U.S. patent application number 15/363991 was filed with the patent office on 2017-03-16 for biomarkers and methods for measuring and monitoring inflammatory disease activity.
The applicant listed for this patent is Crescendo Bioscience, Meso Scale Technologies, LLC., Oklahoma Medical Research Foundation. Invention is credited to Guy L. Cavet, Michael Centola, Steven C. Hoffmann, Nicholas Knowlton, Pankaj Oberoi, Yijing Shen.
Application Number | 20170074878 15/363991 |
Document ID | / |
Family ID | 43876606 |
Filed Date | 2017-03-16 |
United States Patent
Application |
20170074878 |
Kind Code |
A1 |
Oberoi; Pankaj ; et
al. |
March 16, 2017 |
Biomarkers and Methods for Measuring and Monitoring Inflammatory
Disease Activity
Abstract
Biomarkers useful for diagnosing and assessing inflammatory
disease are provided, along with kits for measuring their
expression. The invention also provides predictive models, based on
the biomarkers, as well as computer systems, and software
embodiments of the models for scoring and optionally classifying
samples. The biomarkers include at least two biomarkers selected
from the DAIMRK group and the score is a disease activity index
(DAI).
Inventors: |
Oberoi; Pankaj; (Rockville,
MD) ; Hoffmann; Steven C.; (Columbia, MD) ;
Cavet; Guy L.; (Burlingame, CA) ; Shen; Yijing;
(San Mateo, CA) ; Knowlton; Nicholas; (Chocktaw,
OK) ; Centola; Michael; (Oklahoma City, OK) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Meso Scale Technologies, LLC.
Crescendo Bioscience
Oklahoma Medical Research Foundation |
Rockville
South San Francisco
Oklahoma City |
MD
CA
OK |
US
US
US |
|
|
Family ID: |
43876606 |
Appl. No.: |
15/363991 |
Filed: |
November 29, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14832427 |
Aug 21, 2015 |
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15363991 |
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12905984 |
Oct 15, 2010 |
9200324 |
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14832427 |
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61252110 |
Oct 15, 2009 |
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61304317 |
Feb 12, 2010 |
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61355087 |
Jun 15, 2010 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 33/564 20130101;
G01N 2333/70578 20130101; G01N 2333/4709 20130101; C12Q 1/6883
20130101; G01N 2333/485 20130101; G01N 2333/5412 20130101; G01N
2333/96494 20130101; G01N 33/53 20130101; G01N 2333/72 20130101;
G01N 2800/60 20130101; G01N 2333/70503 20130101; G01N 33/6893
20130101; C12Q 2600/118 20130101; G01N 2333/475 20130101; G01N
2333/4737 20130101; A61P 19/02 20180101; C12Q 2600/158 20130101;
G16B 40/00 20190201; G01N 2800/102 20130101 |
International
Class: |
G01N 33/564 20060101
G01N033/564 |
Claims
1.-231. (canceled)
232. A method for generating quantitative data for a first subject
comprising: performing at least one immunoassay on a first sample
from the first subject to generate a first dataset comprising the
quantitative data, wherein the quantitative data represents at
least twelve protein markers comprising: chitinase 3-like 1
(cartilage glycoprotein-39) (CHI3L1); C-reactive protein,
pentraxin-related (CRP); epidermal growth factor (beta-urogastrone)
(EGF); interleukin 6 (interferon, beta 2) (IL6); leptin (LEP);
matrix metallopeptidase 1 (interstitial collagenase) (MMP1); matrix
metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin
(RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor
superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule
1 (VCAM1); and, vascular endothelial growth factor A (VEGFA); and
wherein the first subject has rheumatoid arthritis (RA) or is
suspected of having RA.
233. The method of claim 232, wherein the at least twelve protein
markers consist of: IL6, EGF, VEGFA, LEP, SAA1, VCAM1, CRP, MMP1,
MMP3, TNFRSF1A, RETN, and CHI3L1.
234. The method of claim 232, wherein performance of the at least
one immunoassay comprises: obtaining the first sample, wherein the
first sample comprises the protein markers; contacting the first
sample with a plurality of distinct reagents; generating a
plurality of distinct complexes between the reagents and markers;
and detecting the complexes to generate the data.
235. The method of claim 232, wherein the at least one immunoassay
comprises a multiplex assay.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is related to and claims the benefit of
U.S. Provisional Application No. 61/252,110, filed on Oct. 15,
2009, U.S. Provisional Application No. 61/304,317 and U.S.
Provisional Application 61/355,087, filed on Jun. 15, 2010, all of
which are herein incorporated by reference in their entirety for
all purposes.
INTRODUCTION
[0002] The present teachings are generally directed to biomarkers
associated with inflammatory disease, and methods of characterizing
biological conditions by scoring quantitative datasets derived from
a subject sample, as well as various other embodiments as described
herein.
[0003] The section headings used herein are for convenience and
organizational purposes only, and are not to be construed as
limiting the subject matter described in any way. All literature
and similar materials cited in this application, including but not
limited to scientific publications, articles, books, treatises,
published patent applications, issued patents, and internet web
pages, regardless the format of such literature and similar
materials, are expressly incorporated by reference in their
entirety for any purpose.
BACKGROUND
[0004] This application is directed to the fields of bioinformatics
and inflammatory and autoimmune diseases, with rheumatoid arthritis
(RA) as an example of these diseases. The present teachings relate
to methods and compositions for assessing, diagnosing, monitoring,
and selecting treatment for inflammatory disease and autoimmune
disease: e.g., RA.
[0005] RA is an example of an inflammatory disease, and is a
chronic, systemic autoimmune disorder. It is one of the most common
systemic autoimmune diseases worldwide. The immune system of the RA
subject targets his/her own joints as well as other organs
including the lung, blood vessels and pericardium, leading to
inflammation of the joints (arthritis), widespread endothelial
inflammation, and even destruction of joint tissue. Erosions and
joint space narrowing are largely irreversible and result in
cumulative disability.
[0006] The precise etiology of RA has not been established, but
underlying disease pathogenesis is complex and includes
inflammation and immune dysregulation. The precise mechanisms
involved are different in individual subjects, and can change in
those subjects over time. Variables such as race, sex, genetics,
hormones, and environmental factors can impact the development and
severity of RA disease. Emerging data are also beginning to reveal
the characteristics of new RA subject subgroups and complex
overlapping relationships with other autoimmune disorders. Disease
duration and level of inflammatory activity is also associated with
other comorbidities such as risk of lymphoma, extra-articular
manifestations, and cardiovascular disease. See, e.g., S. Banerjee
et al., Am. J. Cardiol. 2008, 101(8):1201-1205; E. Baecklund et at,
Arth. Rheum. 2006, 54(3):692-701; and, N. Goodson et al., Ann.
Rheum. Dis. 2005, 64(11):1595-1601. Because of the complexity of
RA, it is difficult to develop a single test that can accurately
and consistently assess, quantify, and monitor RA disease activity
in every subject.
[0007] Traditional models for treating RA are based on the
expectation that controlling disease activity (i.e., inflammation)
in an RA subject should slow or prevent disease progression, in
terms of tissue destruction, cartilage loss and joint erosion.
There is evidence, however, that disease activity and disease
progression can be uncoupled, and may not always function
completely in tandem. Indeed, different cell signaling pathways and
mediators are involved in these two processes. See W. van den Berg
et al., Arth. Rheum. 2005, 52:995-999. The uncoupling of disease
progression and disease activity is described in a number of RA
clinical trials and animal studies. See, e.g., P E Lipsky et al.,
N. Engl. J. Med. 2003, 343:1594-602; A K Brown et al., Arth. Rheum.
2006, 54:3761-3773; and, A R Pettit et al., Am. J. Pathol. 2001,
159:1689-99. Studies of RA subjects indicate limited association
between clinical and radiographic responses. See E. Zatarain and V.
Strand, Nat. Clin. Pract. Rheum. 2006, 2(11):611-618 (Review). RA
subjects have been described who demonstrated radiographic benefits
from combination treatment with infliximab and methotrexate (MTX),
yet did not demonstrate any clinical improvement, as measured by
DAS (Disease Activity Score) and CRP (C-reactive protein). See J S
Smolen et al., Arth. Rheum. 2005, 52(4):1020-30. To best study the
uncoupling of disease progression and activity (erosion and
inflammation, respectively), and to analyze the relationship
between disease activity and progression, RA subjects should be
assessed frequently for both disease activity and progression.
[0008] An increasing number of studies have demonstrated that
frequent monitoring of disease activity (known as "tight control")
results in quicker improvement in and better subject outcomes. The
underlying reason for regularly monitoring an RA subject's disease
activity, using appropriate and validated assessment tools, is
because RA disease in general displays a highly variable and
unpredictable course of progression. In chronic inflammatory
diseases, and RA in particular, treatment is ultimately aimed at
remission. It has been shown that a greater proportion of subjects
with monthly disease activity assessments were in remission at one
year compared to those receiving standard of care (standard of care
being no assessment of disease activity, or assessments made less
frequently than monthly); and further, that subjects with monthly
disease activity assessments had better radiographic outcomes and
physical function compared to those with standard of care. See Y P
M Goekoop-Ruiterman et al., Ann. Rheum. Dis. 2009 (Epublication
Jan. 20, 2009); C. Grigor et al., Lancet 2004, 364:263-269; W.
Kievit et al., Ann. Rheum. Dis. 2008, 67(9):1229-1234; T. Mottonen
et al., Arth. Rheum. 2002, 46(4):894-898; V K Ranganath et al., J.
Rhewn. 2008, 35:1966-1971; T. Sokka et al., Clin. Exp. Rheum. 2006,
24(Suppl. 43):S74-76; L H D van Tuyl et al., Ann. Rheum. Dis. 2008,
67:1574-1577; and, S M M Verstappen et al., Ann. Rheum. Dis. 2007,
66:1443-1449. The ability to effectively monitor disease activity
would allow for tight control of subjects, thus leading to better
subject outcomes.
[0009] There is a need to classify subjects by disease activity in
order to ensure that each receives treatment that is appropriate
and optimized for that patient. In treatment for RA, for example,
the use of disease-modifying anti-rheumatic drug (DMARD)
combinations has become accepted for subjects who fail to respond
to a single DMARD. Studies analyzing treatment with MTX alone and
treatment with MTX in combination with other DMARDs demonstrate
that in DMARD-naive subjects, the balance of efficacy versus
toxicity favors MTX monotherapy, while in DMARD-inadequate
responders, the evidence is inconclusive. In regards to biologics
(e.g., anti-TNF.alpha.), studies support the use of biologics in
combination with MTX in subjects with early RA, or in subjects with
established RA who have not yet been treated with MTX. The number
of drugs available for treating RA is increasing; from this it
follows that the number of possible combinations of these drugs is
increasing as well. In addition, the chronological order in which
each drug in a combination is administered can be varied depending
on the needs of the subject. For the clinician to apply a simple
trial-and-error process to find the optimum treatment for the RA
subject from among the myriad of possible combinations, the
clinician runs the risk of under- or over treating the subject.
Irreversible joint damage for the subject could be the result. See,
e.g., A K Brown et al., Arth. Rheum. 2008, 58(10):2958-2967, and G.
Cohen et al., Ann. Rheum. Dis. 2007, 66:358-363. Clearly there
exists a need to accurately classify subjects by disease activity,
in order to establish their optimal treatment regimen.
[0010] Current clinical management and treatment goals, in the case
of RA, focus on the suppression of disease activity with the goal
of improving the subject's functional ability and slowing the
progression of joint damage. Clinical assessments of RA disease
activity include measuring the subject's difficulty in performing
activities, morning stiffness, pain, inflammation, and number of
tender and swollen joints, an overall assessment of the subject by
the physician, an assessment by the subject of how good s/he feels
in general, and measuring the subject's erythrocyte sedimentation
rate (ESR) and levels of acute phase reactants, such as CRP.
Composite indices comprising multiple variables, such as those just
described, have been developed as clinical assessment tools to
monitor disease activity. The most commonly used are: American
College of Rheumatology (ACR) criteria (D T Felson et al., Arth.
Rheum. 1993, 36(6):729-740 and D T Felson et al., Arth. Rheum.
1995, 38(6):727-735); Clinical Disease Activity Index (CDAI) (D.
Aletaha et al., Arth. Rheum. 2005, 52(9):2625-2636); the DAS (MLL
Prevoo et al., Arth. Rheum. 1995, 38(1):44-48 and A M van Gestel et
al., Arth. Rheum. 1998, 41(10):1845-1850); Rheumatoid Arthritis
Disease Activity Index (RADAI) (G. Stucki et al., Arth. Rheum.
1995, 38(6):795-798); and, Simplified Disease Activity Index (SDAI)
(J S Smolen et al., Rheumatology (Oxford) 2003, 42:244-257).
[0011] Current laboratory tests routinely used to monitor disease
activity in RA subjects, such as CRP and ESR, are relatively
non-specific (e.g., are not RA-specific and cannot be used to
diagnose RA), and cannot be used to determine response to treatment
or predict future outcomes. See, e.g., L. Gossec et al., Ann.
Rheum. Dis. 2004, 63(6):675-680; E J A Kroot et al., Arth. Rheum.
2000, 43(8):1831-1835; H. Makinen et al., Ann. Rheum. Dis. 2005,
64(10):1410-1413; Z. Nadareishvili et al., Arth. Rheum. 2008,
59(8): 1090-1096; N A Khan et al., Abstract, ACR/ARHP Scientific
Meeting 2008; T A Pearson et al., Circulation 2003, 107(3):499-511;
M J Plant et al., Arth. Rheum. 2000, 43(7):1473-1477; T. Pincus et
al., Clin. Exp. Rheum. 2004, 22(Suppl. 35):S50-S56; and, P M Ridker
et al., NEJM 2000, 342(12):836-843. In the case of ESR and CRP, RA
subjects may continue to have elevated ESR or CRP levels despite
being in clinical remission (and non-RA subjects may display
elevated ESR or CRP levels). Some subjects in clinical remission,
as determined by DAS, continue to demonstrate continued disease
progression radiographically, by erosion. Furthermore, some
subjects who do not demonstrate clinical benefits still demonstrate
radiographic benefits from treatment. See, e.g., F C Breedveld et
al., Arth. Rheum. 2006, 54(1):26-37. Clearly, in order to predict
future outcome and treat the RA subject accordingly, there is a
need for clinical assessment tools that accurately assess an RA
subject's disease activity level and that act as predictors of
future course of disease.
[0012] Clinical assessments of disease activity contain subjective
measurements of RA, such as signs and symptoms, and
subject-reported outcomes, all difficult to quantify consistently.
In clinical trials, the DAS is generally used for assessing RA
disease activity. The DAS is an index score of disease activity
based in part on these subjective parameters. Besides its
subjectivity component, another drawback to use of the DAS as a
clinical assessment of RA disease activity is its invasiveness. The
physical examination required to derive a subject's DAS can be
painful, because it requires assessing the amount of tenderness and
swelling in the subject's joints, as measured by the level of
discomfort felt by the subject when pressure is applied to the
joints. Assessing the factors involved in DAS scoring is also
time-consuming. Furthermore, to accurately determine a subject's
DAS requires a skilled assessor so as to minimize wide inter- and
intra-operator variability. A method of clinically assessing
disease activity is needed that is less invasive and time-consuming
than DAS, and more consistent, objective and quantitative, while
being specific to the disease assessed (such as RA).
[0013] Developing biomarker-based tests (e.g., measuring
cytokines), e.g. specific to the clinical assessment of RA, has
proved difficult in practice because of the complexity of RA
biology--the various molecular pathways involved and the
intersection of autoimmune dysregulation and inflammatory response.
Adding to the difficulty of developing RA-specific biomarker-based
tests are the technical challenges involved; e.g., the need to
block non-specific matrix binding in serum or plasma samples, such
as rheumatoid factor (RF) in the case of RA. The detection of
cytokines using bead-based immunoassays, for example, is not
reliable because of interference by RF; hence, RF-positive subjects
cannot be tested for RA-related cytokines using this technology
(and RF removal methods attempted did not significantly improve
results). See S. Churchman et al., Ann. Rheum. Dis. 2009,
68:A1-A56, Abstract A77. Approximately 70% of RA subjects are
RF-positive, so any biomarker-based test that cannot assess
RF-positive patients is obviously of limited use.
[0014] To achieve the maximum therapeutic benefits for individual
subjects, it is important to be able to specifically quantify and
assess the subject's disease activity at any particular time,
determine the effects of treatment on disease activity, and predict
future outcomes. No existing single biomarker or multi-biomarker
test produces results demonstrating a high association with level
of RA disease activity. The embodiments of the present teachings
identify multiple serum biomarkers for the accurate clinical
assessment of disease activity in subjects with chronic
inflammatory disease, such as RA, along with methods of their
use.
SUMMARY
[0015] The present teachings relate to biomarkers associated with
inflammatory disease, and with autoimmune disease, including RA,
and methods of using the biomarkers to measure disease activity in
a subject.
[0016] One embodiment provides a method for scoring a sample, said
method comprising: receiving a first dataset associated with a
first sample obtained from a first subject, wherein said first
dataset comprises quantitative data for at least two markers
selected from the group consisting of: apolipoprotein A-I (APOA1);
apolipoprotein C-III (APOC3); calprotectin (heteropolymer of
protein subunits S100A8 and S100A9); chemokine (C-C motif) ligand
22 (CCL22); chitinase 3-like 1 (cartilage glycoprotein-39)
(CHI3L1); C-reactive protein, pentraxin-related (CRP); epidermal
growth factor (beta-urogastrone) (EGF); intercellular adhesion
molecule 1 (ICAM1); ICTP interleukin 18 (interferon-gamma-inducing
factor) (IL18); interleukin 1, beta (IL1B); interleukin 1 receptor
antagonist (IL1RN); interleukin 6 (interferon, beta 2) (IL6);
interleukin 6 receptor (IL6R); interleukin 8 (IL8); keratan
sulfate; leptin (LEP); matrix metallopeptidase 1 (interstitial
collagenase) (MMP1); matrix metallopeptidase 3 (stromelysin 1,
progelatinase) (MMP3); pyridinoline (PYD); resistin (RETN); serum
amyloid A1 (SAA1); tumor necrosis factor receptor superfamily,
member 1A (TNFRSF1A); tumor necrosis factor (ligand) superfamily,
member 13b (TNFSF13B, or BAFF); vascular cell adhesion molecule 1
(VCAM1); and, vascular endothelial growth factor A (VEGFA); and
determining, a first DAI score from said first dataset using an
interpretation function, wherein said first DAI score provides a
quantitative measure of inflammatory disease activity in said first
subject.
[0017] In one embodiment first dataset is obtained by a method
comprising obtaining said first sample from said first subject,
wherein said first sample comprises a plurality of analytes;
contacting said first sample with a reagent, generating a plurality
of complexes between said reagent and said plurality of analytes;
and detecting said plurality of complexes to obtain said first
dataset associated with said first sample, wherein said first
dataset comprises quantitative data for said least two markers.
[0018] In one embodiment said at least two markers are selected
from the group consisting of: chitinase 3-like 1 (cartilage
glycoprotein-39) (CHI3L1); C-reactive protein, pentraxin-related
(CRP); epidermal growth factor (beta-urogastrone) (EGF);
interleukin 6 (interferon, beta 2) (IL6); leptin (LEP); matrix
metallopeptidase 1 (interstitial collagenase) (MMP1); matrix
metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin
(RETN); serum amyloid A1 (SAA1); tumor necrosis factor receptor
superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule
1 (VCAM1) and vascular endothelial growth factor A (VEGFA).
[0019] In one embodiment said at least two markers are selected
from the group consisting of IL6, EGF, VEGFA, LEP, SAA1, VCAM1,
CRP, MMP1, MMP3, TNFRSF1A, RETN, and CHI3L1.
[0020] In one embodiment the method further comprises reporting
said DAI score to said first subject.
[0021] In one embodiment said inflammatory disease activity is
rheumatoid arthritis disease activity and further comprising
predicting a Sharp score change for said first subject, based on
said DAI score.
[0022] In one embodiment said interpretation function is based on a
predictive model.
[0023] In one embodiment said predictive model is developed using
an algorithm comprising a forward linear stepwise regression
algorithm; a Lasso shrinkage and selection method for linear
regression; or an Elastic Net for regularization and variable
selection for linear regression.
[0024] In one embodiment said algorithm is DAI
score=(0.56*sqrt(IPTJC))+(0.28*sqrt(IPSJC))+(0.14*(PPOA))+(0.36*ln(CRP/10-
.sup.6+1))+0.96; wherein IPTJC=Improved
PTJC=max(0.1739*PTJC+0.7865*PSJC,0); IPSJC=Improved
PSJC=max(0.1734*PTJC+0.7839*PSJC,0); PTJC=Prediction of Tender
Joint
Count=-38.564+3.997*(SAA1).sup.1/10+17.331*(IL6).sup.1/10+4.665*(CHI3L1).-
sup.1/10-15.236*(EGF).sup.1/10+2.651*(TNFRSF1A).sup.1/10+2.641*(LEP).sup.1-
/10+4.026*(VEGFA).sup.1/10-1.47*(VCAM1).sup.1/10; PSJC=Prediction
of Swollen Joint
Count=-25.444+4.051*(SAA1).sup.1/10+16.154*(IL6).sup.1/10-11.847*(EGF).su-
p.1/10+3.091*(CHI3L1).sup.1/10+0.353*(TNFRSF1A).sup.1/10;
PPGA=Prediction of Patient Global
Assessment=-13.489+5.474*(IL6).sup.1/10+0.486*(SAA1).sup.1/10+2.246*(MMP1-
).sup.1/10+1.684*(leptin).sup.1/10+4.14*(TNFRSF1A).sup.1/10+2.292*(VEGFA).-
sup.1/10-1.898*(EGF).sup.1/10+0.028*(MMP3).sup.1/10-2.892*(VCAM1).sup.1/10-
-0.506*(RETN).sup.1/10 wherein units for all biomarkers are
pg/mL.
[0025] In one embodiment said algorithm is DAI
score=(0.56*sqrt(IPTJC))+(0.28*sqrt(IPSJC))+(0.14*(PPGA))+(0.36*ln(CRP+1)-
)+0.96; wherein IPTJC=Improved PTJC=max(0.1739*PTJC+0.7865*PSJC,0);
IPSJC=Improved PSJC=max(0.1734*PTJC+0.7839*PSJC,0); PTJC=Prediction
of Tender Joint
Count=-38.564+3.997*(SAA1).sup.1/10+17.331*(IL6).sup.1/10+4.665*(CHI3L1).-
sup.1/10-15.236*(EGF).sup.1/10+2.651*(TNFRSF1A).sup.1/10+2.641*(LEP).sup.1-
/10+4.026*(VEGFA).sup.1/10-1.47*(VCAM1).sup.1/10; PSJC=Prediction
of Swollen Joint
Count=-25.444+4.051*(SAA1).sup.1/10+16.154*(IL6).sup.1/10-11.847*(EGF).su-
p.1/10+3.091*(CHI3L).sup.1/10+0.353*(TNFRSF1A).sup.1/10;
PPGA=Prediction of Patient Global
Assessment=-13.489+5.474*(IL6).sup.1/10+0.486*(SAA1).sup.1/10+2.246*(MMP1-
).sup.1/10+1.684*(leptin).sup.1/10+4.14*(TNFRSF1A).sup.1/10+2.292*(VEGFA).-
sup.1/10-1.898*(EGF).sup.1/10+0.028*(MMP3).sup.1/10-2.892*(VCAM1).sup.1/10-
-0.506*(RETN).sup.1/10 wherein units for CRP are mg/L and for other
biomarkers are pg/mL.
[0026] In one embodiment, the method further comprises determining
a scaled DAI score wherein said scaled DAI score=round(max(min((DAI
score)*10.53+1, 100),1)).
[0027] In one embodiment said first DAI score is predictive of a
clinical assessment.
[0028] In one embodiment said clinical assessment is selected from
the group consisting of: a DAS, a DAS28, a Sharp score, a tender
joint count (TJC), and a swollen joint count (SJC).
[0029] In one embodiment said clinical assessment is a DAS.
[0030] In one embodiment said clinical assessment is a DAS28.
[0031] In one embodiment said DAS28 comprises a component selected
from the group consisting of tender joint count (TJC), the swollen
joint count (SJC), and the patient global health assessment.
[0032] In one embodiment said clinical assessment is TJC and said
first dataset comprises quantitative data for at least one marker
selected from the group consisting of CHI3L1, EGF, IL6, LEP, SAA1,
TNFRSF1A, VCAM1, and VEGFA.
[0033] In one embodiment said clinical assessment is SJC and said
first dataset comprises quantitative data for at least one marker
selected from the group consisting of CHI3L1, EGF, IL6, SAA1, and
TNFRSF1A.
[0034] In one embodiment said clinical assessment is patient global
health assessment and said first dataset comprises quantitative
data for at least one marker selected from the group consisting of
EGF, IL6, LEP, MMP1, MMP3, RETN, SAA1, TNFRSF1A, VCAM1, and
VEGFA.
[0035] In one embodiment, the method further comprises receiving a
second dataset associated with a second sample obtained from said
first subject, wherein said first sample and said second sample are
obtained from said first subject at different times; determining a
second DAI score from said second dataset using said interpretation
function; and comparing said first DAI score and said second DAI
score to determine a change in said DAI scores, wherein said change
indicates a change in said inflammatory disease activity in said
first subject.
[0036] In one embodiment said inflammatory disease activity is
rheumatoid arthritis activity and said indicated change in
rheumatoid arthritis disease activity indicates the presence,
absence or extent of the subject's response to a therapeutic
regimen.
[0037] In one embodiment, the method further comprises determining
a rate of said change in DAI scores, wherein said rate indicates
the extent of said first subject's response to a therapeutic
regimen.
[0038] In one embodiment said inflammatory disease activity is
rheumatoid arthritis disease activity and further comprising
predicting a Sharp score change rate for said first subject, based
on said indicated change in rheumatoid arthritis disease
activity.
[0039] In one embodiment the method further comprises determining a
prognosis for rheumatoid arthritis progression in said first
subject based on said predicted Sharp score change rate.
[0040] In one embodiment said inflammatory disease is rheumatoid
arthritis.
[0041] In one embodiment said inflammatory disease is
undifferentiated arthritis.
[0042] In one embodiment one of said at least two markers is CRP or
SAA1.
[0043] In one embodiment said DAI score is used as an inflammatory
disease surrogate endpoint, the inflammatory disease may be
rheumatoid arthritis.
[0044] In one embodiment a method for determining a presence or
absence of rheumatoid arthritis in a subject is provided, the
method comprising determining DAI scores according the disclosed
methods for subjects in a population wherein said subjects are
negative for rheumatoid arthritis; deriving an aggregate DAI value
for said population based on said determined DAI scores;
determining a second DAI score for a second subject; comparing the
aggregate DAI value to the second DAI score; and determining a
presence or absence of rheumatoid arthritis in said second subject
based on said comparison.
[0045] In one embodiment said first subject has received a
treatment for rheumatoid arthritis, and the method further
comprises the steps of determining a second DAI score according to
the disclosed method for a second subject wherein said second
subject is of the same species as said first subject and wherein
said second subject has received treatment for rheumatoid
arthritis; comparing said first DAI score to said second DAI score;
and determining a treatment efficacy for said first subject based
on said score comparison.
[0046] In one embodiment the method further comprises determining a
response to rheumatoid arthritis therapy based on said DAI
score.
[0047] In one embodiment the method further comprises selecting a
rheumatoid arthritis therapeutic regimen based on said DAI
score.
[0048] In one embodiment the method further comprises determining a
rheumatoid arthritis treatment course based on said DAI score.
[0049] In one embodiment the method further comprises rating a
rheumatoid arthritis disease activity as low or high based on said
DAI score.
[0050] In one embodiment said predictive model performance is
characterized by an AUC ranging from 0.60 to 0.99.
[0051] In one embodiment said predictive model performance is
characterized by an AUC ranging from 0.70 to 0.79.
[0052] In one embodiment said predictive model performance is
characterized by an AUC ranging from 0.80 to 0.89.
[0053] In one embodiment said at least two markers comprise (APOA1
and IL8), (Calprotectin and CRP), (Calprotectin and EGF),
(Calprotectin and IL8), (CRP and APOA1), (CRP and APOC3), (CRP and
CCL22), (CRP and CHI3L1), (CRP and EGF), (CRP and ICAM1), (CRP and
IL1B), (CRP and IL6), (CRP and IL6R), (CRP and IL8), (CRP and LEP),
(CRP and MMP1), (CRP and MMP3), (CRP and RETN), (CRP and SAA1),
(CRP and TNFRSF1A), (CRP and VCAM1), (CRP and VEGF), (EGF and
APOA1), (EGF and CHI3L1), (EGF and ICAM1), (EGF and IL8), (EGF and
LEP), (EGF and MMP1), (EGF and TNFRSF1A), (EGF and VCAM1), (ICAM1
and IL8), (IL1RN and CRP), (IL1RN and EGF), (IL1RN and IL8), (IL8
and APOC3), (IL8 and CCL22), (IL8 and CHI3L), (IL8 and IL6), (IL8
and IL6R), (IL8 and TNFRSF1A), (LEP and IL8), (MMP3 and IL8), (RETN
and IL8), (SAA1 and EGF), (SAA1 and IL8), (SAA1 and LEP), (SAA1 and
RETN), or (VCAM1 and IL8).
[0054] In one embodiment said at least two markers comprise
(calprotectin and CHI3L1), (calprotectin and interleukin),
(calprotectin and LEP), (calprotectin and pyridinoline),
(calprotectin and RETN), (CCL22 and calpotectin), (CCL22 and CRP),
(CCL22 and IL6), (CCL22 and SAA1), (CRP and calprotectin), (CRP and
CHI3L1), (CRP and EGF), (CRP and ICAM1), (CRP and IL1B), (CRP and
IL1RN), (CRP and IL6), (CRP and IL6R), (CRP and IL8), (CRP and
LEP), (CRP and MMP1), (CRP and MMP3), (CRP and pyridinoline), (CRP
and RETN), (CRP and SAA1), (CRP and TNFRSF1A), (CRP and VCAM1),
(CRP and VEGFA), (EGF and calprotectin), (EGF and IL6), (EGF and
SAA1), (ICAM1 and calprotectin), (ICAM1 and IL6), (ICAM1 and SAA1),
(IL1B and calprotectin), (IL1B and IL6), (IL1B and MMP3), (IL1B and
SAA1), (IL6 and calprotectin), (IL6 and CHI3L1), (IL6 and IL1RN),
(IL6 and IL8), (IL6 and LEP), (IL6 and MMP1), (IL6 and MMP3), (IL6
and pyridinoline), (IL6 and RETN), (IL6 and SAA1), (IL6 and
TNFRSF1A), (IL6 and VCAM1), (IL6 and VEGFA), (IL6R and
calprotectin), (IL6R and IL6), (IL6R and SAA1), (IL8 and
calprotectin), (IL8 and MMP3), (IL8 and SAA1), (MMP1 and
calprotectin), (MMP1 and SAA1), (MMP3 and calprotectin), (MMP3 and
CHI3L1), (MMP3 and SAA1), (SAA1 and calprotectin), (SAA1 and
CHI3L1), (SAA1 and IL1RN), (SAA1 and LEP), (SAA1 and pyridinoline),
(SAA1 and RETN), (SAA1 and TNFRSF1A), (SAA1 and VCAM1), (SAA1 and
VEGFA), (TNFRSF1A and calprotectin), (VCAM1 and calprotectin); or,
(VEGFA and calprotectin)
[0055] In one embodiment said at least two markers comprise one set
of markers selected from the group consisting of TWOMRK Set Nos. 1
through 208 of FIG. 1.
[0056] In one embodiment said at least two markers comprise one set
of markers selected from the group consisting of TWOMRK Set Nos. 1
through 157 of FIG. 17.
[0057] In one embodiment said at least two markers comprises at
least three markers selected from the group consisting of:
apolipoprotein A-I (APOA1); apolipoprotein C-III (APOC3); chemokine
(C-C motif) ligand 22 (CCL22); chitinase 3-like 1 (cartilage
glycoprotein-39) (CHI3L1); ICTP; C-reactive protein,
pentraxin-related (CRP); epidermal growth factor (beta-urogastrone)
(EGF); intercellular adhesion molecule 1 (ICAM1); interleukin 18
(interferon-gamma-inducing factor) (IL18); interleukin 1, beta
(IL1B); interleukin 1 receptor antagonist (IL1RN); interleukin 6
(interferon, beta 2) (IL6); interleukin 6 receptor (IL6R);
interleukin 8 (IL8); keratan sulfate; leptin (LEP); matrix
metallopeptidase 1 (interstitial collagenase) (MMP1); matrix
metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin
(RETN); calprotectin (heteropolymer of protein subunits S100A8 and
S100A9); serum amyloid A1 (SAA1); tumor necrosis factor receptor
superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule
1 (VCAM1); vascular endothelial growth factor A (VEGFA); and,
pyridinoline (PYD).
[0058] In one embodiment said at least two markers comprises one
set of three markers selected from the group consisting of THREEMRK
Set Nos. 1 through 378 of FIG. 2 and THREEMRK Set Nos. 1 through
236 of FIG. 18.
[0059] In one embodiment said at least two markers comprises one
set of three markers selected from the group consisting of THREEMRK
Set Nos. 1 through 236 of FIG. 18.
[0060] In one embodiment said at least two markers comprises at
least four markers selected from the group consisting of:
apolipoprotein A-I (APOA1); apolipoprotein C-III (APOC3); chemokine
(C-C motif) ligand 22 (CCL22); chitinase 3-like 1 (cartilage
glycoprotein-39) (CHI3L); ICTP; C-reactive protein,
pentraxin-related (CRP); epidermal growth factor (beta-urogastrone)
(EGF); intercellular adhesion molecule 1 (ICAM1); interleukin 18
(interferon-gamma-inducing factor) (IL18); interleukin 1, beta
(IL1B); interleukin 1 receptor antagonist (IL1RN); interleukin 6
(interferon, beta 2) (IL6); interleukin 6 receptor (IL6R);
interleukin 8 (IL8); keratan sulfate; leptin (LEP); matrix
metallopeptidase 1 (interstitial collagenase) (MMP1); matrix
metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin
(RETN); calprotectin (heteropolymer of protein subunits S100AS and
S100A9); serum amyloid A1 (SAA1); tumor necrosis factor receptor
superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule
1 (VCAM1); vascular endothelial growth factor A (VEGFA); and,
pyridinoline (PYD).
[0061] In one embodiment said at least two markers comprises one
set of four markers selected from the group consisting of FOURMRK
Set Nos. 1 through 54 of FIG. 3.
[0062] In one embodiment said at least two markers comprises one
set of four markers selected from the group consisting of FOURMRK
Set Nos. 1 through 266 of FIG. 19.
[0063] In one embodiment said at least two markers comprises at
least five markers selected from the group consisting of:
apolipoprotein A-I (APOA1); apolipoprotein C-III (APOC3); chemokine
(C-C motif) ligand 22 (CCL22); chitinase 3-like 1 (cartilage
glycoprotein-39) (CHI3L1); ICTP; C-reactive protein,
pentraxin-related (CRP); epidermal growth factor (beta-urogastrone)
(EGF); intercellular adhesion molecule 1 (ICAM1); interleukin 18
(interferon-gamma-inducing factor) (IL18); interleukin 1, beta
(IL1B); interleukin 1 receptor antagonist (IL1RN); interleukin 6
(interferon, beta 2) (IL6); interleukin 6 receptor (IL6R);
interleukin 8 (IL8); keratan sulfate; leptin (LEP); matrix
metallopeptidase 1 (interstitial collagenase) (MMP1); matrix
metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin
(RETN); calprotectin (heteropolymer of protein subunits S100A8 and
S100A9); serum amyloid A1 (SAA1); tumor necrosis factor receptor
superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule
1 (VCAM1); vascular endothelial growth factor A (VEGFA); and,
pyridinoline (PYD).
[0064] In one embodiment said at least two markers comprises one
set of five markers selected from the group consisting of FIVEMRK
Set Nos. 1 through 44 of FIG. 4.
[0065] In one embodiment said at least two markers comprises one
set of five markers selected from the group consisting of FIVEMRK
Set Nos. 1 through 236 of FIG. 20.
[0066] In one embodiment said at least two markers comprises at
least six markers selected from the group consisting of:
apolipoprotein A-I (APOA1); apolipoprotein C-III (APOC3); chemokine
(C-C motif) ligand 22 (CCL22); chitinase 3-like 1 (cartilage
glycoprotein-39) (CHI3L1); ICTP, C-reactive protein,
pentraxin-related (CRP); epidermal growth factor (beta-urogastrone)
(EGF); intercellular adhesion molecule 1 (ICAM1); interleukin 18
(interferon-gamma-inducing factor) (IL18); interleukin 1, beta
(IL1B); interleukin 1 receptor antagonist (IL1RN); interleukin 6
(interferon, beta 2) (IL6); interleukin 6 receptor (IL6R);
interleukin 8 (IL8); keratan sulfate; leptin (LEP); matrix
metallopeptidase 1 (interstitial collagenase) (MMP1); matrix
metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin
(RETN); calprotectin (heteropolymer of protein subunits S100A8 and
S100A9); serum amyloid A1 (SAA1); tumor necrosis factor receptor
superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule
1 (VCAM1); vascular endothelial growth factor A (VEGFA); and,
pyridinoline (PYD).
[0067] In one embodiment said at least two markers comprises one
set of six markers selected from the group consisting of SIXMRK Set
Nos. 1 through 84 of FIG. 5.
[0068] In one embodiment said at least two markers comprises one
set of six markers selected from the group consisting of SIXMRK Set
Nos. 1 through 192 of FIG. 21.
[0069] In one embodiment said at least two markers comprises
calprotectin. CCL22, CRP, EGF, ICAM1, CHI3L1, ICTP, IL1B, IL1RA,
IL6, IL6R, IL8, LEP, MMP1, MMP3, pyridinoline, RETN, SAA1,
TNFRSF1A, VCAM1 and VEGFA.
[0070] In one embodiment said at least two markers comprises IL6,
EGF, VEGFA, LEP, SAA1, VCAM1, CRP, MMP1, MMP3, TNFRSF1A, RETN, and
CHI3L1.
[0071] Also provided are computer-implemented methods, systems and
computer-readable storage mediums with program code for carrying
out the disclosed methods.
BRIEF DESCRIPTION OF THE DRAWINGS
[0072] The skilled artisan will understand that the drawings,
described below, are for illustration purposes only. The drawings
are not intended to limit the scope of the present teachings in any
way.
[0073] FIG. 1 depicts a list of two-biomarker (TWOMRK) sets or
panels, as described in certain embodiments of the present
teachings, and according to Example 1. Models were run for all
possible two-biomarker combinations of the DAIMRK biomarkers
analyzed in Example 1. DAI scores derived from the levels of a set
of biomarkers comprising the TWOMRK sets of biomarkers in FIG. 1
demonstrated a strong predictive ability to classify subject
disease activity, as evidenced by the AUC values shown (greater
than or equal to 0.60). In this and following figures, correlations
of the DAI scores with DAS28 are shown by r, as estimated using 100
test set cross-validation.
[0074] FIG. 2 depicts a list of three-biomarker (THREEMRK) sets or
panels, as described in certain embodiments of the present
teachings, and according to the methods of Example 1. DAI scores
derived from the levels of a set of biomarkers comprising the
THREEMRK sets of biomarkers in FIG. 2 demonstrated a strong
association with DAS28-CRP, as evidenced by the AUC values shown
(greater than or equal to 0.65). Note that the list of THREEMRK
sets in FIG. 2 does not contain any panels comprising the two
biomarkers of FIG. 1, as this would be redundant (FIG. 1 describes
biomarker sets comprising the TWOMRK sets, not consisting of the
TWOMRK sets).
[0075] FIG. 3 depicts a list of four-biomarker (FOURMRK) sets or
panels, as described in certain embodiments of the present
teachings, and according to Example 1. DAI scores derived from the
levels of a set of biomarkers comprising the FOURMRK sets of
biomarkers in FIG. 3 demonstrated a strong association with
DAS28-CRP, as evidenced by the AUC values shown (greater than or
equal to 0.70). Note that the list of FOURMRK sets in FIG. 3 does
not contain any panels comprising the three biomarkers of FIG. 2,
as this would be redundant (FIG. 2 describes biomarker sets
comprising the THREEMRK sets, not consisting of the THREEMRK
sets).
[0076] FIG. 4 depicts a list of five-biomarker (FIVEMRK) sets or
panels, as described in certain embodiments of the present
teachings, and according to Example 1. DAI scores derived from the
levels of a set of biomarkers comprising the FIVEMRK sets of
biomarkers in FIG. 4 demonstrated a strong association with
DAS28-CRP, as evidenced by the AUC values shown (greater than or
equal to 0.70). Note that the list of FIVEMRK sets in FIG. 4 does
not contain any panels comprising the four biomarkers of FIG. 3, as
this would be redundant (FIG. 3 describes biomarker sets comprising
the FOURMRK sets, not consisting of the FOURMRK sets).
[0077] FIG. 5 depicts a list of six-biomarker (SIXMRK) sets or
panels, as described in certain embodiments of the present
teachings, and according to Example 1. DAI scores derived from the
levels of a set of biomarkers comprising the SIXMRK sets of
biomarkers in FIG. 5 demonstrated a strong association with
DAS28-CRP, as evidenced by the AUC values shown (greater than or
equal to 0.70). Note that the list of SIXMRK sets in FIG. 5 does
not contain any panels comprising the five biomarkers of FIG. 4, as
this would be redundant (FIG. 4 describes biomarker sets comprising
the FIVEMRK sets, not consisting of the FIVEMRK sets).
[0078] FIG. 6 is a flow diagram, which describes an example of a
method for developing a model that can be used to determine the
inflammatory disease activity of a person or population.
[0079] FIG. 7 is a flow diagram, which describes an example of a
method for using the model of FIG. 6 to determine the inflammatory
disease activity of a subject or population.
[0080] FIG. 8 depicts the cumulative distribution function for
p-values and False Discovery Rate, "FDR," as related to the output
of the DAS28 and other response variables of Example 1, where the
FDR was used as multiple testing correction, according to the
following: let k be the largest i for which
p.sub.i.ltoreq.i/m*.alpha.; reject all H.sub.i, i=1, . . . , m. In
this equation the variable a is a pre-specified probability of a
false-positive (Type I) error, typically 0.05, and H is a
hypothesis.
[0081] FIG. 9 depicts a correlation matrix between the continuous
clinical variables and biomarkers of Example 1. Darker gray
indicates positive correlation, and lighter gray indicates negative
correlation.
[0082] FIG. 11 depicts the three-dimensional PCA plot of Example 1.
Each point represents a subject.
[0083] FIG. 12 depicts the use of ROC and AUC to show the ability
of DAI scores to classify subjects into high/low disease groups
(dichotomized on a DAS of 2.67, where DAS<2.67 is remission)
across all DAI cut-off points in 100 cross-validations. The curve
represents the average ROC curves across 100 cross-validations. See
Example 1.
[0084] FIG. 13 depicts the use of ROC and AUC to show the ability
of the DAI score to classify subjects into high/low disease groups
(dichotomized on a DAS of 3.9, the median of the DAS values in the
data) across all DAI cut-off points in 100 cross-validations. The
curve represents the average ROC curves across 100
cross-validations.
[0085] FIG. 14 depicts the accuracy (ACC) and error rates (ERR) of
the 100 cross-validation iterations of Example 2, where a DAS28-CRP
cut-off of 2.67 was used. Shown are the results of applying the
Lasso and Elastic Net models.
[0086] FIG. 15 depicts the accuracy and error rates of the 100
cross-validation iterations of Example 2, where a DAS28-CRP cut-off
of 3.94 was used. Shown are the results of applying the Lasso and
Elastic Net models.
[0087] FIG. 16 is a high-level block diagram of a computer (1600).
Illustrated are at least one processor (1602) coupled to a chipset
(1604). Also coupled to the chipset (1604) are a memory (1606), a
storage device (1608), a keyboard (1610), a graphics adapter
(1612), a pointing device (1614), and a network adapter (1616). A
display (1618) is coupled to the graphics adapter (1612). In one
embodiment, the functionality of the chipset (1604) is provided by
a memory controller hub 1620) and an I/O controller hub (1622). In
another embodiment, the memory (1606) is coupled directly to the
processor (1602) instead of the chipset (1604). The storage device
1608 is any device capable of holding data, like a hard drive,
compact disk read-only memory (CD-ROM), DVD, or a solid-state
memory device. The memory (1606) holds instructions and data used
by the processor (1602). The pointing device (1614) may be a mouse,
track ball, or other type of pointing device, and is used in
combination with the keyboard (1610) to input data into the
computer system (1600). The graphics adapter (1612) displays images
and other information on the display (1618). The network adapter
(1616) couples the computer system (1600) to a local or wide area
network.
[0088] FIG. 17 depicts another list of two-biomarker (TWOMRK) sets
or panels, as described in certain embodiments of the present
teachings, and according to Example 7. Models were run for all
possible two-biomarker combinations of the DAIMRK biomarkers
analyzed in Example 7. DAI scores derived from the levels of a set
of biomarkers comprising the TWOMRK sets of biomarkers in FIG. 17
demonstrated a strong predictive ability to classify subject
disease activity, as evidenced by the AUC values shown (greater
than or equal to 0.60).
[0089] FIG. 18 depicts another list of three-biomarker (THREEMRK)
sets or panels, as described in certain embodiments of the present
teachings, and according to the methods of Example 7. DAI scores
derived from the levels of a set of biomarkers comprising the
THREEMRK sets of biomarkers in FIG. 18 demonstrated a strong
association with DAS28-CRP, as evidenced by the AUC values shown
(greater than or equal to 0.60). Note that the list of THREEMRK
sets in FIG. 2 does not contain any panels comprising the two
biomarkers of FIG. 17, as this would be redundant (FIG. 17
describes biomarker sets comprising the TWOMRK sets, not consisting
of the TWOMRK sets).
[0090] FIG. 19 depicts another list of four-biomarker (FOURMRK)
sets or panels, as described in certain embodiments of the present
teachings, and according to Example 7. DAI scores derived from the
levels of a set of biomarkers comprising the FOURMRK sets of
biomarkers in FIG. 19 demonstrated a strong association with
DAS28-CRP, as evidenced by the AUC values shown (greater than or
equal to 0.65). Note that the list of FOURMRK sets in FIG. 19 does
not contain any panels comprising the three biomarkers of FIG. 18,
as this would be redundant (FIG. 18 describes biomarker sets
comprising the THREEMRK sets, not consisting of the THREEMRK
sets).
[0091] FIG. 20 depicts another list of five-biomarker (FIVEMRK)
sets or panels, as described in certain embodiments of the present
teachings, and according to Example 7. DAI scores derived from the
levels of a set of biomarkers comprising the FIVEMRK sets of
biomarkers in FIG. 20 demonstrated a strong association with
DAS28-CRP, as evidenced by the AUC values shown (greater than
0.65). Note that the list of FIVEMRK sets in FIG. 20 does not
contain any panels comprising the four biomarkers of FIG. 19, as
this would be redundant (FIG. 19 describes biomarker sets
comprising the FOURMRK sets, not consisting of the FOURMRK
sets).
[0092] FIG. 21 depicts another list of six-biomarker (SIXMRK) sets
or panels, as described in certain embodiments of the present
teachings, and according to Example 7. DAI scores derived from the
levels of a set of biomarkers comprising the SIXMRK sets of
biomarkers in FIG. 21 demonstrated a strong association with
DAS28-CRP, as evidenced by the AUC values shown (greater than
0.65). Note that the list of SIXMRK sets in FIG. 21 does not
contain any panels comprising the five biomarkers of FIG. 20, as
this would be redundant (FIG. 20 describes biomarker sets
comprising the FIVEMRK sets, not consisting of the FIVEMRK
sets).
[0093] FIG. 22 depicts a Venn diagram indicating biomarkers that
were used to predict various DAS components in deriving a DAI
score, as described in Example 11.
[0094] FIG. 23 depicts correlations of the DAI algorithm
predictions and CRP with clinical assessments of disease activity,
as described in Example 11.
[0095] FIG. 24 depicts the DAI scores for subjects at baseline and
six-month visits, according to the description in Example 11. DAI
scores are shown by treatment arm and time point. Only subjects
with DAI scores available at both baseline and six months are
shown.
DESCRIPTION OF VARIOUS EMBODIMENTS
[0096] These and other features of the present teachings will
become more apparent from the description herein. While the present
teachings are described in conjunction with various embodiments, it
is not intended that the present teachings be limited to such
embodiments. On the contrary, the present teachings encompass
various alternatives, modifications, and equivalents, as will be
appreciated by those of skill in the art.
[0097] The present teachings relate generally to the identification
of biomarkers associated with subjects having inflammatory and/or
autoimmune diseases, such as for example RA, and that are useful in
determining or assessing disease activity.
[0098] Most of the words used in this specification have the
meaning that would be attributed to those words by one skilled in
the art. Words specifically defined in the specification have the
meaning provided in the context of the present teachings as a
whole, and as are typically understood by those skilled in the art.
In the event that a conflict arises between an art-understood
definition of a word or phrase and a definition of the word or
phrase as specifically taught in this specification, the
specification shall control. It must be noted that, as used in the
specification and the appended claims, the singular forms "a,"
"an," and "the" include plural referents unless the context clearly
dictates otherwise.
DEFINITIONS
[0099] "Accuracy" refers to the degree that a measured or
calculated value conforms to its actual value. "Accuracy" in
clinical testing relates to the proportion of actual outcomes (true
positives or true negatives, wherein a subject is correctly
classified as having disease or as healthy/normal, respectively)
versus incorrectly classified outcomes (false positives or false
negatives, wherein a subject is incorrectly classified as having
disease or as healthy/normal, respectively). Other and/or
equivalent terms for "accuracy" can include, for example,
"sensitivity," "specificity," "positive predictive value (PPV),"
"the AUC," "negative predictive value (NPV)," "likelihood," and
"odds ratio." "Analytical accuracy," in the context of the present
teachings, refers to the repeatability and predictability of the
measurement process. Analytical accuracy can be summarized in such
measurements as, e.g., coefficients of variation (CV), and tests of
concordance and calibration of the same samples or controls at
different times or with different assessors, users, equipment,
and/or reagents. See, e.g., R. Vasan, Circulation 2006,
113(19):2335-2362 for a summary of considerations in evaluating new
biomarkers.
[0100] The term "algorithm" encompasses any formula, model,
mathematical equation, algorithmic, analytical or programmed
process, or statistical technique or classification analysis that
takes one or more inputs or parameters, whether continuous or
categorical, and calculates an output value, index, index value or
score. Examples of algorithms include but are not limited to
ratios, sums, regression operators such as exponents or
coefficients, biomarker value transformations and normalizations
(including, without limitation, normalization schemes that are
based on clinical parameters such as age, gender, ethnicity, etc.),
rules and guidelines, statistical classification models, and neural
networks trained on populations. Also of use in the context of
biomarkers are linear and non-linear equations and statistical
classification analyses to determine the relationship between (a)
levels of biomarkers detected in a subject sample and (b) the level
of the respective subject's disease activity.
[0101] "ALLMRK" in the present teachings refers to a specific
group, panel or set of biomarkers, as the term "biomarkers" is
defined herein. Where the biomarkers of certain embodiments of the
present teachings are proteins, the gene symbols and names used
herein are to be understood to refer to the protein products of
these genes, and the protein products of these genes are intended
to include any protein isoforms of these genes, whether or not such
isoform sequences are specifically described herein. Where the
biomarkers are nucleic acids, the gene symbols and names used
herein are to refer to the nucleic acids (DNA or RNA) of these
genes, and the nucleic acids of these genes are intended to include
any transcript variants of these genes, whether or not such
transcript variants are specifically described herein. The ALLMRK
group of the present teachings is the group of markers consisting
of the following, where the name(s) or symbols in parentheses at
the end of the marker name generally refers to the gene name, if
known, or an alias: adiponectin, C1Q and collagen domain containing
(ADIPOQ); adrenomedullin (ADM); alkaline phosphatase,
liver/bone/kidney (ALPL); amyloid P component, serum (APCS);
advanced glycosylation end product-specific receptor (AGER);
apolipoprotein A-I (APOA1); apolipoprotein A-II (APOA2);
apolipoprotein B (including Ag(x) antigen) (APOB); apolipoprotein
C-II (APOC2); apolipoprotein C-III (APOC3); apolipoprotein E
(APOE); bone gamma-carboxyglutamate (gla) protein (BGLAP, or
osteocalcin); bone morphogenetic protein 6 (BMP6);
calcitonin-related polypeptide beta (CALCB); calprotectin (dimer of
S100A8 and S100A9 protein subunits); chemokine (C-C motif) ligand
22 (CCL22); CD40 ligand (CD40LG); chitinase 3-like 1 (cartilage
glycoprotein-39) (CHI3L1, or YKL-40); cartilage oligomeric matrix
protein (COMP); C-reactive protein, pentraxin-related (CRP); CS3B3
epitope, a cartilage fragment; colony stimulating factor 1
(macrophage) (CSF1, or MCSF); colony stimulating factor 2
(granulocyte-macrophage) (CSF2); colony stimulating factor 3
(granulocyte) (CSF3); cystatin C (CST3); epidermal growth factor
(beta-urogastrone) (EGF); epidermal growth factor receptor
(erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian)
(EGFR); erythropoietin (EPO); Fas (TNF receptor superfamily, member
6) (FAS); fibrinogen alpha chain (FGA); fibroblast growth factor 2
(basic) (FGF2); fibrinogen; fms-related tyrosine kinase 1 (vascular
endothelial growth factor/vascular permeability factor receptor)
(FLT1); fms-related tyrosine kinase 3 ligand (FLT3LG); fms-related
tyrosine kinase 4 (FLT4); follicle stimulating hormone; follicle
stimulating hormone, beta polypeptide (FSHB); gastric inhibitory
polypeptide (GIP); ghrelin; ghrelin/obestatin prepropeptide (GHRL);
growth hormone 1 (GH1); GLP1; hepatocyte growth factor (HGF);
haptoglobin (HP); intercellular adhesion molecule 1 (ICAM1);
intercellular adhesion molecule 3 (ICAM3); ICTP; interferon, alpha
1 (IFNA1); interferon, alpha 2 (IFNA2); glial cell derived
neurotrophic factor (GDNF); interferon, gamma (IFNG); insulin-like
growth factor binding protein 1 (IGFBP1); interleukin 10 (IL10);
interleukin 12; interleukin 12A (natural killer cell stimulatory
factor 1, cytotoxic lymphocyte maturation factor 1, p35) (IL12A);
interleukin 12B (natural killer cell stimulatory factor 2,
cytotoxic lymphocyte maturation factor 2, p40) (IL12B); interleukin
13 (IL13); interleukin 15 (IL15); interleukin 17A (IL17A);
interleukin 18 (interferon-gamma-inducing factor) (L8); interleukin
1, alpha (IL1A); interleukin 1, beta (IL1B); interleukin 1
receptor, type I (IL1R1); interleukin 1 receptor, type II (IL1R2);
interleukin 1 receptor antagonist (IL1RN, or IL1RA); interleukin 2
(IL2); interleukin 2 receptor, interleukin 2 receptor, alpha
(IL2RA); intedrleukin 3 (colony-stimulating factor, multiple)
(IL3); interleukin 4 (IL4); interleukin 4 receptor (IL4R);
interleukin 5 (colony-stimulating factor, eosinophil) (IL5);
interleukin 6 (interferon, beta 2) (IL6); interleukin 6 receptor
(IL6R); interleukin 6 signal transducer (gp130, oncostatin M
receptor) (IL6ST); interleukin 7 (IL7); interleukin 8 (IL8);
insulin (INS); interleukin 9 (IL9); kinase insert domain receptor
(a type III receptor tyrosine kinase) (KDR); v-kit Hardy-Zuckerman
4 feline sarcoma viral oncogene homolog (KIT); keratan sulfate, or
KS; leptin (LEP); leukemia inhibitory factor (cholinergic
differentiation factor) (LIF); lymphotoxin alpha (TNF superfamily,
member 1) (LTA); lysozyme (renal amyloidosis) (LYZ); matrix
metallopeptidase 1 (interstitial collagenase) (MMP1); matrix
metallopeptidase 10 (stromelysin 2) (MMP10); matrix
metallopeptidase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa type IV
collagenase) (MMP2); matrix metallopeptidase 3 (stromelysin 1,
progelatinase) (MMP3); matrix metallopeptidase 9 (gelatinase B, 92
kDa gelatinase, 92 kDa type IV collagenase) (MMP9); myeloperoxidase
(MPO); nerve growth factor (beta polypeptide) (NOF); natriuretic
peptide precursor B (NPPB, or NT-proBNP); neurotrophin 4 (NTF4);
platelet-derived growth factor alpha polypeptide (PDGFA); the dimer
of two PDGFA subunits (or PDGF-AA); the dimer of one PDGFA subunit
and one PDGFB subunit (or PDGF-AB); platelet-derived growth factor
beta polypeptide (PDGFB); prostaglandin E2 (PGE2);
phosphatidylinositol glycan anchor biosynthesis, class F (PIGF);
proopiomelanocortin (POMC); pancreatic polypeptide (PPY); prolactin
(PRL); pentraxin-related gene, rapidly induced by IL1 beta (PTX3,
or pentraxin 3); pyridinoline (PYD); peptide YY (PYY); resistin
(RETN); serum amyloid A1 (SAA1); selectin E (SELE); selectin L
(SELL); selectin P (granule membrane protein 140 kDa, antigen CD62)
(SELP); serpin peptidase inhibitor, clade E (nexin, plasminogen
activator inhibitor type 1), member 1 (SERPINE1); secretory
leukocyte peptidase inhibitor (SLPI); sclerostin (SOST); secreted
protein, acidic, cysteine-rich (SPARC, or osteonectin); secreted
phosphoprotein 1 (SPP1, or osteopontin); transforming growth
factor, alpha (TGFA); thrombomodulin (THBD); tumor necrosis factor
(TNF superfamily, member 2; or TNF-alpha) (TNF); tumor necrosis
factor receptor superfamily, member 11b (TNFRSF11B, or
osteoprotegerin); tumor necrosis factor receptor superfamily,
member 1A (TNFRSF1A); tumor necrosis factor receptor superfamily,
member 1B (TNFRSF1B); tumor necrosis factor receptor superfamily,
member 8 (TNFRSF8); tumor necrosis factor receptor superfamily,
member 9 (TNFRSF9); tumor necrosis factor (ligand) superfamily,
member 11 (TNFSF11, or RANKL); tumor necrosis factor (ligand)
superfamily, member 12 (TNFSF12, or TWEAK); tumor necrosis factor
(ligand) superfamily, member 13 (TNFSF13, or APRIL); tumor necrosis
factor (ligand) superfamily, member 13b (TNFSF13B, or BAFF); tumor
necrosis factor (ligand) superfamily, member 14 (TNFSF14, or
LIGHT); tumor necrosis factor (ligand) superfamily, member 18
(TNFSFS1); thyroid peroxidase (TPO); vascular cell adhesion
molecule 1 (VCAM1); and, vascular endothelial growth factor A
(VEGFA).
[0102] The term "analyte" in the context of the present teachings
can mean any substance to be measured, and can encompass
biomarkers, markers, nucleic acids, electrolytes, metabolites,
proteins, sugars, carbohydrates, fats, lipids, cytokines,
chemokines, growth factors, proteins, peptides, nucleic acids,
oligonucleotides, metabolites, mutations, variants, polymorphisms,
modifications, fragments, subunits, degradation products and other
elements. For simplicity, standard gene symbols may be used
throughout to refer not only to genes but also gene
products/proteins, rather than using the standard protein symbol;
e.g., APOA1 as used herein can refer to the gene APOA and also the
protein ApoAI. In general, hyphens are dropped from analyte names
and symbols herein (IL-6=IL6).
[0103] To "analyze" includes determining a value or set of values
associated with a sample by measurement of analyte levels in the
sample. "Analyze" may further comprise and comparing the levels
against constituent levels in a sample or set of samples from the
same subject or other subject(s). The biomarkers of the present
teachings can be analyzed by any of various conventional methods
known in the art. Some such methods include but are not limited to:
measuring serum protein or sugar or metabolite or other analyte
level, measuring enzymatic activity, and measuring gene
expression.
[0104] The term "antibody" refers to any immunoglobulin-like
molecule that reversibly binds to another with the required
selectivity. Thus, the term includes any such molecule that is
capable of selectively binding to a biomarker of the present
teachings. The term includes an immunoglobulin molecule capable of
binding an epitope present on an antigen. The term is intended to
encompass not only intact immunoglobulin molecules, such as
monoclonal and polyclonal antibodies, but also antibody isotypes,
recombinant antibodies, bi-specific antibodies, humanized
antibodies, chimeric antibodies, anti-idiopathic (anti-ID)
antibodies, single-chain antibodies, Fab fragments, F(ab')
fragments, fusion protein antibody fragments, immunoglobulin
fragments, F.sub.v fragments, single chain F.sub.v fragments, and
chimeras comprising an immunoglobulin sequence and any
modifications of the foregoing that comprise an antigen recognition
site of the required selectivity.
[0105] "Autoimmune disease" encompasses any disease, as defined
herein, resulting from an immune response against substances and
tissues normally present in the body. Examples of suspected or
known autoimmune diseases include rheumatoid arthritis, juvenile
idiopathic arthritis, seronegative spondyloarthropathies,
ankylosing spondylitis, psoriatic arthritis, antiphospholipid
antibody syndrome, autoimmune hepatitis, Behcet's disease, bullous
pemphigoid, coeliac disease, Crohn's disease, dermatomyositis,
Goodpasture's syndrome, Graves' disease, Hashimoto's disease,
idiopathic thrombocytopenic purpura, IgA nephropathy, Kawasaki
disease, systemic lupus erythematosus, mixed connective tissue
disease, multiple sclerosis, myasthenia gravis, polymyositis,
primary biliary cirrhosis, psoriasis, scleroderma, Sjogren's
syndrome, ulcerative colitis, vasculitis, Wegener's granulomatosis,
temporal arteritis, Takayasu's arteritis, Henoch-Schonlein purpura,
leucocytoclastic vasculitis, polyarteritis nodosa, Churg-Strauss
Syndrome, and mixed cryoglobulinemic vasculitis.
[0106] "Biomarker," "biomarkers," "marker" or "markers" in the
context of the present teachings encompasses, without limitation,
cytokines, chemokines, growth factors, proteins, peptides, nucleic
acids, oligonucleotides, and metabolites, together with their
related metabolites, mutations, isoforms, variants, polymorphisms,
modifications, fragments, subunits, degradation products, elements,
and other analytes or sample-derived measures. Biomarkers can also
include mutated proteins, mutated nucleic acids, variations in copy
numbers and/or transcript variants. Biomarkers also encompass
non-blood borne factors and non-analyte physiological markers of
health status, and/or other factors or markers not measured from
samples (e.g., biological samples such as bodily fluids), such as
clinical parameters and traditional factors for clinical
assessments. Biomarkers can also include any indices that are
calculated and/or created mathematically. Biomarkers can also
include combinations of any one or more of the foregoing
measurements, including temporal trends and differences.
[0107] A "clinical assessment," or "clinical datapoint" or
"clinical endpoint," in the context of the present teachings can
refer to a measure of disease activity or severity. A clinical
assessment can include a score, a value, or a set of values that
can be obtained from evaluation of a sample (or population of
samples) from a subject or subjects under determined conditions. A
clinical assessment can also be a questionnaire completed by a
subject. A clinical assessment can also be predicted by biomarkers
and/or other parameters. One of skill in the art will recognize
that the clinical assessment for RA, as an example, can comprise,
without limitation, one or more of the following: DAS, DAS28,
DAS28-ESR, DAS28-CRP, HAQ, mHAQ, MDHAQ, physician global assessment
VAS, patient global assessment VAS, pain VAS, fatigue VAS, overall
VAS, sleep VAS, SDAI, CDAI, RAPID3, RAPID4, RAPID5, ACR20, ACR50,
ACR70, SF-36 (a well-validated measure of general health status),
RA MRI score (RAMRIS; or RA MRI scoring system), total Sharp score
(TSS), van der Heijde-modified TSS, van der Heijde-modified Sharp
score (or Sharp-van der Heijde score (SHS)), Larsen score, TJC,
swollen joint count (SJC), CRP titer (or level), and ESR.
[0108] The term "clinical parameters" in the context of the present
teachings encompasses all measures of the health status of a
subject. A clinical parameter can be used to derive a clinical
assessment of the subject's disease activity. Clinical parameters
can include, without limitation: therapeutic regimen (including but
not limited to DMARDs, whether conventional or biologics, steroids,
etc.), TJC, SJC, morning stiffness, arthritis of three or more
joint areas, arthritis of hand joints, symmetric arthritis,
rheumatoid nodules, radiographic changes and other imaging,
gender/sex, age, race/ethnicity, disease duration, diastolic and
systolic blood pressure, resting heart rate, height, weight,
body-mass index, family history, CCP status (i.e., whether subject
is positive or negative for anti-CCP antibody), CCP titer, RF
status, RF titer, ESR, CRP titer, menopausal status, and whether a
smoker/non-smoker.
[0109] "Clinical assessment" and "clinical parameter" are not
mutually exclusive terms. There may be overlap in members of the
two categories. For example, CRP titer can be used as a clinical
assessment of disease activity; or, it can be used as a measure of
the health status of a subject, and thus serve as a clinical
parameter.
[0110] The term "computer" carries the meaning that is generally
known in the art; that is, a machine for manipulating data
according to a set of instructions. For illustration purposes only,
FIG. 16 is a high-level block diagram of a computer (1600). As is
known in the art, a "computer" can have different and/or other
components than those shown in FIG. 16. In addition, the computer
1600 can lack certain illustrated components. Moreover, the storage
device (1608) can be local and/or remote from the computer (1600)
(such as embodied within a storage area network (SAN)). As is known
in the art, the computer (1600) is adapted to execute computer
program modules for providing functionality described herein. As
used herein, the term "module" refers to computer program logic
utilized to provide the specified functionality. Thus, a module can
be implemented in hardware, firmware, and/or software. In one
embodiment, program modules are stored on the storage device
(1608), loaded into the memory (1606), and executed by the
processor (1602). Embodiments of the entities described herein can
include other and/or different modules than the ones described
here. In addition, the functionality attributed to the modules can
be performed by other or different modules in other embodiments.
Moreover, this description occasionally omits the term "module" for
purposes of clarity and convenience.
[0111] The term "cytokine" in the present teachings refers to any
substance secreted by specific cells of the immune system that
carries signals locally between cells and thus has an effect on
other cells. The term "cytokines" encompasses "growth factors."
"Chemokines" are also cytokines. They are a subset of cytokines
that are able to induce chemotaxis in cells; thus, they are also
known as "chemotactic cytokines."
[0112] "DAIMRK" in the present teachings refers to a specific
group, set or panel of biomarkers, as the term "biomarkers" is
defined herein. Where the biomarkers of certain embodiments of the
present teachings are proteins, the gene symbols and names used
herein are to be understood to refer to the protein products of
these genes, and the protein products of these genes are intended
to include any protein isoforms of these genes, whether or not such
isoform sequences are specifically described herein. Where the
biomarkers are nucleic acids, the gene symbols and names used
herein are to refer to the nucleic acids (DNA or RNA) of these
genes, and the nucleic acids of these genes are intended to include
any transcript variants of these genes, whether or not such
transcript variants are specifically described herein. The DAIMRK
group of the present teachings is the group consisting of:
apolipoprotein A-I (APOA1); apolipoprotein C-III (APOC3);
calprotectin; chemokine (C-C motif) ligand 22 (CCL22); chitinase
3-like 1 (cartilage glycoprotein-39) (CHI3L1, or YKL-40);
C-reactive protein, pentraxin-related (CRP); epidermal growth
factor (beta-urogastrone) (EGF); intercellular adhesion molecule 1
(ICAM1); ICTP; interleukin 18 (interferon-gamma-inducing factor)
(IL18); interleukin 1, beta (IL1B); interleukin 1 receptor
antagonist (IL1RN); interleukin 6 (interferon, beta 2) (IL6);
interleukin 6 receptor (IL6R); interleukin 8 (IL8); keratan
sulfate, or KS; leptin (LEP); matrix metallopeptidase 1
(interstitial collagenase) (MMP1); matrix metallopeptidase 3
(stromelysin 1, progelatinase) (MMP3); pyridinoline (cross-links
formed in collagen, derived from three lysine residues), which may
be referred to herein as PYD; resistin (RETN); serum amyloid A1
(SAA1); tumor necrosis factor receptor superfamily, member 1A
(TNFRSF1A); tumor necrosis factor (ligand) superfamily, member 13b
(TNFSF13B, or BAFF); vascular cell adhesion molecule 1 (VCAM1);
and, vascular endothelial growth factor A (VEGFA).
[0113] Calprotectin is a heteropolymer, comprising two protein
subunits of gene symbols S100A8 and S100A9. ICTP is the
carboxyterminal telopeptide region of type I collagen, and is
liberated during the degradation of mature type I collagen. Type I
collagen is present as fibers in tissue; in bone, the type I
collagen molecules are crosslinked. The ICTP peptide is
immunochemically intact in blood. (For the type I collagen gene,
see official symbol COL1A1, HUGO Gene Nomenclature Committee; also
known as OI4; alpha 1 type I collagen; collagen alpha 1 chain type
I; collagen of skin, tendon and bone, alpha-1 chain; and,
pro-alpha-1 collagen type 1). Keratan sulfate (KS, or
keratosulfate) is not the product of a discrete gene, but refers to
any of several sulfated glycosaminoglycans. They are synthesized in
the central nervous system, and are found especially in cartilage
and bone. Keratan sulfates are large, highly hydrated molecules,
which in joints can act as a cushion to absorb mechanical
shock.
[0114] "DAS" refers to the Disease Activity Score, a measure of the
activity of RA in a subject, well-known to those of skill in the
art. See D. van der Heijde et al., Ann. Rheum. Dis. 1990,
49(11):916-920. "DAS" as used herein refers to this particular
Disease Activity Score. The "DAS28" involves the evaluation of 28
specific joints. It is a current standard well-recognized in
research and clinical practice. Because the DAS28 is a
well-recognized standard, it is often simply referred to as "DAS."
Unless otherwise specified, "DAS" herein will encompass the DAS28.
A DAS28 can be calculated for an RA subject according to the
standard as outlined at the das-score.nl website, maintained by the
Department of Rheumatology of the University Medical Centre in
Nijmegen, the Netherlands. The number of swollen joints, or swollen
joint count out of a total of 28 (SJC28), and tender joints, or
tender joint count out of a total of 28 (TJC28) in each subject is
assessed. In some DAS28 calculations the subject's general health
(GH) is also a factor, and can be measured on a 100 mm Visual
Analogue Scale (VAS). OH may also be referred to herein as PG or
PGA, for "patient global health assessment" (or merely "patient
global assessment"). A "patient global health assessment VAS,"
then, is GH measured on a Visual Analogue Scale.
[0115] "DAS28-CRP" (or "DAS28CRP") is a DAS28 assessment calculated
using CRP in place of ESR (see below). CRP is produced in the
liver. Normally there is little or no CRP circulating in an
individual's blood serum--CRP is generally present in the body
during episodes of acute inflammation or infection, so that a high
or increasing amount of CRP in blood serum can be associated with
acute infection or inflammation. A blood serum level of CRP greater
than 1 mg/dL is usually considered high. Most inflammation and
infections result in CRP levels greater than 10 mg/dL. The amount
of CRP in subject sera can be quantified using, for example, the
DSL-10-42100 ACTIVE.RTM. US C-Reactive Protein Enzyme-Linked
Immunosorbent Assay (ELISA), developed by Diagnostics Systems
Laboratories, Inc. (Webster, Tex.). CRP production is associated
with radiological progression in RA. See M. Van Leeuwen et al., Br.
J. Rheum. 1993, 32(suppl.):9-13). CRP is thus considered an
appropriate alternative to ESR in measuring RA disease activity.
See R. Mallya et al., J. Rheum. 1982, 9(2):224-228, and F. Wolfe,
J. Rheum. 1997, 24:1477-1485.
[0116] The DAS28-CRP can be calculated according to either of the
formulas below, with or without the GH factor, where "CRP"
represents the amount of this protein present in a subject's blood
serum in mg/L, "sqrt" represents the square root, and "In"
represents the natural logarithm:
DAS28-CRP with GH (or
DAS28-CRP4)=(0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.36*ln(CRP+1))+(0.014*GH)-
+0.96; or, (a)
DAS28-CRP without GH (or
DAS28-CRP3)=(0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.36*ln(CRP+1))*1.10+1.15.
(b)
[0117] The "DAS28-ESR" is a DAS28 assessment wherein the ESR for
each subject is also measured (in mm/hour). The DAS28-ESR can be
calculated according to the formula:
DAS28-ESR with GH (or
DAS28-ESR4)=0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.70*ln(ESR)+0.014*GH;
or, (a)
DAS28-ESR without
GH=0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.70*ln(ESR)*1.08+0.16.
(b)
[0118] Unless otherwise specified herein, the term "DAS28," as used
in the present teachings, can refer to a DAS28-ESR or DAS28-CRP, as
obtained by any of the four formulas described above; or, DAS28 can
refer to another reliable DAS28 formula as may be known in the
art.
[0119] A "dataset" is a set of numerical values resulting from
evaluation of a sample (or population of samples) under a desired
condition. The values of the dataset can be obtained, for example,
by experimentally obtaining measures from a sample and constructing
a dataset from these measurements; or alternatively, by obtaining a
dataset from a service provider such as a laboratory, or from a
database or a server on which the dataset has been stored.
[0120] In certain embodiments of the present teachings, a dataset
of values is determined by measuring at least two biomarkers from
the DAIMRK group. This dataset is used by an interpretation
function according to the present teachings to derive a DAI score
(see definition, "DAI score," below), which provides a quantitative
measure of inflammatory disease activity in a subject. In the
context of RA, the DAI score thus derived from this dataset is also
useful in predicting a DAS28 score, with a high degree of
association, as is shown in the Examples below. The at least two
markers can comprise: (APOA1 and IL8), (Calprotectin and CRP),
(Calprotectin and EGF), (Calprotectin and IL8), (CRP and APOA1),
(CRP and APOC3), (CRP and CCL22), (CRP and CHI3L), (CRP and EGF),
(CRP and ICAM1), (CRP and IL1B), (CRP and IL6), (CRP and IL6R),
(CRP and IL8), (CRP and LEP), (CRP and MMP1), (CRP and MMP3), (CRP
and RETN), (CRP and SAA1), (CRP and TNFRSF1A), (CRP and VCAM1),
(CRP and VEGF), (EGF and APOA1), (EGF and CHI3L1), (EGF and ICAM1),
(EGF and IL8), (EGF and LEP), (EGF and MMP1), (EGF and TNFRSF1A),
(EGF and VCAM1), (ICAM1 and IL8), (IL1RN and CRP), (IL1RN and EGF),
(IL1RN and IL8), (IL8 and APOC3), (IL and CCL22), (IL8 and CHI3L1),
(IL8 and IL6), (IL8 and IL6R), (IL8 and TNFRSF1A), (LEP and IL8),
(MMP3 and IL8), (RETN and IL8), (SAA1 and EGF), (SAA1 and IL8),
(SAA1 and LEP), (SAA1 and REIN), or (VCAM1 and IL8). The at least
two markers can also comprise (calprotectin and CHI3L1),
(calprotectin and interleukin), (calprotectin and LEP),
(calprotectin and pyridinoline), (calprotectin and RETN), (CCL22
and calprotectin), (CCL22 and CRP), (CCL22 and 1A6), (CCL22 and
SAA1), (CRP and calprotectin), (CRP and CHI3L1), (CRP and EGF),
(CRP and ICAM1), (CRP and IL1B), (CRP and IL1RN), (CRP and IL6),
(CRP and IL6R), (CRP and IL8), (CRP and LEP), (CRP and MMP1), (CRP
and MMP3), (CRP and pyridinoline), (CRP and RETN), (CRP and SAA1),
(CRP and TNFRSF1A), (CRP and VCAM1), (CRP and VEGFA), (EGF and
calprotectin), (EGF and L6), (EGF and SAA1), (ICAM1 and
calprotectin), (ICAM1 and IL6), (ICAM1 and SAA1), (IL1B and
calprotectin), (IL1B and IL6), (IL1B and MMP3), (IL1B and SAA1),
(IL6 and calprotectin), (IL6 and CHI3L1), (IL6 and IL1RN), (IL6 and
IL8), (IL6 and LEP), (IL6 and MMP1), (L6 and MMP3), (IL6 and
pyridinoline), (IL6 and RETN), (IL6 and SAA1), (IL6 and TNFRSF1A),
(IL6 and VCAM1), (IL6 and VEGFA), (IL6R and calprotectin), (IL6R
and IL6), (IL6R and SAA1), (IL8 and calprotectin), (IL8 and MMP3),
(IL8 and SAA1), (MMP1 and calprotectin), (MMP1 and SAA1), (MMP3 and
calprotectin), (MMP3 and CHI3L1), (MMP3 and SAA1), (SAA1 and
calprotectin), (SAA1 and CHI3L1), (SAA1 and IL1RN), (SAA1 and LEP),
(SAA1 and pyridinoline), (SAA1 and RETN), (SAA1 and TNFRSF1A),
(SAA1 and VCAM1), (SAA1 and VEGFA), (TNFRSF1A and calprotectin),
(VCAM1 and calprotectin); or, (VEGFA and calprotectin).
[0121] The term "disease" in the context of the present teachings
encompasses any disorder, condition, sickness, ailment, etc. that
manifests in, e.g., a disordered or incorrectly functioning organ,
part, structure, or system of the body, and results from, e.g.,
genetic or developmental errors, infection, poisons, nutritional
deficiency or imbalance, toxicity, or unfavorable environmental
factors.
[0122] A "disease activity index score," "DAI score," or simply
"DAI," in the context of the present teachings, is a score that
provides a quantitative measure of inflammatory disease activity or
the state of inflammatory disease in a subject. A set of data from
particularly selected biomarkers, such as markers from the DAIMRK
or ALLMRK set, is input into an interpretation function according
to the present teachings to derive the DAI score. The
interpretation function, in some embodiments, can be created from
predictive or multivariate modeling based on statistical
algorithms. Input to the interpretation function can comprise the
results of testing two or more of the DAIMRK or ALLMRK set of
biomarkers, alone or in combination with clinical parameters and/or
clinical assessments, also described herein. In some embodiments of
the present teachings, the DAI score is a quantitative measure of
autoimmune disease activity. In some embodiments, the DAI score is
a quantitative measure of RA disease activity.
[0123] A DMARD can be conventional or biologic. Examples of DMARDs
that are generally considered conventional include, but are not
limited to, MTX, azathioprine (AZA), bucillamine (BUC), chloroquine
(CQ), ciclosporin (CSA, or cyclosporine, or cyclosporin),
doxycycline (DOXY), hydroxychloroquine (HCQ), intramuscular gold
(IM gold), leflunomide (LEF), levofloxacin (LEV), and sulfasalazine
(SSZ). Examples of other conventional DMARDs include, but are not
limited to, folinic acid, D-pencillamine, gold auranofin, gold
aurothioglucose, gold thiomalate, cyclophosphamide, and
chlorambucil. Examples of biologic DMARDs (or biologic drugs)
include but are not limited to biological agents that target the
tumor necrosis factor (TNF)-alpha molecules and the TNF inhibitors,
such as infliximab, adalimumab, etanercept and golimumab. Other
classes of biologic DMARDs include IL1 inhibitors such as anakinra,
T-cell modulators such as abatacept, B-cell modulators such as
rituximab, and IL6 inhibitors such as tocilizumab.
[0124] "Inflammatory disease" in the context of the present
teachings encompasses, without limitation, any disease, as defined
herein, resulting from the biological response of vascular tissues
to harmful stimuli, including but not limited to such stimuli as
pathogens, damaged cells, irritants, antigens and, in the case of
autoimmune disease, substances and tissues normally present in the
body. Examples of inflammatory disease include RA, atherosclerosis,
asthma, autoimmune diseases, chronic inflammation, chronic
prostatitis, glomerulonephritis, hypersensitivities, inflammatory
bowel diseases, pelvic inflammatory disease, reperfusion injury,
transplant rejection, and vasculitis.
[0125] "Interpretation function," as used herein, means the
transformation of a set of observed data into a meaningful
determination of particular interest; e.g., an interpretation
function may be a predictive model that is created by utilizing one
or more statistical algorithms to transform a dataset of observed
biomarker data into a meaningful determination of disease activity
or the disease state of a subject.
[0126] "Measuring" or "measurement" in the context of the present
teachings refers to determining the presence, absence, quantity,
amount, or effective amount of a substance in a clinical or
subject-derived sample, including the concentration levels of such
substances, or evaluating the values or categorization of a
subject's clinical parameters.
[0127] "Performance" in the context of the present teachings
relates to the quality and overall usefulness of, e.g., a model,
algorithm, or diagnostic or prognostic test. Factors to be
considered in model or test performance include, but are not
limited to, the clinical and analytical accuracy of the test, use
characteristics such as stability of reagents and various
components, ease of use of the model or test, health or economic
value, and relative costs of various reagents and components of the
test.
[0128] A "population" is any grouping of subjects of like specified
characteristics. The grouping could be according to, for example
but without limitation, clinical parameters, clinical assessments,
therapeutic regimen, disease status (e.g. with disease or healthy),
level of disease activity, etc. In the context of using the DAI
score in comparing disease activity between populations, an
aggregate value can be determined based on the observed DAI scores
of the subjects of a population; e.g., at particular timepoints in
a longitudinal study. The aggregate value can be based on, e.g.,
any mathematical or statistical formula useful and known in the art
for arriving at a meaningful aggregate value from a collection of
individual datapoints; e.g., mean, median, median of the mean,
etc.
[0129] A "predictive model," which term may be used synonymously
herein with "multivariate model" or simply a "model," is a
mathematical construct developed using a statistical algorithm or
algorithms for classifying sets of data. The term "predicting"
refers to generating a value for a datapoint without actually
performing the clinical diagnostic procedures normally or otherwise
required to produce that datapoint; "predicting" as used in this
modeling context should not be understood solely to refer to the
power of a model to predict a particular outcome. Predictive models
can provide an interpretation function; e.g., a predictive model
can be created by utilizing one or more statistical algorithms or
methods to transform a dataset of observed data into a meaningful
determination of disease activity or the disease state of a
subject. See Calculation of the DAI score for some examples of
statistical tools useful in model development.
[0130] A "prognosis" is a prediction as to the likely outcome of a
disease. Prognostic estimates are useful in, e.g., determining an
appropriate therapeutic regimen for a subject.
[0131] A "quantitative dataset," as used in the present teachings,
refers to the data derived from, e.g., detection and composite
measurements of a plurality of biomarkers (i.e., two or more) in a
subject sample. The quantitative dataset can be used in the
identification, monitoring and treatment of disease states, and in
characterizing the biological condition of a subject. It is
possible that different biomarkers will be detected depending on
the disease state or physiological condition of interest.
[0132] A "sample" in the context of the present teachings refers to
any biological sample that is isolated from a subject. A sample can
include, without limitation, a single cell or multiple cells,
fragments of cells, an aliquot of body fluid, whole blood,
platelets, serum, plasma, red blood cells, white blood cells or
leucocytes, endothelial cells, tissue biopsies, synovial fluid,
lymphatic fluid, ascites fluid, and interstitial or extracellular
fluid. The term "sample" also encompasses the fluid in spaces
between cells, including gingival crevicular fluid, bone marrow,
cerebrospinal fluid (CSF), saliva, mucous, sputum, semen, sweat,
urine, or any other bodily fluids. "Blood sample" can refer to
whole blood or any fraction thereof, including blood cells, red
blood cells, white blood cells or leucocytes, platelets, serum and
plasma. Samples can be obtained from a subject by means including
but not limited to venipuncture, excretion, ejaculation, massage,
biopsy, needle aspirate, lavage, scraping, surgical incision, or
intervention or other means known in the art.
[0133] A "score" is a value or set of values selected so as to
provide a quantitative measure of a variable or characteristic of a
subject's condition, and/or to discriminate, differentiate or
otherwise characterize a subject's condition. The value(s)
comprising the score can be based on, for example, a measured
amount of one or more sample constituents obtained from the
subject, or from clinical parameters, or from clinical assessments,
or any combination thereof. In certain embodiments the score can be
derived from a single constituent, parameter or assessment, while
in other embodiments the score is derived from multiple
constituents, parameters and/or assessments. The score can be based
upon or derived from an interpretation function; e.g., an
interpretation function derived from a particular predictive model
using any of various statistical algorithms known in the art. A
"change in score" can refer to the absolute change in score, e.g.
from one timepoint to the next, or the percent change in score, or
the change in the score per unit time (i.e., the rate of score
change).
[0134] "Statistically significant" in the context of the present
teachings means an observed alteration is greater than what would
be expected to occur by chance alone (e.g., a "false positive").
Statistical significance can be determined by any of various
methods well-known in the art. An example of a commonly used
measure of statistical significance is the p-value. The p-value
represents the probability of obtaining a given result equivalent
to a particular datapoint, where the datapoint is the result of
random chance alone. A result is often considered highly
significant (not random chance) at a p-value less than or equal to
0.05.
[0135] A "subject" in the context of the present teachings is
generally a mammal. The subject can be a patient. The term "mammal"
as used herein includes but is not limited to a human, non-human
primate, dog, cat, mouse, rat, cow, horse, and pig. Mammals other
than humans can be advantageously used as subjects that represent
animal models of inflammation. A subject can be male or female. A
subject can be one who has been previously diagnosed or identified
as having an inflammatory disease. A subject can be one who has
already undergone, or is undergoing, a therapeutic intervention for
an inflammatory disease. A subject can also be one who has not been
previously diagnosed as having an inflammatory disease; e.g., a
subject can be one who exhibits one or more symptoms or risk
factors for an inflammatory condition, or a subject who does not
exhibit symptoms or risk factors for an inflammatory condition, or
a subject who is asymptomatic for inflammatory disease.
[0136] A "therapeutic regimen," "therapy" or "treatment(s)," as
described herein, includes all clinical management of a subject and
interventions, whether biological, chemical, physical, or a
combination thereof, intended to sustain, ameliorate, improve, or
otherwise alter the condition of a subject. These terms may be used
synonymously herein. Treatments include but are not limited to
administration of prophylactics or therapeutic compounds (including
conventional DMARDs, biologic DMARDs, non-steroidal
anti-inflammatory drugs (NSAID's) such as COX-2 selective
inhibitors, and corticosteroids), exercise regimens, physical
therapy, dietary modification and/or supplementation, bariatric
surgical intervention, administration of pharmaceuticals and/or
anti-inflammatories (prescription or over-the-counter), and any
other treatments known in the art as efficacious in preventing,
delaying the onset of, or ameliorating disease. A "response to
treatment" includes a subject's response to any of the
above-described treatments, whether biological, chemical, physical,
or a combination of the foregoing. A "treatment course" relates to
the dosage, duration, extent, etc. of a particular treatment or
therapeutic regimen.
Use of the Present Teachings in the Diagnosis and Prognosis of
Disease
[0137] In some embodiments of the present teachings, biomarkers
selected from the DAIMARK or ALLMRK group can be used in the
derivation of a DAI score, as described herein, which DAI score can
be used to provide diagnosis, prognosis and monitoring of disease
state and/or disease activity in inflammatory disease and in
autoimmune disease. In certain embodiments, the DAI score can be
used to provide diagnosis, prognosis and monitoring of disease
state and/or disease activity of RA.
[0138] Identifying the state of inflammatory disease in a subject
allows for a prognosis of the disease, and thus for the informed
selection of, initiation of, adjustment of or increasing or
decreasing various therapeutic regimens in order to delay, reduce
or prevent that subject's progression to a more advanced disease
state. In some embodiments, therefore, subjects can be identified
as having a particular level of inflammatory disease activity
and/or as being at a particular state of disease, based on the
determination of their DAI scores, and so can be selected to begin
or accelerate treatment, as treatment is defined herein, to prevent
or delay the further progression of inflammatory disease. In other
embodiments, subjects that are identified via their DAI scores as
having a particular level of inflammatory disease activity, and/or
as being at a particular state of inflammatory disease, can be
selected to have their treatment decreased or discontinued, where
improvement or remission in the subject is seen.
[0139] Blood-based biomarkers that report on the current rate of
joint destructive processes could also present a powerful
prognostic approach to identifying subjects at highest risk of
accelerated bone and cartilage damage. In some embodiments of the
present teachings, biomarkers from the DAIMRK or ALLMRK group can
be measured from subjects' or a subject's samples obtained at
various time points (e.g., longitudinally), to obtain a series of
DAI scores, and the scores can then be associated with radiological
results (such as, e.g., those obtained by TSS) at various time
points and so provide a measurement of disease progression. See
Example 2. The association of the DAI scores with, e.g., change of
TSS results can be analyzed statistically for correlation (e.g.,
Spearman correlation) using multivariate analysis to create single
time point or longitudinal hierarchical linear models and ensure
accuracy. Serum biomarkers of the DAIMRK or ALLMRK group can thus
be used as an alternative to US/radiological results in estimating
rates of progression of disease, and predicting joint damage in RA.
Predictive models using biomarkers can thus identify subjects who
need more aggressive treatment, and earlier, and can thereby
improve subject outcomes. In other embodiments, the DAI scores from
one subject can be compared with each other, for observations of
longitudinal trending as an effect of, e.g., choice or
effectiveness of therapeutic regimen, or as a result of the
subject's response to treatment regimens, or a comparison of the
subject's responses to different regimens.
[0140] The present teachings indicate that DAIMRK- or
ALLMRK-derived formulas developed in cross-sectional analysis are a
strong predictor of disease activity over time; e.g.,
longitudinally. See Example 2. This is a significant finding from a
clinical care perspective. Currently no tests are available to
accurately measure and track RA disease activity over time in the
clinic. Several recent studies have demonstrated that optimal
treatment intervention can dramatically improve clinical outcomes.
See Y P M Goekoop-Ruitenman et al., Ann. Rheum. Dis. 2009
(Epublication Jan. 20, 2009); C. Grigor et al., Lancet 2004,
364:263-269; S M M Verstappen et al., Ann. Rheum. Dis. 2007,
66:1443-1449. In these studies disease activity levels are
frequently monitored and treatment is increased in nonremission
subjects. This concept of treating to remission has been denoted,
"Tight Control." Numbers of subjects achieving low disease activity
and remission in Tight Control trials is high. In addition, Tight
Control cohorts achieve dramatically improved outcomes relative to
cohorts receiving standard of care in clinical practice, where
remission is less achievable. This is in part due to a lack of easy
and sensitive tools to quantitatively monitor disease activity in a
real-world clinical practice. Monitoring in these controlled trials
is via clinical trial measures, such as DAS and Sharp Scores
changes, which are not widely practiced in the real-world clinical
setting. The tests developed from various embodiments of the
present teachings will facilitate the monitoring of disease
activity and Tight Control practices, and result in improved
control of disease activity and improved clinical outcomes.
[0141] In regards to the need for early and accurate diagnosis of
RA, recent advances in RA treatment provide a means for more
profound disease management and optimal treatment of RA within the
first months of symptom onset, which in turn result in
significantly improved outcomes. See F. Wolfe, Arth. Rheum. 2000,
43(12):2751-2761; M. Matucci-Cerinic, Clin. Exp. Rheum. 2002,
20(4):443-444; and, V. Nell et. al., Lancet 2005,
365(9455):199-200. Unfortunately, most subjects do not receive
optimal treatment within this narrow window of opportunity,
resulting in poorer outcomes and irreversible joint damage, in part
because of the limits of current diagnostic laboratory tests.
Numerous difficulties exist in diagnosing RA subject. This is in
part because at their early stages, symptoms may not be fully
differentiated. It is also because diagnostic tests for RA were
developed based on phenomenological findings, not the biological
basis of disease. In various embodiments of the present teachings,
multi-biomarker algorithms can be derived from biomarkers of the
DAIMRK set, which have diagnostic potential. See Example 4. This
aspect of the present teachings has the potential to improve both
the accuracy of RA diagnosis, and the speed of detection of RA.
Rating Disease Activity
[0142] In some embodiments of the present teachings, the DAI score,
derived as described herein, can be used to rate inflammatory
disease activity; e.g., as high, medium or low. In some embodiments
of the present teachings, autoimmune disease activity can be so
rated. In other embodiments, RA disease activity can be so rated.
Using RA disease as an example, because the DAI score correlates
well and with high accuracy with clinical assessments of RA (e.g.,
with the DAS28 score), DAI cut-off scores can be set at
predetermined levels to indicate levels of RA disease activity, and
to correlate with the cut-offs traditionally established for rating
RA activity via DAS28 scores. See Example 3. Because the DAI score
correlates well with traditional clinical assessments of
inflammatory disease activity, e.g. in RA, in other embodiments of
the present teachings bone damage itself in a subject or
population, and thus disease progression, can be tracked via the
use and application of the DAI score.
[0143] These properties of the DAIMRK set of biomarkers can be used
for several purposes. On a subject-specific basis, they provide a
context for understanding the relative level of disease activity.
The DAIMRK-based rating of disease activity can be used, e.g., to
guide the clinician in determining treatment, in setting a
treatment course, and/or to inform the clinician that the subject
is in remission. Moreover, it provides a means to more accurately
assess and document the qualitative level of disease activity in a
subject. It is also useful from the perspective of assessing
clinical differences among populations of subjects within a
practice. For example, this tool can be used to assess the relative
efficacy of different treatment modalities. Moreover, it is also
useful from the perspective of assessing clinical differences among
different practices. This would allow physicians to determine what
global level of disease control is achieved by their colleagues,
and/or for healthcare management groups to compare their results
among different practices for both cost and comparative
effectiveness.
Subject Screening
[0144] Certain embodiments of the present teachings can also be
used to screen subject populations in any number of settings. For
example, a health maintenance organization, public health entity or
school health program can screen a group of subjects to identify
those requiring interventions, as described above. Other
embodiments of these teachings can be used to collect disease
activity data on one or more populations of subjects, to identify
subject disease status in the aggregate, in order to, e.g.,
determine the effectiveness of the clinical management of a
population, or determine gaps in clinical management. Insurance
companies (e.g., health, life, or disability) may request the
screening of applicants in the process of determining coverage for
possible intervention. Data collected in such population screens,
particularly when tied to any clinical progression to conditions
such as inflammatory disease and RA, will be of value in the
operations of, for example, health maintenance organizations,
public health programs and insurance companies.
[0145] Such data arrays or collections can be stored in
machine-readable media and used in any number of health-related
data management systems to provide improved healthcare services,
cost-effective healthcare, and improved insurance operation, among
other things. See, e.g., U.S. Patent Application No. 2002/0038227;
U.S. Patent Application No. 2004/0122296; U.S. Patent Application
No. 2004/0122297; and U.S. Pat. No. 5,018,067. Such systems can
access the data directly from internal data storage or remotely
from one or more data storage sites as further detailed herein.
Thus, in a health-related data management system, wherein it is
important to manage inflammatory disease progression for a
population in order to reduce disease-related employment
productivity loss, disability and surgery, and thus reduce
healthcare costs in the aggregate, various embodiments of the
present teachings provide an improvement comprising the use of a
data array encompassing the biomarker measurements as defined
herein, and/or the resulting evaluation of disease status and
activity from those biomarker measurements.
Measuring Accuracy and Performance of the Present Teachings
[0146] The performance of the present teachings can be assessed in
any of various ways. Assessing the performance of an embodiment of
the present teachings can provide a measurement of the accuracy of
that embodiment, where, e.g., that embodiment is a predictive
model, or a test, assay, method or procedure, whether diagnostic or
prognostic. This accuracy assessment can relate to the ability of
the predictive model or the test to determine the inflammatory
disease activity status of a subject or population. In other
embodiments, the performance assessment relates to the accuracy of
the predictive model or test in distinguishing between subjects
with or without inflammatory disease. In other embodiments, the
assessment relates to the accuracy of the predictive model or test
in distinguishing between states of inflammatory disease in one
subject at different time points.
[0147] The distinguishing ability of the predictive model or test
can be based on whether the subject or subjects have a significant
alteration in the levels of one or more biomarkers. In some
embodiments a significant alteration, in the context of the present
teachings, can mean that the measurement of the biomarkers, as
represented by the DAI score computed by the DAI formula as
generated by the predictive model, is different than some
predetermined DAI cut-off point (or threshold value) for those
biomarkers when input to the DAI formula as described herein. This
significant alteration in biomarker levels as reflected in
differing DAI scores can therefore indicate that the subject has
inflammatory disease, or is at a particular state or severity of
inflammatory disease. The difference in the levels of biomarkers
between the subject and normal, in those embodiments when such
comparisons are done, is preferably statistically significant, and
can be an increase in biomarker level or levels, or a decrease in
biomarker level or levels. In some embodiments of the present
teachings, a significant alteration can mean that a DAI score is
derived from measuring the levels of one or more biomarkers, and
this score alone, without comparison to some predetermined cut-off
point (or threshold value) for those biomarkers, indicates that the
subject has inflammatory disease or has a particular state of
inflammatory disease. Further, achieving increased analytical and
clinical accuracy may require that combinations of two or more
biomarkers be used together in panels, and combined with
mathematical algorithms derived from predictive models to obtain
the DAI score.
[0148] Use of statistical values such as the AUC, and specifically
the AUC as it relates to the ROC curve, encompassing all potential
threshold or cut-off point values is generally used to quantify
predictive model performance. Acceptable degrees of accuracy can be
defined. In certain embodiments of the present teachings, an
acceptable degree of accuracy can be one in which the AUC for the
ROC curve is 0.60 or higher.
[0149] In general, defining the degree of accuracy for the relevant
predictive model or test (e.g., cut-off points on a ROC curve),
defining an acceptable AUC value, and determining the acceptable
ranges in relative concentration of what constitutes an effective
amount of the biomarkers of the present teachings, allows one of
skill in the art to use the biomarkers of the present teachings to
identify inflammatory disease activity in subjects or populations
with a pre-determined level of predictability and performance.
[0150] In various embodiments of the present teachings,
measurements from multiple biomarkers, such as those of the DAIMRK
set, can be combined into a single value, the DAI score, using
various statistical analyses and modeling techniques as described
herein. Because the DAI score demonstrates strong association with
established disease activity assessments, such as the DAS28, the
DAI score can provide a quantitative measure for monitoring the
extent of subject disease activity, and response to treatment.
Example 1 below, e.g., demonstrates that DAI scores are strongly
associated with DAS28; thus, DAI provides an accurate quantitative
measure of subject disease activity. See also FIG. 1 et seq., in
which are shown DAI scores based on sets of biomarkers, which
scores demonstrate a strong association with DAS28-CRP, as
evidenced by the AUC values shown (e.g., greater than or equal to
0.65).
Calculation of the DAI Score
[0151] In some embodiments of the present teachings, inflammatory
disease activity in a subject is measured by: determining the
levels in inflammatory disease subject serum of two or more
biomarkers selected from the DAIMRK set, then applying an
interpretation function to transform the biomarker levels into a
single DAI score, which provides a quantitative measure of
inflammatory disease activity in the subject, correlating well with
traditional clinical assessments of inflammatory disease activity
(e.g., a DAS28 or CDAI score in RA), as is demonstrated in the
Examples below. In some embodiments, the disease activity so
measured relates to an autoimmune disease. In some embodiments, the
disease activity so measured relates to RA.
[0152] In some embodiments, the interpretation function is based on
a predictive model. Established statistical algorithms and methods
well-known in the art, useful as models or useful in designing
predictive models, can include but are not limited to: analysis of
variants (ANOVA); Bayesian networks; boosting and Ada-boosting;
bootstrap aggregating (or bagging) algorithms; decision trees
classification techniques, such as Classification and Regression
Trees (CART), boosted CART, Random Forest (RF), Recursive
Partitioning Trees (RPART), and others; Curds and Whey (CW); Curds
and Whey-Lasso; dimension reduction methods, such as principal
component analysis (PCA) and factor rotation or factor analysis;
discriminant analysis, including Linear Discriminant Analysis
(LDA), Eigengene Linear Discriminant Analysis (ELDA), and quadratic
discriminant analysis; Discriminant Function Analysis (DFA); factor
rotation or factor analysis; genetic algorithms; Hidden Markov
Models; kernel based machine algorithms such as kernel density
estimation, kernel partial least squares algorithms, kernel
matching pursuit algorithms, kernel Fisher's discriminate analysis
algorithms, and kernel principal components analysis algorithms;
linear regression and generalized linear models, including or
utilizing Forward Linear Stepwise Regression, Lasso (or LASSO)
shrinkage and selection method, and Elastic Net regularization and
selection method; glmnet (Lasso and Elastic Net-regularized
generalized linear model); Logistic Regression (LogReg);
meta-learner algorithms; nearest neighbor methods for
classification or regression, e.g. Kth-nearest neighbor (KNN);
non-linear regression or classification algorithms; neural
networks; partial least square; rules based classifiers; shrunken
centroids (SC): sliced inverse regression; Standard for the
Exchange of Product model data, Application Interpreted Constructs
(StepAIC); super principal component (SPC) regression; and, Support
Vector Machines (SVM) and Recursive Support Vector Machines (RSVM),
among others. Additionally, clustering algorithms as are known in
the art can be useful in determining subject sub-groups.
[0153] Logistic Regression is the traditional predictive modeling
method of choice for dichotomous response variables; e.g.,
treatment 1 versus treatment 2. It can be used to model both linear
and non-linear aspects of the data variables and provides easily
interpretable odds ratios.
[0154] Discriminant Function Analysis (DFA) uses a set of analytes
as variables (roots) to discriminate between two or more naturally
occurring groups. DFA is used to test analytes that are
significantly different between groups. A forward step-wise DFA can
be used to select a set of analytes that maximally discriminate
among the groups studied. Specifically, at each step all variables
can be reviewed to determine which will maximally discriminate
among groups. This information is then included in a discriminative
function, denoted a root, which is an equation consisting of linear
combinations of analyte concentrations for the prediction of group
membership. The discriminatory potential of the final equation can
be observed as a line plot of the root values obtained for each
group. This approach identifies groups of analytes whose changes in
concentration levels can be used to delineate profiles, diagnose
and assess therapeutic efficacy. The DFA model can also create an
arbitrary score by which new subjects can be classified as either
"healthy" or "diseased." To facilitate the use of this score for
the medical community the score can be rescaled so a value of 0
indicates a healthy individual and scores greater than 0 indicate
increasing disease activity.
[0155] Classification and regression trees (CART) perform logical
splits (if/then) of data to create a decision tree. All
observations that fall in a given node are classified according to
the most common outcome in that node. CART results are easily
interpretable--one follows a series of if/then tree branches until
a classification results.
[0156] Support vector machines (SVM) classify objects into two or
more classes. Examples of classes include sets of treatment
alternatives, sets of diagnostic alternatives, or sets of
prognostic alternatives. Each object is assigned to a class based
on its similarity to (or distance from) objects in the training
data set in which the correct class assignment of each object is
known. The measure of similarity of a new object to the known
objects is determined using support vectors, which define a region
in a potentially high dimensional space (>R6).
[0157] The process of bootstrap aggregating, or "bagging," is
computationally simple. In the first step, a given dataset is
randomly resampled a specified number of times (e.g., thousands),
effectively providing that number of new datasets, which are
referred to as "bootstrapped resamples" of data, each of which can
then be used to build a model. Then, in the example of
classification models, the class of every new observation is
predicted by the number of classification models created in the
first step. The final class decision is based upon a "majority
vote" of the classification models; i.e., a final classification
call is determined by counting the number of times a new
observation is classified into a given group, and taking the
majority classification (33%+ for a three-class system). In the
example of logistical regression models, if a logistical regression
is bagged 1000 times, there will be 1000 logistical models, and
each will provide the probability of a sample belonging to class 1
or 2.
[0158] Curds and Whey (CW) using ordinary least squares (OLS) is
another predictive modeling method. See L Breiman and J H Friedman,
J. Royal Stat. Soc. B 1997, 59(1):3-54. This method takes advantage
of the correlations between response variables to improve
predictive accuracy, compared with the usual procedure of
performing an individual regression of each response variable on
the common set of predictor variables X. In CW, Y=XB*S, where
Y=(y.sub.kj) with k for the k.sup.th patient and j for j.sup.th
response (j=1 for TJC, j=2 for SJC, etc.), B is obtained using OLS,
and S is the shrinkage matrix computed from the canonical
coordinate system. Another method is Curds and Whey and Lasso in
combination (CW-Lasso). Instead of using OLS to obtain B, as in CW,
here Lasso is used, and parameters are adjusted accordingly for the
Lasso approach.
[0159] Many of these techniques are useful either combined with a
biomarker selection technique (such as, for example, forward
selection, backwards selection, or stepwise selection), or for
complete enumeration of all potential panels of a given size, or
genetic algorithms, or they can themselves include biomarker
selection methodologies in their own techniques. These techniques
can be coupled with information criteria, such as Akaike's
Information Criterion (AIC), Bayes Information Criterion (BIC), or
cross-validation, to quantify the tradeoff between the inclusion of
additional biomarkers and model improvement, and to minimize
overfit. The resulting predictive models can be validated in other
studies, or cross-validated in the study they were originally
trained in, using such techniques as, for example, Leave-One-Out
(LOO) and 10-Fold cross-validation (10-Fold CV).
[0160] One example of an interpretation function that provides a
DAI score, derived from a statistical modeling method as described
above, is given by the following function:
DAI=b.sub.0+b.sub.1*DAIMRK.sub.1.sup.x-b.sub.2*DAIMRK.sub.2.sup.x-b.sub.-
3*DAIMRK.sub.3.sup.x . . . -b.sub.n*DAIMRK.sub.n.sup.x;
where DAI is the DAI score, b.sub.0-n are constants, and
DAIMRK.sub.1-n.sup.x are the serum concentrations to the x.sup.th
power of n different biomarkers selected from the DAIMRK panel. DAI
scores thus obtained for RA subjects with known clinical
assessments (e.g., DAS28 scores) can then be compared to those
known assessments to determine the level of correlation between the
two assessments, and hence determine the accuracy of the DAI score
and its underlying predictive model. See Examples below for
specific formulas and constants.
[0161] More generally, the function can be described as:
DAI=F(DAIMRK.sub.1.sup.x, DAIMRK.sub.2.sup.x, . . . ,
DAIMRK.sub.n.sup.x) where DAI is the DAI score, F is the function,
and DAIMRK.sub.1-n.sup.x are the serum concentrations to the
x.sup.th power of n different biomarkers selected from the DAIMRK
panel. The function is described in the following paragraph.
[0162] An interpretation function for providing a DAI score can
also be derived based on models built to predict components of a
disease activity assessment, such as DAS28-CRP, rather than
predicting disease activity entirely. See Example 11. An example of
such a function is given by the following, wherein biomarkers are
used to provide improved predicted components of the DAS score:
DAI
score=((0.56*sqrt(IPTJC))+(0.28*sqrt(IPSJC))+(0.14*PPGA)+(0.36*ln(CR-
P/10.sup.6+1))+0.96)*10.53+1;
IPTJC=Improved PTJC=max(0.1739*PTJC+0.7865*PSJC,0);
IPSJC=Improved PSJC=max(0.1734*PTJC+0.7839*PSJC,0);
PTJC=Prediction of Tender Joint
Count=-38.564+3.997*(SAA1).sup.1/10+17.331*(IL6).sup.1/10+4.665*(CHI3L1).-
sup.1/10-15.236*(EGF).sup.1/10+2.651*(TNFRSF1A).sup.1/10+2.641*(LEP).sup.1-
/10+4.026*(VEGFA).sup.1/10-1.47*(VCAM1).sup.1/10;
PSJC=Prediction of Swollen Joint
Count=-25.444+4.051*(SAA1).sup.1/10+16.154*(IL6).sup.1/10-11.847*(EGF).su-
p.1/10+3.091*(CHI3L1).sup.1/10+0.353*(TNFRSF1A).sup.1/10;
PPGA=Prediction of Patient Global
Assessment=-13.489+5.474*(IL6).sup.1/10+0.486*(SAA1).sup.1/10+2.246*(MMP1-
).sup.1/10+1.684*(leptin).sup.1/10+4.14*(TNFRSF1A).sup.1/10+2.292*(VEGFA).-
sup.1/10-1.898*(EGF).sup.1/10+0.028*(MMP3).sup.1/10-2.892*(VCAM1).sup.1/10-
-506*(RETN).sup.1/10
in which serum levels x for all biomarkers but CRP are transformed
as x.sup.1/10 units for all biomarkers are in pg/mL, and n is
natural log, or log.sub.e.
[0163] Where CRP units are obtained in mg/L and other markers are
pg/mL DAI
score=((0.56*sqrt(IPTJC))+(0.28*sqrt(IPSJC))+(0.14*(PPGA))+(0.36*ln(C-
RP+1))+0.96)*10.53+1.
[0164] It is understood that if biomarkers are measured in other
units, appropriate conversion can be applied to use those
measurements in the above interpretation function.
[0165] The DAI score can be further rounded and capped, in order to
provide a whole number between 1 and 100, the scaled DAI score. To
accomplish this, the immediately preceding function can be
re-written: scaled DAI
score=round(max(min((0.56*sqrt(IPTJC)+(0.28*sqrt(IPSJC))+(0.14*(PPGA))+(0-
.36*ln(CRP+1)+0.96)*10.53+1, 1001)). Biomarker gene names provided
in the above formulas represent the concentrations of those
markers, and will depend on the types of assays used.
[0166] In some embodiments of the present teachings, it is not
required that the DAI score be compared to any pre-determined
"reference," "normal," "control," "standard," "healthy,"
"pre-disease" or other like index, in order for the DAI score to
provide a quantitative measure of inflammatory disease activity in
the subject.
[0167] In other embodiments of the present teachings, the amount of
the biomarker(s) can be measured in a sample and used to derive a
DAI score, which DAI score is then compared to a "normal" or
"control" level or value, utilizing techniques such as, e.g.,
reference or discrimination limits or risk defining thresholds, in
order to define cut-off points and/or abnormal values for
inflammatory disease. The normal level then is the level of one or
more biomarkers or combined biomarker indices typically found in a
subject who is not suffering from the inflammatory disease under
evaluation. Other terms for "normal" or "control" are, e.g.,
"reference," "index," "baseline," "standard," "healthy,"
"pre-disease," etc. Such normal levels can vary, based on whether a
biomarker is used alone or in a formula combined with other
biomarkers to output a score. Alternatively, the normal level can
be a database of biomarker patterns from previously tested subjects
who did not convert to the inflammatory disease under evaluation
over a clinically relevant time period. Reference (normal, control)
values can also be derived from, e.g., a control subject or
population whose inflammatory disease activity level or state is
known. In some embodiments of the present teachings, the reference
value can be derived from one or more subjects who have been
exposed to treatment for inflammatory disease, or from one or more
subjects who are at low risk of developing inflammatory disease, or
from subjects who have shown improvements in inflammatory disease
activity factors (such as, e.g., clinical parameters as defined
herein) as a result of exposure to treatment. In some embodiments
the reference value can be derived from one or more subjects who
have not been exposed to treatment; for example, samples can be
collected from (a) subjects who have received initial treatment for
inflammatory disease, and (b) subjects who have received subsequent
treatment for inflammatory disease, to monitor the progress of the
treatment. A reference value can also be derived from disease
activity algorithms or computed indices from population
studies.
Systems for Implementing Disease Activity Tests
[0168] Tests for measuring disease activity according to various
embodiments of the present teachings can be implemented on a
variety of systems typically used for obtaining test results, such
as results from immunological or nucleic acid detection assays.
Such systems may comprise modules that automate sample preparation,
that automate testing such as measuring biomarker levels, that
facilitate testing of multiple samples, and/or are programmed to
assay the same test or different tests on each sample. In some
embodiments, the testing system comprises one or more of a sample
preparation module, a clinical chemistry module, and an immunoassay
module on one platform. Testing systems are typically designed such
that they also comprise modules to collect, store, and track
results, such as by connecting to and utilizing a database residing
on hardware. Examples of these modules include physical and
electronic data storage devices as are well-known in the art, such
as a hard drive, flash memory, and magnetic tape. Test systems also
generally comprise a module for reporting and/or visualizing
results. Some examples of reporting modules include a visible
display or graphical user interface, links to a database, a
printer, etc. See section Machine-readable storage medium,
below.
[0169] One embodiment of the present invention comprises a system
for determining the inflammatory disease activity of a subject. In
some embodiments, the system employs a module for applying a DAIMRK
or ALLMRK formula to an input comprising the measured levels of
biomarkers in a panel, as described herein, and outputting a
disease activity index score. In some embodiments, the measured
biomarker levels are test results, which serve as inputs to a
computer that is programmed to apply the DAIMRK or ALLMRK formula.
The system may comprise other inputs in addition to or in
combination with biomarker results in order to derive an output
disease activity index; e.g., one or more clinical parameters such
as therapeutic regimen, TJC, SJC, morning stiffness, arthritis of
three or more joint areas, arthritis of hand joints, symmetric
arthritis, rheumatoid nodules, radiographic changes and other
imaging, gender/sex, age, race/ethnicity, disease duration, height,
weight, body-mass index, family history, CCP status, RF status,
ESR, smoker/non-smoker, etc. In some embodiments the system can
apply the DAIMRK/ALLMRK formula to biomarker level inputs, and then
output a disease activity score that can then be analyzed in
conjunction with other inputs such as other clinical parameters. In
other embodiments, the system is designed to apply the
DAIMRK/ALLMRK formula to the biomarker and non-biomarker inputs
(such as clinical parameters) together, and then report a composite
output disease activity index.
[0170] A number of testing systems are presently available that
could be used to implement various embodiments of the present
teachings. See, for example, the ARCHITECT series of integrated
immunochemistry systems--high-throughput, automated, clinical
chemistry analyzers (ARCHITECT is a registered trademark of Abbott
Laboratories, Abbott Park, Ill. 60064). See C. Wilson et al.,
"Clinical Chemistry Analyzer Sub-System Level Performance,"
American Association for Clinical Chemistry Annual Meeting,
Chicago, Ill., Jul. 23-27, 2006; and, H J Kisner, "Product
development: the making of the Abbott ARCHITECT," Clin. Lab.
Manage. Rev. 1997 November-December, 11(6):419-21; A. Ognibene et
al., "A new modular chemiluminescence immunoassay analyser
evaluated," Clin. Chem. Lab. Med. 2000 March, 38(3):251-60; J W
Park et al., "Three-year experience in using total laboratory
automation system," Southeast Asian J. Trop. Med. Public Health
2002, 33 Suppl 2:68-73; D. Pauli et al., "The Abbott Architect
c8000: analytical performance and productivity characteristics of a
new analyzer applied to general chemistry testing," Clin. Lab.
2005, 51(1-2):31-41.
[0171] Another testing system useful for embodiments of the present
teachings is the VITROS system (VITROS is a registered trademark of
Johnson & Johnson Corp., New Brunswick, N.J.)--an apparatus for
chemistry analysis that is used to generate test results from blood
and other body fluids for laboratories and clinics. Another testing
system is the DIMENSION system (DIMENSION is a registered trademark
of Dade Behring Inc., Deerfield Ill.)--a system for the analysis of
body fluids, comprising computer software and hardware for
operating the analyzers, and analyzing the data generated by the
analyzers.
[0172] The testing required for various embodiments of the present
teachings, e.g. measuring biomarker levels, can be performed by
laboratories such as those certified under the Clinical Laboratory
Improvement Amendments (42 U.S.C. Section 263(a)), or by
laboratories certified under any other federal or state law, or the
law of any other country, state or province that governs the
operation of laboratories that analyze samples for clinical
purposes. Such laboratories include, for example, Laboratory
Corporation of America, 358 South Main Street, Burlington, N.C.
27215 (corporate headquarters); Quest Diagnostics, 3 Giralda Farms,
Madison, N.J. 07940 (corporate headquarters); and other reference
and clinical chemistry laboratories.
Biomarker Selection
[0173] The biomarkers and methods of the present teachings allow
one of skill in the art to monitor or assess a subject's
inflammatory and/or autoimmune disease activity, such as for RA,
with a high degree of accuracy. Over 100 markers were initially
identified as having increased or decreased concentration levels in
subjects or populations with RA relative to subjects without
disease, or at different states of disease, or to the subject
himself at other timepoints in the evolution or activity of the
disease. For the initial comparison of observed biomarker with RA
disease activity, the disease activity for each subject was based
upon traditional clinical parameters, such as the DAS28 score.
DAIMRK Group of Markers
[0174] Analyte biomarkers can be selected for use in the present
teachings to form a panel or group of markers. Table 1 describes
several specific biomarkers, collectively referred to as the DAIMRK
group of biomarkers. The present teachings describe the DAIMRK set
of biomarkers as one set or panel of markers that is strongly
associated with inflammatory disease, and especially RA, when used
in particular combinations to derive a DAI score, based on their
correlation with traditional clinical assessments of disease; in
the example of RA, by their correlation with DAS28. See Example 1.
As an example, one embodiment of the present teachings comprises a
method of determining RA disease activity in a subject comprising
measuring the levels of at least two biomarkers from Table 1,
wherein the at least two biomarkers are selected from the group
consisting of apolipoprotein A-I (APOA1); apolipoprotein C-III
(APOC3); chemokine (C-C motif) ligand 22 (CCL22); chitinase 3-like
1 (cartilage glycoprotein-39) (CHI3L1); ICTP; C-reactive protein,
pentraxin-related (CRP); epidermal growth factor (beta-urogastrone)
(EGF); intercellular adhesion molecule 1 (ICAM1); interleukin 18
(interferon-gamma-inducing factor) (IL18); interleukin 1, beta
(IL1B); interleukin 1 receptor antagonist (IL1RN); interleukin 6
(interferon, beta 2) (IL6); interleukin 6 receptor (IL6R);
interleukin 8 (IL8); keratan sulfate; leptin (LEP); matrix
metallopeptidase 1 (interstitial collagenase) (MMP1); matrix
metallopeptidase 3 (stromelysin 1, progelatinase) (MMP3); resistin
(RETN); calprotectin (heteropolymer of protein subunits S100AS and
S100A9); serum amyloid A1 (SAA1); tumor necrosis factor receptor
superfamily, member 1A (TNFRSF1A); vascular cell adhesion molecule
1 (VCAM1); vascular endothelial growth factor A (VEGPFA); and,
pyridinoline (PYD); then, using these observed biomarker levels to
derive a disease activity index score for the subject via an
interpretation function, which score provides a quantitative
measure of RA disease activity in that subject.
[0175] One skilled in the art will recognize that the DAIMRK
biomarkers presented herein encompass all forms and variants of
these biomarkers, including but not limited to polymorphisms,
isoforms, mutants, derivatives, transcript variants, precursors
(including nucleic acids and pre- or pro-proteins), cleavage
products, receptors (including soluble and transmembrane
receptors), ligands, protein-ligand complexes, protein-protein
homo- or heteropolymers, post-translationally modified variants
(such as, e.g., via cross-linking or glycosylation), fragments, and
degradation products, as well as any multi-unit nucleic acid,
protein, and glycoprotein structures comprising any of the DAIMRK
biomarkers as constituent subunits of the fully assembled
structure.
TABLE-US-00001 TABLE 1 Entrez DAIMRK Official Official Other NCBI
Gene No. Symbol* Name* Name(s) RefSeq ID 1 APOA1 Apolipoprotein
MGC117399; NP_000030.1 335 A-I ApoAI 2 APOC3 Apolipoprotein
ApoCIII; NP_000031.1 345 C-III MGC150353 3 CCL22 Chemokine MDC; A-
NP_002981.2 6367 (C-C motif) 152E5.1; ABCD- ligand 22 1; DC/B-CK;
MGC34554; SCYA22; STCP- 1; CC chemokine STCP-1; macrophage- derived
chemokine; small inducible cytokine A22; small inducible cytokine
subfamily A (Cys-Cys), member 22; stimulated T cell chemotactic
protein 1 4 CHI3L Chitinase 3-like YKL-40; NP_001267.2 1116 1
(cartilage ASRT7; glycoprotein-39) DKFZp686N19119; FLJ38139; GP39;
HC-gp39; HCGP-3P; YYL- 40; cartilage glycoprotein-39; chitinase
3-like 1 5 CRP C-reactive MGC149895; NP_000558.2 1401 protein,
MGC88244; pentraxin-related PTX1 6 EGF Epidermal HOMG4; URG;
NP_001954.2 1950 growth factor beta-urogastrone; (beta- epidermal
growth urogastrone) factor 7 ICAMI Intercellular intercellular
NP_000192.2 3383 adhesion adhesion molecule 1 molecule 1 (CD54);
human rhinovirus receptor; ICAM-1 8 N/A N/A ICTP N/A N/A 9 IL18
Interleukin 18 IGIF; IL-1g; NP_001553.1 3606 (interferon- IL1F4;
IL-18; gamma-inducing MGC12320; IL-1 factor) gamma; interferon-
gamma-inducing factor; interleukin-1 gamma 10 IL1B Interleukin 1,
IL-1; IL1-BETA; NP_000567.1 3553 Beta IL1.beta.; IL1F2; catabolin;
preinterleukin 1 beta; pro- interleukin-1- beta 11 IL1RN
Interleukin 1 DIRA; ICIL- NP_000568.1 3557 receptor 1RA; IL-1ra3;
antagonist IL1F3; IL1RA; IRAP; MGC10430; MVCD4; IL1RN (IL1F3);
OTTHUMP00000203730; intracellular IL- 1 receptor antagonist type
II; intracellular interleukin- 1 receptor antagonist (icIL- 1ra);
type II interleukin-1 receptor antagonist 12 IL6 Interleukin 6
IL-6; BSF2; NP_000591.1 3569 (interferon, HGF; HSF; beta 2) IFNB2;
B cell stimulatory factor-2; B-cell differentiation factor; CTL
differentiation factor; OTTHUMP00000158544; hybridoma growth
factor; interleukin BSF-2 13 IL6R Interleukin 6 IL-6R; CD126;
NP_000556.1 3570 receptor IL-6R-alpha; IL6RA; MGC104991; CD126
antigen; interleukin 6 receptor alpha subunit 14 IL8 Interleukin 8
IL-8; CXCL8; NP_000575.1 3576 GCP1; LECT; LUCT; LYNAP; MDNCF;
MONAP; NAF; NAP-1; T cell chemotactic factor; beta- thromboglobulm-
like protein; chemokine (C-X- C motif) ligand 8; emoctakin;
granulocyte chemotactic protein 1; lymphocyte- derived neutrophil-
activating factor; monocyte- derived neutrophil chemotactic factor;
neutrophil- activating peptide 1; small inducible cytokine
subfamily B, member 8 15.sup..dagger. N/A N/A keratan sulfate; N/A
N/A KS 16 LEP Leptin FLJ94114; OB; NP_000221.1 3952 OBS; leptin
(murine obesity homolog); leptin (obesity homolog, mouse); obese,
mouse, homolog of; obesity factor 17 MMP1 Matrix MMP-1; CLG;
NP_002412.1 4312 metallopeptidase CLGN; fibroblast 1 (interstitial
collagenase; collagenase) matrix metalloprotease 1 18 MMP3 Matrix
MMP-3; NP_002413.1 4314 metallopeptidase CHDS6; 3 (stromelysin
MGC126102; 1, progelatinase) MGC126103; MGC126104; SL-1; STMY;
STMY1; STR1; proteoglycanase; transin-1 19 RETN Resistin ADSF;
FIZZ3; NP_065148.1 56729 MGC126603; MGC126609; RETN1; RSTN; XCP1;
C/EBP- epsilon regulated myeloid-specific secreted cysteine-rich
protein precursor 1; found in inflammatory zone 3
20.sup..dagger-dbl. S100A8 S100 calcium Calprotectin; NP_002955.2
6279 binding protein 60B8AG; A8 CAGA; CFAG; CGLA; CP-10; L1Ag;
MA387; MIF; MRP8; NIF; P8; myeloid related protein 8;
OTTHUMP00000015330; S100 calcium-binding protein A8; S100
calcium-binding protein A8 (calgranulin A); calgranulin A; cystic
fibrosis antigen S100A9 S100 calcium Calprotectin; NP_002956.1 6280
binding protein 60B8AG; A9 CAGB; CFAG; CGLB; L1AG; L1AG; MAC387;
MIF; MRP14; NIF; P14; myeloid related protein 9; S100
calcium-binding protein A9; S100 calcium-binding protein A9
(calgranulin B); calgranulin B 21 SAA1 Serum amyloid MGC111216;
NP_000322.2 6288 A1 PIG4; SAA; TP53I4; tumor protein p53 inducible
protein 4 22 TNFRSF1A Tumor necrosis TNFR1; NP_001056.1 7132 factor
receptor CD120a; FPF; superfamily, MGC19588; member 1A TBP1; TNF-R;
TNF-R55; TNFAR; TNFR55; TNFR60; p55; p55-R; p60; tumor necrosis
factor binding protein 1; tumor necrosis factor receptor 1; tumor
necrosis factor receptor type 1; tumor necrosis factor-alpha
receptor 23 TNFSF13B Tumor necrosis BAFF; BLYS; NP_001139117.1;
10673 factor (ligand) CD257; DTL; NP_006564.1 superfamily, TALL1;
member 13b THANK; TNFSF20; ZTNF4; B cell activation factor 24 VCAM1
Vascular cell VCAM-1; NP_001069.1 7412 adhesion CID106; molecule 1
DKFZp779G2333; INCAM-100; MGC99561; CD106 antigen
25 VEGFA Vascular RP1-261G23.1; NP_001020539.2 7422 endothelial
MGC70609; growth factor MVCD1; VEGF; A VPF; vascular endothelial
growth factor isoform VEGF165; vascular permeability factor 26 N/A
N/A PYD, N/A N/A pyridinoline *HUGO Gene Nomenclature Committee, as
of Sep. 25, 2009; accession numbers refer to sequence versions in
NCBI database as of Sep. 25, 2009. .sup..dagger.Keratan sulfate;
not a discrete gene .sup..dagger-dbl.Calprotectin heteropolymer N/A
= Not applicable to this analyte
Biological Significance of the DAIMRK Group of Markers
[0176] The present teachings describe a robust, stepwise
development process for identifying a panel or panels of biomarkers
that are strongly predictive of autoimmune disease activity.
Multivariate algorithmic combinations of specific biomarkers as
described herein exceed the prognostic and predictive power of
individual biomarkers known in the art, because the combinations
comprise biomarkers that represent a broad range of disease
mechanisms, which no individual biomarker does. As a consequence of
the diversity of pathways represented by the combinations as taught
herein, the methods of the present teachings are useful in the
clinical assessment of individual subjects, despite the
heterogeneity of the pathology of the disease assessed.
[0177] The group of biomarkers comprising the DAIMRK set, as an
example, was identified through a selection process comprising
rigorous correlation studies of an initial large, comprehensive set
of candidate protein biomarkers, the ALLMRK set (also described
herein). See, e.g., Example 1. All of the biomarkers that resulted
from these correlation studies, and that make up the DAIMRK set,
are known in the art to play key roles in the pathology of the
autoimmune disease, RA. The methodology employed in selecting the
DAIMRK biomarkers thus resulted in a set of markers especially
useful in quantifying RA disease activity, by providing the
clinician with a unique and broad look at RA disease biology. The
DAIMRK set of biomarkers of the present teachings are thus more
effective in quantifying disease activity than single biomarkers or
randomly selected groupings of biomarkers.
[0178] By demonstration of the key roles of the resulting DAIMRK
markers in RA pathology, the DAIMRK set comprises: the endogenous
form of the recombinant molecule anakinra, an FDA-approved biologic
therapy for RA (IL1RN); the target of anakinra, IL1B, an
inflammatory mediator and key pathologic regulator in RA; key
mediators of the IL6 pathway (IL6 and IL6R) and the TNF pathway
(TNFRSF1A), which are also targets of biologic therapies in RA;
IL8, which modulates neutrophil migration and activation,
neutrophils having a key role in RA disease, as they comprise the
majority of infiltrating inflammatory cells in RA synovial fluid
and release a variety of disease mediators; calprotectin, which has
a role in modulating neutrophil activation, in addition to its role
in TLR4 inflammatory signaling; CCL22, a key modulator of humoral
immunity and B cell activation, and which recruits T cells to the
rheumatoid synovium; the pro-angiogenic proteins VEGFA and IL8,
which also attract leukocytes to the RA joint; the endothelial
adhesion and activation biomarkers ICAM1 and VCAM1; markers derived
in large part from fibroblasts, including IL6, IL8, VEGPFA, EGF,
MMP1 and MMP3; CHI3L1, which is highly elevated in RA joints and
thought to modulate intra-articular matrix; bone and cartilage
matrix breakdown products of RA joints, including ICTP, keratan
sulfate, and PYD; lipid-associated proteins LEP, RETN, APOA1 and
APOC3; and, two key acute phase proteins, CRP and SAA1, which
reflect the role of RA inflammation in inducing the hepatic acute
phase response.
[0179] Additionally, because the serum levels of certain protein
biomarkers of the DAIMRK set are known to fluctuate in an
individual, depending on disease activity, in some embodiments of
the present teachings the clinician could select those biomarkers
for generating a DAI score, and thus obtain a more concise overview
of the subject's present disease activity status.
[0180] Moreover, the process of comprehensive candidate biomarker
identification and subsequent staged correlation-based analyses in
a series of independent cohorts, as described in the Examples that
follow, results in the identification of a panel or panels of
biomarkers that have significant correlation to disease
activity.
Model Development Process
[0181] An exemplary method for developing predictive models to
determine the inflammatory disease activity of a subject or
population is shown by the flow diagram of FIG. 6 (200). Biomarker
data from a representative population, as described herein, is
obtained (202). This biomarker data can be derived through a
variety of methods, including prospective, retrospective,
cross-sectional, or longitudinal studies, that involve
interventions or observations of the representative subjects or
populations from one or more timepoints. The biomarker data can be
obtained from a single study or multiple studies. Subject and
population data can generally include data pertaining to the
subjects' disease status and/or clinical assessments, which can be
used for training and validating the algorithms for use in the
present teachings, wherein the values of the biomarkers described
herein are correlated to the desired clinical measurements.
[0182] Data within the representative population dataset is then
prepared (204) so as to fit the requirements of the model that will
be used for biomarker selection, described below. A variety of
methods of data preparation can be used, such as transformations,
normalizations, and gap-fill techniques including nearest neighbor
interpolation or other pattern recognition techniques. The data
preparation techniques that are useful for different model types
are well-known in the art. See Examples, below.
[0183] Biomarkers are then selected for use in the training of the
model to determine inflammatory disease activity (206). Various
models can be used to inform this selection, and biomarker data are
chosen from the dataset providing the most reproducible results.
Methods to evaluate biomarker performance can include, e.g.,
bootstrapping and cross-validation.
[0184] After the biomarkers are selected, the model to be used to
determine inflammatory disease activity can be selected. For
specific examples of statistical methods useful in designing
predictive models, see Calculation of the DAI score.
[0185] For the particular selection model used with a dataset,
biomarkers can be selected based on such criteria as the
biomarker's ranking among all candidate markers, the biomarker's
statistical significance in the model, and any improvement in model
performance when the biomarker is added to the model. Tests for
statistical significance can include, for example, correlation
tests, t-tests, and analysis of variance (ANOVA). Models can
include, for example, regression models such as regression trees
and linear models, and classification models such as logistic
regression, Random Forest, SVM, tree models, and LDA. Examples of
these are described herein.
[0186] In those cases where individual biomarkers are not alone
indicative of inflammatory disease activity, biomarker combinations
can be applied to the selection model. Instead of univariate
biomarker selection, for example, multivariate biomarker selection
can be used. One example of an algorithm useful in multivariate
biomarker selection is a recursive feature selection algorithm.
Biomarkers that are not alone good indicators of inflammatory
disease activity may still be useful as indicators when in
combination with other biomarkers, in a multivariate input to the
model, because each biomarker may bring additional information to
the combination that would not be informative where taken
alone.
[0187] Next, selection, training and validation is performed on the
model for assessing disease activity (208). Models can be selected
based on various performance and/or accuracy criteria, such as are
described herein. By applying datasets to different models, the
results can be used to select the best models, while at the same
time the models can be used to determine which biomarkers are
statistically significant for inflammatory disease activity.
Combinations of models and biomarkers can be compared and validated
in different datasets. The comparisons and validations can be
repeated in order to train and/or choose a particular model.
[0188] FIG. 7 is a flow diagram of an exemplary method (250) of
using a model as developed above to determine the inflammatory
disease activity of a subject or a population. Biomarker data is
obtained from the subject at (252). This data can be obtained by a
variety of means, including but not limited to physical
examinations, self-reports by the subject, laboratory testing,
medical records and charts. Subject data can then be prepared (254)
via transformations, logs, normalizations, and so forth, based on
the particular model selected and trained in FIG. 6. The data is
then input into the model for evaluation (256), which outputs an
index value (258); e.g., a DAI score. Examples as to how a model
can be used to evaluate a subject's biomarkers and output a DAI
value are provided herein.
Modifications for Response to Treatment
[0189] In certain embodiments of the present teachings, biomarkers
from the DAIMRK group can be used to determine a subject's response
to treatment for inflammatory disease. Measuring levels of an
effective amount of biomarkers also allows for the course of
treatment of inflammatory disease to be monitored. In these
embodiments, a biological sample can be provided from a subject
undergoing therapeutic regimens for inflammatory disease. If
desired, biological samples are obtained from the subject at
various time points before, during, or after treatment.
[0190] Various embodiments of the present teachings can be used to
provide a guide to the selection of a therapeutic regimen for a
subject; meaning, e.g., that treatment may need to be more or less
aggressive, or a subject may need a different therapeutic regimen,
or the subject's current therapeutic regimen may need to be changed
or stopped, or a new therapeutic regimen may need to be adopted,
etc.
[0191] Treatment strategies are confounded by the fact that RA is a
classification given to a group of subjects with a diverse array of
related symptoms. This suggests that certain subtypes of RA are
driven by specific cell type or cytokine. As a likely consequence,
no single therapy has proven optimal for treatment. Given the
increasing numbers of therapeutic options available for RA, the
need for an individually tailored treatment directed by
immunological prognostic factors of treatment outcome is
imperative. In various embodiments of the present teachings, a
DAIMRK biomarker-derived algorithm can be used to quantify therapy
response in RA subjects. See Example 5. Measuring DAIMRK biomarker
levels over a period time can provide the clinician with a dynamic
picture of the subject's biological state, and the DAI scores are
highly correlated to DAS28. Overlaying the DAS28 score with the DAI
score can provide a deeper understanding of how a subject is
responding to therapy. These embodiments of the present teachings
thus will provide subject-specific biological information, which
will be informative for therapy decision and will facilitate
therapy response monitoring, and should result in more rapid and
more optimized treatment, better control of disease activity, and
an increase in the proportion of subjects achieving remission.
[0192] Differences in the genetic makeup of subjects can result in
differences in their relative abilities to metabolize various
drugs, which may modulate the symptoms or state of inflammatory
disease. Subjects that have inflammatory disease can vary in age,
ethnicity, body mass index (BMI), total cholesterol levels, blood
glucose levels, blood pressure, LDL and HDL levels, and other
parameters. Accordingly, use of the biomarkers disclosed herein,
both alone and together in combination with known genetic factors
for drug metabolism, allow for a pre-determined level of
predictability that a putative therapeutic or prophylactic to be
tested in a selected subject will be suitable for treating or
preventing inflammatory disease in the subject.
[0193] To identify therapeutics or drugs that are appropriate for a
specific subject, a test sample from the subject can also be
exposed to a therapeutic agent or a drug, and the level of one or
more biomarkers can be determined. The level of one or more
biomarkers can be compared to sample derived from the subject
before and after treatment or exposure to a therapeutic agent or a
drug, or can be compared to samples derived from one or more
subjects who have shown improvements in inflammatory disease state
or activity (e.g., clinical parameters or traditional laboratory
risk factors) as a result of such treatment or exposure.
Combination with Clinical Parameters
[0194] Any of the aforementioned clinical parameters can also be
used in the practice of the present teachings, as input to the
DAIMRK formula or as a pre-selection criteria defining a relevant
population to be measured using a particular DAIMRK panel and
formula. As noted above, clinical parameters can also be useful in
the biomarker normalization and pre-processing, or in selecting
particular biomarkers from DAIMRK, panel construction, formula type
selection and derivation, and formula result post-processing.
Clinical Assessments of the Present Teachings
[0195] In some embodiments of the present teachings, panels of
DAIMRK biomarkers and formulas are tailored to the population,
endpoints or clinical assessment, and/or use that is intended. For
example, the DAIMRK panels and formulas can be used to assess
subjects for primary prevention and diagnosis, and for secondary
prevention and management. For the primary assessment, the DAIMRK
panels and formulas can be used for prediction and risk
stratification for future conditions or disease sequelae, for the
diagnosis of inflammatory disease, for the prognosis of disease
activity and rate of change, and for indications for future
diagnosis and therapeutic regimens. For secondary prevention and
clinical management, the DAIMRK panels and formulas can be used for
prognosis and risk stratification. The DAIMRK panels and formulas
can be used for clinical decision support, such as determining
whether to defer intervention or treatment, to recommend preventive
check-ups for at-risk patients, to recommend increased visit
frequency, to recommend increased testing, and to recommend
intervention. The DAIMRK panels and formulas can also be useful for
therapeutic selection, determining response to treatment,
adjustment and dosing of treatment, monitoring ongoing therapeutic
efficiency, and indication for change in therapeutic regimen.
[0196] In some embodiments of the present teachings, the DAIMRK
panels and formulas can be used to aid in the diagnosis of
inflammatory disease, and in the determination of the severity of
inflammatory disease. The DAIMRK panels and formulas can also be
used for determining the future status of intervention such as, for
example in RA, determining the prognosis of future joint erosion
with or without treatment. Certain embodiments of the present
teachings can be tailored to a specific treatment or a combination
of treatments. X-ray is currently considered the gold standard for
assessment of disease progression, but it has limited capabilities
since subjects may have long periods of active symptomatic disease
while radiographs remain normal or show only nonspecific changes.
Conversely, subjects who seem to have quiescent disease
(subclinical disease) may slowly progress over time, undetected
clinically until significant radiographic progression has occurred.
If subjects with a high likelihood of disease progression could be
identified in advance, the opportunity for early aggressive
treatment could result in much more effective disease outcomes.
See, e.g., M. Weinblatt et al., N. Engl. J. Med. 1999, 340:253-259.
In certain embodiments of the present teachings, an algorithm
developed from the DAIMRK set of biomarkers can be used, with
significant power, to characterize the level of bone or cartilage
damage activity in RA subjects. See Example 6. In other
embodiments, an algorithm developed from the DAIMRK set of
biomarkers can be used, with significant power, to prognose joint
destruction over time. See Example 6. In other embodiments, the DAI
score can be used as a strong predictor of radiographic
progression, giving the clinician a novel way to identify subjects
at risk of RA-induced joint damage and allowing for early
prescription of joint-sparing agents, prophylactically.
[0197] In some embodiments of the present teachings, the DAIMRK
panels and formulas can be used as surrogate markers of clinical
events necessary for the development of inflammatory
disease-specific agents; e.g., pharmaceutical agents. That is, the
DAI surrogate marker, derived from a DAIMRK panel, can be used in
the place of clinical events in a clinical trial for an
experimental RA treatment. DAIMRK panels and formulas can thus be
used to derive an inflammatory disease surrogate endpoint to assist
in the design of experimental treatments for RA.
Measurement of Biomarker
[0198] The quantity of one or more biomarkers of the present
teachings can be indicated as a value. The value can be one or more
numerical values resulting from the evaluation of a sample, and can
be derived, e.g., by measuring level(s) of the biomarker(s) in a
sample by an assay performed in a laboratory, or from dataset
obtained from a provider such as a laboratory, or from a dataset
stored on a server. Biomarker levels can be measured using any of
several techniques known in the art. The present teachings
encompass such techniques, and further include all subject fasting
and/or temporal-based sampling procedures for measuring
biomarkers.
[0199] The actual measurement of levels of the biomarkers can be
determined at the protein or nucleic acid level using any method
known in the art. "Protein" detection comprises detection of
full-length proteins, mature proteins, pre-proteins, polypeptides,
isoforms, mutations, variants, post-translationally modified
proteins and variants thereof, and can be detected in any suitable
manner. Levels of biomarkers can be determined at the protein
level, e.g., by measuring the serum levels of peptides encoded by
the gene products described herein, or by measuring the enzymatic
activities of these protein biomarkers. Such methods are well-known
in the art and include, e.g., immunoassays based on antibodies to
proteins encoded by the genes, aptamers or molecular imprints. Any
biological material can be used for the detection/quantification of
the protein or its activity. Alternatively, a suitable method can
be selected to determine the activity of proteins encoded by the
biomarker genes according to the activity of each protein analyzed.
For biomarker proteins, polypeptides, isoforms, mutations, and
variants thereof known to have enzymatic activity, the activities
can be determined in vitro using enzyme assays known in the art.
Such assays include, without limitation, protease assays, kinase
assays, phosphatase assays, reductase assays, among many others.
Modulation of the kinetics of enzyme activities can be determined
by measuring the rate constant KM using known algorithms, such as
the Hill plot, Michaelis-Menten equation, linear regression plots
such as Lineweaver-Burk analysis, and Scatchard plot.
[0200] Using sequence information provided by the public database
entries for the biomarker, expression of the biomarker can be
detected and measured using techniques well-known to those of skill
in the art. For example, nucleic acid sequences in the sequence
databases that correspond to nucleic acids of biomarkers can be
used to construct primers and probes for detecting and/or measuring
biomarker nucleic acids. These probes can be used in, e.g.,
Northern or Southern blot hybridization analyses, ribonuclease
protection assays, and/or methods that quantitatively amplify
specific nucleic acid sequences. As another example, sequences from
sequence databases can be used to construct primers for
specifically amplifying biomarker sequences in, e.g.,
amplification-based detection and quantitation methods such as
reverse-transcription based polymerase chain reaction (RT-PCR) and
PCR. When alterations in gene expression are associated with gene
amplification, nucleotide deletions, polymorphisms,
post-translational modifications and/or mutations, sequence
comparisons in test and reference populations can be made by
comparing relative amounts of the examined DNA sequences in the
test and reference populations.
[0201] As an example, Northern hybridization analysis using probes
which specifically recognize one or more of these sequences can be
used to determine gene expression. Alternatively, expression can be
measured using RT-PCR; e.g., polynucleotide primers specific for
the differentially expressed biomarker mRNA sequences
reverse-transcribe the mRNA into DNA, which is then amplified in
PCR and can be visualized and quantified. Biomarker RNA can also be
quantified using, for example, other target amplification methods,
such as TMA, SDA, and NASBA, or signal amplification methods (e.g.,
bDNA), and the like. Ribonuclease protection assays can also be
used, using probes that specifically recognize one or more
biomarker mRNA sequences, to determine gene expression.
[0202] Alternatively, biomarker protein and nucleic acid
metabolites can be measured. The term "metabolite" includes any
chemical or biochemical product of a metabolic process, such as any
compound produced by the processing, cleavage or consumption of a
biological molecule (e.g., a protein, nucleic acid, carbohydrate,
or lipid). Metabolites can be detected in a variety of ways known
to one of skill in the art, including the refractive index
spectroscopy (RI), ultra-violet spectroscopy (UV), fluorescence
analysis, radiochemical analysis, near-infrared spectroscopy
(near-IR), nuclear magnetic resonance spectroscopy (NMR), light
scattering analysis (LS), mass spectrometry, pyrolysis mass
spectrometry, nephelometry, dispersive Raman spectroscopy, gas
chromatography combined with mass spectrometry, liquid
chromatography combined with mass spectrometry, matrix-assisted
laser desorption ionization-time of flight (MALDI-TOF) combined
with mass spectrometry, ion spray spectroscopy combined with mass
spectrometry, capillary electrophoresis, NMR and IR detection. See
WO 04/056456 and WO 04/088309, each of which is hereby incorporated
by reference in its entirety. In this regard, other biomarker
analytes can be measured using the above-mentioned detection
methods, or other methods known to the skilled artisan. For
example, circulating calcium ions (Ca.sup.2+) can be detected in a
sample using fluorescent dyes such as the Fluo series, Fura-2A,
Rhod-2, among others. Other biomarker metabolites can be similarly
detected using reagents that are specifically designed or tailored
to detect such metabolites.
[0203] In some embodiments, a biomarker is detected by contacting a
subject sample with reagents, generating complexes of reagent and
analyte, and detecting the complexes. Examples of "reagents"
include but are not limited to nucleic acid primers and
antibodies.
[0204] In some embodiments of the present teachings an antibody
binding assay is used to detect a biomarker, e.g., a sample from
the subject is contacted with an antibody reagent that binds the
biomarker analyte, a reaction product (or complex) comprising the
antibody reagent and analyte is generated, and the presence (or
absence) or amount of the complex is determined. The antibody
reagent useful in detecting biomarker analytes can be monoclonal,
polyclonal, chimeric, recombinant, or a fragment of the foregoing,
as discussed in detail above, and the step of detecting the
reaction product can be carried out with any suitable immunoassay.
The sample from the subject is typically a biological fluid as
described above, and can be the same sample of biological fluid as
is used to conduct the method described above.
[0205] Immunoassays carried out in accordance with the present
teachings can be homogeneous assays or heterogeneous assays. In a
homogeneous assay the immunological reaction can involve the
specific antibody (e.g., anti-biomarker protein antibody), a
labeled analyte, and the sample of interest. The label produces a
signal, and the signal arising from the label becomes modified,
directly or indirectly, upon binding of the labeled analyte to the
antibody. Both the immunological reaction of binding, and detection
of the extent of binding, can be carried out in a homogeneous
solution. Immunochemical labels which can be employed include but
are not limited to free radicals, radioisotopes, fluorescent dyes,
enzymes, bacteriophages, and coenzymes. Immunoassays include
competition assays.
[0206] In a heterogeneous assay approach, the reagents can be the
sample of interest, an antibody, and a reagent for producing a
detectable signal. Samples as described above can be used. The
antibody can be immobilized on a support, such as a bead (such as
protein A and protein G agarose beads), plate or slide, and
contacted with the sample suspected of containing the biomarker in
liquid phase. The support is separated from the liquid phase, and
either the support phase or the liquid phase is examined using
methods known in the art for detecting signal. The signal is
related to the presence of the analyte in the sample. Methods for
producing a detectable signal include but are not limited to the
use of radioactive labels, fluorescent labels, or enzyme labels.
For example, if the antigen to be detected contains a second
binding site, an antibody which binds to that site can be
conjugated to a detectable (signal-generating) group and added to
the liquid phase reaction solution before the separation step. The
presence of the detectable group on the solid support indicates the
presence of the biomarker in the test sample. Examples of suitable
immunoassays include but are not limited to oligonucleotides,
immunoblotting, immunoprecipitation, immunofluorescence methods,
chemiluminescence methods, electrochemiluminescence (ECL), and/or
enzyme-linked immunoassays (ELISA).
[0207] Those skilled in the art will be familiar with numerous
specific immunoassay formats and variations thereof which can be
useful for carrying out the method disclosed herein. See, e.g., E.
Maggio, Enzyme-Immunoassay (1980), CRC Press, Inc., Boca Raton,
Fla. See also U.S. Pat. No. 4,727,022 to C. Skold et al., titled
"Novel Methods for Modulating Ligand-Receptor Interactions and
their Application"; U.S. Pat. No. 4,659,678 to CC Forrest et al.,
titled "Immunoassay of Antigens"; U.S. Pat. No. 4,376,110 to GS
David et al., tided "Immunometric Assays Using Monoclonal
Antibodies"; U.S. Pat. No. 4,275,149 to D. Litman et al., titled
"Macromolecular Environment Control in Specific Receptor Assays";
U.S. Pat. No. 4,233,402 to E. Maggio et al., titled "Reagents and
Method Employing Channeling"; and, U.S. Pat. No. 4,230,797 to R.
Boguslaski et al., titled "Heterogenous Specific Binding Assay
Employing a Coenzyme as Label."
[0208] Antibodies can be conjugated to a solid support suitable for
a diagnostic assay (e.g., beads such as protein A or protein G
agarose, microspheres, plates, slides or wells formed from
materials such as latex or polystyrene) in accordance with known
techniques, such as passive binding. Antibodies as described herein
can likewise be conjugated to detectable labels or groups such as
radiolabels (e.g., 35S, 125I, 131I), enzyme labels (e.g.,
horseradish peroxidase, alkaline phosphatase), and fluorescent
labels (e.g., fluorescein, Alexa, green fluorescent protein,
rhodamine) in accordance with known techniques.
[0209] Antibodies may also be useful for detecting
post-translational modifications of biomarkers. Examples of
post-translational modifications include, but are not limited to
tyrosine phosphorylation, threonine phosphorylation, serine
phosphorylation, citrullination and glycosylation (e.g., O-GlcNAc).
Such antibodies specifically detect the phosphorylated amino acids
in a protein or proteins of interest, and can be used in the
immunoblotting, immunofluorescence, and ELISA assays described
herein. These antibodies are well-known to those skilled in the
art, and commercially available. Post-translational modifications
can also be determined using metastable ions in reflector
matrix-assisted laser desorption ionization-time of flight mass
spectrometry (MALDI-TOF). See U. Wirth et al., Proteomics 2002,
2(10):1445-1451.
Kits
[0210] Other embodiments of the present teachings comprise
biomarker detection reagents packaged together in the form of a kit
for conducting any of the assays of the present teachings. In
certain embodiments, the kits comprise oligonucleotides that
specifically identify one or more biomarker nucleic acids based on
homology and/or complementarity with biomarker nucleic acids. The
oligonucleotide sequences may correspond to fragments of the
biomarker nucleic acids. For example, the oligonucleotides can be
more than 200, 200, 150, 100, 50, 25, 10, or fewer than 10
nucleotides in length. In other embodiments, the kits comprise
antibodies to proteins encoded by the biomarker nucleic acids. The
kits of the present teachings can also comprise aptamers. The kit
can contain in separate containers a nucleic acid or antibody (the
antibody either bound to a solid matrix, or packaged separately
with reagents for binding to a matrix), control formulations
(positive and/or negative), and/or a detectable label, such as but
not limited to fluorescein, green fluorescent protein, rhodamine,
cyanine dyes, Alexa dyes, luciferase, and radiolabels, among
others. Instructions for carrying out the assay, including,
optionally, instructions for generating a DAI score, can be
included in the kit; e.g., written, tape, VCR, or CD-ROM. The assay
can for example be in the form of a Northern hybridization or a
sandwich ELISA as known in the art.
[0211] In some embodiments of the present teachings, biomarker
detection reagents can be immobilized on a solid matrix, such as a
porous strip, to form at least one biomarker detection site. In
some embodiments, the measurement or detection region of the porous
strip can include a plurality of sites containing a nucleic acid.
In some embodiments, the test strip can also contain sites for
negative and/or positive controls. Alternatively, control sites can
be located on a separate strip from the test strip. Optionally, the
different detection sites can contain different amounts of
immobilized nucleic acids, e.g., a higher amount in the first
detection site and lesser amounts in subsequent sites. Upon the
addition of test sample, the number of sites displaying a
detectable signal provides a quantitative indication of the amount
of biomarker present in the sample. The detection sites can be
configured in any suitably detectable shape and can be, e.g., in
the shape of a bar or dot spanning the width of a test strip.
[0212] In other embodiments of the present teachings, the kit can
contain a nucleic acid substrate array comprising one or more
nucleic acid sequences. The nucleic acids on the array specifically
identify one or more nucleic acid sequences represented by DAIMRK
biomarker Nos. 1-25. In various embodiments, the expression of one
or more of the sequences represented by DAIMRK Nos. 1-25 can be
identified by virtue of binding to the array. In some embodiments
the substrate array can be on a solid substrate, such as what is
known as a "chip." See, e.g., U.S. Pat. No. 5,744,305. In some
embodiments the substrate array can be a solution array; e.g., xMAP
(Luminex, Austin, Tex.), Cyvera (Illumina, San Diego, Calif.),
RayBio Antibody Arrays (RayBiotech, Inc., Norcross, Ga.), CellCard
(Vitra Bioscience, Mountain View, Calif.) and Quantum Dots' Mosaic
(Invitrogen, Carlsbad, Calif.).
Machine-Readable Storage Medium
[0213] A machine-readable storage medium can comprise, for example,
a data storage material that is encoded with machine-readable data
or data arrays. The data and machine-readable storage medium are
capable of being used for a variety of purposes, when using a
machine programmed with instructions for using said data. Such
purposes include, without limitation, storing, accessing and
manipulating information relating to the inflammatory disease
activity of a subject or population over time, or disease activity
in response to inflammatory disease treatment, or for drug
discovery for inflammatory disease, etc. Data comprising
measurements of the biomarkers of the present teachings, and/or the
evaluation of disease activity or disease state from these
biomarkers, can be implemented in computer programs that are
executing on programmable computers, which comprise a processor, a
data storage system, one or more input devices, one or more output
devices, etc. Program code can be applied to the input data to
perform the functions described herein, and to generate output
information. This output information can then be applied to one or
more output devices, according to methods well-known in the art.
The computer can be, for example, a personal computer, a
microcomputer, or a workstation of conventional design.
[0214] The computer programs can be implemented in a high-level
procedural or object-oriented programming language, to communicate
with a computer system such as for example, the computer system
illustrated in FIG. 16. The programs can also be implemented in
machine or assembly language. The programming language can also be
a compiled or interpreted language. Each computer program can be
stored on storage media or a device such as ROM, magnetic diskette,
etc., and can be readable by a programmable computer for
configuring and operating the computer when the storage media or
device is read by the computer to perform the described procedures.
Any health-related data management systems of the present teachings
can be considered to be implemented as a computer-readable storage
medium, configured with a computer program, where the storage
medium causes a computer to operate in a specific manner to perform
various functions, as described herein.
[0215] The biomarkers disclosed herein can be used to generate a
"subject biomarker profile" taken from subjects who have
inflammatory disease. The subject biomarker profiles can then be
compared to a reference biomarker profile, in order to diagnose or
identify subjects with inflammatory disease, to monitor the
progression or rate of progression of inflammatory disease, or to
monitor the effectiveness of treatment for inflammatory disease.
The biomarker profiles, reference and subject, of embodiments of
the present teachings can be contained in a machine-readable
medium, such as analog tapes like those readable by a CD-ROM or USB
flash media, among others. Such machine-readable media can also
contain additional test results, such as measurements of clinical
parameters and clinical assessments. The machine-readable media can
also comprise subject information; e.g., the subject's medical or
family history. The machine-readable media can also contain
information relating to other disease activity algorithms and
computed scores or indices, such as those described herein.
EXAMPLES
[0216] Aspects of the present teachings can be further understood
in light of the following examples, which should not be construed
as limiting the scope of the present teachings in any way.
[0217] The practice of the present teachings employ, unless
otherwise indicated, conventional methods of protein chemistry,
biochemistry, recombinant DNA techniques and pharmacology, within
the skill of the art. Such techniques are explained fully in the
literature. See, e.g., T. Creighton, Proteins: Structures and
Molecular Properties, 1993, W. Freeman and Co.; A. Lehninger,
Biochemistry, Worth Publishers, Inc. (current addition); J.
Sambrook et al., Molecular Cloning: A Laboratory Manual, 2nd
Edition, 1989; Methods In Enzymology, S. Colowick and N. Kaplan,
eds., Academic Press, Inc.; Remington's Pharmaceutical Sciences,
18th Edition, 1990, Mack Publishing Company, Easton, Pa.; Carey and
Sundberg, Advanced Organic Chemistry, Vols. A and B, 3rd Edition,
1992, Plenum Press.
[0218] The practice of the present teachings also employ, unless
otherwise indicated, conventional methods of statistical analysis,
within the skill of the art. Such techniques are explained fully in
the literature. See, e.g., J. Little and D. Rubin. Statistical
Analysis with Missing Data, 2nd Edition 2002, John Wiley and Sons.
Inc., NJ; M. Pepe, The Statistical Evaluation of Medical Tests for
Classification and Prediction (Oxford Statistical Science Series)
2003, Oxford University Press, Oxford, UK; X. Zhoue et al.,
Statistical Methods in Diagnostic Medicine 2002, John Wiley and
Sons, Inc., NJ; T. Hastie et. al, The Elements of Statistical
Leaning: Data Mining, Inference, and Prediction, Second Edition
2009, Springer, N.Y.; W. Cooley and P. Lohnes, Multivariate
procedures for the behavioral science 1962, John Wiley and Sons,
Inc. NY; E. Jackson, A User's Guide to Principal Components 2003,
John Wiley and Sons, Inc., NY.
Example 1
Association of DAI with DAS28 Scores in a Large Clinical Cohort
[0219] Example 1 demonstrates the transformation of observed
biomarker levels into a DAI score by various statistical modeling
methodologies, which DAI score serves as a quantitative measurement
of disease activity that correlates well with observed DAS28, as
for measuring the extent of subject inflammation status and disease
activity at any single timepoint. Certain embodiments of the
present teachings comprise utilizing the DAIMRK set of biomarkers
for determining a DAI score with high correlation with disease
activity status.
[0220] Samples were obtained from the Brigham and Women's Hospital
Rheumatoid Arthritis Sequential Study (BRASS). The appropriate
Research Ethics Committee approval was obtained for the study, and
all subjects gave informed consent. Since 2003, 1,000 subjects with
confirmed RA under the care of the Brigham and Women's hospital
have been enrolled in BRASS. The cohort for this study had the
following characteristics: 80% female, 62% CCP positive, 83% RF
positive, 13% smokers, 61% on MTX, 76% on non-biologic DMARDs, 53%
on biologic DMARDs, and 27% on steroids. Additionally, the mean age
of the cohort was 59 years (standard deviation (SD)+/-13.1), with a
minimum age of 22 and a maximum age of 94. The mean DAS28-CRP for
this cohort was 4.1 (SD+/-1.7), with a minimum of 1.2 and a maximum
of 8.2.
[0221] All subjects fulfilled the American College of Rheumatology
criteria for RA, and every subject in the study will be followed
for five years. At six-month intervals throughout the study, data
are collected from all subjects, comprising medical or clinical
information such as disease activity scores, radiological results,
subject health status and other clinical assessments. Blood samples
are collected at twelve-month intervals from each subject for five
years. A subpopulation of one hundred and eighty subjects was
selected from the BRASS cohort. Within the subjects selected, a
wide distribution of DAS28-CRP scores was represented (DAS28
range=1.19-8.2).
[0222] Assays were designed, in multiplex or ELISA format, for
measuring multiple disease-related protein biomarkers selected from
the ALLMRK set, as that set is described herein. These assays were
identified through a screening and optimization process, prior to
assaying the BRASS samples. The respective biomarker assays,
vendors, and platforms used were as follows: APOA1, Millipore,
LUMINEX.RTM.; APOC3, Millipore, LUMINEX.RTM.; calprotectin, Alpco,
ELISA; CCL22, Meso Scale Discovery, MSD.RTM.; CHL3L1 (YKL-40),
Quidel, ELISA; CRP, Meso Scale Discovery, MSD.RTM.; EGF, R&D
Systems, LUMINEX.RTM.; ICAM1, Meso Scale Discovery, MSD.RTM.; ICTP,
IDS (Immunodiagnostic Systems), ELISA; IL18, R&D Systems,
ELISA; IL1B, Meso Scale Discovery, MSD.RTM.; IL1RN, R&D
Systems, LUMINEX.RTM.; IL6. R&D Systems, LUMINEX.RTM.; IL6R,
Millipore, LUMINEX.RTM.; IL8, R&D Systems, LUMINEX.RTM.;
keratan sulfate, Cape Cod, Inc., ELISA; LEP, R&D Systems,
LUMINEX.RTM.; MMP1, R&D Systems, LUMINEX.RTM.; MMP3, R&D
Systems, LUMINEX.RTM.; RETN, R&D Systems, LUMINEX.RTM.; SAA1,
Meso Scale Discovery, MSD.RTM.; TNFRSF1A, Meso Scale Delivery,
MSD.RTM.; TNFSF13B, R&D Systems, ELISA; VCAM1, Meso Scale
Discovery, MSD.RTM.; and, VEGFA, R&D Systems, LUMINEX.RTM..
[0223] All assays were performed following the manufacturer's
instructions, with cohort samples randomly assigned to the sample
positions on the plate layouts. Four pooled sera, from healthy, RA,
SLE and osteoarthritis (OA) subjects, were included in each assay
plate as process controls. All samples were assayed at least in
duplicate. Seven-point calibration curves were constructed for each
biomarker for an accurate determination of the measurable range of
test sera.
[0224] Prior to statistical analyses, all assay data were reviewed
for pass/fail criteria as predefined by standard operating
procedures, including inter-assay CV, intra-assay CV, percent
number of samples within the measurable range of the calibration
curve, and four serum process controls within the range of the
calibration curve. The biomarker values that were not in the
measurable range of the calibration curves were marked as missing
data, and imputed by the lowest/highest detected value across all
the samples within a given biomarker assay. No imputation was
performed for the univariate analyses. For multivariate analysis,
missing data imputation methods commonly used in microarray
expression data and well-known in the art were used. See, e.g., R.
Little and D. Rubin, Statistical Analysis with Missing Data, 2nd
Edition 2002, John Wiley and Sons, Inc., NJ. Biomarkers were
excluded from analysis where more than 20% of the data were
missing, and the remaining data were imputed by the KNN algorithm
(where k=5 nearest neighbors). KNN functions on the intuitive idea
that close objects are more likely to be in the same category.
Thus, in KNN, predictions are based on a set of prototype examples
that are used to predict new (i.e., unseen) data based on the
majority vote (for classification tasks) over a set of k-nearest
prototypes. Given a new case of dependent values (query point), we
would like to estimate the outcome based on the KNN examples. KNN
achieves this by finding k examples that are closest in Euclidian
distance to the query point.
Univariate Analysis
[0225] Biomarker assay data were normalized by plate before
correlations were calculated between individual proteins and
measurements were transformed into DAI scores. Associations were
calculated between the DAI scores and DAS28-CRP scores, SJC, TJC,
or CDAI. The correlation results were then compared using
univariate analysis. See Table 10.
TABLE-US-00002 TABLE 10 Biomarker Correlation coefficient Nominal
p-value APOA1 -0.177 <0.0001 calprotectin 0.42 <0.0001 CHI3LI
0.178 <0.0001 CRP 0.476 <0.0001 EGF -0.358 <0.0001 ICAM1
0.242 <0.0001 IL1B -0.282 <0.0001 IL6 0.289 <0.0001 IL6R
0.082 <0.0001 IL8 -0.393 <0.0001 IL1RN 0.211 <0.0001 LEP
0.21 <0.0001 RETN 0.256 <0.0001 SAA1 0.386 <0.0001
TNFRSF1A 0.176 <0.0001 VCAM1 0.323 <0.0001 VEGFA 0.198
<0.0001 keratan sulfate -0.258 0.002 TNFSF13B 0.271 0.007 ICTP
0.266 0.014 APOC3 -0.118 0.255 MMP3 0.34 <0.0001 CCL22 0.116 0.2
MMP1 0.261 0.006
[0226] See FIG. 8 for a cumulative distribution function (CDF) plot
of transformation comparisons, wherein the CDF of p-values is the
cumulative distribution function of all the p-values obtained
(i.e., one p-value per DAIMRK biomarker), and thus shows the
distribution of all p-values. See FIG. 9 for a correlation matrix
between 21 DAIMRK biomarkers and continuous clinical variables.
[0227] The False Discovery Rate (FDR) was used as a multiple
testing correction, according to the following: let k be the
largest i for which p.sub.i.ltoreq.i/m*.alpha.; reject all H.sub.i,
where i=1, . . . , m. In this equation the variable a is a
pre-specified probability of a false-positive (Type I) error,
typically 0.05, and H is a hypothesis. As will be clear to one of
skill in the art, where the DAIMRK biomarker is significantly
associated with the DAS score, the q-value (the false discovery
rate) is small. FIG. 8 shows the different results obtained from
different normalizations. A parametric correlation test was also
performed, using the parametric test H.sub.i: .rho..sub.i=0, and
the statistic given by
t = r ( n - 2 ) 1 / 2 ( 1 - r 2 ) 1 / 2 . ##EQU00001##
For this analysis, t represents the test statistics (for which
p-value can be calculated using the T distribution), r is the
correlation coefficient, and n is the sample size.
[0228] Covariation and multicolinearity between all variables were
evaluated; i.e., for both clinical data and biomarkers. Heatmap,
PCA, and correlation matrices were generated. See FIGS. 11 and 9
for PCA and correlation matrices, respectively (heatmap not shown).
If a strong correlation was shown to exist between biomarkers, it
indicated that multicolinearity should be taken into account during
the model building process. If a strong association was detected
between baseline clinical variables and biomarkers, it was
determined that further evaluation was needed. ANOVA and Spearman
correlations, along with p-values and FDR, were used to examine
associations between all clinical variables (without DAS28 scores)
and biomarkers. See FIG. 9.
Multivariate Analysis
[0229] Several multivariate modeling methods were considered. In
general, the linear penalized regression methods were determined to
perform the best.
Model 1: Forward Stepwise Ordinary Least Square Regression
[0230] For this modeling method, the equation Y=X.beta.+.epsilon.
applies, where Y is the column vector with observed values, .beta.
is a matrix of coefficients for the predictor variables X.sub.i,
and .epsilon. is the random error. The forward selection begins
with no variables in the model. Then, given a collection of
predictors X, the predictor having the largest absolute correlation
with the response Y is selected and a simple linear regression of Y
on X.sub.1 is performed, where X.sub.1 is the first predictor
variable. The residual vector is now orthogonal to X.sub.1, and is
taken to be the new response variable. The other predictors are
then projected orthogonally to X.sub.1 and the forward selection
process is repeated. The DAIMRK biomarker selected at each step is
recorded, along with the correlation R.sup.2.
Model 2: Penalized Regressions
[0231] Penalized regression model methods are a set of statistical
techniques that select subsets of variables to include in a model
and determine stable coefficients for the variables. These methods
are particularly useful when variables are correlated, and include
ridge regression, Lasso, Elastic Net, and other methods. All of
these methods have the characteristic that they shrink (penalize)
the coefficients in the regression model.
[0232] In the first penalized regression model, Least Absolute
Shrinkage and Selection Operator (LASSO or Lasso) is used to
prioritize biomarkers (based on R.sup.2 values) and to obtain a
Lasso model. The "lasso" in this model minimizes the residual sum
of the square, subject to the sum of the absolute value of the
coefficients being less than a constant. See R. Tibshirani, J.
Royal Stat. Soc., series B 1996, 58(1):267-288. The Lasso method
produces interpretable models, such as subset selection, and
exhibits the stability of ridge regression (a statistical method
that shrinks and stabilizes coefficients in regression models with
multicolinearity). See W. Mendenhall and T. Sincich, A Second
Course in Statistics: Regression Analysis, 6.sup.th edition 2003,
Pearson Prentice Hall, Inc., Upper Saddle River, N.J.
[0233] In the second penalized regression model, linear regression
is used with Elastic Net and mixtures of Lasso and ridge penalties
to prioritize biomarkers (based on R.sup.2 values) and obtain a
final Elastic Net model. Elastic Net is a relatively new
regularization and variable selection method. It encourages a
grouping effect, where strongly correlated predictors segregate
together, tending to be either in or out of the model together. See
T. Zou, J. Royal Stat. Soc., series B 2005, 67(2):301-320.
[0234] In the third model, the forward variable selection method is
a method of finding the "best" combination of variables by starting
with a single variable, that which results in the best fit for the
dependent variable Y, and increasing the number of variables used,
step by step, testing all combinations of the original variable
with the remaining variables in order to find the "best" pair of
variables, continuing until either all variables are used up or
some stopping criterion is met.
Model 3: Random Forest
[0235] Random Forest models are based upon the idea of creating
hundreds of regression trees as models. See L Breiman, Machine
Learning 2001, 45(1):5-32. Each regression tree model is created
with a uniform number of terminal nodes ("leaves") at the end of
the branches of the tree. To estimate the regression value of a new
subject, or to assign the subject to a class, the subject's data is
evaluated within each of the regression tree models. The output
prediction (i.e., regression value if continuous data,
classification if binary data) from all trees is then averaged to
create the final regression value or class prediction. In the case
of regression values, averaging may be obtained by a weighted
average; in class prediction, simply by voting.
[0236] The Random Forest methodology is as follows. First, a
bootstrap sample (i.e., a sample with replacement) is drawn from
the original data. Then a regression tree is "grown" from each
bootstrap sample; i.e., at each node one randomly samples p of the
n biomarkers measured, and selects the best biomarker and the best
value of that biomarker to split the data into pure subsets from
those biomarkers. Data from "training" subjects are used to build
the tree models. Then, new data is predicted by aggregating the
predictions of the various regression trees thus derived. For each
subject sample k, where the k subject samples are different from
those used in training the model (i.e., all k samples are "out of
the bag"), the response estimates are averaged over the trees,
given as {circumflex over (.gamma.)}.sub.k. The random forest
prediction algorithm is then given by the equation:
PE ^ f = E XY ( Y - h _ ( X ) ) 2 = 1 K ( y k - y k ^ ) 2 ,
##EQU00002##
where PE.sub.f is a test set estimate of the generalization error
of PE.sub.f, and h(X)=(1/L).SIGMA.h(x;.theta..sub.l) is the random
forest prediction. The collection of tree predictors is given by
h(x,.theta..sub.l), l=1 . . . L, where .theta..sub.l is a random
vector. Y represents the actual response variables; e.g., a DAS
score. Y represents the predictor, e.g., biomarker levels.
[0237] The variable importance is then estimated. In every
regression tree thus grown in the random forest, one calculates the
prediction error for that tree,
PE l = 1 K ( y k - y k ^ ) 2 , ##EQU00003##
as predicted by the lth tree predictor, h(x;.theta..sub.l). One
then randomly permutes the values of a biomarker variable i in the
"out of bag" cases, and computes the prediction error
PE li = 1 K ( y k - y ki ^ ) 2 ##EQU00004##
as predicted by the lth tree predictor. Importance (Imp) is given
as the variable i for Imp.sub.i=PE.sub.li-PE.sub.l for the ith
biomarker for lth tree. The variable importance of the ith variable
is computed
I i = I m _ p i SE ( Imp i ) ##EQU00005##
where Imp.sub.i is the average and standard area of importance of
ith variable over all L trees.
Coefficients Representative of a DAI Model
[0238] The following coefficients represent the terms of the
respective DAI models: DAI.sub.k=.SIGMA..beta..sub.ix.sub.ik, where
DAI.sub.ik is the calculated DAI for the kth subject, x.sub.ik
represents the transformed ith biomarker concentration for the kth
subject, and .beta..sub.i is the coefficient for the ith
biomarker.
Cross-Validation
[0239] A random subset of 70% of the total study population was
selected without replacement. The model was fitted using this
subset, then evaluated as to AUC for classification of subjects,
and correlation (r), against the remaining 30% of the study
population. Cross-validation was repeated 100 times, and the
resulting accuracy estimates were averaged to predict future
performance.
Results
[0240] The analyses demonstrated that the DAI scores associate well
with DAS28 scores, and also discriminate between subjects with high
and low DAS28 scores. Correlations of the DAI scores with DAS28
were r=0.57 to r=0.6, as estimated using 100 test set
cross-validations. Specifically, the DAS28 correlation of the DAI
score derived using the Lasso method was r=0.5909, the DAS28
correlation of the DAI score derived using the Elastic Net method
was r=0.5974, and the DAS28 correlation of the DAI score derived
using the forward variable selection method was r=0.5692. These
results show that the DAI score derived from each of these methods,
and using different subsets of the protein biomarkers, all yield
good correlation with DAS28.
[0241] The DAI scores can also be used to discriminate between
subjects with high and low DAS28 scores, and thus classify subjects
by level of disease activity, as shown by the area under the ROC
curve (FIGS. 12 and 13), estimated using 100 cross-validation test
sets. See also Example 3. Specifically, for subjects dichotomized
at a DAS of 2.67, where DAS<2.67 is considered remission, the
area under the ROC curve for the DAI score derived using the Lasso
method was 0.911. The area under the ROC curve for the DAI score
derived using the Elastic Net method was 0.891. For subjects
dichotomized on a DAS of 3.9, which is the median DAS value of this
study, the area under the ROC curve for the DAI score derived using
the Lasso method was 0.869. The area under the ROC curve for the
DAI score derived using the Elastic Net method was 0.856. These
results show that the DAI scores derived using each of these
methods all yield good areas under the ROC curves, and thus good
discrimination between subjects with high and low DAS28 scores.
[0242] The results further show that by specifically selecting
biomarkers from the DAIMRK set, all the DAI scores derived
therefrom, according to each of the above-described methods, yield
good areas under the ROC curves for discriminating subjects with
high and low DAS28 scores.
[0243] A specific instance of a formula for calculating a DAI score
was developed using seven biomarker proteins selected from the
DAIMRK set of biomarkers, according to the methods described above
(starting with an ALLMRK biomarker dataset, using data collected
from 322 RA samples obtained from the BRASS and OMRF cohorts; see
below for a discussion of the OMRF cohort).
[0244] The DAI score in this Example was computed using the
following formula:
DAI=4.49+0.36*CRP-0.29*EGF-0.22*IL8+0.045*LEP+0.21*IL1RN-0.25*AP-
OA1+0.10*CCL22. This formula exhibited a correlation of 0.5801 and
AUC of 0.7772 in predicting DAS28.
Example 2
Correlation of DAI to DAS28 Scores Over Multiple Timepoints in a
Longitudinal Cohort
[0245] Example 2 demonstrates the practice of the present teachings
in a longitudinal study of RA, and the predictive power of DAI
scores to track changes in a subject's DAS28 scores over time. The
DAI score thus provides a quantitative measure to monitor changes
in subject disease activity and response to treatment.
Experimental Design, Biomarker Selection and Quality Control of
Assay Data
[0246] Analyzing data obtained from multiple time points for a
subject is not only useful in monitoring changes in that subject's
disease activity, but can also be useful in increasing the
prediction accuracy of a DAI formula. The objective of this study
was to develop, validate, and compare biomarker-based models
(single time point and longitudinal) that measure disease activity
in RA subjects over time, in order to demonstrate that the
performance of the longitudinal model is better than
cross-sectional.
[0247] For the purpose of the longitudinal study described herein,
a subject group was selected from the BRASS cohort. See Example 1
for a general description of the BRASS cohort. Note that the
specific subject samples used in this study were different from
those analyzed in Example 1. (Therefore, this longitudinal study
can also serve as an independent cohort validation for the study
described in Example 1.) A total of 255 samples were obtained from
the annual physician visits of 85 RA subjects (at years 1, 2 and
4), and were used for this study. The cohort for this study had the
following characteristics: 91% female, 62% CCP positive, 64% RF
positive, 4% smokers, 48% on MTX, 64% on non-biologic DMARDs, 43%
on biologic DMARDs, and 27% on steroids. Additionally, the mean age
of the cohort was 59 years (SD+/-12.7), with a minimum age of 29
and a maximum age of 85. The mean DAS28-CRP for this cohort was 4.1
(SD+/-1.7), with a wide distribution of DAS28-CRP scores (minimum
of 1.2 and a maximum of 8.2).
[0248] Twenty-one biomarkers selected from the DAIMRK set were
assayed in a multiplex format or an ELISA format. (Various
suppliers were identified through a screening and optimization
process prior to the study; e.g., Millipore, R & D Systems,
Meso Scale Discoveries, and various ELISA assay suppliers.) All
assays were performed following the manufacturer's instructions
with cohort samples randomly assigned (or the equivalent) to the
sample positions on the plate layouts. Four pooled sera (Normal,
RA, SLE and OA) were included in each 96-well plate as process
controls. All samples were assayed at least in duplicate.
Seven-point calibration curves were constructed for each biomarker,
to accurately determine the measurable range of test sera. See
Example 3 for a discussion of how study assay data were
qualified.
Performance of the DAI Model in Tracking Longitudinal Changes in
DAS28
[0249] See Example 1 for an explanation of selected statistical
models used to construct the relationship between DAI and DAS28
scores. In addition, DAI models were also built based on
longitudinal hierarchical linear methods (HLM), which incorporated
all timepoint information. The HLM include both time-variant and
time-invariant variables.
Results
[0250] The correlation between change of DAI and change of DAS28
between two time points was r=0.56 in the dataset described in this
example, where the DAI model was built from a single-timepoint
penalized regression model with cross-sectional data from the BRASS
cohort described in Example 1. The correlation increased to 0.69
when a longitudinal HLM was built from the data described in this
example and tested on the Taylor cohort described in Example 5.
[0251] This study demonstrates that a DAIMRK-derived algorithm
developed in both cross-sectional and longitudinal analyses was a
strong predictor of disease activity over time. These results
further demonstrate that the biomarker algorithm utilized in this
study has a high level of accuracy and is robust with respect to
sampling over time.
Example 3
Classification of Subjects by DAI Score
[0252] Example 3 demonstrates the use of a DAI score to classify
subjects according to disease activity. The study was conducted
with 182 samples from the BRASS cohort (see Example 1), and 140
samples from a cohort established by the Oklahoma Medical Research
Foundation (the OMRF cohort). The appropriate Ethics Committee
approval was obtained for the study, and all subjects gave informed
consent. Since 2007, more than 800 subjects with confirmed RA have
been enrolled in OMRF cohort. All subjects fulfilled the American
College of Rheumatology criteria for RA. The cross-sectional study
collected medical or clinical information from all subjects,
comprising disease activity scores, radiological results, subject
health status and other clinical assessments. Blood samples were
collected during office visits. The subjects from the BRASS cohort
for this study had the following characteristics: 86% female, 65%
CCP positive, 70% RF positive, 5% smokers, 60% on MTX, 72% on
non-biologic DMARDs, 55% on biologic DMARDs, and 23% on steroids.
Additionally, the mean age of the subjects of the BRASS cohort was
58 years (SD+/-14.3), with a minimum age of 22 and a maximum age of
94. The mean DAS28-CRP for the subjects of this cohort was 3.2
(SD+/-1.2), with a minimum of 1.2 and a maximum of 7.5. The
subjects from the OMRF cohort for this study had the following
characteristics: 75% female, 60% CCP positive, 98% RF positive, 22%
smokers, 63% on MTX, 81% on non-biologic DMARDs, 49% on biologic
DMARDs, and 32% on steroids. Additionally, the mean age of the
subjects of this cohort was 60 years (SD+/-13.1), with a minimum
age of 26 and a maximum age of 84. The mean DAS28-CRP for the
subjects of this cohort was 5.2 (SD+/-1.5), with a minimum of 2.2
and a maximum of 8.2.
[0253] DAIMRK biomarker assays and assay data quality control were
performed as described in Example 1.
[0254] A cut-off of DAI=3 best separates the low DAS (DAS<2.67)
and high DAS (DAS>2.67) subjects, at an accuracy rate of
>0.8. See FIG. 14. When the DAS threshold is set to 4.0 instead
of 2.67, DAI also reached the accuracy rate of 0.8. See FIG.
15.
[0255] This study demonstrates that a DAI algorithm derived from
the DAIMRK set of biomarkers can be used to classify subjects into
well-established levels of disease activity, relative to the
gold-standard clinically-based measure, the DAS28.
Example 4
Use of DAI to Distinguish Subjects with RA from Unaffected, Healthy
Controls
[0256] Example 4 demonstrates the use of the DAI score in the
diagnosis of RA, by discriminating subjects with RA from
unaffected, healthy controls.
[0257] Data from 24 healthy control subjects and 31 subjects
diagnosed with RA were examined to determine whether mean DAIMRK
biomarker levels were different between the two groups. Twenty-one
biomarkers selected from the DAIMRK set were assayed in a multiplex
format or an ELISA format. Assay suppliers were previously
identified through a screening and optimization process (e.g.,
Millipore, R & D Systems, Meso Scale Discoveries, and various
ELISA assay suppliers). All assays were performed following the
manufacturer's instructions, with cohort samples randomly assigned
(or the equivalent) to the sample positions on the plate layouts.
Four pooled sera (normal, RA, SLE and OA) were included in each
96-well plate as process controls. All samples were assayed at
least in duplicate. Seven-point calibration curves were constructed
for each biomarker protein, for accurate determination of the
measurable range of test sera. See Example 3 for a discussion of
how study assay data were qualified.
Statistical Analysis
[0258] Statistical analyses of data included the t-test, random
forests, boosted trees, and KNN. Boosted Trees models are based
upon the idea of computing a sequence of trees, where each
successive tree is built by predicting the residuals of the
preceding tree. Put another way, boosting will generate a sequence
of classifiers, where each consecutive classifier in the sequence
is an "expert" in classifying observations that were not
well-classified by those preceding it.
[0259] The univariate statistical analysis in this Example was
performed using a two-sample t-test with Satterthwaite adjustment.
The resulting data showed a right-skewed distribution, so a
logarithmic transformation was used to correct for the skew, and a
numeric value of 1 was added to avoid the asymptotic tail of the
resulting logarithmic function between the numeric values of 0 and
1. The univariate analyses indicated that the relative levels of
CCL22, CRP, IL6, IL8, keratan sulfate, and TNFSF1A were
significantly different between healthy (Control) individuals and
RA subjects. See Table 2.
TABLE-US-00003 TABLE 2 DAIMARK variable RA Control p-value CCL22
3.71 (0.19) 3.47 (0.15) 1.14E-06 CRP 4.55 (0.61) 4.22 (0.47)
0.027294 IL6 0.98 (0.37) 0.82 (0.2) 0.049 IL6R 4.23 (0.18) 4.3
(0.09) 0.053 IL8 1.18 (0.26) 1.04 (0.15) 0.015925 keratan sulfate
2.28 (0.08) 2.44 (0.08) 2.21E-09 TNFRSF1A 2.9 (0.19) 3.03 (0.15)
0.007447
Multivariate Analysis
[0260] The Random Forest algorithm was provided with the DMARK
variables from Table 2 and samples were split, 43% into the Test
set and 56% into the Training set. The Training set variables were
ranked based upon their relative importance in the model. Relative
importance is based on the degree to which each variable
contributes to improving the model fit. See R A Belk, "Statistical
Learning from a Regression Perspective," Springer, 2008, p. 213.
See Table 3.
TABLE-US-00004 TABLE 3 Variable Importance CCL22 1 keratan sulfate
0.748 IL6R 0.707 TNFRSF1A 0.452 IL8 0.438 IL6R 0.41 CRP 0.24
[0261] The Training set data showed 96.8% accuracy and the Test set
data showed 87.5% accuracy, as measured by ability to discriminate
subjects with RA from unaffected healthy controls. The test
confusion matrix specifies the error (confusion) in the actual
versus predicted classification. See Table 4.
TABLE-US-00005 TABLE 4 Test confusion* matrix Training confusion*
matrix Actual Predicted Actual Predicted RA 14 11 17 17 Control 10
10 14 13 Total 24 31 Accuracy 87.5% 96.8%
[0262] Here "Predicted RA" refers to samples from subjects that
were predicted to have RA and actually did, while "Predicted
Control" refers to samples from subjects that were predicted to be
healthy and actually were. Thus in the Test confusion matrix shown
in Table 4, of the 24 samples tested, 14 of the RA samples were
correctly predicted to be RA positive and three were incorrectly
predicted to be healthy, while all 10 control samples were
correctly predicted to be healthy. The accuracy then is calculated
as: (number Predicted RA that is Actual RA)+(number Predicted
Control that is Actual Control)/total number samples; or, for the
Test confusion matrix, (11+10)/24=0.875, and for the Training
confusion matrix, (17+13)/31=0.968.
[0263] The boosted tree algorithm was given the DAIMRK variables in
Table 2 and the samples split 33% into the Test set, and 64% into
the Training sets. The Training set variables were ranked based
upon their relative importance in the model. See Table 5.
TABLE-US-00006 TABLE 5 Variable Importance keratan sulfate 1 CCL22
0.95 CRP 0.91 TNFRSF1A 0.84 IL6R 0.77 IL6R 0.72 IL8 0.59
[0264] The Training set data showed 100% accuracy and the Test set
data showed 83.3% accuracy. See Table 6.
TABLE-US-00007 TABLE 6 Test confusion matrix Training confusion
matrix Actual Predicted Actual Predicted RA 9 7 22 22 Control 9 8
15 15 Total 18 37 Accuracy 83.3% 100%
Results
[0265] Using stored blood samples from RA and healthy subjects,
relationships were examined between the protein serum levels of
different DAIMRK biomarkers related to immune activation and
inflammatory response. The mean DAIMRK biomarker levels were
different between the two groups of subject. Additionally, the
levels of CCL22, CRP, IL6, IL8, keratan sulfate, and TNFSF1A were
significantly different between healthy subjects and RA subjects.
These results would indicate that as RA disease progresses,
additional pathological mechanisms are activated to trigger the
onset of clinical symptoms.
Example 5
Assessment of Response to Therapy Using DAI Scores
[0266] This example demonstrates that the DAI score is useful in
assessing a subject's response to a single therapy, and in
comparing subjects' responses to two therapies. The hypothesis that
the DAI score is significantly associated with a subject's response
to infliximab treatment was tested, as was the hypothesis that the
DAI score is associated with differences in response to two
therapies.
[0267] Serum samples and clinical and imaging data were examined
from 24 subjects (the Taylor cohort), who were followed in a
two-year blinded study to compare a therapeutic regimen of MTX and
infliximab against a therapeutic regimen of MTX alone, in
aggressive early RA. Placebo arm subjects were switched to
methotrexate and infliximab after one year. Subjects were evaluated
by ultrasound at 0, 18, 54 and 110 weeks, and scored for synovial
thickening and vascularity by power Doppler area (PDA).
Radiographic examination to determine van der Heijde modified Sharp
(vdH-Sharp) scores was carried out at 0, 30, 54 and 110 weeks.
DAS28 scores were obtained at office examinations carried out every
three to five weeks over the two-year study period. DAIMRK
biomarker levels were determined in blood samples from all 24
subjects collected at 0, 6, 18, 54 and 110 weeks.
[0268] Characteristics of the subjects of the Taylor cohort were as
follows: the mean age of the placebo+MTX subgroup was 51 years
(SD+/-14.0), the inf+MTX subgroup was 55 years (SD+/-11.8); the
mean weight in kg of the placebo+MTX subgroup was 71.1 (SD+/-13.2),
the inf+MTX subgroup was 67.9 (SD+/-16.1); the mean disease
duration of the placebo+MTX subgroup was 1.64 years (SD+/-0.63),
the inf+MTX subgroup was 1.33 (SD+/-0.64).
[0269] To show that DAI score is significantly associated with a
subject's response to infliximab treatment, each subject's DAI
score before infliximab treatment (year 0, week 0) was compared to
his/her score after one year of infliximab treatment (year 1, week
52). Row A of Table 7 shows the results of a test (paired t-test)
of the difference between the DAI scores at year 0 and year 1 for
12 subjects receiving infliximab (inf). The DAI scores were
computed from the model built from BRASS subjects, described
elsewhere herein. The t-stat is the value of the test statistic, t,
for which a p-value can be calculated using the T distribution.
TABLE-US-00008 TABLE 7 t-stat p-value A Change in inf, year 0 to 1
-2.69981 0.007764 B Difference MTX and -1.41064 0.093483 difference
MTX, year 0 to 1
[0270] As Table 7 shows, the paired t-test is significant
(p=0.007764), thus demonstrating that the DAI score changes
significantly following infliximab treatment.
[0271] To show that the DAI score is useful in assessing
differences in subjects' response to two therapies, the DAI scores
of subjects receiving infliximab treatment were compared to the DAI
scores of subjects receiving MTX treatment. The DAI scores of weeks
0 to 52 were subtracted within both MTX and infliximab subjects.
Twelve datapoints (or DAI score differences) were obtained for each
treatment group. Then a non-paired t-test (n=12 for each group) was
used. Row B of Table 7 shows the results of the t-test for the
difference in DAI scores of infliximab subjects and DAI scores of
MTX subjects. The t-test shows a trend to significance (p=0.09). A
sample size of greater than twelve observations would be expected
to yield a significant p-value for this difference.
[0272] This example demonstrates that the DAI score is useful in
assessing a subject's response to a single therapy, and that the
DAI score is useful in comparing subjects' response to two
therapies.
Example 6
Correlation of DAI Scores with Clinical Measures of Erosion
[0273] This example demonstrates that DAI scores track joint
erosion, with a strong correlation between DAI scores and
radiographic changes in subjects, based on changes in Sharp scores
from X-ray imaging and changes in measures of joint damage (i.e.,
synovial thickening, vascularity, and intra-articular blood flow)
assessed by power Doppler (PD) ultrasonography. Synovial
vascularization and mononuclear cell infiltration are known to be
characteristics of RA synovitis. See P. Taylor et al., Arth. Rheum.
2004, 50(4):1107-1116. This example demonstrates that DAI scores
can provide the current rate of joint destructive processes in
subjects, and correlate with ultrasound observations of subclinical
synovitis. Thus, DAI scores are a powerful complementary approach
to identify subjects at highest risk of accelerated bone and
cartilage damage.
[0274] The samples used in this example were the Taylor cohort,
described above. See Example 5. Clinical measures of erosion were
assessed using two radiographic modalities: X-ray and ultrasound.
X-rays of hands and feet taken at 0, 30, 54 and 110 weeks provided
van der Heijde modified Sharp scores. All subjects had erosions at
baseline (week 0), but experienced a wide range of changes in total
Sharp scores (TSS) over the course of the study (median change
6.25, inter-quartile range 4-14.5). Ultrasound studies provided
three measures of joint damage: color Doppler area (CDA), synovial
thickening (SYN), and erosion score (ES). Blood samples from all 24
subjects were collected at 0, 6, 18, 54 and 110 weeks, and were
used to measure the levels of protein biomarkers selected from the
ALLMRK set, described above.
[0275] Correlation coefficients between the DAI scores and the
three ultrasound measures observed were calculated. The DAI score
was calculated for each subject at each given timepoint, and those
DAI score values were then paired with the ultrasound scores for
that subject at same timepoints. The 24 subjects had ultrasound
scores at timepoint 0, 18, 54, and 110 weeks. The correlation (Cor)
was computed as Cor(DAL_kt, ultrasound_kt), where k is 1, . . . ,
24 and t=0, 18, 54, 110. Thus, 24 subjects*4 timepoints per
subject=96 datapoints total were used in computing the Cor. The DAI
score was correlated to all three ultrasound measures
(p<0.05).
[0276] Table 8 shows the correlation between DAI scores and Sharp
scores. The DAI model was built from a separate cohort of subjects
(BRASS) to prevent over-fitting. The DAI scores were computed
across all 24 subjects at week 6, when therapeutic effect was
observable. The results in Table 8 were computed as follows: (a)
build DAI model from BRASS cohort of subjects; (b) calculate the
DAI score in Taylor cohort of subjects (all 24) using week 6 data;
(c) use leave-one-out cross-validation procedure, and for each 23
subjects (i) build a longitudinal model using the week 6 DAI score
to predict rate of change in total Sharp score (TSS) (i.e., change
of TSS/week), (ii) calculate three Sharp score rates of change
(i.e., 0-54 weeks, 0-110 weeks, and 54-110 weeks) for the left-out
subject, (iii) calculate three estimated TSS rates of change (0-54
weeks, 0-110 weeks, and 54-110 weeks) for the left-out subject,
from (i); (d) after obtaining all the estimated TSS changes for
each subject, calculate the correlation between the actual TSS rate
of change and the estimated one based on the DAI scores for all 24
subjects. The correlations were calculated for each interval (e.g.,
0-54 weeks) separately.
TABLE-US-00009 TABLE 8 Interval Correlation Week 0-54 0.769 Week
0-110 0.737 Week 54-110 0.567
[0277] These results demonstrate that DAI scores are correlated
with clinical measures of erosion, as determined by X-ray (i.e.,
Sharp scores) and ultrasound observations of subclinical synovitis
in subjects' joints.
Example 7
Association of DAI with DAS28 Scores in Another Large Clinical
Cohort
[0278] Example 7 demonstrates the transformation of observed
biomarker levels into a DAI score by various statistical modeling
methodologies, which DAI score serves as a quantitative measurement
of disease activity that correlates well with observed DAS28, as
for measuring the extent of subject inflammation status and disease
activity at any single timepoint. This example also demonstrates
the selection of a particular set of 23 biomarkers, all members of
the DAIMRK set; namely, SAA1, IL6, TNFRSF1A, VEGFA, PYD, MMP1,
ICAM1, calprotectin, CHI3L1, MMP3, EGF, IL1RN, VCAM1, LEP, RETN,
CRP, IL8, APOA1, APOC3, CCL22, IL1B, IL6R and IL18. Certain
embodiments of the present teachings comprise utilizing these
biomarkers from the DAIMRK set of biomarkers for determining a DAI
score with significant correlation with disease activity
status.
[0279] Samples were obtained from the Computer Assisted Management
in Early Rheumatoid Arthritis Study (CAMERA). From 1999-2003, all
early rheumatoid arthritis patients (i.e., disease duration of one
year or less) who fulfilled the 1987 revised American College of
Rheumatology (ACR) criteria for rheumatoid arthritis were asked to
participate in this two-year randomized, open-label prospective
multicentre strategy trial. As a result, 299 patients were studied.
Patients visited the outpatient clinic of one of the six
rheumatology departments in the region of Utrecht, the Netherlands,
collaborating in the Utrecht Rheumatoid Arthritis Cohort study
group. Inclusion criteria were that patients must have exhibited
symptoms for less than one year, with age greater than 16 years.
Exclusion criteria were the previous use of glucocorticoids or any
DMARD, use of cytotoxic or immunosuppressive drugs within a period
of three months before inclusion, alcohol abuse, defined as more
than two units per day, and psychological problems, which would
make adherence to the study protocol impossible. At baseline all
patients were monitored for medical conditions that would interfere
with MTX usage. This screening included a chest X-ray, liver
enzymes, albumin, hepatitis serology, serum creatinine and complete
blood count. An independent person performed randomization in
blocks of nine per hospital. The medical ethics committees of all
participating hospitals approved this study, and all patients gave
written informed consent before entering the study.
[0280] The cohort for this study had the following characteristics:
69% female, 68% CCP positive, 74% RF positive, 100% on MTX, 100% on
non-biologic DMARDs, and 0% on biologic DMARDs. Additionally, the
mean age of the cohort was 52 years (standard deviation
(SD)+/-14.7), with a minimum age of 17 and a maximum age of 78. The
mean DAS28-CRP for this cohort was 5.0 (SD+/-1.9), with a minimum
of 0.9 and a maximum of 8.4.
[0281] A subpopulation of 72 subjects was selected from the CAMERA
cohort for this Example. All 72 patients were represented by
baseline (time 0) visits and samples, and 48 were also represented
by six-month visits and samples. Within the visits selected, a wide
distribution of DAS28-CRP scores were represented, ranging from a
minimum of 0.96 to a maximum of 8.4.
[0282] Assays were designed, in multiplex or ELISA format, for
measuring multiple disease-related protein biomarkers selected from
the ALLMRK set, as that set is described herein. These assays were
identified through a screening process and were extensively
optimized prior to assaying the CAMERA samples. SAA1, IL6,
TNFRSF1A, VEGFA, MMP1, ICAM1, calprotectin, CHI3L1, MMP3, EGF,
VCAM1, LEP, RETN, CRP, IL8, APOA1, APOC3, CCL22, IL1B and IL6R were
measured using the MESO SCALE DISCOVERY.RTM. (MSD) platform. IL8
and IL1RN were measured with ELISA technology from R&D Systems,
and PYD was measured with ELISA from Quidel.
[0283] All assays were performed following the manufacturer's
instructions, with cohort samples randomly assigned (or the
equivalent) to the sample positions on the plate layouts. Four
pooled sea (from normal, RA, SLE and osteoarthritis (OA) subjects)
were included in each assay plate as process controls. All samples
were run at least in duplicate. Seven-point calibration curves were
constructed for each biomarker for accurate determination of the
measurable range of test sera.
[0284] Prior to statistical analyses, all assay data were reviewed
for pass/fail criteria as predefined by standard operating
procedures on parameters, including inter-assay CV, intra-assay CV,
percent of samples within the measurable range of the calibration
curve, and four serum process controls within the range of the
calibration curve. The biomarker values that were not in the
measurable range of the calibration curves were marked as missing
data, and imputed by the lowest/highest detected value across all
the samples within a given biomarker assay. No imputation was
performed for the univariate analyses. For multivariate analysis,
missing data imputation methods commonly used in microarray
expression data and well known in the art were used. See, e.g., R.
Little and D. Rubin, Statistical Analysis with Missing Data, 2nd
Edition 2002, John Wiley and Sons, Inc., NJ. Biomarkers were
excluded from analysis where more than 20% of the data were
missing, and the remaining data were imputed by the KNN algorithm
(with k=5 nearest neighbors).
Univariate Analysis
[0285] Biomarker assay data were normalized across each plate
before correlations were calculated between individual proteins and
measurements were transformed into DAI scores. Associations were
calculated between the DAI scores and DAS28-CRP scores, swollen
joint counts, TJCs, or CDAI. The correlation results were then
compared using univariate analysis. See Table 9, results of
univariate analyses for several DAIMRK biomarkers in the CAMERA
training set.
[0286] The False Discovery Rate (FDR) was used as multiple testing
correction, according to the following: let k be the largest i for
which pi.ltoreq.i/m*.alpha. reject all Hi, i=1, . . . , m. As will
be clear to one of skill in the art, where the DAIMRK biomarker is
significantly associated with the DAS score, then the q-value is
small. A parametric correlation test was also performed, using the
parametric test H.sub.i: .rho..sub.i=0, and the statistic given
by
t = r ( n - 2 ) 1 / 2 ( 1 - r 2 ) 1 / 2 . ##EQU00006##
[0287] Covariation and multicolinearity between all variables were
evaluated; i.e., for both clinical data and biomarkers. If a strong
correlation was seen to exist between biomarkers, it indicated that
multicolinearity should be taken into account during the model
building process. If a strong association was detected between
baseline clinical variables and biomarkers, it was determined that
further evaluation was needed. ANOVA and Spearman correlations,
along with p-values and FDR, were used to examine associations
between all continuous clinical variables (without DAS28 scores)
and biomarkers.
TABLE-US-00010 TABLE 9 Correlation Nominal DAIMRK coefficient
p-value IL6 0.693 0 CRP 0.685 0 SAA1 0.658 0 calprotectin 0.557 0
MMP3 0.509 0 IL8 0.466 0 IL1B 0.454 0 CHI3L1 0.423 0 MMP1 0.364 0
TNFRSF1A 0.363 0 VEGFA 0.293 0.001 ICAM1 0.23 0.012 pyridinoline
0.228 0.013 RETN 0.219 0.016
Multivariate Analysis
[0288] Several multivariate modeling methods were considered. In
general, the linear penalized regression model was determined to
perform the best.
Model 1: Forward Stepwise Ordinary Least Square Regression
[0289] See Example 1 for a description of the forward stepwise
ordinary least square regression model.
Model 2: Penalized Regressions
[0290] See Example 1 for a description of the penalized regressions
model.
Coefficients Representative of a DAI Model
[0291] The following coefficients represent the terms of the
respective DAI models: DAI.sub.k=.SIGMA..beta..sub.ix.sub.ik, where
DAI.sub.ik is the calculated DAI for the kth subject, x.sub.ik
represents the standardized ith biomarker concentration for the kth
subject (usually log transformed and plate-to-plate normalized),
and .beta..sub.i is the coefficient for the ith biomarker.
Cross-Validation
[0292] A random subset of 70% of the total study population was
selected without replacement. The model was fitted using this
subset, then evaluated against the remaining 30% of the study
population, using AUC and correlation. Cross-validation was
repeated 100 times, and the resulting accuracy estimates were
averaged to predict future performance.
Results
[0293] The DAI score in the present example was computed using the
following formula:
DAI=(-16.16)-(0.06*calprotectin)+(0.22*CHI3L1)+(1.19*ICAM1)+(2.77*IL6)+(0-
.73*MMP1)-(0.83*MMP3)+(1.03*pyridinoline)+(1.18*SAA1)+(2.44*TNFRSF1A)+(0.3-
3*VEGFA).
[0294] This formula exhibited a correlation of 0.65 and AUC of 0.84
in predicting DAS28 in the independent cohort, CAMERA.
[0295] The analyses demonstrated that the DAI scores correlate well
with DAS28 scores, and also discriminate between subjects with high
and low DAS28 scores, thus allowing for classification of subjects
by disease activity.
[0296] Correlations of the DAI scores with DAS28 were r=0.75 to
r=0.78, as estimated using 100 test set cross-validations.
Specifically, the DAS28 correlation of the DAI score derived using
the Lasso method was 0.776, the DAS28 correlation of the DAI score
derived using the Elastic Net method was 0.762, and the DAS28
correlation of the DAI score derived using the forward variable
selection method was 0.746. (Forward selection is a method of
finding the "best" combination of variables by starting with a
single variable, that which results in the best fit for the
dependent variable Y, and increasing the number of variables used,
step by step, testing all combinations of the original variable
with the remaining variables in order to find the "best" pair of
variables, continuing until either all variables are used up or
some stopping criterion is met.)
[0297] These results show that the DAI scores derived using each of
these modeling methods, and using different subsets of the protein
biomarkers, all yield good correlation with DAS28 scores.
[0298] DAI scores can also be used to discriminate between subjects
with high and low DAS28 scores, as demonstrated by the value of the
area under the ROC curve, estimated using 100 cross-validation test
sets. For subjects dichotomized on a DAS of 4.1, which is the
median DAS value of this study, the area under the ROC curve for
the DAI score derived using the Lasso method was 0.896. The area
under the ROC curve for the DAI score derived using the Elastic Net
method was 0.881. These results show that the DAI scores derived
using each of these methods all yield good areas under the ROC
curves for discriminating subjects with high and low DAS28
scores.
Example 8
Association of DAI Scores with DAS28 Scores by AUC is not Dependent
on Subgroup
[0299] Example 8 demonstrates that the correlation of DAI scores
with DAS by AUC, and thus the usefulness of DAI scores to classify
subjects by disease activity, are not significantly affected by
subject subgroupings, such as by CCP status, sex, age, etc.
[0300] The performance of the 10-marker DAI algorithm (described in
Example 7) relative to DAS28-CRP was further evaluated in patient
subgroups from the CAMERA cohort (see Example 7 for a description
of the CAMERA study) defined by several major clinical variables;
namely, sex, RF status, CCP status, and age. Table 10 presents the
correlation and classification (AUC) results of this analysis.
TABLE-US-00011 TABLE 10 AUC Sex (M; F) 0.828 0.849 RF status (Neg;
Pos) 0.8 0.852 CCP status (Neg; Pos) 0.820 0.837 Age (under 53;
over 53) 0.858 0.851
[0301] This analysis indicates that the capability of DAI scores to
classify subjects by disease activity, as demonstrated by AUC
values, are not significantly affected by the subject subgroupings
of sex, RF status, CCP status, and age.
Example 9
Change in DAI Scores not Strictly Correlated with Single Biomarker
Levels
[0302] Example 9 demonstrates that changes in subjects' disease
activity, as evidenced by changes in their DAI or DAS scores
between first and second clinical visits, do not strictly correlate
with changes in the levels of the single biomarker CHI3L1. In other
words, univariate analysis of the DAIMRK biomarker CHI3L1, which is
positively weighted in an exemplary DAI algorithm (see, e.g.,
example 7), indicated that despite its positive weight, an increase
in CHI3L1 level does not statistically correlate with an increase
in disease activity, and vice versa.
[0303] The Index for Rheumatoid Arthritis Measurement (INFORM)
study is a large multi-center observational study of the North
American RA population. Patients were recruited between April and
September 2009 from 25 sites in the U.S. and Canada. Inclusion
criteria were: age>18 years with a diagnosis of RA made by a
board-certified rheumatologist. Patients concurrently enrolled in a
therapeutic drug trial involving a biologic agent and a placebo arm
were excluded. At their first study visit, 512 patients were
selected for biomarker analysis. The average age of these patients
was 58.9 years (range 20-91), and 76% were female. The mean SJC and
TJC were 4.28 and 5.49, respectively. Of these 512 patients, 128
were tested for CHI3L1 at both the first and second study visits,
which were separated by around 3 months. Of these patients, 53% had
increased DAI values at the second visit. Among the patients with
increased DAI values, 57% also demonstrated an increase in CHI3L1
values. See Table 11.
TABLE-US-00012 TABLE 11 No. patients DAI decreased/stayed No.
patients same DAL increased No. patients CHI3L1 36 29
decrease/stayed same No. patients CHI3L1 24 39 increased
[0304] These results indicate that in the example of the DAIMRK
biomarker CHI3L1, weighted positively in the DAI algorithm of
Example 7, for example, an increase in CHI3L1 level does not
necessarily correlate with an increase in RA disease activity, as
measured by DAI, and vice versa.
[0305] The same holds true when the change in levels of CHI3L1 is
compared to change in disease activity as measured by DAS. In a
study of the INFORM cohort, 44% of the patients demonstrated an
increase in DAS values in second visits, among which 43%
demonstrated an increase in CHI3L1 values. See Table 12.
TABLE-US-00013 TABLE 12 No. patients No. DAS patients decreased/
DAS stayed same increased No. patients CHI3L1 33 32 decreased/
stayed same No. patients 39 24 CHI3L1 increased
[0306] In another analysis, the change in CHI3L1 levels from the
first to second visit was compared to DAI change, where the DAI
change from visit 1 to visit 2 was at least by a magnitude of 10%.
The results are shown in Table 13.
TABLE-US-00014 TABLE 13 No. patients DAI No. patients DAI decreased
by <= 10% increased by >10% No. patients 58 7 CHI3L1
decreased/ stayed same No. patients 44 19 CHI3L1 increased
[0307] These results demonstrate that among patients demonstrating
a DAI decrease of at least 10% in the subsequent visits, 43% of
these demonstrated an increase in CHIL1 levels.
[0308] Changes in CHI3L1 levels were likewise analyzed against
changes in DAS values, where DAS changed by at least 10%. Results
from the INFORM study showed that among all patients where DAS
increased by at least 10%, only 41% also showed an increase in
CHI3L1 level. See Table 14.
TABLE-US-00015 TABLE 14 No. patients No. patients DAS decreased DAS
increased by by <= 10% >10% No. patients 42 23 CHI3L1
decreased/ stayed same No. patients 47 16 CHI3L1 increased
[0309] Taken together, these results demonstrate that in the
example of the DAIMRK biomarker CHI3L1, weighted positively in the
DAI algorithm of Example 7, for example, an increase in CHI3L1
level does not necessarily correlate with an increase in RA disease
activity, as measured by DAI or by DAS, and vice versa.
Example 10
Performance of Univariate Models Across Various Cohorts
[0310] This example demonstrates that the predictive value
univariate (single biomarker) models are weaker across various
cohorts than are the multivariate models of the present
teachings.
[0311] The ability of each single DAIMRK biomarker to predict
disease activity was analyzed for the cohorts indicated in Table
15, and the correlation values obtained. (For a description of
BRASS, see Example 1; for CAMERA, see Example 7; for INFORM, see
Example 9).
TABLE-US-00016 TABLE 15 BRASS CAMERA INFORM corre- corre- corre-
DAIMRK lation p-value lation p-value lation p-value calprotectin
0.42 0 0.557 0 0.251 0 CCL22 0.167 0.034 N/D* N/D 0.123 0.005
CHI3L1 0.498 0 0.423 0 0.207 0 CRP 0.803 0 0.685 0 0.421 0 EGF
-0.218 0.005 N/D N/D N/D N/D ICAM1 0.366 0 0.23 0.012 0.186 0 ICTP
N/D N/D N/D N/D 0.162 0 IL1B N/D N/D 0.454 0 0.161 0.001 IL1RA 0.31
0 N/D N/D 0.183 0 IL6 0.597 0 0.693 0 0.325 0 IL6R 0.224 0.004 N/D
N/D 0.132 0.003 IL8 N/D N/D 0.466 0 0.139 0.002 LEP 0.176 0.023 N/D
0.151 0.001 MMP1 0.411 0 0.364 0 0.135 0.003 MMP3 0.562 0 0.509 0
0.189 0 pyridinoline 0.379 0 0.228 0.013 0.115 0.01 RETN 0.236
0.002 0.219 0.016 N/D N/D SAA1 0.746 0 0.658 0 0.318 0 TNFRSF1A
0.506 0 0.363 0 0.201 0 VCAM1 0.291 0 N/D N/D N/D N/D VEGFA 0.43 0
0.293 0.001 0.17 0 *N/D: "Not Done"
[0312] As is evident from this table, these univariate markers
cannot be used with consistency to predict disease activity across
cohort populations. By comparison, the 10-marker panel of Example 7
demonstrated, in CAMERA, a correlation of 0.65 and an AUROC of
0.84; in BRASS, representative Lasso models achieved an average
correlation of 0.76 and AUROC of 0.88; and, in INFORM,
representative Lasso models in the 512 samples achieved an average
correlation of 0.44 and AUROC of 0.67 in cross-validation.
Example 11
Alternative Modeling for Deriving DAI Score
[0313] This example demonstrates another, alternative method of
deriving a Disease Activity Index score, based on a dataset of
quantitative data for biomarkers. In this example, a DAI score is
determined from the biomarker data using an interpretation function
that is based on a set of predictive models, where each predictive
model is predictive of a component of the DAS28-CRP, in this
example TJC, SJC and patient global health assessment (GHA).
DAI Algorithm Development and Evaluation
Training Data
[0314] A DAI algorithm was trained using clinical and biomarker
data for patients in the InFoRM and BRASS studies. The InFoRM study
(Index For Rheumatoid Arthritis Measurement) is a multi-center
observational study of the North American RA population. The
patients used in algorithm training were recruited between April
and September 2009 from 25 sites in the U.S. and Canada. Inclusion
criteria were: age>18 years with a diagnosis of RA made by a
board-certified rheumatologist. Patients concurrently enrolled in
therapeutic drug trials involving a biologic agent and a placebo
arm were excluded. The study includes three visits for each
patient, each with clinical data and biological sample collection,
at approximately three-month intervals.
[0315] BRASS is an observational study of approximately 1,000 RA
patients receiving care at the RB Brigham Arthritis and
Musculoskeletal Diseases Clinical Research Center, at the Brigham
and Women's Hospital, Boston. Mass. Inclusion criteria were:
age>18 years with a diagnosis of RA made by a board-certified
rheumatologist. The study includes annual visits with clinical data
and biological sample collection, and patient questionnaires
between visits.
[0316] The first data set used in training consisted of visit 1
data for 512 InFoRM patients. The 512 patient visits were chosen to
have clinical characteristics representative of the entire study
population at the time of selection, and also to have been
evaluated by a limited number of joint assessors. The number of
joint assessors was limited to 12 so that assessor-specific biases
could be evaluated and taken into account in algorithm development.
The average age of these patients was 58.9 years (range 20-91), and
76% were female. The mean SJC and TJC were 4.28 and 5.49,
respectively.
[0317] Assays for 25 candidate biomarkers were run on serum from
the 512 InFoRM visits. Those biomarkers were SAA1, IL6, TNFRSF1A,
VEGFA, PYD, MMP1, ICAM1, calprotectin, CHI3L1, MMP3, EGF, IL1RA,
VCAM1, LEP, RETN, CRP, IL8, APOA1, APOC3, CCL22, IL1B, IL6R, IL8,
keratan sulfate and ICTP. All the biomarker assays were run on the
Meso Scale Discovery (MSD.RTM.) platform. See Example 1 for
specifics of biomarker assay development and evaluation.
[0318] The biomarkers were prioritized based on (1) univariate
association with disease activity, (2) contribution to multivariate
models for disease activity, and (3) assay performance.
[0319] The assays for 20 candidate biomarkers were run in a second
set of patient samples, comprising 167 samples from BRASS and 29
from InFoRM. These 20 candidate biomarkers were SAA1, IL6,
TNFRSF1A, VEGFA, PYD, MMP1, ICAM1, calprotectin, YKL40, MMP3, EGF,
IL1RA. VCAM1, leptin, resistin, CRP, IL8, CCL22, IL1B and IL6R. The
samples were selected to enrich the overall training data for
extremes of disease activity, while also having good representation
of patients with moderate disease activity. Enriching for extreme
phenotypes can result in improved algorithm training, as long as
the resulting training population still fully represents the types
of patients on which the algorithm will used in independent
validation and intended use populations. The 167 BRASS samples were
intended to represent similar numbers of patients with low,
moderate and high disease activity. The 29 InFoRM samples were
selected to represent patients with high disease activity, since
low and moderate activity patients were already well represented by
the first 512 training samples.
Data Analysis
[0320] Prior to statistical analyses, all assay data were reviewed
for pass/fail criteria on parameters including inter-assay CV,
intra-assay CV, percent of samples within the measurable range of
the calibration curve, and four serum process controls within the
range of the calibration curve. The biomarker values that were not
in the measurable range of the calibration curves were marked as
missing data, and imputed with the lowest/highest detected value
across all the samples within a given biomarker assay during the
data export process. If the intra-assay CV of the biomarker
concentration, computed from two replicates, was greater than 30%,
it was also considered missing and excluded from univariate
analyses. For multivariate analysis, individual biomarkers were
excluded entirely if more than 20% of their data values were
missing, and other missing data were imputed by the KNN algorithm
(with k=5 nearest neighbors). In the data used for algorithm
training, no biomarkers were excluded from multivariate analysis
because they all had less than 20% missing values. Concentration
values were transformed as .times.0.1 prior to further analysis in
order to make the distribution of values for each biomarker more
normal. This transformation has a similar effect to log
transformation but avoids the generation of negative values. The
transformed, imputed biomarker dataset is denoted as X_(n.times.m),
where X is the protein data from n markers and m samples.
[0321] In univariate analysis, the Pearson correlations between the
levels of each biomarker and disease activity measures including
DAS28-CRP4, DAS28-ESR4, SJC, TJC, GHA, SDAI and CDAI were
calculated.
[0322] In multivariate analysis, statistical models were developed
by five different regression methods. In the first regression
method (1), forward stepwise ordinary least square regression, the
equation Y=X.beta.+.epsilon. applies, where Y is the column vector
with observed values, .beta. is the vector of coefficients, and
.epsilon. is the residuals. The forward selection begins with no
variables in the model. Then, given a collection of predictors X,
the one having the largest absolute correlation with the response Y
is selected and a simple linear regression of Y on X1 is performed.
The residual vector is now orthogonal to X1, and is taken to be the
new response variable. The other predictors are then projected
orthogonally to X1 and the forward selection process is
repeated.
[0323] In the second method (2), Lasso is used to prioritize
biomarkers (based on R.sup.2 values) and to obtain a Lasso model.
The "lasso" in this model minimizes the residual sum of squares,
subject to the sum of the absolute value of the coefficients being
less than a constant. This method produces interpretable models and
exhibits the stability of ridge regression. See R. Tibshirani, J.
Royal Stat. Soc. B 1996, 58(1):267-288.
[0324] In the third method (3), the Elastic Net, mixtures of Lasso
and ridge penalties are applied. It encourages a grouping effect,
where strongly correlated predictors segregate together, either
tending to be in or out of the model together. See T. Zou, J. Royal
Stat. Soc. B 2005, 67(2):301-320. For each of the above three
methods, the marker selected at each step is recorded.
[0325] The fourth method (4) is Multivariate Response with Curds
and Whey (CW) using ordinary least squares (OLS). See L. Breiman
and J H Friedman, J. Royal Stat. Soc. B 1997, 59(1):3-54. This
method takes advantage of the correlations between the response
variables (e.g., components of DAS) to improve predictive accuracy,
compared with the usual procedure of performing an individual
regression of each response variable on the common set of predictor
variables X. In CW, Y=XB*S, where Y=(y.sub.kj) with k for the
k.sup.th patient and j for j.sup.th response (j=1 for TJC, j=2 for
SJC, etc.), B is obtained using OLS, and S is the shrinkage matrix
computed from the canonical co-ordinate system. Hence, this
approach will yield sub-models corresponding to each component of
DAS.
[0326] The fifth method (5) is Curds and Whey and Lasso in
combination (CW-Lasso). Instead of using OLS to obtain B as in CW,
Lasso was used, and the parameters were adjusted accordingly for
the Lasso approach.
[0327] The performance of the five regression methods was compared
in 70/30 cross validation (repeatedly training in a randomly
selected 70% of the data and testing in the remaining 30%). The
number of markers in each regression model was chosen by using
nested 10-fold cross-validation once the number of markers was
selected for a given analysis method the best-fitting model of that
size was used to represent the method. In the CW approaches
(methods 4 and 5), nested 10 fold cross validation was used for
each sub-model corresponding to each component of DAS. The models
developed using the CW-Lasso method performed best overall. The
following sections consist of results mainly using CW-Lasso
approach.
[0328] The 20 candidate biomarkers examined in all training samples
were prioritized according to a number of criteria, including:
strength of association with disease activity and contribution to
multivariate models: consistency of correlation with disease
activity across feasibility and training data sets; CRP was
excluded from any sub-models for TJC, SJC, and PGA both because it
is included in the DAS28-CRP4 and because it did not increase
sub-model prediction accuracy in independent test samples (CRP is
used, however, in the final DAI score calculation as part of the
DAI formula); robust assay performance (IL1B was excluded from
final modeling because its concentrations too frequently fall below
the limits of detection of immunoassays): known drug effects (IL6R
was excluded from final modeling because it is known to be strongly
affected by tocilizumab, independently of the effects of the drug
on disease activity); and, stability (IL8 was excluded from final
modeling because its measurable levels are known to rise
dramatically when serum samples are not kept cold). These criteria
led to 15 candidate biomarkers being considered for inclusion in
the final algorithm. See Table 16.
TABLE-US-00017 TABLE 16 Biomarker Functional Category calprotectin
cytokines and receptors CHI3L1 skeletal EGF growth factors ICAM1
adhesion molecules IL1RA cytokines and receptors IL6 cytokines and
receptors LEP hormones MMP1 matrix metalloproteinases MMP3 matrix
metalloproteinases PYD skeletal RETN hormones SAA1 acute phase
response TNFRSF1A cytokines and receptors VCAM1 adhesion molecules
VEGFA growth factors
Training the Algorithm
[0329] While all data was used in prioritizing biomarkers, a subset
was used for training the final algorithm. This subset was selected
to have a broad range of disease activity levels, so that patients
at all levels of disease activity were well represented. A
comparison was made of the performance of models trained using:
only BRASS samples (167 total); BRASS samples plus InFoRM samples
(167+.about.100) selected to have a uniform disease activity
distribution; or, BRASS samples plus InFoRM samples
(167+.about.100) with a disease activity distribution like that of
the BRASS samples.
[0330] The model performance was evaluated in an independent set of
BRASS and InFoRM samples (70 total) set aside for this purpose. The
DAS28-CRP distribution of this independent test set was similar to
that of past studies (approximately normal). As shown below,
correlation (r) to the DAS28-CRP and area under the ROC curve
(AUROC) for predicting high and low DAS using median cut off were
higher when training used BRASS samples plus "BRASS-like" InFoRM
samples, although the differences were not statistically
significant. The following Table 17 uses the Lasso regression
method.
TABLE-US-00018 TABLE 17 Training Set r AUROC BRASS only 0.53 0.68
BRASS + Uniform InFoRM 0.54 0.69 BRASS + BRASS-like 0.55 0.71
InFoRM
[0331] For final training, the combination of BRASS plus
"BRASS-like" InFoRM samples was selected. The CW-Lasso regression
method was chosen for development of the final algorithm because of
its superior performance in cross validation within the training
set and in testing using InFoRM 512 patients and CAMERA patients
(see below, DAI algorithm performance, for a description of
algorithm testing in another cohort of samples). In the application
of this method, the shrinkage matrix was applied to the predictions
of TJC and SJC. Ten-fold cross-validation indicated that the
following 13 markers were optimal for performance. See Table
18.
TABLE-US-00019 TABLE 18 Marker TJC SJC PGA calprotecin X CHI3L1 X X
EGF X X X IL6 X X X LEP X X MMP1 X MMP3 X PYD X X RETN X SAA1 X X X
TNFRSF1A X X VCAM1 X X VEGF1 X X
[0332] From this set, PYD and calprotectin were excluded due to
elevated assay failure rates. The remaining 11 biomarkers gave very
similar algorithm performance to the full set of 13. An algorithm
was chosen for validation that was developed by CW-Lasso regression
using this 11-marker to estimate the DAS28-CRP in data from the
BRASS+BRASS-like InFoRM samples. The estimates of TJC, SJC and PGHA
were combined with a CRP test result in a formula similar to that
used to calculate the DAS28-CRP.
DAS28CRP = 0.56 TJC + 0.28 SJC + 0.14 PGHA + 0.36 log ( CRP 10 6 +
1 ) + 0.96 ##EQU00007## PDAS = 0.56 IPTJC + 0.28 IPSJC + 0.14 PPGHA
+ 0.36 log ( CRP 10 6 + 1 ) + 0.96 ##EQU00007.2##
[0333] Here IPTJC=Improved Prediction of TJC, IPSJC=Improved
Prediction of SJC, PPGHA=Predicted PGHA, and PDAS is Predicted
DAS28-CRP. (Details are defined below; see Selected algorithm.) The
DAI score is the result from this formula.
[0334] Table 19 demonstrates the correlation of the values
predicted by the PDAS algorithm with actual values for TJC, SJC,
PGHA and DAS28-CRP, in the two cohorts studied, CAMERA and
InFoRM.
TABLE-US-00020 TABLE 19 Study TJC SJC PGHA DAS28-CRP CAMERA 0.445
0.536 0.427 0.726 InFoRM 0.223 0.328 0.388 0.53 (512 subject)
Selected Algorithm
[0335] The 11-marker+CRP Lasso model selected from the training
process is as follows:
PTJC=-38.564+3.997*(SAA1).sup.1/10+17.331*(IL6).sup.1/10+4.665*(CHI3L).s-
up.1/10-15.236*(EGF).sup.1/10+2.651*(TNFRSF1A).sup.1/10+2.641*(LEP).sup.1/-
10+4.026*(VEGFA).sup.1/10-1.47*(VCAM1).sup.1/10;
PSJC=-25.444+4.051*(SAA1).sup.1/10+16.154*(IL6).sup.1/10-11.847*(EGF).su-
p.1/10+3.091*(CHI3L1).sup.1/10+0.353*(TNFRSF1A).sup.1/10;
PPGHA=-13.489+5.474*(IL6).sup.1/10+0.486*(SAA1).sup.1/10+2.246*(MMP1).su-
p.1/10+1.684*(leptin).sup.1/10+4.14*(TNFRSF1A).sup.1/10+2.292*(VEGFA).sup.-
1/10-1.898*(EGF).sup.1/10+0.028*(MMP3).sup.1/10-2.892*(VCAM1).sup.1/10-0.5-
06*(RETN).sup.1/10;
IPTJC=max(0.1739*PTJC+0.7865*PSJC,0);
IPSJC=max(0.1734*PTJC+0.7839*PSJC,0);
DAI
score=round(max(min((0.56*sqrt(IPTJC)+0.28*sqrt(IPSJC)+0.14*PPGA+0.3-
6*ln(CRP/10.sup.6+1))*10.53+1,100),1)).
[0336] For the final DA algorithm, the results from the
11-marker+CRP CW-Lasso model were scaled and rounded to be integers
on a scale of 1-100 such that a DAI score of 1 would be equivalent
to a DAS28-CRP value of 0, and a DAI score of 100 would be
equivalent to a DAS28-CRP value of 9.4.
[0337] Gene names in the above formulas correspond to serum protein
concentrations, as obtained by the MSD.RTM. platform. Biomarker
concentrations were obtained in the ranges shown in Table 20 (95%
interval).
TABLE-US-00021 TABLE 20 pg/ml Biomarker Lower Limit Upper Limit IL6
2.2 104 EGF 20 383 VEGFA 83 776 LEP 2,226 139,885 SAA1 636,889
99,758,140 VCAM1 354,026 1,054,681 CRP 245,332 76,399,801 MMP1
3,047 39,373 MMP3 9,203 134,262 TNFRSF1A 1,139 4,532 RETN 3,635
19,308 CHI3L1 25,874 442,177
DAI Algorithm Performance
[0338] In order to independently test the performance of the
algorithm developed above in this Example, a total of 120 serum
samples were analyzed, obtained from the CAMERA study (see Example
7 for a description of the CAMERA study). Of these, 72 samples were
taken from subject baseline visits, and 48 were from visits six
months subsequent to baseline. The concentrations of 23 serum
protein biomarker were measured in each sample: APOA1, APOC3,
calprotectin, CCL22, CHI3L1 (YKL40), CRP, EGF, ICAM1, IL18, IL1B,
IL1RA, IL6, IL6R, IL8, LEP, MMP1, MMP3, PYD, RETN, SAA1, TNFRSF1A,
VCAM1, and VEGFA. The concentrations of the markers were determined
by customized immunoassays using either the Meso Scale Discovery
SECTOR Imager 6000 or individual ELISAs.
[0339] The associations between individual biomarkers and the
clinical assessment measurements of DAS28-CRP, SJC28 and TJC28 were
assessed by Pearson correlation (r) for log-transformed
concentrations. The correlation p-values were adjusted for multiple
hypothesis testing by estimating false discovery rates (FDR) using
the method of Benjamini and Hochberg. See J. Royal Stat. Soc. B
1995 57(1):289-300.
[0340] Of the 23 proteins examined, fourteen were statistically
significantly correlated with DAS28-CRP, eleven with SJC28 and nine
with TJC28 (FDR<0.05). See Table 22, which shows the Pearson
correlations (r) between individual biomarkers and each clinical
disease activity measure. The q-values reflect the FDRs, and were
calculated by adjusting the p-values for multiple hypothesis
testing. Statistically significant associations (q<0.05) are in
bold. As Table 21 shows, the individual biomarkers associated with
disease activity represented a range of pathways associated with RA
disease pathophysiology (Functional Category).
TABLE-US-00022 TABLE 21 DAS28-CRP SJC28 TJC28 Biomarker Functional
Category r q-val r q-val r q-val calprotectin cytokines and
receptors 0.56 <0.01 0.38 <0.01 0.33 <0.01 CHI3L1 Skeletal
0.42 <0.01 0.35 <0.01 0.30 <0.01 CCL22 cytokines and
receptors -0.04 0.75 -0.13 0.19 -0.03 0.73 CRP acute phase response
0.69 <0.01 0.41 <0.01 0.36 <0.01 EGF growth factors -0.07
0.46 -0.08 0.42 -0.12 0.28 ICAM1 adhesion molecules 0.23 0.02 0.13
0.20 0.08 0.44 IL1B cytokines and receptors 0.45 <0.01 0.34
<0.01 0.31 <0.01 IL6 cytokines and receptors 0.69 <0.01
0.50 <0.01 0.41 <0.01 IL6R cytokines and receptors 0.01 0.97
0.03 0.71 0.02 0.89 IL8 cytokines and receptors 0.47 <0.01 0.46
<0.01 0.30 <0.01 IL1RA cytokines and receptors 0.01 0.97 0.05
0.58 -0.09 0.44 LEP hormones 0.00 0.97 -0.07 0.53 -0.06 0.56 MMP1
MMPs 0.36 <0.01 0.29 <0.01 0.19 0.06 MMP3 MMPs 0.51 <0.01
0.40 <0.01 0.26 <0.01 PYD skeletal 0.23 0.04 0.29 <0.01
0.21 0.09 RETN hormones 0.22 0.03 0.13 0.20 0.13 0.28 SAA1 acute
phase response 0.66 <0.01 0.43 <0.01 0.37 <0.01 TNFRSF1A
cytokines and receptors 0.36 <0.01 0.30 <0.01 0.24 0.02 VCAM1
adhesion molecules 0.13 0.24 0.14 0.20 0.08 0.56 VEGFA growth
factors 0.29 <0.01 0.18 0.12 0.07 0.56
[0341] Two pre-specified algorithms, a prototype and a final
algorithm, using subsets of these 23 biomarkers were applied to
calculate a total DAI score for each subject at each visit
(baseline and six-month). These algorithms were trained in prior
studies using independent samples from other clinical cohorts.
Algorithm performance was evaluated by Pearson correlation (r) and
area under the ROC curve (AUROC) for identifying high and low
disease activity at the baseline and six-month visits. The
reference classification for ROC analysis was based on a DAS28-CRP
of 2.67, the threshold separating remission/low disease activity
from moderate and high disease activity.
Prototype Algorithm for Multivariate Model
[0342] The first algorithm, or "prototype algorithm," using a
linear combination of protein biomarkers, was trained on subject
samples to estimate the DAS28 directly and was provided by the
formula described elsewhere herein according to:
DAI=b.sub.0+b.sub.1*DAIMRK.sub.1.sup.x-b.sub.2*DAIMRK.sub.2.sup.x-b.sub.-
3*DAIMRK.sub.3.sup.x . . . -b.sub.n*DAIMRK.sub.n.sup.x;
where DAI is the DAI score, b.sub.0-n are constants, and
DAIMRK.sub.1-n.sup.x are the serum concentrations, transformed to
the x.sup.th power, of n different biomarkers selected from the
DAIMRK panel.
[0343] The prototype algorithm used in this Example was:
DAI=(-16.1564)-(0.0606*Calprotectin.sup.1/10)+(0.2194*CH3L1.sup.1/10)+(1-
.1886*ICAM1.sup.1/10)+(2.7738*IL6.sup.1/10)+(0.7254*MMP1.sup.1/10)-(0.8348-
*MMP3.sup.1/10)+(1.0296*PYD.sup.1/10)+(1.1792*SAA1.sup.1/10)+(2.4422*TNFRS-
F1A.sup.1/10)+(0.3272*VEGFA.sup.1/10).
[0344] The prototype algorithm achieved a Pearson correlation (r)
of 0.65 and an AUROC of 0.84 relative to the DAS28-CRP.
Biomarker Selection for Final Algorithm
[0345] The second algorithm was derived using serum biomarker
concentrations to separately estimate the three clinical
assessments of TJC28, SJC28 and PGHA. Note that all of these are
components of the formula used in calculating DAS28-CRP:
DAS28-CRP=0.56*sqrt(TJC28)+0.28*sqrt(SJC28)+0.36*ln(CRP+1)+(0.014*PGHA)+-
0.96.
[0346] Biomarkers were then selected to predict and estimate
clinical assessments of disease activity, specifically PGHA, TJC28
and SJC28. The resulting estimates were combined with a serum CRP
concentration measurement to calculate an overall DAI score. See
FIG. 22, which indicates the three panels of biomarkers predictive
of clinical disease activity measurements, the union thereof, and
CRP. The CW-Lasso method was used to predict the individual
components of the DAS28; i.e., TJC28, SJC28 and PGHA. Note that
biomarker terms are included in the CW-Lasso if they help to
improve cross-validated model performance, and this criterion does
not imply that each term is statistically significant by univariate
analysis. A biomarker could make a significant contribution to a
multivariate model even if it does not have a significant
univariate correlation, and could not make a significant
contribution to a multivariate model even though it has a
significant univariate correlation. Indeed, a comparison of each
algorithm predictive for a clinical assessment, (a)-(c) above, with
the biomarkers of Table 18 shows that not all biomarkers in each
algorithm were individually statistically correlated with that
clinical assessment. For example, values for serum concentrations
of EGF, LEP, VEGFA and VCAM1 are all included in the algorithm for
predicting TJC28, yet each of these markers individually
demonstrated a q-value for correlation with TJC of .gtoreq.0.28.
Including these markers, however, improves multivariate model
performance in independent cross-validation test sets.
[0347] The overall DAI score derived according to the methods of
the present Example was given as a whole number between 1 and 100.
The formula used to derive this score was provided by:
DAI
Score=((0.56*sqrt(PTJC)+0.28*sqrt(PSJC)+0.36*log(CRP/10.sup.6+1)+(0.-
14*PPGHA)+0.96)*10.53)+1,
where PTJC=predicted TJC28, PSJC=predicted SJC28, and
PPGHA=predicted PGA. Unlike other formulas to derive DAI scores
described herein, in the formula of this Example the measurements
of individual biomarkers were weighted based on information such as
that depicted in FIG. 22, and removing redundancy of biomarkers, so
as to derive the best prediction of and correlation with particular
clinical disease activity measurements (TJC28, SJC28, PGHA). This
resulted in the inclusion of data from the following set of
biomarkers: SAA1, IL6, CHI3L1, EGF, TNFRSF1A, LEP, VEGFA and VCAM1
for PTJC; SAA1, IL6, EGF, CHI3L1 and TNFRSF1A for PSJC; SAA1, MMP1,
LEP, TNFRSF1A, VEGFA, EGF, MMP3, VCAM1 and RETN for PPGHA; plus
CRP. In total, therefore, data from the following set of 12 markers
was used to derive a DAI score: CHI3L1, CRP, EGF, IL6, LEP, MMP1,
MMP3, RETN, SAA1, TNFRSF1A, VCAM1 and VEGFA. The predicted clinical
assessments of disease activity were developed according to the
following formulas:
PTJC=-38.564+(3.997*SAA1.sup.1/10)+(17.331*IL6.sup.1/10)+(4.665*CHI3L1.s-
up.1/10)-(15.236*EGF.sup.1/10)+(2.651*TNFRSF1A.sup.1/10)+(2.641*LEP.sup.1/-
10)+(4.026*VEGFA.sup.1/10)-(1.47*VCAM1.sup.1/10) (a)
PSJC=-25.444+(4.051*SAA1.sup.1/10)+(16.154*IL6.sup.1/10)-(11.847*EGF.sup-
.1/10)+(3.091*CHI3L1.sup.1/10)+(0.353*TNFRSF1A.sup.1/10); and,
(b)
PPGHA=-13.489+(5.474*IL6.sup.1/10)+(0.486*SAA1.sup.1/10)+(2.246*MMP1.sup-
.1/10)+(1.684*LEP.sup.1/10)+(4.14*TNFRSF1A.sup.1/10)+(2.292*VEGFA.sup.1/10-
)-(1.898*EGF.sup.1/10)+(0.028*MMP3.sup.1/10)-(2.892*VCAM1.sup.1/10)-(0.506-
*RETN.sup.1/10). (c)
[0348] The performance of the above algorithm in deriving a DAI
score was evaluated by Pearson correlation (r) and area under the
ROC curve (AUROC) for identifying high and low disease activity at
the baseline and six-month visits. The Pearson correlation was
0.73, and the AUROC was 0.87, with the reference classification for
ROC analysis based on a threshold DAS28-CRP of 2.67, the threshold
separating remission/low disease activity from moderate and high
disease activity. The changes in biomarker-based DAI scores between
the baseline and six-month visits were assessed by the paired
Wilcoxon rank sum test.
[0349] To ensure that performance of the second algorithm was not
overestimated due to the inclusion of two samples for some
patients, subsets of samples were also analyzed that included only
one randomly selected visit for each subject. The algorithm
performed equally well in these subsets. Possible bias in the AUROC
due to an imbalance in numbers between low and high disease
activity groups was also analyzed using a DAS28-CRP cutoff of 2.67.
When the cutoff was set at the median DAS28-CRP of 4.6, the AUROC
was 0.83.
[0350] When the predictions of the individual components of the
DAS28 generated by the DAI algorithm were correlated to the actual
TJC28, SJC28 and PGHA, the correlation coefficients were seen to
trend higher (and thus provide better correlation with clinical
disease activity measurements) than the coefficients for CRP, a
marker commonly used alone as an indicator of RA disease activity.
See FIG. 23.
[0351] An analysis was then done to determine whether the DAI score
changed in response to the treatment protocols used in the CAMERA
study. For all subjects for whom DAI Scores were available for both
visits (baseline and six-month), the median score dropped from 52
to 37 (p=2.2E-6; n=46). See FIG. 24. The intensive and conventional
treatment arms were considered separately. There was also a
significant decrease in median DAI Score in the intensive treatment
arm, from 52 to 36 (p=2.5E-5; n=31). In the conventional treatment
arm, the median DAI Score decreased from 59 to 45 (p=0.06;
n=15).
[0352] In conclusion, this Example demonstrates that serum protein
biomarkers representing a variety of biological pathways were
consistently associated with RA disease activity. A pre-specified
DAI algorithm combining information from several of these
biomarkers performed well in predicting RA disease activity when
evaluated in an independent test set. The algorithm's estimates of
TJC, SJC and PGHA correlated to actual clinical measures of disease
activity. Furthermore, subsequent DAI scores of the subjects
analyzed decreased compared to initial DAI scores following and in
response to treatment.
Example 12
Use of DAI to Predict Lint Damage Progression
[0353] Example 12 demonstrates the use of the DAI score to predict
joint damage progression in RA subjects. In this Example serum
samples were analyzed from 89 subject participants in the BeSt
(Dutch, "Behandelstrategieen") study. The BeSt study is a
multicenter, randomized, controlled study designed to compare the
clinical efficacy and radiologic outcomes of four different
treatment strategies in patients with early onset RA. See Y P
Goekoop-Ruiterman et al., Arth. Rheum. 2005, 52:3381-3390. Serum
biomarkers were evaluated in serum collected at year 1. Total Van
der Heijde modified Sharp scores (TSS) from year 1 and year 2 were
used.
[0354] The DAI score at year 1 was evaluated for its ability to
predict the change in TSS from year 1 to year 2. Identifying
patients at risk of increase in TSS is a clinical question of great
importance. The DAI score was correlated with change in TSS
(P<0.001). See Table 22. Moreover, the observed correlation
coefficient for DAI score was greater than for any clinical
variable examined except year 1 TSS. Since TSS is only evaluated in
clinical trials and not available in routine clinical practice,
this suggests that the DAI score has the potential to outperform
conventional clinical variables at predicting progressive joint
damage. DAI score also had a higher observed area under the
receiver operating characteristic curve for identifying patients
with increases in TSS than other clinical variables except year 1
TSS.
TABLE-US-00023 TABLE 22 P value Correlation AUROC TSS <0.001
0.541 0.765 DAI <0.001 0.435 0.686 CRP <0.001 0.366 0.64 ESR
0.027 0.216 0.527 DAS-ESR 0.001 0.33 0.567 DAS-CRP 0.001 0.351
0.595 TJC28 0.012 0.252 0.492 SJC28 0.003 0.3 0.653 RAI 0.164 0.11
0.485 SJC44 0.106 0.14 0.56 VAS 0.06 0.174 0.554
Example 13
DAI Score Unaffected by Comorbidities
[0355] 512 subjects were selected from the InFoRM cohort, to be
representative of the entire cohort in age, sex, DAS28CRP (DAS28)
and disease duration. The ratios in the median CRP, CDAI, DAS28 and
DAI in patients with co-morbidities were compared to patients
without the co-morbidity to assess the robustness of the DAI. To
calculate the DAI, the concentrations of IL-6, EGF, VEGF-A, Leptin,
SAA, CRP, VCAM-1, MMP-1, MMP-3, Resistin, YKL-40, and TNF-RI were
measured using multiplex immunoassays and combined in the algorithm
identified in Example 11. Co-morbidities of interest included
hypertension, osteoarthritis, prior fracture, diabetes, psychiatric
illness, peptic ulcer, Sjogren's syndrome, fibromyalgia, COPD, and
asthma. The significance of differences was determined by Wilcoxon
rank sum test with a multiple testing correction applied. The
multiple testing correction is described in Benjamini and Hochberg.
J. Royal Stat. Soc. B 1995 57(1):289-300. Results are reported as
the ratio of the median value of the measure (e.g. CDAI) among
people with the condition compared to those without the
condition.
[0356] The results showed that several co-morbidities were
associated with differences, mostly increases, in median disease
activity measures. Comparing people with each comorbidity to those
without the comorbidity, the ratios in the median scores were
generally larger for CRP [range 0.8-2.1] and CDAI [range 1.0-1.8]
than for DAS28 [range 1.0-1.4] and DAI [range 1.0-1.2]. Across the
4 outcome measures, the greatest number of significant differences
in median scores was seen in patients with fibromyalgia,
psychiatric illness, Sjogren's, and hypertension (Table 1). The DAI
was not significantly different in males versus females (median:
41.7 vs. 423, p-value: 0.46) or in current smokers versus
non-smokers (median: 38.5 vs. 42.7, p-value: 0.13). The score did
vary significantly with BMI: median DAI score for subjects with
BMI.ltoreq.30 was 38.7, while the median for subjects with a
BMI>30 was 46.3.
TABLE-US-00024 TABLE 23 Ratios in Disease Activity Measure's Median
Value N Subgroup (%) CRP CDAI DAS28 DAI Fibromyalgia 33 1.6* 1.6*
1.3* 1.1 (6) Psychiatric 24 1.7 1.7* 1.4* 1.1 illness (5) Sjogren's
20 1.0 1.8* 1.3* 1.1 (4) Hypertension 223 1.0 1.3* 1.1* 1.1 (44)
Peptic Ulcer 19 0.8 1.5* 1.2 1.0 (4) Osteoarthritis 173 1.0 1.2 1.1
1.0 (34) Osteoporotic 131 0.9 1.0 1.0 1.0 bone fracture (26)
Diabetes 72 0.9 1.1 1.1 1.1 (14) Asthma 50 1.5 1.2 1.1 1.1 (10)
COPD 20 2.1 1.1 10 1.2 (4) A value of 1.0 implies that there is no
difference in the median value of the measure for people with
versus those without the comorbidity *Significant difference from
the population without the co-morbidity, False Discovery Rate
<10%.
[0357] In conclusion, DAI has been validated to assess and monitor
rheumatoid arthritis ("RA") disease activity. When assessing the RA
disease activity of patients with common co-morbidities, the DAI
appears to be less confounded by the presence of co-morbidities
than the other measures tested. This may be due to its inclusion of
multiple biomarkers representing biologic pathways in RA.
Example 14
DAI Score to Measure Disease Activity in Undifferentiated
Arthritis
[0358] It has been shown that DAS is a valid measure of disease
activity in undifferentiated arthritis ("UA"). See Fransen, J. et
al. Arthritis Care and Research, 62(10):1392.8, 2010. Thus, the
model in example 11, which estimates the DAS, calculates a DAI
score which is a measure of UA disease activity. Alternatively a
model similar to that in example 11 is trained such that the DAI
score is a measure of UA disease activity.
[0359] All publications and patent applications cited in this
specification are herein incorporated by reference as if each
individual publication or patent application were specifically and
individually indicated to be incorporated by reference.
[0360] Although the foregoing invention has been described in some
detail by way of illustration and example for purposes of clarity
of understanding, it will be readily apparent to one of ordinary
skill in the art in light of the teachings of this invention that
certain changes and modifications may be made thereto without
departing from the spirit or scope of the invention as defined in
the appended claims.
Sequence CWU 1
1
251267PRTHomo sapiens 1Met Lys Ala Ala Val Leu Thr Leu Ala Val Leu
Phe Leu Thr Gly Ser 1 5 10 15 Gln Ala Arg His Phe Trp Gln Gln Asp
Glu Pro Pro Gln Ser Pro Trp 20 25 30 Asp Arg Val Lys Asp Leu Ala
Thr Val Tyr Val Asp Val Leu Lys Asp 35 40 45 Ser Gly Arg Asp Tyr
Val Ser Gln Phe Glu Gly Ser Ala Leu Gly Lys 50 55 60 Gln Leu Asn
Leu Lys Leu Leu Asp Asn Trp Asp Ser Val Thr Ser Thr 65 70 75 80 Phe
Ser Lys Leu Arg Glu Gln Leu Gly Pro Val Thr Gln Glu Phe Trp 85 90
95 Asp Asn Leu Glu Lys Glu Thr Glu Gly Leu Arg Gln Glu Met Ser Lys
100 105 110 Asp Leu Glu Glu Val Lys Ala Lys Val Gln Pro Tyr Leu Asp
Asp Phe 115 120 125 Gln Lys Lys Trp Gln Glu Glu Met Glu Leu Tyr Arg
Gln Lys Val Glu 130 135 140 Pro Leu Arg Ala Glu Leu Gln Glu Gly Ala
Arg Gln Lys Leu His Glu 145 150 155 160 Leu Gln Glu Lys Leu Ser Pro
Leu Gly Glu Glu Met Arg Asp Arg Ala 165 170 175 Arg Ala His Val Asp
Ala Leu Arg Thr His Leu Ala Pro Tyr Ser Asp 180 185 190 Glu Leu Arg
Gln Arg Leu Ala Ala Arg Leu Glu Ala Leu Lys Glu Asn 195 200 205 Gly
Gly Ala Arg Leu Ala Glu Tyr His Ala Lys Ala Thr Glu His Leu 210 215
220 Ser Thr Leu Ser Glu Lys Ala Lys Pro Ala Leu Glu Asp Leu Arg Gln
225 230 235 240 Gly Leu Leu Pro Val Leu Glu Ser Phe Lys Val Ser Phe
Leu Ser Ala 245 250 255 Leu Glu Glu Tyr Thr Lys Lys Leu Asn Thr Gln
260 265 299PRTHomo sapiens 2Met Gln Pro Arg Val Leu Leu Val Val Ala
Leu Leu Ala Leu Leu Ala 1 5 10 15 Ser Ala Arg Ala Ser Glu Ala Glu
Asp Ala Ser Leu Leu Ser Phe Met 20 25 30 Gln Gly Tyr Met Lys His
Ala Thr Lys Thr Ala Lys Asp Ala Leu Ser 35 40 45 Ser Val Gln Glu
Ser Gln Val Ala Gln Gln Ala Arg Gly Trp Val Thr 50 55 60 Asp Gly
Phe Ser Ser Leu Lys Asp Tyr Trp Ser Thr Val Lys Asp Lys 65 70 75 80
Phe Ser Glu Phe Trp Asp Leu Asp Pro Glu Val Arg Pro Thr Ser Ala 85
90 95 Val Ala Ala 393PRTHomo sapiens 3Met Asp Arg Leu Gln Thr Ala
Leu Leu Val Val Leu Val Leu Leu Ala 1 5 10 15 Val Ala Leu Gln Ala
Thr Glu Ala Gly Pro Tyr Gly Ala Asn Met Glu 20 25 30 Asp Ser Val
Cys Cys Arg Asp Tyr Val Arg Tyr Arg Leu Pro Leu Arg 35 40 45 Val
Val Lys His Phe Tyr Trp Thr Ser Asp Ser Cys Pro Arg Pro Gly 50 55
60 Val Val Leu Leu Thr Phe Arg Asp Lys Glu Ile Cys Ala Asp Pro Arg
65 70 75 80 Val Pro Trp Val Lys Met Ile Leu Asn Lys Leu Ser Gln 85
90 4383PRTHomo sapiens 4Met Gly Val Lys Ala Ser Gln Thr Gly Phe Val
Val Leu Val Leu Leu 1 5 10 15 Gln Cys Cys Ser Ala Tyr Lys Leu Val
Cys Tyr Tyr Thr Ser Trp Ser 20 25 30 Gln Tyr Arg Glu Gly Asp Gly
Ser Cys Phe Pro Asp Ala Leu Asp Arg 35 40 45 Phe Leu Cys Thr His
Ile Ile Tyr Ser Phe Ala Asn Ile Ser Asn Asp 50 55 60 His Ile Asp
Thr Trp Glu Trp Asn Asp Val Thr Leu Tyr Gly Met Leu 65 70 75 80 Asn
Thr Leu Lys Asn Arg Asn Pro Asn Leu Lys Thr Leu Leu Ser Val 85 90
95 Gly Gly Trp Asn Phe Gly Ser Gln Arg Phe Ser Lys Ile Ala Ser Asn
100 105 110 Thr Gln Ser Arg Arg Thr Phe Ile Lys Ser Val Pro Pro Phe
Leu Arg 115 120 125 Thr His Gly Phe Asp Gly Leu Asp Leu Ala Trp Leu
Tyr Pro Gly Arg 130 135 140 Arg Asp Lys Gln His Phe Thr Thr Leu Ile
Lys Glu Met Lys Ala Glu 145 150 155 160 Phe Ile Lys Glu Ala Gln Pro
Gly Lys Lys Gln Leu Leu Leu Ser Ala 165 170 175 Ala Leu Ser Ala Gly
Lys Val Thr Ile Asp Ser Ser Tyr Asp Ile Ala 180 185 190 Lys Ile Ser
Gln His Leu Asp Phe Ile Ser Ile Met Thr Tyr Asp Phe 195 200 205 His
Gly Ala Trp Arg Gly Thr Thr Gly His His Ser Pro Leu Phe Arg 210 215
220 Gly Gln Glu Asp Ala Ser Pro Asp Arg Phe Ser Asn Thr Asp Tyr Ala
225 230 235 240 Val Gly Tyr Met Leu Arg Leu Gly Ala Pro Ala Ser Lys
Leu Val Met 245 250 255 Gly Ile Pro Thr Phe Gly Arg Ser Phe Thr Leu
Ala Ser Ser Glu Thr 260 265 270 Gly Val Gly Ala Pro Ile Ser Gly Pro
Gly Ile Pro Gly Arg Phe Thr 275 280 285 Lys Glu Ala Gly Thr Leu Ala
Tyr Tyr Glu Ile Cys Asp Phe Leu Arg 290 295 300 Gly Ala Thr Val His
Arg Ile Leu Gly Gln Gln Val Pro Tyr Ala Thr 305 310 315 320 Lys Gly
Asn Gln Trp Val Gly Tyr Asp Asp Gln Glu Ser Val Lys Ser 325 330 335
Lys Val Gln Tyr Leu Lys Asp Arg Gln Leu Ala Gly Ala Met Val Trp 340
345 350 Ala Leu Asp Leu Asp Asp Phe Gln Gly Ser Phe Cys Gly Gln Asp
Leu 355 360 365 Arg Phe Pro Leu Thr Asn Ala Ile Lys Asp Ala Leu Ala
Ala Thr 370 375 380 5224PRTHomo sapiens 5Met Glu Lys Leu Leu Cys
Phe Leu Val Leu Thr Ser Leu Ser His Ala 1 5 10 15 Phe Gly Gln Thr
Asp Met Ser Arg Lys Ala Phe Val Phe Pro Lys Glu 20 25 30 Ser Asp
Thr Ser Tyr Val Ser Leu Lys Ala Pro Leu Thr Lys Pro Leu 35 40 45
Lys Ala Phe Thr Val Cys Leu His Phe Tyr Thr Glu Leu Ser Ser Thr 50
55 60 Arg Gly Tyr Ser Ile Phe Ser Tyr Ala Thr Lys Arg Gln Asp Asn
Glu 65 70 75 80 Ile Leu Ile Phe Trp Ser Lys Asp Ile Gly Tyr Ser Phe
Thr Val Gly 85 90 95 Gly Ser Glu Ile Leu Phe Glu Val Pro Glu Val
Thr Val Ala Pro Val 100 105 110 His Ile Cys Thr Ser Trp Glu Ser Ala
Ser Gly Ile Val Glu Phe Trp 115 120 125 Val Asp Gly Lys Pro Arg Val
Arg Lys Ser Leu Lys Lys Gly Tyr Thr 130 135 140 Val Gly Ala Glu Ala
Ser Ile Ile Leu Gly Gln Glu Gln Asp Ser Phe 145 150 155 160 Gly Gly
Asn Phe Glu Gly Ser Gln Ser Leu Val Gly Asp Ile Gly Asn 165 170 175
Val Asn Met Trp Asp Phe Val Leu Ser Pro Asp Glu Ile Asn Thr Ile 180
185 190 Tyr Leu Gly Gly Pro Phe Ser Pro Asn Val Leu Asn Trp Arg Ala
Leu 195 200 205 Lys Tyr Glu Val Gln Gly Glu Val Phe Thr Lys Pro Gln
Leu Trp Pro 210 215 220 61207PRTHomo sapiens 6Met Leu Leu Thr Leu
Ile Ile Leu Leu Pro Val Val Ser Lys Phe Ser 1 5 10 15 Phe Val Ser
Leu Ser Ala Pro Gln His Trp Ser Cys Pro Glu Gly Thr 20 25 30 Leu
Ala Gly Asn Gly Asn Ser Thr Cys Val Gly Pro Ala Pro Phe Leu 35 40
45 Ile Phe Ser His Gly Asn Ser Ile Phe Arg Ile Asp Thr Glu Gly Thr
50 55 60 Asn Tyr Glu Gln Leu Val Val Asp Ala Gly Val Ser Val Ile
Met Asp 65 70 75 80 Phe His Tyr Asn Glu Lys Arg Ile Tyr Trp Val Asp
Leu Glu Arg Gln 85 90 95 Leu Leu Gln Arg Val Phe Leu Asn Gly Ser
Arg Gln Glu Arg Val Cys 100 105 110 Asn Ile Glu Lys Asn Val Ser Gly
Met Ala Ile Asn Trp Ile Asn Glu 115 120 125 Glu Val Ile Trp Ser Asn
Gln Gln Glu Gly Ile Ile Thr Val Thr Asp 130 135 140 Met Lys Gly Asn
Asn Ser His Ile Leu Leu Ser Ala Leu Lys Tyr Pro 145 150 155 160 Ala
Asn Val Ala Val Asp Pro Val Glu Arg Phe Ile Phe Trp Ser Ser 165 170
175 Glu Val Ala Gly Ser Leu Tyr Arg Ala Asp Leu Asp Gly Val Gly Val
180 185 190 Lys Ala Leu Leu Glu Thr Ser Glu Lys Ile Thr Ala Val Ser
Leu Asp 195 200 205 Val Leu Asp Lys Arg Leu Phe Trp Ile Gln Tyr Asn
Arg Glu Gly Ser 210 215 220 Asn Ser Leu Ile Cys Ser Cys Asp Tyr Asp
Gly Gly Ser Val His Ile 225 230 235 240 Ser Lys His Pro Thr Gln His
Asn Leu Phe Ala Met Ser Leu Phe Gly 245 250 255 Asp Arg Ile Phe Tyr
Ser Thr Trp Lys Met Lys Thr Ile Trp Ile Ala 260 265 270 Asn Lys His
Thr Gly Lys Asp Met Val Arg Ile Asn Leu His Ser Ser 275 280 285 Phe
Val Pro Leu Gly Glu Leu Lys Val Val His Pro Leu Ala Gln Pro 290 295
300 Lys Ala Glu Asp Asp Thr Trp Glu Pro Glu Gln Lys Leu Cys Lys Leu
305 310 315 320 Arg Lys Gly Asn Cys Ser Ser Thr Val Cys Gly Gln Asp
Leu Gln Ser 325 330 335 His Leu Cys Met Cys Ala Glu Gly Tyr Ala Leu
Ser Arg Asp Arg Lys 340 345 350 Tyr Cys Glu Asp Val Asn Glu Cys Ala
Phe Trp Asn His Gly Cys Thr 355 360 365 Leu Gly Cys Lys Asn Thr Pro
Gly Ser Tyr Tyr Cys Thr Cys Pro Val 370 375 380 Gly Phe Val Leu Leu
Pro Asp Gly Lys Arg Cys His Gln Leu Val Ser 385 390 395 400 Cys Pro
Arg Asn Val Ser Glu Cys Ser His Asp Cys Val Leu Thr Ser 405 410 415
Glu Gly Pro Leu Cys Phe Cys Pro Glu Gly Ser Val Leu Glu Arg Asp 420
425 430 Gly Lys Thr Cys Ser Gly Cys Ser Ser Pro Asp Asn Gly Gly Cys
Ser 435 440 445 Gln Leu Cys Val Pro Leu Ser Pro Val Ser Trp Glu Cys
Asp Cys Phe 450 455 460 Pro Gly Tyr Asp Leu Gln Leu Asp Glu Lys Ser
Cys Ala Ala Ser Gly 465 470 475 480 Pro Gln Pro Phe Leu Leu Phe Ala
Asn Ser Gln Asp Ile Arg His Met 485 490 495 His Phe Asp Gly Thr Asp
Tyr Gly Thr Leu Leu Ser Gln Gln Met Gly 500 505 510 Met Val Tyr Ala
Leu Asp His Asp Pro Val Glu Asn Lys Ile Tyr Phe 515 520 525 Ala His
Thr Ala Leu Lys Trp Ile Glu Arg Ala Asn Met Asp Gly Ser 530 535 540
Gln Arg Glu Arg Leu Ile Glu Glu Gly Val Asp Val Pro Glu Gly Leu 545
550 555 560 Ala Val Asp Trp Ile Gly Arg Arg Phe Tyr Trp Thr Asp Arg
Gly Lys 565 570 575 Ser Leu Ile Gly Arg Ser Asp Leu Asn Gly Lys Arg
Ser Lys Ile Ile 580 585 590 Thr Lys Glu Asn Ile Ser Gln Pro Arg Gly
Ile Ala Val His Pro Met 595 600 605 Ala Lys Arg Leu Phe Trp Thr Asp
Thr Gly Ile Asn Pro Arg Ile Glu 610 615 620 Ser Ser Ser Leu Gln Gly
Leu Gly Arg Leu Val Ile Ala Ser Ser Asp 625 630 635 640 Leu Ile Trp
Pro Ser Gly Ile Thr Ile Asp Phe Leu Thr Asp Lys Leu 645 650 655 Tyr
Trp Cys Asp Ala Lys Gln Ser Val Ile Glu Met Ala Asn Leu Asp 660 665
670 Gly Ser Lys Arg Arg Arg Leu Thr Gln Asn Asp Val Gly His Pro Phe
675 680 685 Ala Val Ala Val Phe Glu Asp Tyr Val Trp Phe Ser Asp Trp
Ala Met 690 695 700 Pro Ser Val Met Arg Val Asn Lys Arg Thr Gly Lys
Asp Arg Val Arg 705 710 715 720 Leu Gln Gly Ser Met Leu Lys Pro Ser
Ser Leu Val Val Val His Pro 725 730 735 Leu Ala Lys Pro Gly Ala Asp
Pro Cys Leu Tyr Gln Asn Gly Gly Cys 740 745 750 Glu His Ile Cys Lys
Lys Arg Leu Gly Thr Ala Trp Cys Ser Cys Arg 755 760 765 Glu Gly Phe
Met Lys Ala Ser Asp Gly Lys Thr Cys Leu Ala Leu Asp 770 775 780 Gly
His Gln Leu Leu Ala Gly Gly Glu Val Asp Leu Lys Asn Gln Val 785 790
795 800 Thr Pro Leu Asp Ile Leu Ser Lys Thr Arg Val Ser Glu Asp Asn
Ile 805 810 815 Thr Glu Ser Gln His Met Leu Val Ala Glu Ile Met Val
Ser Asp Gln 820 825 830 Asp Asp Cys Ala Pro Val Gly Cys Ser Met Tyr
Ala Arg Cys Ile Ser 835 840 845 Glu Gly Glu Asp Ala Thr Cys Gln Cys
Leu Lys Gly Phe Ala Gly Asp 850 855 860 Gly Lys Leu Cys Ser Asp Ile
Asp Glu Cys Glu Met Gly Val Pro Val 865 870 875 880 Cys Pro Pro Ala
Ser Ser Lys Cys Ile Asn Thr Glu Gly Gly Tyr Val 885 890 895 Cys Arg
Cys Ser Glu Gly Tyr Gln Gly Asp Gly Ile His Cys Leu Asp 900 905 910
Ile Asp Glu Cys Gln Leu Gly Glu His Ser Cys Gly Glu Asn Ala Ser 915
920 925 Cys Thr Asn Thr Glu Gly Gly Tyr Thr Cys Met Cys Ala Gly Arg
Leu 930 935 940 Ser Glu Pro Gly Leu Ile Cys Pro Asp Ser Thr Pro Pro
Pro His Leu 945 950 955 960 Arg Glu Asp Asp His His Tyr Ser Val Arg
Asn Ser Asp Ser Glu Cys 965 970 975 Pro Leu Ser His Asp Gly Tyr Cys
Leu His Asp Gly Val Cys Met Tyr 980 985 990 Ile Glu Ala Leu Asp Lys
Tyr Ala Cys Asn Cys Val Val Gly Tyr Ile 995 1000 1005 Gly Glu Arg
Cys Gln Tyr Arg Asp Leu Lys Trp Trp Glu Leu Arg 1010 1015 1020 His
Ala Gly His Gly Gln Gln Gln Lys Val Ile Val Val Ala Val 1025 1030
1035 Cys Val Val Val Leu Val Met Leu Leu Leu Leu Ser Leu Trp Gly
1040 1045 1050 Ala His Tyr Tyr Arg Thr Gln Lys Leu Leu Ser Lys Asn
Pro Lys 1055 1060 1065 Asn Pro Tyr Glu Glu Ser Ser Arg Asp Val Arg
Ser Arg Arg Pro 1070 1075 1080 Ala Asp Thr Glu Asp Gly Met Ser Ser
Cys Pro Gln Pro Trp Phe 1085 1090 1095 Val Val Ile Lys Glu His Gln
Asp Leu Lys Asn Gly Gly Gln Pro 1100 1105 1110 Val Ala Gly Glu Asp
Gly Gln Ala Ala Asp Gly Ser Met Gln Pro 1115 1120 1125 Thr Ser Trp
Arg Gln Glu Pro Gln Leu Cys Gly Met Gly Thr Glu 1130 1135 1140 Gln
Gly Cys Trp Ile Pro Val Ser Ser Asp Lys Gly Ser Cys Pro 1145 1150
1155 Gln Val Met Glu Arg Ser Phe His Met Pro Ser Tyr Gly Thr Gln
1160 1165 1170 Thr Leu Glu Gly Gly Val Glu Lys Pro His Ser Leu Leu
Ser Ala 1175 1180 1185 Asn Pro Leu Trp Gln Gln Arg Ala Leu Asp Pro
Pro His Gln Met 1190 1195 1200 Glu Leu Thr Gln 1205 7532PRTHomo
sapiens 7Met Ala Pro Ser
Ser Pro Arg Pro Ala Leu Pro Ala Leu Leu Val Leu 1 5 10 15 Leu Gly
Ala Leu Phe Pro Gly Pro Gly Asn Ala Gln Thr Ser Val Ser 20 25 30
Pro Ser Lys Val Ile Leu Pro Arg Gly Gly Ser Val Leu Val Thr Cys 35
40 45 Ser Thr Ser Cys Asp Gln Pro Lys Leu Leu Gly Ile Glu Thr Pro
Leu 50 55 60 Pro Lys Lys Glu Leu Leu Leu Pro Gly Asn Asn Arg Lys
Val Tyr Glu 65 70 75 80 Leu Ser Asn Val Gln Glu Asp Ser Gln Pro Met
Cys Tyr Ser Asn Cys 85 90 95 Pro Asp Gly Gln Ser Thr Ala Lys Thr
Phe Leu Thr Val Tyr Trp Thr 100 105 110 Pro Glu Arg Val Glu Leu Ala
Pro Leu Pro Ser Trp Gln Pro Val Gly 115 120 125 Lys Asn Leu Thr Leu
Arg Cys Gln Val Glu Gly Gly Ala Pro Arg Ala 130 135 140 Asn Leu Thr
Val Val Leu Leu Arg Gly Glu Lys Glu Leu Lys Arg Glu 145 150 155 160
Pro Ala Val Gly Glu Pro Ala Glu Val Thr Thr Thr Val Leu Val Arg 165
170 175 Arg Asp His His Gly Ala Asn Phe Ser Cys Arg Thr Glu Leu Asp
Leu 180 185 190 Arg Pro Gln Gly Leu Glu Leu Phe Glu Asn Thr Ser Ala
Pro Tyr Gln 195 200 205 Leu Gln Thr Phe Val Leu Pro Ala Thr Pro Pro
Gln Leu Val Ser Pro 210 215 220 Arg Val Leu Glu Val Asp Thr Gln Gly
Thr Val Val Cys Ser Leu Asp 225 230 235 240 Gly Leu Phe Pro Val Ser
Glu Ala Gln Val His Leu Ala Leu Gly Asp 245 250 255 Gln Arg Leu Asn
Pro Thr Val Thr Tyr Gly Asn Asp Ser Phe Ser Ala 260 265 270 Lys Ala
Ser Val Ser Val Thr Ala Glu Asp Glu Gly Thr Gln Arg Leu 275 280 285
Thr Cys Ala Val Ile Leu Gly Asn Gln Ser Gln Glu Thr Leu Gln Thr 290
295 300 Val Thr Ile Tyr Ser Phe Pro Ala Pro Asn Val Ile Leu Thr Lys
Pro 305 310 315 320 Glu Val Ser Glu Gly Thr Glu Val Thr Val Lys Cys
Glu Ala His Pro 325 330 335 Arg Ala Lys Val Thr Leu Asn Gly Val Pro
Ala Gln Pro Leu Gly Pro 340 345 350 Arg Ala Gln Leu Leu Leu Lys Ala
Thr Pro Glu Asp Asn Gly Arg Ser 355 360 365 Phe Ser Cys Ser Ala Thr
Leu Glu Val Ala Gly Gln Leu Ile His Lys 370 375 380 Asn Gln Thr Arg
Glu Leu Arg Val Leu Tyr Gly Pro Arg Leu Asp Glu 385 390 395 400 Arg
Asp Cys Pro Gly Asn Trp Thr Trp Pro Glu Asn Ser Gln Gln Thr 405 410
415 Pro Met Cys Gln Ala Trp Gly Asn Pro Leu Pro Glu Leu Lys Cys Leu
420 425 430 Lys Asp Gly Thr Phe Pro Leu Pro Ile Gly Glu Ser Val Thr
Val Thr 435 440 445 Arg Asp Leu Glu Gly Thr Tyr Leu Cys Arg Ala Arg
Ser Thr Gln Gly 450 455 460 Glu Val Thr Arg Lys Val Thr Val Asn Val
Leu Ser Pro Arg Tyr Glu 465 470 475 480 Ile Val Ile Ile Thr Val Val
Ala Ala Ala Val Ile Met Gly Thr Ala 485 490 495 Gly Leu Ser Thr Tyr
Leu Tyr Asn Arg Gln Arg Lys Ile Lys Lys Tyr 500 505 510 Arg Leu Gln
Gln Ala Gln Lys Gly Thr Pro Met Lys Pro Asn Thr Gln 515 520 525 Ala
Thr Pro Pro 530 8193PRTHomo sapiens 8Met Ala Ala Glu Pro Val Glu
Asp Asn Cys Ile Asn Phe Val Ala Met 1 5 10 15 Lys Phe Ile Asp Asn
Thr Leu Tyr Phe Ile Ala Glu Asp Asp Glu Asn 20 25 30 Leu Glu Ser
Asp Tyr Phe Gly Lys Leu Glu Ser Lys Leu Ser Val Ile 35 40 45 Arg
Asn Leu Asn Asp Gln Val Leu Phe Ile Asp Gln Gly Asn Arg Pro 50 55
60 Leu Phe Glu Asp Met Thr Asp Ser Asp Cys Arg Pro Arg Thr Pro Arg
65 70 75 80 Thr Ile Phe Ile Ile Ser Met Tyr Lys Asp Ser Gln Pro Arg
Gly Met 85 90 95 Ala Val Thr Ile Ser Val Lys Cys Glu Lys Ile Ser
Thr Leu Ser Cys 100 105 110 Glu Asn Lys Ile Ile Ser Phe Lys Glu Met
Asn Pro Pro Asp Asn Ile 115 120 125 Lys Asp Thr Lys Ser Asp Ile Ile
Phe Phe Gln Arg Ser Val Pro Gly 130 135 140 His Asp Asn Lys Met Gln
Phe Glu Ser Ser Ser Tyr Glu Gly Tyr Phe 145 150 155 160 Leu Ala Cys
Glu Lys Glu Arg Asp Leu Phe Lys Leu Ile Leu Lys Lys 165 170 175 Glu
Asp Glu Leu Gly Asp Arg Ser Ile Met Phe Thr Val Gln Asn Glu 180 185
190 Asp 9269PRTHomo sapiens 9Met Ala Glu Val Pro Glu Leu Ala Ser
Glu Met Met Ala Tyr Tyr Ser 1 5 10 15 Gly Asn Glu Asp Asp Leu Phe
Phe Glu Ala Asp Gly Pro Lys Gln Met 20 25 30 Lys Cys Ser Phe Gln
Asp Leu Asp Leu Cys Pro Leu Asp Gly Gly Ile 35 40 45 Gln Leu Arg
Ile Ser Asp His His Tyr Ser Lys Gly Phe Arg Gln Ala 50 55 60 Ala
Ser Val Val Val Ala Met Asp Lys Leu Arg Lys Met Leu Val Pro 65 70
75 80 Cys Pro Gln Thr Phe Gln Glu Asn Asp Leu Ser Thr Phe Phe Pro
Phe 85 90 95 Ile Phe Glu Glu Glu Pro Ile Phe Phe Asp Thr Trp Asp
Asn Glu Ala 100 105 110 Tyr Val His Asp Ala Pro Val Arg Ser Leu Asn
Cys Thr Leu Arg Asp 115 120 125 Ser Gln Gln Lys Ser Leu Val Met Ser
Gly Pro Tyr Glu Leu Lys Ala 130 135 140 Leu His Leu Gln Gly Gln Asp
Met Glu Gln Gln Val Val Phe Ser Met 145 150 155 160 Ser Phe Val Gln
Gly Glu Glu Ser Asn Asp Lys Ile Pro Val Ala Leu 165 170 175 Gly Leu
Lys Glu Lys Asn Leu Tyr Leu Ser Cys Val Leu Lys Asp Asp 180 185 190
Lys Pro Thr Leu Gln Leu Glu Ser Val Asp Pro Lys Asn Tyr Pro Lys 195
200 205 Lys Lys Met Glu Lys Arg Phe Val Phe Asn Lys Ile Glu Ile Asn
Asn 210 215 220 Lys Leu Glu Phe Glu Ser Ala Gln Phe Pro Asn Trp Tyr
Ile Ser Thr 225 230 235 240 Ser Gln Ala Glu Asn Met Pro Val Phe Leu
Gly Gly Thr Lys Gly Gly 245 250 255 Gln Asp Ile Thr Asp Phe Thr Met
Gln Phe Val Ser Ser 260 265 10159PRTHomo sapiens 10Met Ala Leu Glu
Thr Ile Cys Arg Pro Ser Gly Arg Lys Ser Ser Lys 1 5 10 15 Met Gln
Ala Phe Arg Ile Trp Asp Val Asn Gln Lys Thr Phe Tyr Leu 20 25 30
Arg Asn Asn Gln Leu Val Ala Gly Tyr Leu Gln Gly Pro Asn Val Asn 35
40 45 Leu Glu Glu Lys Ile Asp Val Val Pro Ile Glu Pro His Ala Leu
Phe 50 55 60 Leu Gly Ile His Gly Gly Lys Met Cys Leu Ser Cys Val
Lys Ser Gly 65 70 75 80 Asp Glu Thr Arg Leu Gln Leu Glu Ala Val Asn
Ile Thr Asp Leu Ser 85 90 95 Glu Asn Arg Lys Gln Asp Lys Arg Phe
Ala Phe Ile Arg Ser Asp Ser 100 105 110 Gly Pro Thr Thr Ser Phe Glu
Ser Ala Ala Cys Pro Gly Trp Phe Leu 115 120 125 Cys Thr Ala Met Glu
Ala Asp Gln Pro Val Ser Leu Thr Asn Met Pro 130 135 140 Asp Glu Gly
Val Met Val Thr Lys Phe Tyr Phe Gln Glu Asp Glu 145 150 155
11212PRTHomo sapiens 11Met Asn Ser Phe Ser Thr Ser Ala Phe Gly Pro
Val Ala Phe Ser Leu 1 5 10 15 Gly Leu Leu Leu Val Leu Pro Ala Ala
Phe Pro Ala Pro Val Pro Pro 20 25 30 Gly Glu Asp Ser Lys Asp Val
Ala Ala Pro His Arg Gln Pro Leu Thr 35 40 45 Ser Ser Glu Arg Ile
Asp Lys Gln Ile Arg Tyr Ile Leu Asp Gly Ile 50 55 60 Ser Ala Leu
Arg Lys Glu Thr Cys Asn Lys Ser Asn Met Cys Glu Ser 65 70 75 80 Ser
Lys Glu Ala Leu Ala Glu Asn Asn Leu Asn Leu Pro Lys Met Ala 85 90
95 Glu Lys Asp Gly Cys Phe Gln Ser Gly Phe Asn Glu Glu Thr Cys Leu
100 105 110 Val Lys Ile Ile Thr Gly Leu Leu Glu Phe Glu Val Tyr Leu
Glu Tyr 115 120 125 Leu Gln Asn Arg Phe Glu Ser Ser Glu Glu Gln Ala
Arg Ala Val Gln 130 135 140 Met Ser Thr Lys Val Leu Ile Gln Phe Leu
Gln Lys Lys Ala Lys Asn 145 150 155 160 Leu Asp Ala Ile Thr Thr Pro
Asp Pro Thr Thr Asn Ala Ser Leu Leu 165 170 175 Thr Lys Leu Gln Ala
Gln Asn Gln Trp Leu Gln Asp Met Thr Thr His 180 185 190 Leu Ile Leu
Arg Ser Phe Lys Glu Phe Leu Gln Ser Ser Leu Arg Ala 195 200 205 Leu
Arg Gln Met 210 12468PRTHomo sapiens 12Met Leu Ala Val Gly Cys Ala
Leu Leu Ala Ala Leu Leu Ala Ala Pro 1 5 10 15 Gly Ala Ala Leu Ala
Pro Arg Arg Cys Pro Ala Gln Glu Val Ala Arg 20 25 30 Gly Val Leu
Thr Ser Leu Pro Gly Asp Ser Val Thr Leu Thr Cys Pro 35 40 45 Gly
Val Glu Pro Glu Pro Arg Thr Thr Val His Trp Val Leu Arg Lys 50 55
60 Pro Ala Ala Gly Ser His Pro Ser Arg Trp Ala Gly Met Gly Arg Arg
65 70 75 80 Leu Leu Leu Arg Ser Val Gln Leu His Asp Ser Gly Asn Tyr
Ser Cys 85 90 95 Tyr Arg Ala Gly Arg Pro Ala Gly Thr Val His Leu
Leu Val Asp Val 100 105 110 Pro Pro Glu Glu Pro Gln Leu Ser Cys Phe
Arg Lys Ser Pro Leu Ser 115 120 125 Asn Val Val Cys Glu Trp Gly Pro
Arg Ser Thr Pro Ser Leu Thr Thr 130 135 140 Lys Ala Val Leu Leu Val
Arg Lys Phe Gln Asn Ser Pro Ala Glu Asp 145 150 155 160 Phe Gln Glu
Pro Cys Gln Tyr Ser Gln Glu Ser Gln Lys Phe Ser Cys 165 170 175 Gln
Leu Ala Val Pro Glu Gly Asp Ser Ser Phe Tyr Ile Val Ser Met 180 185
190 Cys Val Ala Ser Ser Val Gly Ser Lys Phe Ser Lys Thr Gln Thr Phe
195 200 205 Gln Gly Cys Gly Ile Leu Gln Pro Asp Pro Pro Ala Asn Ile
Thr Val 210 215 220 Thr Ala Val Ala Arg Asn Pro Arg Trp Leu Ser Val
Thr Trp Gln Asp 225 230 235 240 Pro His Ser Trp Asn Ser Ser Phe Tyr
Arg Leu Arg Phe Glu Leu Arg 245 250 255 Tyr Arg Ala Glu Arg Ser Lys
Thr Phe Thr Thr Trp Met Val Lys Asp 260 265 270 Leu Gln His His Cys
Val Ile His Asp Ala Trp Ser Gly Leu Arg His 275 280 285 Val Val Gln
Leu Arg Ala Gln Glu Glu Phe Gly Gln Gly Glu Trp Ser 290 295 300 Glu
Trp Ser Pro Glu Ala Met Gly Thr Pro Trp Thr Glu Ser Arg Ser 305 310
315 320 Pro Pro Ala Glu Asn Glu Val Ser Thr Pro Met Gln Ala Leu Thr
Thr 325 330 335 Asn Lys Asp Asp Asp Asn Ile Leu Phe Arg Asp Ser Ala
Asn Ala Thr 340 345 350 Ser Leu Pro Val Gln Asp Ser Ser Ser Val Pro
Leu Pro Thr Phe Leu 355 360 365 Val Ala Gly Gly Ser Leu Ala Phe Gly
Thr Leu Leu Cys Ile Ala Ile 370 375 380 Val Leu Arg Phe Lys Lys Thr
Trp Lys Leu Arg Ala Leu Lys Glu Gly 385 390 395 400 Lys Thr Ser Met
His Pro Pro Tyr Ser Leu Gly Gln Leu Val Pro Glu 405 410 415 Arg Pro
Arg Pro Thr Pro Val Leu Val Pro Leu Ile Ser Pro Pro Val 420 425 430
Ser Pro Ser Ser Leu Gly Ser Asp Asn Thr Ser Ser His Asn Arg Pro 435
440 445 Asp Ala Arg Asp Pro Arg Ser Pro Tyr Asp Ile Ser Asn Thr Asp
Tyr 450 455 460 Phe Phe Pro Arg 465 1399PRTHomo sapiens 13Met Thr
Ser Lys Leu Ala Val Ala Leu Leu Ala Ala Phe Leu Ile Ser 1 5 10 15
Ala Ala Leu Cys Glu Gly Ala Val Leu Pro Arg Ser Ala Lys Glu Leu 20
25 30 Arg Cys Gln Cys Ile Lys Thr Tyr Ser Lys Pro Phe His Pro Lys
Phe 35 40 45 Ile Lys Glu Leu Arg Val Ile Glu Ser Gly Pro His Cys
Ala Asn Thr 50 55 60 Glu Ile Ile Val Lys Leu Ser Asp Gly Arg Glu
Leu Cys Leu Asp Pro 65 70 75 80 Lys Glu Asn Trp Val Gln Arg Val Val
Glu Lys Phe Leu Lys Arg Ala 85 90 95 Glu Asn Ser 14167PRTHomo
sapiens 14Met His Trp Gly Thr Leu Cys Gly Phe Leu Trp Leu Trp Pro
Tyr Leu 1 5 10 15 Phe Tyr Val Gln Ala Val Pro Ile Gln Lys Val Gln
Asp Asp Thr Lys 20 25 30 Thr Leu Ile Lys Thr Ile Val Thr Arg Ile
Asn Asp Ile Ser His Thr 35 40 45 Gln Ser Val Ser Ser Lys Gln Lys
Val Thr Gly Leu Asp Phe Ile Pro 50 55 60 Gly Leu His Pro Ile Leu
Thr Leu Ser Lys Met Asp Gln Thr Leu Ala 65 70 75 80 Val Tyr Gln Gln
Ile Leu Thr Ser Met Pro Ser Arg Asn Val Ile Gln 85 90 95 Ile Ser
Asn Asp Leu Glu Asn Leu Arg Asp Leu Leu His Val Leu Ala 100 105 110
Phe Ser Lys Ser Cys His Leu Pro Trp Ala Ser Gly Leu Glu Thr Leu 115
120 125 Asp Ser Leu Gly Gly Val Leu Glu Ala Ser Gly Tyr Ser Thr Glu
Val 130 135 140 Val Ala Leu Ser Arg Leu Gln Gly Ser Leu Gln Asp Met
Leu Trp Gln 145 150 155 160 Leu Asp Leu Ser Pro Gly Cys 165
15469PRTHomo sapiens 15Met His Ser Phe Pro Pro Leu Leu Leu Leu Leu
Phe Trp Gly Val Val 1 5 10 15 Ser His Ser Phe Pro Ala Thr Leu Glu
Thr Gln Glu Gln Asp Val Asp 20 25 30 Leu Val Gln Lys Tyr Leu Glu
Lys Tyr Tyr Asn Leu Lys Asn Asp Gly 35 40 45 Arg Gln Val Glu Lys
Arg Arg Asn Ser Gly Pro Val Val Glu Lys Leu 50 55 60 Lys Gln Met
Gln Glu Phe Phe Gly Leu Lys Val Thr Gly Lys Pro Asp 65 70 75 80 Ala
Glu Thr Leu Lys Val Met Lys Gln Pro Arg Cys Gly Val Pro Asp 85 90
95 Val Ala Gln Phe Val Leu Thr Glu Gly Asn Pro Arg Trp Glu Gln Thr
100 105 110 His Leu Thr Tyr Arg Ile Glu Asn Tyr Thr Pro Asp Leu Pro
Arg Ala 115 120 125 Asp Val Asp His Ala Ile Glu Lys Ala Phe Gln Leu
Trp Ser Asn Val 130 135 140 Thr Pro Leu Thr Phe Thr Lys Val Ser Glu
Gly Gln Ala Asp Ile Met 145 150 155 160 Ile Ser Phe Val Arg Gly Asp
His Arg Asp Asn Ser Pro Phe Asp Gly 165 170 175 Pro Gly Gly Asn Leu
Ala His Ala Phe Gln Pro Gly Pro Gly Ile Gly
180 185 190 Gly Asp Ala His Phe Asp Glu Asp Glu Arg Trp Thr Asn Asn
Phe Arg 195 200 205 Glu Tyr Asn Leu His Arg Val Ala Ala His Glu Leu
Gly His Ser Leu 210 215 220 Gly Leu Ser His Ser Thr Asp Ile Gly Ala
Leu Met Tyr Pro Ser Tyr 225 230 235 240 Thr Phe Ser Gly Asp Val Gln
Leu Ala Gln Asp Asp Ile Asp Gly Ile 245 250 255 Gln Ala Ile Tyr Gly
Arg Ser Gln Asn Pro Val Gln Pro Ile Gly Pro 260 265 270 Gln Thr Pro
Lys Ala Cys Asp Ser Lys Leu Thr Phe Asp Ala Ile Thr 275 280 285 Thr
Ile Arg Gly Glu Val Met Phe Phe Lys Asp Arg Phe Tyr Met Arg 290 295
300 Thr Asn Pro Phe Tyr Pro Glu Val Glu Leu Asn Phe Ile Ser Val Phe
305 310 315 320 Trp Pro Gln Leu Pro Asn Gly Leu Glu Ala Ala Tyr Glu
Phe Ala Asp 325 330 335 Arg Asp Glu Val Arg Phe Phe Lys Gly Asn Lys
Tyr Trp Ala Val Gln 340 345 350 Gly Gln Asn Val Leu His Gly Tyr Pro
Lys Asp Ile Tyr Ser Ser Phe 355 360 365 Gly Phe Pro Arg Thr Val Lys
His Ile Asp Ala Ala Leu Ser Glu Glu 370 375 380 Asn Thr Gly Lys Thr
Tyr Phe Phe Val Ala Asn Lys Tyr Trp Arg Tyr 385 390 395 400 Asp Glu
Tyr Lys Arg Ser Met Asp Pro Gly Tyr Pro Lys Met Ile Ala 405 410 415
His Asp Phe Pro Gly Ile Gly His Lys Val Asp Ala Val Phe Met Lys 420
425 430 Asp Gly Phe Phe Tyr Phe Phe His Gly Thr Arg Gln Tyr Lys Phe
Asp 435 440 445 Pro Lys Thr Lys Arg Ile Leu Thr Leu Gln Lys Ala Asn
Ser Trp Phe 450 455 460 Asn Cys Arg Lys Asn 465 16477PRTHomo
sapiens 16Met Lys Ser Leu Pro Ile Leu Leu Leu Leu Cys Val Ala Val
Cys Ser 1 5 10 15 Ala Tyr Pro Leu Asp Gly Ala Ala Arg Gly Glu Asp
Thr Ser Met Asn 20 25 30 Leu Val Gln Lys Tyr Leu Glu Asn Tyr Tyr
Asp Leu Lys Lys Asp Val 35 40 45 Lys Gln Phe Val Arg Arg Lys Asp
Ser Gly Pro Val Val Lys Lys Ile 50 55 60 Arg Glu Met Gln Lys Phe
Leu Gly Leu Glu Val Thr Gly Lys Leu Asp 65 70 75 80 Ser Asp Thr Leu
Glu Val Met Arg Lys Pro Arg Cys Gly Val Pro Asp 85 90 95 Val Gly
His Phe Arg Thr Phe Pro Gly Ile Pro Lys Trp Arg Lys Thr 100 105 110
His Leu Thr Tyr Arg Ile Val Asn Tyr Thr Pro Asp Leu Pro Lys Asp 115
120 125 Ala Val Asp Ser Ala Val Glu Lys Ala Leu Lys Val Trp Glu Glu
Val 130 135 140 Thr Pro Leu Thr Phe Ser Arg Leu Tyr Glu Gly Glu Ala
Asp Ile Met 145 150 155 160 Ile Ser Phe Ala Val Arg Glu His Gly Asp
Phe Tyr Pro Phe Asp Gly 165 170 175 Pro Gly Asn Val Leu Ala His Ala
Tyr Ala Pro Gly Pro Gly Ile Asn 180 185 190 Gly Asp Ala His Phe Asp
Asp Asp Glu Gln Trp Thr Lys Asp Thr Thr 195 200 205 Gly Thr Asn Leu
Phe Leu Val Ala Ala His Glu Ile Gly His Ser Leu 210 215 220 Gly Leu
Phe His Ser Ala Asn Thr Glu Ala Leu Met Tyr Pro Leu Tyr 225 230 235
240 His Ser Leu Thr Asp Leu Thr Arg Phe Arg Leu Ser Gln Asp Asp Ile
245 250 255 Asn Gly Ile Gln Ser Leu Tyr Gly Pro Pro Pro Asp Ser Pro
Glu Thr 260 265 270 Pro Leu Val Pro Thr Glu Pro Val Pro Pro Glu Pro
Gly Thr Pro Ala 275 280 285 Asn Cys Asp Pro Ala Leu Ser Phe Asp Ala
Val Ser Thr Leu Arg Gly 290 295 300 Glu Ile Leu Ile Phe Lys Asp Arg
His Phe Trp Arg Lys Ser Leu Arg 305 310 315 320 Lys Leu Glu Pro Glu
Leu His Leu Ile Ser Ser Phe Trp Pro Ser Leu 325 330 335 Pro Ser Gly
Val Asp Ala Ala Tyr Glu Val Thr Ser Lys Asp Leu Val 340 345 350 Phe
Ile Phe Lys Gly Asn Gln Phe Trp Ala Ile Arg Gly Asn Glu Val 355 360
365 Arg Ala Gly Tyr Pro Arg Gly Ile His Thr Leu Gly Phe Pro Pro Thr
370 375 380 Val Arg Lys Ile Asp Ala Ala Ile Ser Asp Lys Glu Lys Asn
Lys Thr 385 390 395 400 Tyr Phe Phe Val Glu Asp Lys Tyr Trp Arg Phe
Asp Glu Lys Arg Asn 405 410 415 Ser Met Glu Pro Gly Phe Pro Lys Gln
Ile Ala Glu Asp Phe Pro Gly 420 425 430 Ile Asp Ser Lys Ile Asp Ala
Val Phe Glu Glu Phe Gly Phe Phe Tyr 435 440 445 Phe Phe Thr Gly Ser
Ser Gln Leu Glu Phe Asp Pro Asn Ala Lys Lys 450 455 460 Val Thr His
Thr Leu Lys Ser Asn Ser Trp Leu Asn Cys 465 470 475 17108PRTHomo
sapiens 17Met Lys Ala Leu Cys Leu Leu Leu Leu Pro Val Leu Gly Leu
Leu Val 1 5 10 15 Ser Ser Lys Thr Leu Cys Ser Met Glu Glu Ala Ile
Asn Glu Arg Ile 20 25 30 Gln Glu Val Ala Gly Ser Leu Ile Phe Arg
Ala Ile Ser Ser Ile Gly 35 40 45 Leu Glu Cys Gln Ser Val Thr Ser
Arg Gly Asp Leu Ala Thr Cys Pro 50 55 60 Arg Gly Phe Ala Val Thr
Gly Cys Thr Cys Gly Ser Ala Cys Gly Ser 65 70 75 80 Trp Asp Val Arg
Ala Glu Thr Thr Cys His Cys Gln Cys Ala Gly Met 85 90 95 Asp Trp
Thr Gly Ala Arg Cys Cys Arg Val Gln Pro 100 105 1893PRTHomo sapiens
18Met Leu Thr Glu Leu Glu Lys Ala Leu Asn Ser Ile Ile Asp Val Tyr 1
5 10 15 His Lys Tyr Ser Leu Ile Lys Gly Asn Phe His Ala Val Tyr Arg
Asp 20 25 30 Asp Leu Lys Lys Leu Leu Glu Thr Glu Cys Pro Gln Tyr
Ile Arg Lys 35 40 45 Lys Gly Ala Asp Val Trp Phe Lys Glu Leu Asp
Ile Asn Thr Asp Gly 50 55 60 Ala Val Asn Phe Gln Glu Phe Leu Ile
Leu Val Ile Lys Met Gly Val 65 70 75 80 Ala Ala His Lys Lys Ser His
Glu Glu Ser His Lys Glu 85 90 19114PRTHomo sapiens 19Met Thr Cys
Lys Met Ser Gln Leu Glu Arg Asn Ile Glu Thr Ile Ile 1 5 10 15 Asn
Thr Phe His Gln Tyr Ser Val Lys Leu Gly His Pro Asp Thr Leu 20 25
30 Asn Gln Gly Glu Phe Lys Glu Leu Val Arg Lys Asp Leu Gln Asn Phe
35 40 45 Leu Lys Lys Glu Asn Lys Asn Glu Lys Val Ile Glu His Ile
Met Glu 50 55 60 Asp Leu Asp Thr Asn Ala Asp Lys Gln Leu Ser Phe
Glu Glu Phe Ile 65 70 75 80 Met Leu Met Ala Arg Leu Thr Trp Ala Ser
His Glu Lys Met His Glu 85 90 95 Gly Asp Glu Gly Pro Gly His His
His Lys Pro Gly Leu Gly Glu Gly 100 105 110 Thr Pro 20122PRTHomo
sapiens 20Met Lys Leu Leu Thr Gly Leu Val Phe Cys Ser Leu Val Leu
Gly Val 1 5 10 15 Ser Ser Arg Ser Phe Phe Ser Phe Leu Gly Glu Ala
Phe Asp Gly Ala 20 25 30 Arg Asp Met Trp Arg Ala Tyr Ser Asp Met
Arg Glu Ala Asn Tyr Ile 35 40 45 Gly Ser Asp Lys Tyr Phe His Ala
Arg Gly Asn Tyr Asp Ala Ala Lys 50 55 60 Arg Gly Pro Gly Gly Ala
Trp Ala Ala Glu Val Ile Ser Asp Ala Arg 65 70 75 80 Glu Asn Ile Gln
Arg Phe Phe Gly His Gly Ala Glu Asp Ser Leu Ala 85 90 95 Asp Gln
Ala Ala Asn Glu Trp Gly Arg Ser Gly Lys Asp Pro Asn His 100 105 110
Phe Arg Pro Ala Gly Leu Pro Glu Lys Tyr 115 120 21455PRTHomo
sapiens 21Met Gly Leu Ser Thr Val Pro Asp Leu Leu Leu Pro Leu Val
Leu Leu 1 5 10 15 Glu Leu Leu Val Gly Ile Tyr Pro Ser Gly Val Ile
Gly Leu Val Pro 20 25 30 His Leu Gly Asp Arg Glu Lys Arg Asp Ser
Val Cys Pro Gln Gly Lys 35 40 45 Tyr Ile His Pro Gln Asn Asn Ser
Ile Cys Cys Thr Lys Cys His Lys 50 55 60 Gly Thr Tyr Leu Tyr Asn
Asp Cys Pro Gly Pro Gly Gln Asp Thr Asp 65 70 75 80 Cys Arg Glu Cys
Glu Ser Gly Ser Phe Thr Ala Ser Glu Asn His Leu 85 90 95 Arg His
Cys Leu Ser Cys Ser Lys Cys Arg Lys Glu Met Gly Gln Val 100 105 110
Glu Ile Ser Ser Cys Thr Val Asp Arg Asp Thr Val Cys Gly Cys Arg 115
120 125 Lys Asn Gln Tyr Arg His Tyr Trp Ser Glu Asn Leu Phe Gln Cys
Phe 130 135 140 Asn Cys Ser Leu Cys Leu Asn Gly Thr Val His Leu Ser
Cys Gln Glu 145 150 155 160 Lys Gln Asn Thr Val Cys Thr Cys His Ala
Gly Phe Phe Leu Arg Glu 165 170 175 Asn Glu Cys Val Ser Cys Ser Asn
Cys Lys Lys Ser Leu Glu Cys Thr 180 185 190 Lys Leu Cys Leu Pro Gln
Ile Glu Asn Val Lys Gly Thr Glu Asp Ser 195 200 205 Gly Thr Thr Val
Leu Leu Pro Leu Val Ile Phe Phe Gly Leu Cys Leu 210 215 220 Leu Ser
Leu Leu Phe Ile Gly Leu Met Tyr Arg Tyr Gln Arg Trp Lys 225 230 235
240 Ser Lys Leu Tyr Ser Ile Val Cys Gly Lys Ser Thr Pro Glu Lys Glu
245 250 255 Gly Glu Leu Glu Gly Thr Thr Thr Lys Pro Leu Ala Pro Asn
Pro Ser 260 265 270 Phe Ser Pro Thr Pro Gly Phe Thr Pro Thr Leu Gly
Phe Ser Pro Val 275 280 285 Pro Ser Ser Thr Phe Thr Ser Ser Ser Thr
Tyr Thr Pro Gly Asp Cys 290 295 300 Pro Asn Phe Ala Ala Pro Arg Arg
Glu Val Ala Pro Pro Tyr Gln Gly 305 310 315 320 Ala Asp Pro Ile Leu
Ala Thr Ala Leu Ala Ser Asp Pro Ile Pro Asn 325 330 335 Pro Leu Gln
Lys Trp Glu Asp Ser Ala His Lys Pro Gln Ser Leu Asp 340 345 350 Thr
Asp Asp Pro Ala Thr Leu Tyr Ala Val Val Glu Asn Val Pro Pro 355 360
365 Leu Arg Trp Lys Glu Phe Val Arg Arg Leu Gly Leu Ser Asp His Glu
370 375 380 Ile Asp Arg Leu Glu Leu Gln Asn Gly Arg Cys Leu Arg Glu
Ala Gln 385 390 395 400 Tyr Ser Met Leu Ala Thr Trp Arg Arg Arg Thr
Pro Arg Arg Glu Ala 405 410 415 Thr Leu Glu Leu Leu Gly Arg Val Leu
Arg Asp Met Asp Leu Leu Gly 420 425 430 Cys Leu Glu Asp Ile Glu Glu
Ala Leu Cys Gly Pro Ala Ala Leu Pro 435 440 445 Pro Ala Pro Ser Leu
Leu Arg 450 455 22266PRTHomo sapiens 22Met Asp Asp Ser Thr Glu Arg
Glu Gln Ser Arg Leu Thr Ser Cys Leu 1 5 10 15 Lys Lys Arg Glu Glu
Met Lys Leu Lys Glu Cys Val Ser Ile Leu Pro 20 25 30 Arg Lys Glu
Ser Pro Ser Val Arg Ser Ser Lys Asp Gly Lys Leu Leu 35 40 45 Ala
Ala Thr Leu Leu Leu Ala Leu Leu Ser Cys Cys Leu Thr Val Val 50 55
60 Ser Phe Tyr Gln Val Ala Ala Leu Gln Gly Asp Leu Ala Ser Leu Arg
65 70 75 80 Ala Glu Leu Gln Gly His His Ala Glu Lys Leu Pro Ala Gly
Ala Gly 85 90 95 Ala Pro Lys Ala Gly Leu Glu Glu Ala Pro Ala Val
Thr Ala Gly Leu 100 105 110 Lys Ile Phe Glu Pro Pro Ala Pro Gly Glu
Gly Asn Ser Ser Gln Asn 115 120 125 Ser Arg Asn Lys Arg Ala Val Gln
Gly Pro Glu Glu Thr Gly Ser Tyr 130 135 140 Thr Phe Val Pro Trp Leu
Leu Ser Phe Lys Arg Gly Ser Ala Leu Glu 145 150 155 160 Glu Lys Glu
Asn Lys Ile Leu Val Lys Glu Thr Gly Tyr Phe Phe Ile 165 170 175 Tyr
Gly Gln Val Leu Tyr Thr Asp Lys Thr Tyr Ala Met Gly His Leu 180 185
190 Ile Gln Arg Lys Lys Val His Val Phe Gly Asp Glu Leu Ser Leu Val
195 200 205 Thr Leu Phe Arg Cys Ile Gln Asn Met Pro Glu Thr Leu Pro
Asn Asn 210 215 220 Ser Cys Tyr Ser Ala Gly Ile Ala Lys Leu Glu Glu
Gly Asp Glu Leu 225 230 235 240 Gln Leu Ala Ile Pro Arg Glu Asn Ala
Gln Ile Ser Leu Asp Gly Asp 245 250 255 Val Thr Phe Phe Gly Ala Leu
Lys Leu Leu 260 265 23285PRTHomo sapiens 23Met Asp Asp Ser Thr Glu
Arg Glu Gln Ser Arg Leu Thr Ser Cys Leu 1 5 10 15 Lys Lys Arg Glu
Glu Met Lys Leu Lys Glu Cys Val Ser Ile Leu Pro 20 25 30 Arg Lys
Glu Ser Pro Ser Val Arg Ser Ser Lys Asp Gly Lys Leu Leu 35 40 45
Ala Ala Thr Leu Leu Leu Ala Leu Leu Ser Cys Cys Leu Thr Val Val 50
55 60 Ser Phe Tyr Gln Val Ala Ala Leu Gln Gly Asp Leu Ala Ser Leu
Arg 65 70 75 80 Ala Glu Leu Gln Gly His His Ala Glu Lys Leu Pro Ala
Gly Ala Gly 85 90 95 Ala Pro Lys Ala Gly Leu Glu Glu Ala Pro Ala
Val Thr Ala Gly Leu 100 105 110 Lys Ile Phe Glu Pro Pro Ala Pro Gly
Glu Gly Asn Ser Ser Gln Asn 115 120 125 Ser Arg Asn Lys Arg Ala Val
Gln Gly Pro Glu Glu Thr Val Thr Gln 130 135 140 Asp Cys Leu Gln Leu
Ile Ala Asp Ser Glu Thr Pro Thr Ile Gln Lys 145 150 155 160 Gly Ser
Tyr Thr Phe Val Pro Trp Leu Leu Ser Phe Lys Arg Gly Ser 165 170 175
Ala Leu Glu Glu Lys Glu Asn Lys Ile Leu Val Lys Glu Thr Gly Tyr 180
185 190 Phe Phe Ile Tyr Gly Gln Val Leu Tyr Thr Asp Lys Thr Tyr Ala
Met 195 200 205 Gly His Leu Ile Gln Arg Lys Lys Val His Val Phe Gly
Asp Glu Leu 210 215 220 Ser Leu Val Thr Leu Phe Arg Cys Ile Gln Asn
Met Pro Glu Thr Leu 225 230 235 240 Pro Asn Asn Ser Cys Tyr Ser Ala
Gly Ile Ala Lys Leu Glu Glu Gly 245 250 255 Asp Glu Leu Gln Leu Ala
Ile Pro Arg Glu Asn Ala Gln Ile Ser Leu 260 265 270 Asp Gly Asp Val
Thr Phe Phe Gly Ala Leu Lys Leu Leu 275 280 285 24739PRTHomo
sapiens 24Met Pro Gly Lys Met Val Val Ile Leu Gly Ala Ser Asn Ile
Leu Trp 1 5 10 15 Ile Met Phe Ala Ala Ser Gln Ala Phe Lys Ile Glu
Thr Thr Pro Glu 20 25 30 Ser Arg Tyr Leu Ala Gln Ile Gly Asp Ser
Val Ser Leu Thr Cys Ser 35 40 45 Thr Thr Gly Cys Glu Ser Pro Phe
Phe Ser Trp Arg Thr Gln Ile Asp 50 55 60 Ser Pro Leu Asn Gly Lys
Val Thr Asn Glu Gly Thr Thr Ser Thr Leu 65
70 75 80 Thr Met Asn Pro Val Ser Phe Gly Asn Glu His Ser Tyr Leu
Cys Thr 85 90 95 Ala Thr Cys Glu Ser Arg Lys Leu Glu Lys Gly Ile
Gln Val Glu Ile 100 105 110 Tyr Ser Phe Pro Lys Asp Pro Glu Ile His
Leu Ser Gly Pro Leu Glu 115 120 125 Ala Gly Lys Pro Ile Thr Val Lys
Cys Ser Val Ala Asp Val Tyr Pro 130 135 140 Phe Asp Arg Leu Glu Ile
Asp Leu Leu Lys Gly Asp His Leu Met Lys 145 150 155 160 Ser Gln Glu
Phe Leu Glu Asp Ala Asp Arg Lys Ser Leu Glu Thr Lys 165 170 175 Ser
Leu Glu Val Thr Phe Thr Pro Val Ile Glu Asp Ile Gly Lys Val 180 185
190 Leu Val Cys Arg Ala Lys Leu His Ile Asp Glu Met Asp Ser Val Pro
195 200 205 Thr Val Arg Gln Ala Val Lys Glu Leu Gln Val Tyr Ile Ser
Pro Lys 210 215 220 Asn Thr Val Ile Ser Val Asn Pro Ser Thr Lys Leu
Gln Glu Gly Gly 225 230 235 240 Ser Val Thr Met Thr Cys Ser Ser Glu
Gly Leu Pro Ala Pro Glu Ile 245 250 255 Phe Trp Ser Lys Lys Leu Asp
Asn Gly Asn Leu Gln His Leu Ser Gly 260 265 270 Asn Ala Thr Leu Thr
Leu Ile Ala Met Arg Met Glu Asp Ser Gly Ile 275 280 285 Tyr Val Cys
Glu Gly Val Asn Leu Ile Gly Lys Asn Arg Lys Glu Val 290 295 300 Glu
Leu Ile Val Gln Glu Lys Pro Phe Thr Val Glu Ile Ser Pro Gly 305 310
315 320 Pro Arg Ile Ala Ala Gln Ile Gly Asp Ser Val Met Leu Thr Cys
Ser 325 330 335 Val Met Gly Cys Glu Ser Pro Ser Phe Ser Trp Arg Thr
Gln Ile Asp 340 345 350 Ser Pro Leu Ser Gly Lys Val Arg Ser Glu Gly
Thr Asn Ser Thr Leu 355 360 365 Thr Leu Ser Pro Val Ser Phe Glu Asn
Glu His Ser Tyr Leu Cys Thr 370 375 380 Val Thr Cys Gly His Lys Lys
Leu Glu Lys Gly Ile Gln Val Glu Leu 385 390 395 400 Tyr Ser Phe Pro
Arg Asp Pro Glu Ile Glu Met Ser Gly Gly Leu Val 405 410 415 Asn Gly
Ser Ser Val Thr Val Ser Cys Lys Val Pro Ser Val Tyr Pro 420 425 430
Leu Asp Arg Leu Glu Ile Glu Leu Leu Lys Gly Glu Thr Ile Leu Glu 435
440 445 Asn Ile Glu Phe Leu Glu Asp Thr Asp Met Lys Ser Leu Glu Asn
Lys 450 455 460 Ser Leu Glu Met Thr Phe Ile Pro Thr Ile Glu Asp Thr
Gly Lys Ala 465 470 475 480 Leu Val Cys Gln Ala Lys Leu His Ile Asp
Asp Met Glu Phe Glu Pro 485 490 495 Lys Gln Arg Gln Ser Thr Gln Thr
Leu Tyr Val Asn Val Ala Pro Arg 500 505 510 Asp Thr Thr Val Leu Val
Ser Pro Ser Ser Ile Leu Glu Glu Gly Ser 515 520 525 Ser Val Asn Met
Thr Cys Leu Ser Gln Gly Phe Pro Ala Pro Lys Ile 530 535 540 Leu Trp
Ser Arg Gln Leu Pro Asn Gly Glu Leu Gln Pro Leu Ser Glu 545 550 555
560 Asn Ala Thr Leu Thr Leu Ile Ser Thr Lys Met Glu Asp Ser Gly Val
565 570 575 Tyr Leu Cys Glu Gly Ile Asn Gln Ala Gly Arg Ser Arg Lys
Glu Val 580 585 590 Glu Leu Ile Ile Gln Val Thr Pro Lys Asp Ile Lys
Leu Thr Ala Phe 595 600 605 Pro Ser Glu Ser Val Lys Glu Gly Asp Thr
Val Ile Ile Ser Cys Thr 610 615 620 Cys Gly Asn Val Pro Glu Thr Trp
Ile Ile Leu Lys Lys Lys Ala Glu 625 630 635 640 Thr Gly Asp Thr Val
Leu Lys Ser Ile Asp Gly Ala Tyr Thr Ile Arg 645 650 655 Lys Ala Gln
Leu Lys Asp Ala Gly Val Tyr Glu Cys Glu Ser Lys Asn 660 665 670 Lys
Val Gly Ser Gln Leu Arg Ser Leu Thr Leu Asp Val Gln Gly Arg 675 680
685 Glu Asn Asn Lys Asp Tyr Phe Ser Pro Glu Leu Leu Val Leu Tyr Phe
690 695 700 Ala Ser Ser Leu Ile Ile Pro Ala Ile Gly Met Ile Ile Tyr
Phe Ala 705 710 715 720 Arg Lys Ala Asn Met Lys Gly Ser Tyr Ser Leu
Val Glu Ala Gln Lys 725 730 735 Ser Lys Val 25371PRTHomo sapiens
25Met Thr Asp Arg Gln Thr Asp Thr Ala Pro Ser Pro Ser Tyr His Leu 1
5 10 15 Leu Pro Gly Arg Arg Arg Thr Val Asp Ala Ala Ala Ser Arg Gly
Gln 20 25 30 Gly Pro Glu Pro Ala Pro Gly Gly Gly Val Glu Gly Val
Gly Ala Arg 35 40 45 Gly Val Ala Leu Lys Leu Phe Val Gln Leu Leu
Gly Cys Ser Arg Phe 50 55 60 Gly Gly Ala Val Val Arg Ala Gly Glu
Ala Glu Pro Ser Gly Ala Ala 65 70 75 80 Arg Ser Ala Ser Ser Gly Arg
Glu Glu Pro Gln Pro Glu Glu Gly Glu 85 90 95 Glu Glu Glu Glu Lys
Glu Glu Glu Arg Gly Pro Gln Trp Arg Leu Gly 100 105 110 Ala Arg Lys
Pro Gly Ser Trp Thr Gly Glu Ala Ala Val Cys Ala Asp 115 120 125 Ser
Ala Pro Ala Ala Arg Ala Pro Gln Ala Leu Ala Arg Ala Ser Gly 130 135
140 Arg Gly Gly Arg Val Ala Arg Arg Gly Ala Glu Glu Ser Gly Pro Pro
145 150 155 160 His Ser Pro Ser Arg Arg Gly Ser Ala Ser Arg Ala Gly
Pro Gly Arg 165 170 175 Ala Ser Glu Thr Met Asn Phe Leu Leu Ser Trp
Val His Trp Ser Leu 180 185 190 Ala Leu Leu Leu Tyr Leu His His Ala
Lys Trp Ser Gln Ala Ala Pro 195 200 205 Met Ala Glu Gly Gly Gly Gln
Asn His His Glu Val Val Lys Phe Met 210 215 220 Asp Val Tyr Gln Arg
Ser Tyr Cys His Pro Ile Glu Thr Leu Val Asp 225 230 235 240 Ile Phe
Gln Glu Tyr Pro Asp Glu Ile Glu Tyr Ile Phe Lys Pro Ser 245 250 255
Cys Val Pro Leu Met Arg Cys Gly Gly Cys Cys Asn Asp Glu Gly Leu 260
265 270 Glu Cys Val Pro Thr Glu Glu Ser Asn Ile Thr Met Gln Ile Met
Arg 275 280 285 Ile Lys Pro His Gln Gly Gln His Ile Gly Glu Met Ser
Phe Leu Gln 290 295 300 His Asn Lys Cys Glu Cys Arg Pro Lys Lys Asp
Arg Ala Arg Gln Glu 305 310 315 320 Asn Pro Cys Gly Pro Cys Ser Glu
Arg Arg Lys His Leu Phe Val Gln 325 330 335 Asp Pro Gln Thr Cys Lys
Cys Ser Cys Lys Asn Thr Asp Ser Arg Cys 340 345 350 Lys Ala Arg Gln
Leu Glu Leu Asn Glu Arg Thr Cys Arg Cys Asp Lys 355 360 365 Pro Arg
Arg 370
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