLE-US-00003 TABLE 1 Differential protein expression between
baseline and week 8 in paired samples from n = 39 patients treated
for pulmonary TB Intrasubject Shift.sup.b (baseline to week 8)
Swiss Up Down p- q- Rank Target Prot.sup.a (n) (n) value value 1)
TIMP-2 P16035 39 5.3e-08 5.48e-07 2) GFR.alpha.-2 O00451 39 5.3e-08
5.48e-07 3) MRC2 Q9UBG0 39 5.3e-08 5.48e-07 4) Haptoglobin, P00738
39 5.3e-08 5.48e-07 Mixed Type 5) LBP P18428 39 5.3e-08 5.48e-07 6)
amyloid precursor P05067 39 5.3e-08 5.48e-07 protein 7) BGH3 Q15582
39 5.3e-08 5.48e-07 8) TSP4 P35443 39 5.3e-08 5.48e-07 9) FETUB
Q9UGM5 39 5.3e-08 5.48e-07 10) PCI P05154 39 5.3e-08 5.48e-07 11)
Kallistatin P29622 39 5.3e-08 5.48e-07 12) .alpha.2-HS- P02765 39
5.3e-08 5.48e-07 Glycoprotein 13) CHL1 O00533 39 5.3e-08 5.48e-07
14) CDON Q4KMG0 39 5.3e-08 5.48e-07 15) D-dimer P02671, 39 5.3e-08
5.48e-07 P02675, P02679 16) MMP-1 P03956 39 5.3e-08 5.48e-07 17)
contactin-1 Q12860 38 5.7e-08 5.48e-07 18) CD 109 Q6YHK3 38 5.7e-08
5.48e-07 19) IGFBP-7 Q16270 38 5.7e-08 5.48e-07 20) Sphingosine
Q9NYA1 38 5.7e-08 5.48e-07 kinase 1 21) CRP P02741 38 5.7e-08
5.48e-07 22) SEPR Q12884 38 5.7e-08 5.48e-07 23) TIMP-3 P35625 38
6.1e-08 5.48e-07 24) Lipocalin 2 P80188 38 6.1e-08 5.48e-07 25)
NAP-2 P02775 38 6.1e-08 5.48e-07 26) Nectin-like Q9BY67 38 6.1e-08
5.48e-07 protein 2 27) Proteinase-3 P24158 38 6.1e-08 5.48e-07 28)
PDGF-BB P01127 38 6.1e-08 5.48e-07 29) MMP-2 P08253 38 6.1e-08
5.48e-07 30) TIMP-1 P01033 37 6.6e-08 5.48e-07 31) ROR1 Q01973 37
6.6e-08 5.48e-07 32) IGFBP-6 P24592 37 6.6e-08 5.48e-07 33) PAI-1
P05121 38 6.6e-08 5.48e-07 34) Protein C P04070 37 6.6e-08 5.48e-07
35) C9 P02748 38 6.6e-08 5.48e-07 36) GDF-9 O60383 37 6.6e-08
5.48e-07 37) Carbonic P23280 37 6.6e-08 5.48e-07 anhydrase 6 38)
RBP P02753 37 6.6e-08 5.48e-07 39) Albumin P02768 38 6.6e-08
5.48e-07 40) Fibronectin P02751 37 6.6e-08 5.48e-07 41)
Antithrombin III P01008 38 7.2e-08 5.48e-07 42) a1-Antitrypsin
P01009 37 7.2e-08 5.48e-07 43) HRG P04196 38 7.2e-08 5.48e-07 44)
Angiopoietin-1 Q15389 38 7.8e-08 5.48e-07 45) ATS13 Q76LX8 37
7.8e-08 5.48e-07 46) Coagulation P08709 37 7.8e-08 5.48e-07 Factor
VII 47) Afamin P43652 38 7.8e-08 5.48e-07 48) TrkB Q16620 37
7.8e-08 5.48e-07 49) GOT1 P17174 38 7.8e-08 5.48e-07 50) Azurocidin
P20160 38 8.4e-08 5.80e-07 51) NCAM-L1 P32004 38 9.1e-08 6.03e-07
52) PLXC1 O60486 36 9.1e-08 6.03e-07 53) I-TAC O14625 38 1.1e-07
6.41e-07 54) CYTF O76096 37 1.1e-07 6.41e-07 55) BPI P17213 36
1.1e-07 6.41e-07 56) HNRPQ O60506 36 1.1e-07 6.41e-07 57) PHI
P06744 36 1.1e-07 6.41e-07 58) Cathepsin G P08311 36 1.1e-07
6.47e-07 59) Osteoblast- Q13950 37 1.1e-07 6.47e-07 specific
transcription factor 2 60) SAA P02735 38 1.1e-07 6.47e-07 61) TXD12
O95881 36 1.1e-07 6.47e-07 62) gp130, soluble P40189 36 1.2e-07
6.87e-07 63) ITI heavy chain Q14624 36 1.3e-07 7.30e-07 H4 64)
CDK8/cyclin C P49336, 36 1.4e-07 7.63e-07 P24863 65) VEGF121 P15692
38 1.4e-07 7.63e-07 66) LRIG3 Q6UXM1 36 1.5e-07 7.98e-07 67) MAPK14
Q16539 36 1.5e-07 7.98e-07 68) PGRP-S O75594 36 1.7e-07 8.24e-07
69) RGM-C Q6ZVN8 36 1.7e-07 8.24e-07 70) Fibrinogen P02679 37
1.7e-07 8.24e-07 g-chain dimer 71) MMP-9 P14780 36 1.8e-07 8.52e-07
72) Thyroxine- P05543 35 1.8e-07 8.52e-07 Binding Globulin 73)
Cadherin-5 P33151 35 1.8e-07 8.52e-07 74) NPS-PLA2 P14555 37
1.9e-07 9.07e-07 75) NAP-2 P02775 36 2.1e-07 9.51e-07 76) FN1.3
P02751 34 2.1e-07 9.51e-07 77) Protease nexin I P07093 38 2.3e-07
9.99e-07 78) Plasminogen P00747 34 2.3e-07 9.99e-07 .sup.aSwiss
Prot is a large protein sequence database widely used for protein
resources .sup.bNumber of patients showing up or
down-regulation
TABLE-US-00004 TABLE 2 Differential protein expression between
baseline and week 8 in paired samples from n = 39 patients treated
for pulmonary TB. Intrasubject Shift.sup.b (baseline to week 8)
Swiss Up p- q- Rank Target Prot.sup.a (n) Down value value 1)
TIMP-2 P16035 39 5.3e-08 5.48e-07 2) GFR.alpha.-2 O00451 39 5.3e-08
5.48e-07 3) MRC2 Q9UBG0 39 5.3e-08 5.48e-07 4) Haptvoglobin, P00738
39 5.3e-08 5.48e-07 Mixed 5) LBP P18428 39 5.3e-08 5.48e-07 6)
amyloid precursor P05067 39 5.3e-08 5.48e-07 protein 7) BGH3 Q15582
39 5.3e-08 5.48e-07 8) TSP4 P35443 39 5.3e-08 5.48e-07 9) FETUB
Q9UGM5 39 5.3e-08 5.48e-07 10) PCI P05154 39 5.3e-08 5.48e-07 11)
Kallistatin P29622 39 5.3e-08 5.48e-07 12) .alpha.2-HS- P02765 39
5.3e-08 5.48e-07 Glycoprotein 13) CHL1 O00533 39 5.3e-08 5.48e-07
14) CDON Q4KMG0 39 5.3e-08 5.48e-07 15) D-dimer P02671, 39 5.3e-08
5.48e-07 16) MMP-1 P03956 39 5.3e-08 5.48e-07 17) contactin-1
Q12860 38 5.7e-08 5.48e-07 18) CD109 Q6YHK3 38 5.7e-08 5.48e-07 19)
IGFBP-7 Q16270 38 5.7e-08 5.48e-07 20) Sphingosine Q9NYA1 38
5.7e-08 5.48e-07 kinase 1 21) CRP P02741 38 5.7e-08 5.48e-07 22)
SEPR Q12884 38 5.7e-08 5.48e-07 23) TIMP-3 P35625 38 6.1e-08
5.48e-07 24) Lipocalin 2 P80188 38 6.1e-08 5.48e-07 25) NAP-2
P02775 38 6.1e-08 5.48e-07 26) Nectin-like Q9BY67 38 6.1e-08
5.48e-07 protein 2 27) Proteinase-3 P24158 38 6.1e-08 5.48e-07 28)
PDGF-BB P01127 38 6.1e-08 5.48e-07 29) MMP-2 P08253 38 6.1e-08
5.48e-07 30) TIMP-1 P01033 37 6.6e-08 5.48e-07 31) ROR1 Q01973 37
6.6e-08 5.48e-07 32) IGFBP-6 P24592 37 6.6e-08 5.48e-07 33) PAI-1
P05121 38 6.6e-08 5.48e-07 34) Protein C P04070 37 6.6e-08 5.48e-07
35) C9 P02748 38 6.6e-08 5.48e-07 36) GDF-9 O60383 37 6.6e-08
5.48e-07 37) Carbonic P23280 37 6.6e-08 5.48e-07 anhydrase 6 38)
RBP P02753 37 6.6e-08 5.48e-07 39) Albumin P02768 38 6.6e-08
5.48e-07 40) Fibronectin P02751 37 6.6e-08 5.48e-07 41)
Antithrombin III P01008 38 7.2e-08 5.48e-07 42) a1-Antitrypsin
P01009 37 7.2e-08 5.48e-07 43) HRG P04196 38 7.2e-08 5.48e-07 44)
Angiopoietin-1 Q15389 38 7.8e-08 5.48e-07 45) ATS13 Q76LX8 37
7.8e-08 5.48e-07 46) Coagulation P08709 37 7.8e-08 5.48e-07 Factor
VII 47) Afamin P43652 38 7.8e-08 5.48e-07 48) TrkB Q16620 37
7.8e-08 5.48e-07 49) GOT1 P17174 38 7.8e-08 5.48e-07 50) Azurocidin
P20160 38 8.4e-08 5.80e-07 51) NCAM-L1 P32004 38 9.1e-08 6.03e-07
52) PLXC1 O60486 36 9.1e-08 6.03e-07 53) I-TAC O14625 38 1.1e-07
6.41e-07 54) CYTF O76096 37 1.1e-07 6.41e-07 55) BPI P17213 36
1.1e-07 6.41e-07 56) HNRPQ O60506 36 1.1e-07 6.41e-07 57) PHI
P06744 36 1.1e-07 6.41e-07 58) Cathepsin G P08311 36 1.1e-07
6.47e-07 59) Osteoblast- Q13950 37 1.1e-07 6.47e-07 specific 60)
SAA P02735 38 1.1e-07 6.47e-07 61) TXD12 O95881 36 1.1e-07 6.47e-07
62) gp130, soluble P40189 36 1.2e-07 6.87e-07 63) ITI heavy chain
Q14624 36 1.3e-07 7.30e-07 H4 64) CDK8/cyclin C P49336, 36 1.4e-07
7.63e-07 65) VEGF121 P15692 38 1.4e-07 7.63e-07 66) LRIG3 Q6UXM1 36
1.5e-07 7.98e-07 67) MAPK14 Q16539 36 1.5e-07 7.98e-07 68) PGRP-S
O75594 36 1.7e-07 8.24e-07 69) RGM-C Q6ZVN8 36 1.7e-07 8.24e-07 70)
Fibrinogen P02679 37 1.7e-07 8.24e-07 g-chain dimer 71) MMP-9
P14780 36 1.8e-07 8.52e-07 72) Thyroxine- P05543 35 1.8e-07
8.52e-07 Binding 73) Cadherin-5 P33151 35 1.8e-07 8.52e-07 74)
NPS-PLA2 P14555 37 1.9e-07 9.07e-07 75) NAP-2 P02775 36 2.1e-07
9.51e-07 76) FN1.3 P02751 34 2.1e-07 9.51e-07 77) Protease nexin I
P07093 38 2.3e-07 9.99e-07 78) Plasminogen P00747 34 2.3e-07
9.99e-07 79) Lactoferrin P02788 36 2.4e-07 1.04e-06 80) Dkk-4
Q9UBT3 35 2.4e-07 1.04e-06 81) PDGF-AA P04085 36 2.4e-07 1.04e-06
82) Gelsolin P06396 33 2.6e-07 1.10e-06 83) Macrophage P22897 35
2.8e-07 1.15e-06 mannose 84) Alkaline P05186 35 2.8e-07 1.15e-06
phosphatase, 85) FUT5 Q11128 34 2.8e-07 1.15e-06 86) Apo A-I P02647
35 3.0e-07 1.22e-06 87) SDF-1a P48061 35 3.3e-07 1.27e-06 88) MIA
Q16674 34 3.3e-07 1.27e-06 89) GDF-11 O95390 37 3.3e-07 1.27e-06
90) VEGF P15692 37 3.5e-07 1.31e-06 91) MPIF-1 P55773 37 3.5e-07
1.31e-06 92) FN1.4 P02751 34 3.5e-07 1.31e-06 93) Cofilin-1 P23528
36 3.5e-07 1.31e-06 94) Contactin-4 Q8IWV2 37 3.8e-07 1.39e-06 95)
Calpain I P07384, 37 4.1e-07 1.47e-06 96) RET P07949 34 4.1e-07
1.47e-06 97) Contactin-5 O94779 35 4.4e-07 1.53e-06 98) LEAP-1
P81172 36 4.4e-07 1.53e-06 99) MASP3 P48740 33 4.4e-07 1.53e-06
100) LSAMP Q13449 35 4.7e-07 1.60e-06 101) BMPER Q8N8U9 35 4.7e-07
1.60e-06 102) PAFAH Q13093 36 4.7e-07 1.60e-06 103) C2 P06681 35
5.1e-07 1.67e-06 104) HGFA Q04756 38 5.1e-07 1.67e-06 105) CTAP-III
P02775 35 5.1e-07 1.67e-06 106) Protein S P07225 34 5.4e-07
1.78e-06 107) IGFBP-3 P17936 33 5.9e-07 1.84e-06 108) HSP 90a
P07900 34 5.9e-07 1.84e-06 109) TrkC Q16288 36 5.9e-07 1.84e-06
110) PSA-ACT P07288, 36 5.9e-07 1.84e-06 111) a2-Macroglobulin
P01023 33 6.3e-07 1.95e-06 112) IP-10 P02778 36 6.3e-07 1.95e-06
113) resistin Q9HD89 33 6.8e-07 2.04e-06 114) RASA1 P20936 37
6.8e-07 2.04e-06 115) CATZ Q9UBR2 34 6.8e-07 2.04e-06 116) ZAP70
P43403 35 7.8e-07 2.31e-06 117) Factor B P00751 36 7.8e-07 2.31e-06
118) TGF-b R III Q03167 35 8.4e-07 2.44e-06 119) CAPG P40121 35
8.4e-07 2.44e-06 120) Flt3 ligand P49771 33 9.0e-07 2.60e-06 121)
ERBB1 P00533 35 9.7e-07 2.74e-06 122) Gro-b P19875 35 9.7e-07
2.74e-06 123) bFGF-R P11362 35 1.0e-06 2.92e-06 124) BMP-1 P13497
36 1.1e-06 3.09e-06 125) Angiostatin P00747 33 1.1e-06 3.09e-06
126) SAP P02743 34 1.3e-06 3.50e-06 127) Coagulation P00740 36
1.3e-06 3.50e-06 Factor IX 128) IGFBP-5 P24593 36 1.4e-06 3.66e-06
129) CNDP1 Q96KN2 36 1.4e-06 3.66e-06 130) Cadherin-2 P19022 35
1.4e-06 3.66e-06 131) VEGF sR3 P35916 34 1.5e-06 3.87e-06 132)
Siglec-9 Q9Y336 33 1.5e-06 3.87e-06 133) IDUA P35475 35 1.7e-06
4.42e-06 134) suPAR Q03405 33 1.8e-06 4.66e-06 135) Coagulation
P00740 36 1.8e-06 4.66e-06 Factor IX 136) CD30 Ligand P32971 32
1.9e-06 4.92e-06 137) 14-3-3 eta Q04917 33 1.9e-06 4.92e-06 138)
TIG2 Q99969 34 2.2e-06 5.61e-06 139) HGF P14210 36 2.4e-06 5.92e-06
140) TNF sR-II P20333 33 2.4e-06 5.92e-06 141) Factor I P05156 37
2.6e-06 6.13e-06 142) OLR1 P78380 33 2.6e-06 6.13e-06 143) Thrombin
P00734 31 2.6e-06 6.13e-06 144) Collectin Q9BWP8 35 2.6e-06
6.13e-06 Kidney 1 145) Endoglin P17813 35 2.6e-06 6.13e-06 146) ON
P09486 32 2.7e-06 6.43e-06 147) CATC P53634 32 2.7e-06 6.43e-06
148) DKK3 Q9UBP4 32 2.7e-06 6.43e-06 149) Fibrinogen P02671, 34
2.9e-06 6.83e-06 150) MMP-8 P22894 34 3.1e-06 7.17e-06 151) TPSB2
P20231 33 3.1e-06 7.17e-06 152) CK-MB P12277, 33 3.1e-06 7.17e-06
153) BASI P35613 33 3.4e-06 7.62e-06 154) Adiponectin Q15848 33
3.6e-06 8.10e-06 155) Myeloperoxidase P05164 34 3.9e-06 8.44e-06
156) Aurora kinase A O14965 34 3.9e-06 8.44e-06 157) IL-18 R.alpha.
Q13478 32 3.9e-06 8.44e-06 158) 14-3-3 protein P61981 33 3.9e-06
8.44e-06 gamma 159) Ck-b-8-1 P55773 35 4.1e-06 8.97e-06 160) FCG3B
O75015 33 4.4e-06 9.54e-06 161) MIP-1a P10147 32 4.7e-06 9.95e-06
162) TFPI P10646 32 4.7e-06 9.95e-06 163) NCAM-120 P13591 33
4.7e-06 9.95e-06 164) TSG-6 P98066 33 4.7e-06 9.95e-06 165) HAI-1
O43278 34 5.0e-06 1.06e-05 166) CNTFR alpha P26992 31 5.4e-06
1.10e-05 167) Factor D P00746 35 5.4e-06 1.10e-05 168) IL-17 RC
Q8NAC3 35 5.4e-06 1.10e-05 169) BOC Q9BWV1 34 5.4e-06 1.10e-05 170)
Spondin-1 Q9HCB6 35 5.8e-06 1.17e-05 171) RGMB Q6NW40 31 6.1e-06
1.23e-05 172) Lysozyme P61626 33 6.1e-06 1.23e-05 173) C1QBP Q07021
31 6.1e-06 1.23e-05 174) Gro-g P19876 33 7.0e-06 1.39e-05 175) CD5L
O43866 31 8.0e-06 1.55e-05 176) MAPK2 P49137 31 8.0e-06 1.55e-05
177) C6 P13671 33 8.0e-06 1.55e-05 178) JAK2 O60674 33 8.0e-06
1.55e-05 179) Apo E3 P02649 34 8.5e-06 1.65e-05 180) Apo B P04114
32 9.1e-06 1.73e-05 181) LYVE1 Q9Y5Y7 32 9.1e-06 1.73e-05 182)
Endocan Q9NQ30 34 9.1e-06 1.73e-05 183) ASAHL Q02083 34 9.7e-06
1.84e-05 184) PBEF P43490 33 1.0e-05 1.95e-05 185) CD23 P06734 35
1.1e-05 2.07e-05 186) WFKN2 Q8TEU8 32 1.2e-05 2.17e-05 187) RGMA
Q96B86 32 1.2e-05 2.17e-05 188) ENTP3 O75355 35 1.2e-05 2.17e-05
189) PF-4 P02776 31 1.3e-05 2.29e-05 190) HPG- P15428 31 1.3e-05
2.29e-05 191) ZAP70 P43403 34 1.3e-05 2.42e-05 192) Cystatin C
P01034 31 1.4e-05 2.56e-05 193) C3d P01024 33 1.4e-05 2.56e-05 194)
AIF1 P55008 34 1.5e-05 2.67e-05 195) URB Q76M96 33 1.5e-05 2.67e-05
196) GSK-3 beta P49841 32 1.5e-05 2.67e-05 197) MATN2 O00339 33
1.5e-05 2.67e-05 198) Ephrin-A5 P52803 32 1.6e-05 2.83e-05 199)
CYTD P28325 35 1.8e-05 3.16e-05 200) HDGR2 Q7Z4V5 32 1.8e-05
3.16e-05 201) PH P01298 35 1.8e-05 3.16e-05 202) TNF sR-I P19438 30
2.0e-05 3.28e-05 203) IL-2 sRa P01589 34 2.0e-05 3.28e-05 204)
IFN-g R1 P15260 33 2.0e-05 3.28e-05 205) DHH O43323 32 2.0e-05
3.28e-05 206) Coactosin-like Q14019 32 2.0e-05 3.28e-05 protein
207) SCF sR P10721 34 2.1e-05 3.46e-05 208) HSP 70 P08107 34
2.1e-05 3.46e-05 209) IL-13 P35225 34 2.2e-05 3.65e-05 210) DKK1
O94907 34 2.2e-05 3.65e-05 211) M-CSF R P07333 31 2.4e-05 3.84e-05
212) Bcl-2 P10415 31 2.4e-05 3.84e-05 213) TARC Q92583 32 2.5e-05
4.07e-05 214) Apo E P02649 33 2.7e-05 4.23e-05 215) CD36 ANTIGEN
P16671 33 2.7e-05 4.23e-05 216) IL-19 Q9UHD0 34 2.7e-05 4.23e-05
217) Kininogen, P01042 32 2.7e-05 4.23e-05 HMW, Single 218) IL-1a
P01583 33 2.7e-05 4.23e-05 219) Apo E4 P02649 33 2.8e-05 4.48e-05
220) SDF-1b P48061 32 3.4e-05 5.33e-05
221) Macrophage P21757 33 3.4e-05 5.33e-05 scavenger 222) Midkine
P21741 31 3.6e-05 5.59e-05 223) IGF-I P05019 30 3.6e-05 5.59e-05
224) AK1A1 P14550 33 3.6e-05 5.59e-05 225) EDAR Q9UNE0 31 4.1e-05
6.25e-05 226) Hemoglobin P69905, 33 4.1e-05 6.25e-05 227) GP1BA
P07359 32 4.3e-05 6.61e-05 228) BARK1 P25098 31 4.6e-05 6.99e-05
229) Calpastatin P20810 30 5.2e-05 7.81e-05 230) OBCAM Q14982 30
5.2e-05 7.81e-05 231) Growth hormone P10912 32 5.5e-05 8.25e-05
receptor 232) pTEN P60484 33 5.8e-05 8.68e-05 233) IF4G2 P78344 31
5.8e-05 8.68e-05 234) GASP-2 Q96D09 31 6.2e-05 9.06e-05 235)
Cystatin M Q15828 29 6.2e-05 9.06e-05 236) dopa P20711 34 6.2e-05
9.06e-05 decarboxylase 237) LCMT1 Q9UIC8 30 6.2e-05 9.06e-05 238)
Myoglobin P02144 30 6.6e-05 9.53e-05 239) NRP1 O14786 32 6.6e-05
9.53e-05 .sup.aSwiss Prot is a large protein sequence database
widely used for protein resources .sup.bNumber of patients showing
up or down-regulation
TABLE-US-00005 TABLE 3 DAVID analysis - Paired sample data set
Anno- p-value tation (Bonferroni Cluster Corrected) Enrichment
Score: 83.01749576763416 signal 6.01E-94 signal peptide 4.54E-93
disulfide bond 7.85E-69 disulfide bond 4.23E-68 Enrichment Score:
58.445486770722454 Secreted 6.48E-71 GO: 0005576~extracellular
region 5.14E-59 GO: 0005615~extracellular space 1.36E-49 GO:
0044421~extracellular region part 1.49E-47 Enrichment Score:
28.642746811176313 GO: 0009611~response to wounding 1.73E-36 GO:
0006952~defense response 4.33E-22 GO: 0006954~inflammatory response
1.61E-19 Enrichment Score: 16.859976579934965 hsa04610: Complement
and coagulation cascades 1.89E-20 GO: 0042060~wound healing
2.47E-16 GO: 0007599~hemostasis 1.63E-15 blood coagulation 3.19E-16
GO: 0007596~blood coagulation 8.30E-15 GO: 0050817~coagulation
8.30E-15 GO: 0050878~regulation of body fluid levels 7.23E-13 GO:
0030168~platelet activation 4.89E-05 Enrichment Score:
14.250642339320128 GO: 0002526~acute inflammatory response 6.45E-14
acute phase 3.88E-12 GO: 0006953~acute-phase response 1.18E-09
Enrichment Score: 13.460056934270545 GO: 0005539~glycosaminoglycan
binding 5.38E-13 GO: 0030246~carbohydrate binding 2.98E-12 GO:
0030247~polysaccharide binding 3.73E-12 GO: 0001871~pattern binding
3.73E-12 GO: 0008201~heparin binding 5.83E-11 heparin-binding
3.48E-09 Enrichment Score: 12.995486525261226 GO: 0031983~vesicle
lumen 2.66E-23 GO: 0060205~cytoplasmic membrane-bounded 4.44E-22
vesicle lumen GO: 0031091~platelet alpha granule 1.64E-19 GO:
0031093~platelet alpha granule lumen 2.13E-19 GO: 0030141~secretory
granule 7.15E-15 GO: 0044433~cytoplasmic vesicle part 2.12E-12 GO:
0031988~membrane-bounded vesicle 1.13E-07 GO: 0016023~cytoplasmic
membrane-bounded 2.06E-07 vesicle GO: 0031982~vesicle 1.95E-06 GO:
0031410~cytoplasmic vesicle 8.62E-06 Enrichment Score:
11.192778045345973 domain: Peptidase S1 4.97E-12 GO:
0004252~serine-type endopeptidase activity 3.73E-12 IPR001314:
Peptidase S1A, chymotrypsin 1.24E-11 GO: 0004175~endopeptidase
activity 1.39E-11 GO: 0008236~serine-type peptidase activity
7.06E-11 IPR001254: Peptidase S1 and S6, 8.01E-11 chymotrypsin/Hap
GO: 0017171~serine hydrolase activity 8.84E-11 zymogen 1.21E-10
serine proteinase 3.31E-10 Protease 8.38E-10 GO: 0008233~peptidase
activity 1.37E-09 Serine protease 1.58E-09 IPR018114: Peptidase
S1/S6, chymotrypsin/Hap, 3.20E-09 active site SM00020: Tryp_SPc
7.34E-09 GO: 0070011~peptidase activity, acting on L- 4.66E-08
amino acid peptides active site: Charge relay system 1.12E-06 GO:
0006508~proteolysis 0.029938092 hydrolase 0.016036551 Enrichment
Score: 10.955845478585891 GO: 0004866~endopeptidase inhibitor
activity 1.49E-18 GO: 0030414~peptidase inhibitor activity 6.20E-18
GO: 0004857~enzyme inhibitor activity 1.28E-12 protease inhibitor
2.06E-12 GO: 0004867~serine-type endopeptidase 1.99E-09 inhibitor
activity Serine protease inhibitor 2.58E-09 site: Reactive bond
7.46E-08 serine proteinase inhibitor 1.30E-06 IPR000215: Protease
inhibitor I4, serpin 3.35E-04 PIRSF001630: serpin 5.94E-04 GO:
0002020~protease binding 0.002520743 SM00093: SERPIN 0.001060892
Enrichment Score: 8.121728432887654 cell adhesion 1.50E-06 GO:
0007155~cell adhesion 2.16E-05 GO: 0022610~biological adhesion
2.23E-05 Enrichment Score: 7.857494699405822 GO: 0051241~negative
regulation of 1.70E-07 multicellular organismal process GO:
0030195~negative regulation of blood 2.57E-06 coagulation GO:
0030193~regulation of blood coagulation 6.27E-06 GO:
0050819~negative regulation of coagulation 7.87E-06 GO:
0050818~regulation of coagulation 2.18E-05 Enrichment Score:
6.887800010777238 hsa04060: Cytokine~cytokine receptor interaction
3.34E-11 GO: 0005125~cytokine activity 4.31E-10 cytokine 1.27E-09
GO: 0006935~chemotaxis 9.75E-07 GO: 0042330~taxis 9.75E-07
IPR001811: Small chemokine, interleukin-8-like 1.03E-06 chemotaxis
3.51E-06 inflammatory response 6.83E-06 GO: 0008009~chemokine
activity 7.52E-06 GO: 0042379~chemokine receptor binding 1.35E-05
IPR018048: Small chemokine, C--X--C, conserved 2.22E-05 site
SM00199: SCY 6.93E-06 PIRSF002522: CXC chemokine 2.86E-05 GO:
0007626~locomotory behavior 5.85E-04 IPR001089: Small chemokine,
C--X--C 1.85E-04 GO: 0007610~behavior 0.008360072 IPR002473: Small
chemokine, C-X- 0.010862324 C/Interleukin 8 Enrichment Score:
6.760362997866251 domain: Ig-like C2-type 1 8.09E-10 domain:
Ig-like C2-type 2 8.88E-10 IPR003598: Immunoglobulin subtype 2
1.24E-08 IPR013098: Immunoglobulin I-set 1.64E-08 Immunoglobulin
domain 1.46E-08 domain: Ig-like C2-type 3 1.39E-07 IPR013151:
Immunoglobulin 8.02E-08 domain: Ig-like C2-type 5 1.20E-05 SM00408:
IGc2 1.51E-06 domain: Ig-like C2-type 4 1.63E-05 IPR007110:
Immunoglobulin-like 2.15E-05 IPR013783: Immunoglobulin-like fold
1.34E-04 IPR003599: Immunoglobulin subtype 1.71E-04 domain:
Fibronectin type-III 2 0.002634843 domain: Fibronectin type-III 1
0.00282371 domain: Fibronectin type-III 3 0.004969958 IPR008957:
Fibronectin, type III-like fold 0.01383263 Enrichment Score:
6.513631012511141 GO: 0030334~regulation of cell migration 3.24E-08
GO: 0040012~regulation of locomotion 3.80E-08 GO:
0051270~regulation of cell motion 3.32E-07 GO: 0040017~positive
regulation of locomotion 6.87E-07 GO: 0030335~positive regulation
of cell 2.27E-06 migration GO: 0051272~positive regulation of cell
motion 7.66E-06 GO: 0008284~positive regulation of cell 2.59E-04
proliferation Enrichment Score: 6.382973768072646 gpi-anchor
7.76E-08 propeptide: Removed in mature form 1.05E-05 lipoprotein
0.001999218 GO: 0031225~anchored to membrane 0.005219445 lipid
moiety-binding region: GPI-anchor 0.03344798 amidated serine
Enrichment Score: 6.381892989930859 GO: 0050727~regulation of
inflammatory 1.19E-09 response GO: 0031348~negative regulation of
defense 0.002426561 response GO: 0050728~negative regulation of
0.014941367 inflammatory response Enrichment Score:
5.766209887182889 GO: 0048589~developmental growth 0.002009295 GO:
0040007~growth 0.002141443 GO: 0031099~regeneration 0.002418875 GO:
0042246~tissue regeneration 0.018123619 Enrichment Score:
5.093484237271428 GO: 0030334~regulation of cell migration 3.24E-08
GO: 0051270~regulation of cell motion 3.32E-07 GO:
0014910~regulation of smooth muscle cell 2.42E-05 migration
Enrichment Score: 5.03912245884617 GO: 0043067~regulation of
programmed cell 7.06E-05 death GO: 0010941~regulation of cell death
7.74E-05 GO: 0042981~regulation of apoptosis 1.81E-04 GO:
0043069~negative regulation of 0.02653394 programmed cell death GO:
0060548~negative regulation of cell death 0.027885969 Enrichment
Score: 5.007846862267697 Antimicrobial 5.64E-04 GO: 0042742~defense
response to bacterium 0.020111498 antibiotic 0.004714378 Enrichment
Score: 4.847304827557594 GO: 0007584~response to nutrient
0.003955906 GO: 0048545~response to steroid hormone 0.019987437
stimulus GO: 0031667~response to nutrient levels 0.026212926
Enrichment Score: 4.652312171554018 glycosylation site: N-linked
(Glc) (glycation) 1.01E-05 glycation 3.05E-05 Enrichment Score:
4.569289937199638 GO: 0031012~extracellular matrix 0.005426686
extracellular matrix 0.008814367 GO: 0005578~proteinaceous
extracellular matrix 0.007324162 Enrichment Score:
4.4413411706947885 GO: 0002526~acute inflammatory response 6.45E-14
GO: 0048584~positive regulation of response to 3.27E-08 stimulus
GO: 0002684~positive regulation of immune 6.92E-05 system process
GO: 0006959~humoral immune response 7.56E-05 GO: 0006956~complement
activation 4.80E-04 GO: 0002541~activation of plasma proteins
5.82E-04 involved in acute inflammatory response GO:
0050778~positive regulation of immune 8.69E-04 response GO:
0002252~immune effector process 0.016200769 GO: 0016485~protein
processing 0.020111498 GO: 0045087~innate immune response
0.021422415 complement alternate pathway 0.004054285 GO:
0002253~activation of immune response 0.031999554 complement
pathway 0.006599395 GO: 0051604~protein maturation 0.042326373
innate immunity 0.011613247 h_alternativePathway: Alternative
Complement 0.011241058 Pathway IPR016060: Complement control module
0.04106339 h_compPathway: Complement Pathway 0.021008529 Enrichment
Score: 4.130616183620647 IPR000010: Proteinase inhibitor 125,
cystatin 3.30E-05 SM00043: CY 7.98E-05 domain: Cystatin 1
0.005588456 domain: Cystatin 2 0.005588456 GO:
0004869~cysteine-type endopeptidase 0.005881805 inhibitor activity
Enrichment Score: 4.052462047448763 GO: 0042592~homeostatic process
1.17E-04 GO: 0048878~chemical homeostasis 8.30E-04 GO:
0030005~cellular di-, tri-valent inorganic 0.026234271 cation
homeostasis GO: 0055066~di-, tri-valent inorganic cation
0.046516885 homeostasis Enrichment Score: 4.041695851166527 GO:
0001501~skeletal system development 0.02123564 GO:
0001503~ossification 0.025372783 GO: 0060348~bone development
0.045384573 Enrichment Score: 3.9213291824047216 IPR000562: Type II
fibronectin, collagen- 2.85E-04 binding SM00059: FN2 4.81E-04
Enrichment Score: 3.8727407217543153 GO: 0006928~cell motion
1.15E-05 Enrichment Score: 3.7683817948224463 GO: 0051050~positive
regulation of transport 0.00444447 GO: 0051222~positive regulation
of protein 0.018855521 transport
GO: 0032880~regulation of protein localization 0.021422415
Enrichment Score: 3.6480936797487855 growth factor 1.41E-05 mitogen
9.96E-05 Enrichment Score: 3.638806829653257 kringle 0.016478159
IPR018059: Kringle, subgroup 0.019261763 IPR018056: Kringle,
conserved site 0.031522348 IPR000001: Kringle 0.031522348 SM00130:
KR 0.03548799 Enrichment Score: 3.5255469961665273 GO:
0010033~response to organic substance 1.24E-05 GO: 0009725~response
to hormone stimulus 0.009694324 GO: 0009719~response to endogenous
stimulus 0.010969202 GO: 0048545~response to steroid hormone
0.019987437 stimulus Enrichment Score: 3.4007875204513938 GO:
0014910~regulation of smooth muscle cell 2.42E-05 migration
Enrichment Score: 3.3534540539683504 metalloprotease inhibitor
0.028637424 metalloenzyme inhibitor 0.042292475 Enrichment Score:
3.3314605750410893 GO: 0034358~plasma lipoprotein particle
0.005392824 GO: 0032994~protein-lipid complex 0.005392824
Enrichment Score: 3.2795760321542433 h_amiPathway: Acute Myocardial
Infarction 1.78E-04 gamma-carboxyglutamic acid 7.12E-04
beta-hydroxyaspartic acid 7.42E-04 carboxyglutamic acid 7.42E-04
calcium binding 0.001081267 h_extrinsicPathway: Extrinsic
Prothrombin 0.001023605 Activation Pathway IPR002383: Coagulation
factor, Gla region 0.005499108 domain: Gla 0.014684251 IPR000294:
Gamma-carboxyglutamic acid-rich 0.010862324 (GLA) domain vitamin K
0.018148778 thrombophilia 0.018148778 SM00069: GLA 0.012479961
h_intrinsicPathway: Intrinsic Prothrombin 0.021008529 Activation
Pathway Enrichment Score: 3.2363675396592373 GO: 0040007~growth
0.002141443 Enrichment Score: 2.8661130704947184 GO:
0010035~response to inorganic substance 0.001535929 Enrichment
Score: 2.8570723350751956 GO: 0007167~enzyme linked receptor
protein 3.72E-05 signaling pathway tyrosine-specific protein kinase
2.51E-05 IPR008266: Tyrosine protein kinase, active site 2.48E-04
phosphotransferase 2.27E-04 tyrosine-protein kinase 2.28E-04 ATP
2.37E-04 GO: 0007169~transmembrane receptor protein 0.004712502
tyrosine kinase signaling pathway IPR001245: Tyrosine protein
kinase 0.001511608 GO: 0004714~transmembrane receptor protein
0.002367407 tyrosine kinase activity SM00219: TyrKc 0.009888893
Enrichment Score: 2.832111814427249 GO: 0010743~regulation of foam
cell 0.003906039 differentiation GO: 0034381~lipoprotein particle
clearance 0.007062906 Enrichment Score: 2.632089925155713
extracellular matrix 0.008814367 Enrichment Score:
2.589728231561291 GO: 0030162~regulation of proteolysis 0.035240925
Enrichment Score: 2.541055191143139 calcium 5.08E-06 GO:
0005509~calcium ion binding 0.005178201 Enrichment Score:
2.536907630353502 propeptide: Activation peptide 1.52E-04 metal
ion-binding site: Calcium 1 0.011635726 collagen degradation
0.012956123 metal ion-binding site: Calcium 2 0.037112738
Enrichment Score: 1.8866108235227257 GO: 0014910~regulation of
smooth muscle cell 2.42E-05 migration Enrichment Score:
1.6588499771985241 GO: 0034381~lipoprotein particle clearance
0.007062906 GO: 0034358~plasma lipoprotein particle 0.005392824 GO:
0032994~protein-lipid complex 0.005392824 Enrichment Score:
1.3901891582691086 GO: 0005577~fibrinogen complex 0.035496696
TABLE-US-00006 TABLE 4 Unpaired analysis of proteins showing
differential expression between baseline and week 8 in n = 39 TB
patients. KS- Swiss distance Target Prot* (Signed) p-value q-value
1) TSP4 P35443 0.821 1.37e-12 1.06e-09 2) SEPR Q12884 0.769
4.08e-11 1.05e-08 3) MRC2 Q9UBG0 0.769 4.08e-11 1.05e-08 4)
Antithrom- P01008 0.744 2.05e-10 3.15e-08 bin III 5) PCI P05154
0.744 2.05e-10 3.15e-08 6) LBP P18428 -0.718 9.73e-10 6.25e-08 7)
a2-HS- P02765 0.718 9.73e-10 6.25e-08 Glycoprotein 8) NPS-PLA2
P14555 -0.718 9.73e-10 6.25e-08 9) Haptoglobin, P00738 -0.718
9.73e-10 6.25e-08 Mixed Type 10) Kallistatin P29622 0.718 9.73e-10
6.25e-08 11) MMP-2 P08253 0.718 9.73e-10 6.25e-08 12) NCAM-L1
P32004 0.718 9.73e-10 6.25e-08 13) CDON Q4KMG0 0.692 4.38e-09
2.60e-07 14) Fibronectin P02751 0.667 1.87e-08 1.03e-06 15)
Cathepsin G P08311 -0.641 7.53e-08 2.23e-06 16) gp130, P40189 0.641
7.53e-08 2.23e-06 soluble 17) Nectin-like Q9BY67 0.641 7.53e-08
2.23e-06 protein 2 18) LEAP-1 P81172 -0.641 7.53e-08 2.23e-06 19)
CRP P02741 -0.641 7.53e-08 2.23e-06 20) Fibrinogen P02679 -0.641
7.53e-08 2.23e-06 g-chain dimer 21) TIMP-2 P16035 0.641 7.53e-08
2.23e-06 22) IL-19 Q9UHD0 0.641 7.53e-08 2.23e-06 23) CDK8/cyclin C
P49336, -0.641 7.53e-08 2.23e-06 P24863 24) CHL1 O00533 0.641
7.53e-08 2.23e-06 25) D-dimer P02671, -0.641 7.53e-08 2.23e-06
P02675, P02679 26) CATZ Q9UBR2 0.641 7.53e-08 2.23e-06 27) TrkC
Q16288 0.615 2.88e-07 7.15e-06 28) Fibrinogen P02671, -0.615
2.88e-07 7.15e-06 P02675, P02679 29) Angiopoietin-1 Q15389 -0.615
2.88e-07 7.15e-06 30) Lipocalin 2 P80188 -0.615 2.88e-07 7.15e-06
31) C9 P02748 -0.615 2.88e-07 7.15e-06 32) MMP-9 P14780 -0.590
1.04e-06 1.82e-05 33) I-TAC O14625 -0.590 1.04e-06 1.82e-05 34)
BMP-1 P13497 0.590 1.04e-06 1.82e-05 35) BMPER Q8N8U9 0.590
1.04e-06 1.82e-05 36) Plasminogen P00747 0.590 1.04e-06 1.82e-05
37) PHI P06744 -0.590 1.04e-06 1.82e-05 38) TrkB Q16620 0.590
1.04e-06 1.82e-05 39) Coagulation P00740 -0.590 1.04e-06 1.82e-05
Factor IX 40) GOT1 P17174 -0.590 1.04e-06 1.82e-05 41) RBP P02753
0.590 1.04e-06 1.82e-05 42) Albumin P02768 0.590 1.04e-06 1.82e-05
43) Sphingosine Q9NYA1 -0.590 1.04e-06 1.82e-05 kinase 1 44) Afamin
P43652 0.590 1.04e-06 1.82e-05 45) TIMP-1 P01033 -0.564 3.56e-06
4.58e-05 46) GFRa-2 O00451 0.564 3.56e-06 4.58e-05 47) Azurocidin
P20160 -0.564 3.56e-06 4.58e-05 48) Lactoferrin P02788 -0.564
3.56e-06 4.58e-05 49) amyloid precur- P05067 -0.564 3.56e-06
4.58e-05 sor protein 50) RET P07949 0.564 3.56e-06 4.58e-05 51)
LRIG3 Q6UXM1 0.564 3.56e-06 4.58e-05 52) CD30 Ligand P32971 0.564
3.56e-06 4.58e-05 53) Osteoblast- Q13950 0.564 3.56e-06 4.58e-05
specific transcription factor 2 54) Proteinase-3 P24158 -0.564
3.56e-06 4.58e-05 55) MASP3 P48740 0.564 3.56e-06 4.58e-05 56)
HNRPQ O60506 -0.564 3.56e-06 4.58e-05 57) SAA P02735 -0.564
3.56e-06 4.58e-05 58) PLXC1 O60486 0.564 3.56e-06 4.58e-05 59)
Coagulation P00740 -0.564 3.56e-06 4.58e-05 Factor IX 60) CAPG
P40121 -0.564 3.56e-06 4.58e-05 *Swiss Prot is a large protein
sequence database widely used for protein resources
TABLE-US-00007 TABLE 5 Unpaired analysis of proteins showing
differential expression between baseline and week 8 in n = 39 TB
patients. KS- Swiss distance Target Prot.sup.a (Signed) p-value
q-value 1) TSP4 P35443 0.821 1.37e-12 1.06e-09 2) SEPR Q12884 0.769
4.08e-11 1.05e-08 3) MRC2 Q9UBG0 0.769 4.08e-11 1.05e-08 4)
Antithrom- P01008 0.744 2.05e-10 3.15e-08 bin III 5) PCI P05154
0.744 2.05e-10 3.15e-08 6) LBP P18428 -0.718 9.73e-10 6.25e-08 7)
a2-HS- P02765 0.718 9.73e-10 6.25e-08 Glycoprotein 8) NPS-PLA2
P14555 -0.718 9.73e-10 6.25e-08 9) Haptoglobin, P00738 -0.718
9.73e-10 6.25e-08 Mixed Type 10) Kallistatin P29622 0.718 9.73e-10
6.25e-08 11) MMP-2 P08253 0.718 9.73e-10 6.25e-08 12) NCAM-L1
P32004 0.718 9.73e-10 6.25e-08 13) CDON Q4KMG0 0.692 4.38e-09
2.60e-07 14) Fibronectin P02751 0.667 1.87e-08 1.03e-06 15)
Cathepsin G P08311 -0.641 7.53e-08 2.23e-06 16) gp130, P40189 0.641
7.53e-08 2.23e-06 soluble 17) Nectin-like Q9BY67 0.641 7.53e-08
2.23e-06 protein 2 18) LEAP-1 P81172 -0.641 7.53e-08 2.23e-06 19)
CRP P02741 -0.641 7.53e-08 2.23e-06 20) Fibrinogen P02679 -0.641
7.53e-08 2.23e-06 g-chain dimer 21) TIMP-2 P16035 0.641 7.53e-08
2.23e-06 22) IL-19 Q9UHD0 0.641 7.53e-08 2.23e-06 23) CDK8/cyclin C
P49336, -0.641 7.53e-08 2.23e-06 P24863 24) CHL1 O00533 0.641
7.53e-08 2.23e-06 25) D-dimer P02671, -0.641 7.53e-08 2.23e-06
P02675, 26) CATZ Q9UBR2 0.641 7.53e-08 2.23e-06 27) TrkC Q16288
0.615 2.88e-07 7.15e-06 28) Fibrinogen P02671, -0.615 2.88e-07
7.15e-06 P02675, 29) Angiopoietin-1 Q15389 -0.615 2.88e-07 7.15e-06
30) Lipocalin 2 P80188 -0.615 2.88e-07 7.15e-06 31) C9 P02748
-0.615 2.88e-07 7.15e-06 32) MMP-9 P14780 -0.590 1.04e-06 1.82e-05
33) I-TAC O14625 -0.590 1.04e-06 1.82e-05 34) BMP-1 P13497 0.590
1.04e-06 1.82e-05 35) BMPER Q8N8U9 0.590 1.04e-06 1.82e-05 36)
Plasminogen P00747 0.590 1.04e-06 1.82e-05 37) PHI P06744 -0.590
1.04e-06 1.82e-05 38) TrkB Q16620 0.590 1.04e-06 1.82e-05 39)
Coagulation P00740 -0.590 1.04e-06 1.82e-05 Factor IX 40) GOT1
P17174 -0.590 1.04e-06 1.82e-05 41) RBP P02753 0.590 1.04e-06
1.82e-05 42) Albumin P02768 0.590 1.04e-06 1.82e-05 43) Sphingosine
Q9NYA1 -0.590 1.04e-06 1.82e-05 kinase 1 44) Afamin P43652 0.590
1.04e-06 1.82e-05 45) TIMP-1 P01033 -0.564 3.56e-06 4.58e-05 46)
GFRa-2 O00451 0.564 3.56e-06 4.58e-05 47) Azurocidin P20160 -0.564
3.56e-06 4.58e-05 48) Lactoferrin P02788 -0.564 3.56e-06 4.58e-05
49) amyloid precur- P05067 -0.564 3.56e-06 4.58e-05 sor protein 50)
RET P07949 0.564 3.56e-06 4.58e-05 51) LRIG3 Q6UXM1 0.564 3.56e-06
4.58e-05 52) CD30 Ligand P32971 0.564 3.56e-06 4.58e-05 53)
Osteoblast- Q13950 0.564 3.56e-06 4.58e-05 specific transcription
54) Proteinase-3 P24158 -0.564 3.56e-06 4.58e-05 55) MASP3 P48740
0.564 3.56e-06 4.58e-05 56) HNRPQ O60506 -0.564 3.56e-06 4.58e-05
57) SAA P02735 -0.564 3.56e-06 4.58e-05 58) PLXC1 O60486 0.564
3.56e-06 4.58e-05 59) Coagulation P00740 -0.564 3.56e-06 4.58e-05
Factor IX 60) CAPG P40121 -0.564 3.56e-06 4.58e-05 61) HSP 90a
P07900 -0.538 1.15e-05 1.27e-04 62) TNF sR-I P19438 -0.538 1.15e-05
1.27e-04 63) Angiostatin P00747 0.538 1.15e-05 1.27e-04 64)
Gelsolin P06396 0.538 1.15e-05 1.27e-04 65) MAPK14 Q16539 -0.538
1.15e-05 1.27e-04 66) PBEF P43490 -0.538 1.15e-05 1.27e-04 67)
Contactin-4 Q8IWV2 0.538 1.15e-05 1.27e-04 68) IGFBP-7 Q16270 0.538
1.15e-05 1.27e-04 69) PGRP-S O75594 -0.538 1.15e-05 1.27e-04 70)
FN1.3 P02751 0.538 1.15e-05 1.27e-04 71) Myeloperoxidase P05164
-0.513 3.54e-05 2.94e-04 72) ROR1 Q01973 0.513 3.54e-05 2.94e-04
73) Cadherin-5 P33151 0.513 3.54e-05 2.94e-04 74) Carbonic P23280
0.513 3.54e-05 2.94e-04 anhydrase 6 75) FETUB Q9UGM5 0.513 3.54e-05
2.94e-04 76) FN1.4 P02751 0.513 3.54e-05 2.94e-04 77) DKK3 Q9UBP4
0.513 3.54e-05 2.94e-04 78) BPI P17213 -0.513 3.54e-05 2.94e-04 79)
Factor B P00751 -0.513 3.54e-05 2.94e-04 80) Lysozyme P61626 -0.513
3.54e-05 2.94e-04 81) bFGF-R P11362 0.513 3.54e-05 2.94e-04 82)
Protein S P07225 -0.513 3.54e-05 2.94e-04 83) Apo A-I P02647 0.513
3.54e-05 2.94e-04 84) MPIF-1 P55773 -0.513 3.54e-05 2.94e-04 85)
GDF-9 O60383 -0.513 3.54e-05 2.94e-04 86) BGH3 Q15582 0.513
3.54e-05 2.94e-04 87) .alpha.1-Antitrypsin P01009 -0.513 3.54e-05
2.94e-04 88) CYTD P28325 0.513 3.54e-05 2.94e-04 89) RACK1 P63244
0.513 3.54e-05 2.94e-04 90) C6 P13671 -0.513 3.54e-05 2.94e-04 91)
14-3-3 eta Q04917 -0.513 3.54e-05 2.94e-04 92) ITI heavy Q14624
-0.513 3.54e-05 2.94e-04 chain H4 93) HRG P04196 0.513 3.54e-05
2.94e-04 94) SDF-1a P48061 0.487 1.03e-04 6.84e-04 95) SAP P02743
-0.487 1.03e-04 6.84e-04 96) Thyroxine- P05543 0.487 1.03e-04
6.84e-04 Binding Globulin 97) NAP-2 P02775 -0.487 1.03e-04 6.84e-04
98) contactin-1 Q12860 0.487 1.03e-04 6.84e-04 99) TIG2 Q99969
-0.487 1.03e-04 6.84e-04 100) CATC P53634 -0.487 1.03e-04 6.84e-04
101) C2 P06681 -0.487 1.03e-04 6.84e-04 102) CD109 Q6YHK3 0.487
1.03e-04 6.84e-04 103) Thrombin P00734 -0.487 1.03e-04 6.84e-04
104) 14-3-3 protein P61981 -0.487 1.03e-04 6.84e-04 gamma 105) C3b
P01024 -0.487 1.03e-04 6.84e-04 106) CTAP-III P02775 -0.487
1.03e-04 6.84e-04 107) C3d P01024 -0.487 1.03e-04 6.84e-04 108) HGF
P14210 -0.487 1.03e-04 6.84e-04 109) Alkaline phos- P05186 -0.487
1.03e-04 6.84e-04 phatase, bone 110) HAI-1 O43278 0.487 1.03e-04
6.84e-04 111) PAI-1 P05121 -0.487 1.03e-04 6.84e-04 112) IP-10
P02778 -0.487 1.03e-04 6.84e-04 113) AK1A1 P14550 -0.487 1.03e-04
6.84e-04 114) ZAP70 P43403 -0.487 1.03e-04 6.84e-04 115) VEGF121
P15692 -0.487 1.03e-04 6.84e-04 116) Sonic Q15465 0.487 1.03e-04
6.84e-04 Hedgehog indicates data missing or illegible when
filed
TABLE-US-00008 TABLE 6 Disease severity score calculation Range
Normali- Normalized (min- zation Range (min- Weigh Parameter max)
factor max) factor Scoring CXRCLASS 1-3 1 1-3 1 added CXREXTNT A-C
(1-3) 1 1-3 1 added dtd_base 3.67-17.5 0.2 0.734-3.5 1 sub- tracted
smearb 2-4 1 2-4 1 added bmi 15.2-26.7 0.25 3.8-6.675 2 sub-
tracted anycav 0-1 1 0-1 1 added bilatcav 0-1 1 0-1 2 added
bilatabn 0-1 1 0-1 1 added
TABLE-US-00009 TABLE 7 Calculation of the disease severity score
CXR- CXREX- pid CLASS TNT dtd_base smearb anycav bilatcav bilatabn
bmi Score 1016 1 2 6.21 2 0 0 0 26.7 0.026 1029 1 1 17.5 3 0 0 0 19
0.125 1015 1 2 10.46 3 0 0 0 23.1 0.147 1039 1 1 5.67 3 0 0 0 19.3
0.264 1021 1 1 5.9 3 0 0 0 19.2 0.264 1020 2 2 8.38 2 1 0 0 21.1
0.298 1038 1 3 8.33 2 0 0 1 20.8 0.308 1026 1 2 5.96 3 0 0 0 19.7
0.310 1034 1 2 9.54 4 0 0 0 20 0.318 1009 1 3 4.21 4 0 0 1 26 0.322
1004 1 2 7.38 4 0 0 0 20.5 0.330 1033 1 2 6.71 4 0 0 0 20 0.354
1040 1 2 7.54 4 0 0 0 19.1 0.371 1023 2 2 7.29 4 1 0 0 21.6 0.421
1019 3 2 5.5 3 1 0 0 22.1 0.428 1011 1 3 5.33 4 0 0 1 21.6 0.446
1012 2 2 5.83 3 1 0 0 19 0.458 1010 1 3 7.63 3 0 0 1 18.2 0.461
1006 1 3 7.92 3 0 0 1 17.9 0.467 1024 2 2 7.25 3 1 0 0 17.3 0.494
1031 1 2 4.38 4 0 0 1 18.3 0.498 1032 2 2 5.63 3 1 0 1 18.8 0.530
1022 3 2 13.17 4 1 0 1 19.6 0.535 1037 1 3 5.71 4 0 0 1 18.5 0.538
1028 2 3 5.5 3 1 0 1 18.8 0.594 1030 3 2 4.29 4 1 0 0 19.1 0.600
1027 3 2 8.96 4 1 0 0 17.1 0.604 1017 2 2 3.67 4 1 0 0 17.1 0.607
1014 3 3 6.13 4 1 0 0 19.9 0.614 1001 3 2 4.42 4 1 0 0 18.1 0.629
1003 2 3 9.58 3 1 0 1 15.9 0.633 1036 3 2 4.67 4 1 0 1 17.9 0.695
1025 3 3 5.46 4 1 0 1 19.3 0.704 1005 2 3 5.38 4 1 0 1 16.9 0.717
1018 3 3 6.92 4 1 0 1 18 0.726 1002 3 3 5.25 4 1 0 1 16.6 0.791
1007 3 2 4.5 4 1 1 1 17.1 0.847 1008 3 3 5.67 4 1 1 1 17.4 0.885
1013 3 3 5.33 4 1 1 1 15.2 0.958 min 1 1 3.67 2 0 0 0 15.2 max 3 3
17.5 4 1 1 1 26.7 a* x 1 x 1 x 0.2 x 1 x 1 x 1 x 1 c* b* 1 1 1 1 1
2 2 2 a scale/normalize b weight factor c (bmi - 12)/4
TABLE-US-00010 TABLE 8 Biomarkers for overall TB treatment changes,
without specific analysis* KS Empirical Protein SwissProt Distance
P-Value Q-Value MRC2 Q9UBG0 0.8947 2.829e-011 7.859e-010 TSP4
P35443 0.8947 2.829e-011 7.859e-010 gp130, P40189 0.8947 2.829e-011
7.859e-010 soluble SEPR Q12884 0.8421 2.829e-011 7.859e-010 CATZ
Q9UBR2 0.8421 2.829e-011 7.859e-010 CDON Q4KMG0 0.8421 2.829e-011
7.859e-010 LEAP-1 P81172 0.8421 2.829e-011 7.859e-010 Antithrom-
P01008 0.8421 2.829e-011 7.859e-010 bin III Nectin-like Q9BY67
0.8421 2.829e-011 7.859e-010 protein 2 LBP P18428 0.8421 2.829e-011
7.859e-010 CRP P02741 0.7895 2.829e-011 7.859e-010 NCAM-L1 P32004
0.7895 2.829e-011 7.859e-010 MMP-2 P08253 0.7895 2.829e-011
7.859e-010 Fibronectin P02751 0.7895 2.829e-011 7.859e-010 a2-HS-
P02765 0.7895 2.829e-011 7.859e-010 Glycoprotein Osteoblast- Q13950
0.7895 2.829e-011 7.859e-010 specif transcr fact 2 Kallistatin
P29622 0.7895 2.829e-011 7.859e-010 CD30 Ligand P32971 0.7895
2.829e-011 7.859e-010 PCI P05154 0.7895 2.829e-011 7.859e-010 BGH3
Q15582 0.7895 2.829e-011 7.859e-010 Haptoglobin, P00738 0.7895
2.829e-011 7.859e-010 Mixed Type TrkB Q16620 0.7368 9.569e-006
0.0001861 BMPER Q8N8U9 0.7368 9.569e-006 0.0001861 Contactin-4
Q8IWV2 0.7368 9.569e-006 0.0001861 NPS-PLA2 P14555 0.7368
9.569e-006 0.0001861 Coagulation P00740 0.7368 1.914e-005 0.0001861
Factor IX Afamin P43652 0.7368 1.914e-005 0.0001861 SAA P02735
0.7368 1.914e-005 0.0001861 LRIG3 Q6UXM1 0.7368 1.914e-005
0.0001861 RET P07949 0.7368 1.914e-005 0.0001861 I-TAC O14625
0.7368 1.914e-005 0.0001861 ROR1 Q01973 0.7368 1.914e-005 0.0001861
GFRa-2 O00451 0.7368 1.914e-005 0.0001861 Cathepsin G P08311 0.7368
1.914e-005 0.0001861 bFGF-R P11362 0.7368 1.914e-005 0.0001861
Fibrinogen P02679 0.6842 1.914e-005 0.0001861 g-chain dimer
Lysozyme P61626 0.6842 1.914e-005 0.0001861 GOT1 P17174 0.6842
1.914e-005 0.0001861 D-dimer P02671 0.6842 1.914e-005 0.0001861
Coagulation P43652 0.6842 1.914e-005 0.0001861 Factor IX ITI heavy
Q14624 0.6842 1.914e-005 0.0001861 chain H4 Gelsolin P06396 0.6842
1.914e-005 0.0001861 PHI P06744 0.6842 1.914e-005 0.0001861 HSP 70
P08107 0.6842 1.914e-005 0.0001861 Albumin P02768 0.6842 1.914e-005
0.0001861 MASP3 P48740 0.6842 1.914e-005 0.0001861 a1-Antitrypsin
P01009 0.6842 1.914e-005 0.0001861 CDK8/cyclin C P49336 0.6842
1.914e-005 0.0001861 BMP-1 P13497 0.6842 1.914e-005 0.0001861 C9
P02748 0.6842 1.914e-005 0.0001861 IL-19 Q9UHD0 0.6842 1.914e-005
0.0001861 HAI-1 O43278 0.6842 1.914e-005 0.0001861 Protein S P07225
0.6842 1.914e-005 0.0001861 Myeloperoxidase P05164 0.6842
1.914e-005 0.0001861 TRAIL R1 O00220 0.6842 1.914e-005 0.0001861
DKK3 Q9UBP4 0.6842 1.914e-005 0.0001861 amyloid precur- P05067
0.6842 1.914e-005 0.0001861 sor protein Angiopoietin-1 Q15389
0.6842 1.914e-005 0.0001861 Fibrinogen P02671, 0.6842 1.914e-005
0.0001861 P02675 MMP-9 P14780 0.6842 1.914e-005 0.0001861 MAPK14
Q16539 0.6316 0.0005856 0.0028 HRG P04196 0.6316 0.0005856 0.0028
CAPG P40121 0.6316 0.0005856 0.0028 PLXC1 O60486 0.6316 0.0005856
0.0028 PDGF-BB P01127 0.6316 0.0005856 0.0028 C6 P13671 0.6316
0.0005856 0.0028 BPI P17213 0.6316 0.0005856 0.0028 CYTD P28325
0.6316 0.0005856 0.0028 a2-Macroglobulin P01023 0.6316 0.0005856
0.0028 RBP P02753 0.6316 0.0005856 0.0028 CHL1 O00533 0.6316
0.0005856 0.0028 Proteinase-3 P24158 0.6316 0.0005856 0.0028 FETUB
Q9UGM5 0.6316 0.0005856 0.0028 Carbonic P23280 0.6316 0.0005856
0.0028 anhydrase 6 RGMB Q6NW40 0.6316 0.0005856 0.0028 IGFBP-7
Q16270 0.6316 0.0005856 0.0028 CD23 P06734 0.6316 0.0005856 0.0028
CD109 Q6YHK3 0.6316 0.0005856 0.0028 TGF-b R III Q03167 0.6316
0.0005856 0.0028 NAP-2 P02775 0.6316 0.0005856 0.0028 Lipocalin 2
P80188 0.6316 0.0005856 0.0028 Cadherin-5 P33151 0.6316 0.0005856
0.0028 Azurocidin P20160 0.6316 0.0005856 0.0028 Apo A-I P02647
0.6316 0.0005856 0.0028 CNTFR alpha P26992 0.6316 0.0005856 0.0028
Thyroxine- P05543 0.6316 0.0005856 0.0028 Binding Globulin HGF
P14210 0.6316 0.0005856 0.0028 TrkC Q16288 0.6316 0.0005856 0.0028
SDF-1a P48061 0.6316 0.0005856 0.0028 TIMP-2 P16035 0.6316
0.0005856 0.0028 TSG-6 P98066 0.5789 0.0005856 0.0028 JAK2 O60674
0.5789 0.0005856 0.0028 C3d P01024 0.5789 0.0005856 0.0028 14-3-3
eta P63104 0.5789 0.0005856 0.0028 CTAP-III P02775 0.5789 0.0005856
0.0028 BOC Q9BWV1 0.5789 0.0005856 0.0028 HNRPQ O60506 0.5789
0.0005856 0.0028 PSA-ACT P07288 0.5789 0.0005856 0.0028 Plasminogen
P00747 0.5789 0.0005856 0.0028 IP-10 P02778 0.5789 0.0005856 0.0028
BASI P35613 0.5789 0.0005856 0.0028 FN1.4 P02751 0.5789 0.0005856
0.0028 FN1.3 P02751 0.5789 0.0005856 0.0028 PGRP-S O75594 0.5789
0.0005856 0.0028 Contactin-5 O94779 0.5789 0.0005856 0.0028 C2
P06681 0.5789 0.0005856 0.0028 IDUA P35475 0.5789 0.0005856 0.0028
TIG2 Q99969 0.5789 0.0005856 0.0028 GDF-9 O60383 0.5789 0.0005856
0.0028 LSAMP Q13449 0.5789 0.0005856 0.0028 NG36 Q96KQ7 0.5789
0.0005856 0.0028 PAI-1 P05121 0.5789 0.0005856 0.0028 MPIF-1 P55773
0.5789 0.0005856 0.0028 Lactoferrin P02788 0.5789 0.0005856 0.0028
Sonic Q15465 0.5789 0.0005856 0.0028 Hedgehog MIA Q16674 0.5789
0.0005856 0.0028 IGFBP-6 P24592 0.5789 0.0005856 0.0028 IGFBP-5
P24593 0.5789 0.0005856 0.0028 HSP 90a P07900 0.5789 0.0005856
0.0028 SCF sR P07900 0.5789 0.0005856 0.0028 SAP P02743 0.5789
0.0005856 0.0028 TIMP-1 P01033 0.5789 0.0005856 0.0028 Sphingosine
Q9NYA1 0.5789 0.0005856 0.0028 kinase *Based upon 8-week treatment
response
TABLE-US-00011 TABLE 9 Top candidate biomarkers for predicting
treatment response at 8 weeks* Analysis Protein SwissProt Signed KS
p-value q-value Baseline PSME1 Q06323 0.639 0.000307 0.29 IL-11 RA
Q14626 -0.597 0.00094 0.29 HSP 70 P11142 0.589 0.00115 0.29
Galectin-8 O00214 -0.584 0.00131 0.29 a2-Antiplasmin P08697 0.582
0.0014 0.29 IFN-lambda 2 Q8IZJ0 0.545 0.00342 0.60 MMP-13 P45452
-0.532 0.00463 0.64 iC3b P01024 -0.529 0.00492 0.64 APRIL O75888
-0.495 0.0104 0.99 MMP-12 P39900 0.495 0.0104 0.99 Week 8
Coagulation P12259 0.645 0.000266 0.28 Factor V XPNPEP1 Q9NQW7
0.589 0.00115 0.60 gp130, P40189 0.547 0.00321 0.80 soluble BGH3
Q15582 0.539 0.00386 0.80 TIMP-2 P16035 0.532 0.00463 0.80 APRIL
O75888 -0.500 0.00932 0.80 ECM1 Q16610 0.500 0.00932 0.80 IFN-aA
P01563 0.495 0.0104 0.80 Vasoactive P01282 0.495 0.0104 0.80
Intestinal Peptide IL-11 P20809 0.492 0.011 0.80 Log2 ratio EphA1
P21709 -0.550 0.00302 0.85 of base- gp130, P40189 -0.547 0.00321
0.85 line-to- soluble wk 8 TIMP-1 P01033 0.537 0.0041 0.85 MMP-8
P22894 0.534 0.00436 0.85 AMPM2 P50579 0.532 0.00463 0.85 MMP-12
P39900 0.492 0.011 0.85 Macrophage P21757 0.487 0.0123 0.85
scavenger receptor Nectin-like Q9BY67 -0.487 0.0123 0.85 protein 2
CNDP1 Q96KN2 -0.487 0.0123 0.85 DKK3 Q9UBP4 -0.487 0.0123 0.85
*Ranked according to KS score. For the analyses at baseline and at
8 weeks, positive KS values indicate higher protein levels in
responders than slow-responders. For the analysis using the Log2
ratio of baseline-to-wk 8 signals, positive KS values indicate a
larger differential change between the two time points in the
responders compared to slow-responders.
TABLE-US-00012 TABLE 10 Top candidate biomarkers for predicting
treatment response at 8 weeks* Rank Sum Rank Protein SwissProt Z
score p-value q-value 1 Nectin-like Q9BY67 3.301 0.00096 0.37
protein 1 2 EphAl P21709 3.077 0.0021 0.65 3 gp130, soluble P40189
2.852 0.0044 0.65 4 CATZ Q9UBR2 2.768 0.0057 0.65 5 CNDP1 Q96KN2
2.627 0.0086 0.65 6 TGF-.beta. R III Q03167 2.599 0.0094 0.65 7
MRC2 Q9UBG0 2.515 0.012 0.65 8 ADAM 9 Q13443 2.459 0.014 0.76 9
CDON Q4KMG0 2.459 0.014 0.76 10 IL-2 sR.alpha. P01589 -2.430 0.015
0.76 *Ranked according to their Rank Sum Z-score. Positive rank sum
values indicate a larger differential change between the two time
points in the responders compared to slow-responders.
TABLE-US-00013 TABLE 11 Additional TB treatment response markers*
Protein SwissProt BGN P21810 IL-7 P13232 LYNB P07948 YES P07947
*Identified by comparison of week-8 responders to slow-responders
vs. week 8 slow-responders to all baseline
TABLE-US-00014 TABLE 12 Top 10 proteins at baseline and at 8 weeks
that correlate with TTCC in univariate regression of log10 RFU on
TTCC (measured in weeks), ranked by R.sup.2 Anal- p- q- ysis
Protein SwissProt Slope* R.sup.2 value value Base- ERP29 P30040
-0.012 0.30 0.00037 0.39 line Peroxiredoxin-5 P30044 -0.0078 0.24
0.0018 0.69 HSP 70 P08107 -0.016 0.23 0.002 0.69
.alpha.2-Antiplasmin P08697 -0.0058 0.21 0.0035 0.90 RANTES P13501
0.0059 0.19 0.0051 1.00 IgG P01857 0.0063 0.19 0.0091 1.00
Transketolase P29401 -0.01 0.17 0.0095 1.00 NKG2D P26718 0.0041
0.17 0.0096 1.00 (KLRK1) Coagulation P12259 -0.0089 0.16 0.012 1.00
Factor V Coagulation P00740 -0.0056 0.15 0.014 1.00 Factor IX 8
NKG2D P26718 0.0043 0.26 0.00081 0.65 weeks (KLRK1) XPNPEP1 Q9NQW7
-0.0024 0.32 0.0012 0.65 BGH3 (TGFBI) Q15582 -0.0085 0.22 0.0026
0.71 CDK8/cyclin C P49336 0.0041 0.23 0.0027 0.71 SAA P02735 0.076
0.19 0.0051 0.83 Coagulation P12259 -0.0069 0.19 0.0053 0.83 Factor
V YES P07947 -0.0038 0.19 0.0059 0.83 PARC P55774 0.019 0.17 0.0095
0.83 CD39 P49961 0.0031 0.17 0.011 0.83 LRIG3 Q6UXM1 -0.0055 0.16
0.011 0.83 *Linear model coefficient gives change in log RFU signal
per week
* * * * *
References