U.S. patent application number 15/531747 was filed with the patent office on 2017-09-21 for marker combinations for diagnosing infections and methods of use thereof.
The applicant listed for this patent is MeMed Diagnostics Ltd.. Invention is credited to Olga BOICO, Assaf COHEN-DOTAN, Eran EDEN, Gali KRONENFELD, Roy NAVON, Kfir OVED.
Application Number | 20170269081 15/531747 |
Document ID | / |
Family ID | 56106839 |
Filed Date | 2017-09-21 |
United States Patent
Application |
20170269081 |
Kind Code |
A1 |
OVED; Kfir ; et al. |
September 21, 2017 |
MARKER COMBINATIONS FOR DIAGNOSING INFECTIONS AND METHODS OF USE
THEREOF
Abstract
A method of determining an infection type in a subject is
disclosed. The method comprises measuring the concentration of a
first determinant selected from the group consisting of the
determinants which are set forth in Table 1 and a second
determinant selected from the group of the determinants which are
set forth in Table 2 in a subject derived sample, wherein the
concentration is indicative of the infection type.
Inventors: |
OVED; Kfir; (Hof HaCarmel,
IL) ; EDEN; Eran; (Haifa, IL) ; KRONENFELD;
Gali; (Haifa, IL) ; BOICO; Olga; (Haifa,
IL) ; NAVON; Roy; (Tel-Aviv, IL) ;
COHEN-DOTAN; Assaf; (Natania, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MeMed Diagnostics Ltd. |
Tirat Ha Carmel |
|
IL |
|
|
Family ID: |
56106839 |
Appl. No.: |
15/531747 |
Filed: |
December 10, 2015 |
PCT Filed: |
December 10, 2015 |
PCT NO: |
PCT/IL2015/051201 |
371 Date: |
May 31, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62136725 |
Mar 23, 2015 |
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62090606 |
Dec 11, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 2800/60 20130101;
G01N 33/56966 20130101; G01N 2800/26 20130101; G01N 33/56983
20130101; G01N 33/56911 20130101 |
International
Class: |
G01N 33/569 20060101
G01N033/569 |
Claims
1. A method of determining an infection type in a subject
comprising measuring the concentration of a first determinant
selected from the group consisting of the determinants which are
set forth in Table 1 and a second determinant selected from the
group of the determinants which are set forth in Table 2 in a
sample derived from the subject, wherein said concentration is
indicative of the infection type.
2-3. (canceled)
4. The method of claim 1, wherein said first determinant is NGAL,
MMP8 or neopterin.
5. The method of claim 1, wherein said first determinant is a
polypeptide.
6. A method of determining an infection type in a subject
comprising measuring the concentration of at least two determinants
which are set forth in Table 1 in a sample derived from the
subject, wherein said concentration is indicative of the infection
type.
7-8. (canceled)
9. The method of claim 6, wherein at least one of said at least two
determinants is NGAL, MMP8 or neopterin.
10-16. (canceled)
17. The method of claim 1, wherein said second determinant is
selected from the group consisting of TRAIL, CRP and IP-10.
18. The method of claim 17, wherein said determinants comprise: (i)
CRP and NGAL; (ii) CRP and MMP8; (iii) CRP and neopterin; (iv)
TRAIL and NGAL; (v) TRAIL and MMP8; (vi) TRAIL and neopterin; (vii)
IP10 and NGAL; (viii) IP10 and MMP8; or (ix) IP10 and neopterin; or
(x) Neopterin and PCT; or (xi) NGAL and PCT.
19. The method of claim 6, wherein said at least two determinants
are: (i) NGAL and MMP8; (ii) NGAL and neopterin; or (iii) neopterin
and MMP8.
20. The method of claim 1, wherein said second determinant is
TRAIL.
21. The method of claim 20, wherein when the concentration of said
TRAIL is higher than a pre-determined threshold value, a bacterial
infection is ruled out for the subject.
22. The method of claim 20, wherein when the concentration of said
TRAIL is higher than a pre-determined threshold value, a viral
infection is ruled in for the subject.
23. The method of claim 20, further comprising measuring the
concentration of CRP and/or IP-10.
24. A method of determining an infection type in a child,
comprising measuring the concentration of the determinant neopterin
and/or the determinant NGAL in a sample derived from the child,
wherein said concentration is indicative of the infection type.
25. The method of claim 24, further comprising determining the
concentration of at least one of the determinants set forth in
Table 2.
26. A method of determining an infection type in an adult,
comprising measuring the concentration of the determinant
osteopontin in a sample derived from the adult, and at least one of
the determinants set forth in Table 2, wherein said concentration
is indicative of the infection type.
27. The method of claim 1, wherein no more than two determinants
are measured.
28. The method of claim 1, wherein no more than three determinants
are measured.
29. The method of claim 1, wherein no more than four determinants
are measured.
30. The method of claim 1, wherein the sample is whole blood or a
fraction thereof.
31. The method of claim 30, wherein said blood fraction sample
comprises cells selected from the group consisting of lymphocytes,
monocytes and granulocytes.
32. The method of claim 30, wherein said blood fraction sample
comprises serum or plasma.
33. The method of claim 1, wherein the concentration of the
determinant is determined electrophoretically or
immunochemically.
34. The method of claim 33, wherein the immunochemical detection is
by flow cytometry, radioimmunoassay, immunofluorescence assay or by
an enzyme-linked immunosorbent assay.
35. The method of claim 1, wherein the concentration of the
determinant is measured within about 24 hours after the sample is
obtained.
36. The method of claim 20, wherein the concentration of TRAIL is
measured in a sample that was stored at 12.degree. C. or lower,
wherein said storage begins less than 24 hours after the sample is
obtained.
37. The method of claim 1, wherein said determinant of Table 1 is
set forth in Table 5.
38. The method of claim 37, wherein said determinant set forth in
Table 5 is selected from the group consisting of NGAL, neopterin,
and osteopontin.
39. The method of claim 37, further comprising age normalization of
the determinant concentration.
40. The method of claim 37, further comprising stratifying the
subject according to age and wherein the threshold is an
appropriate age dependent threshold.
41. A kit comprising a plurality of determinant detection reagents
that specifically detect a first determinant selected from the
group consisting of the determinants which are set forth in Table 1
and a second determinant selected from the group of the
determinants which are set forth in Table 2.
42. A kit comprising a plurality of detection reagents that
specifically detect at least two determinants which are set forth
in Table 1.
43. (canceled)
44. The kit of claim 41, wherein the detection reagent is an
antibody or fragment thereof.
45. The kit of claim 41, wherein said kit comprises antibodies that
detect no more than 10 determinants.
Description
FIELD AND BACKGROUND OF THE INVENTION
[0001] The present invention, in some embodiments thereof, relates
to the identification of biological signatures and determinants
associated with bacterial and viral infections and methods of using
such biological signatures in the screening diagnosis, therapy, and
monitoring of infection.
[0002] Antibiotics (Abx) are the world's most prescribed class of
drugs with a 25-30 billion $US global market. Abx are also the
world's most misused drug with a significant fraction of all drugs
(40-70%) being wrongly prescribed (Linder, J. A. and R. S. Stafford
2001; Scott, J. G. and D. Cohen, et al. 2001; Davey, P. and E.
Brown, et al. 2006; Cadieux, G. and R. Tamblyn, et al. 2007;
Pulcini, C. and E. Cua, et al. 2007), ("CDC--Get Smart: Fast Facts
About Antibiotic Resistance" 2011).
[0003] One type of Abx misuse is when the drug is administered in
case of a non-bacterial disease, such as a viral infection, for
which Abx is ineffective. For example, according to the USA center
for disease control and prevention CDC, over 60 Million wrong Abx
prescriptions are given annually to treat flu in the US. The
health-care and economic consequences of the Abx over-prescription
include: (i) the cost of antibiotics that are unnecessarily
prescribed globally, estimated at >$10 billion annually; (ii)
side effects resulting from unnecessary Abx treatment are reducing
quality of healthcare, causing complications and prolonged
hospitalization (e.g. allergic reactions, Abx associated diarrhea,
intestinal yeast etc.) and (iii) the emergence of resistant strains
of bacteria as a result of the overuse (the CDC has declared the
rise in antibiotic resistance of bacteria as "one of the world's
most pressing health problems in the 21.sup.st century" (Arias, C.
A. and B. E. Murray 2009; "CDC--About Antimicrobial Resistance"
2011)).
[0004] Antibiotics under-prescription is not uncommon either. For
example up to 15% of adult bacterial pneumonia hospitalized
patients in the US receive delayed or no Abx treatment, even though
in these instances early treatment can save lives and reduce
complications (Houck, P. M. and D. W. Bratzler, et al 2002).
[0005] Technologies for infectious disease diagnostics have the
potential to reduce the associated health and financial burden
associated with Abx misuse. Ideally, such a technology should: (i)
accurately differentiate between a bacterial and viral infections;
(ii) be rapid (within minutes); (iii) be able to differentiate
between pathogenic and non-pathogenic bacteria that are part of the
body's natural flora; (iv) differentiate between mixed
co-infections and pure viral infections and (v) be applicable in
cases where the pathogen is inaccessible (e.g. sinusitis,
pneumonia, otitis-media, bronchitis, etc).
[0006] Current solutions (such as culture, PCR and immunoassays) do
not fulfill all these requirements: (i) Some of the assays yield
poor diagnostic accuracy (e.g. low sensitivity or
specificity)(Uyeki et al. 2009), and are restricted to a limited
set of bacterial or viral strains; (ii) they often require hours to
days; (iii) they do not distinguish between pathogenic and
non-pathogenic bacteria (Del Mar, C 1992), thus leading to false
positives; (iv) they often fail to distinguish between a mixed and
a pure viral infections and (v) they require direct sampling of the
infection site in which traces of the disease causing agent are
searched for, thus prohibiting the diagnosis in cases where the
pathogen resides in an inaccessible tissue, which is often the
case.
[0007] Consequentially, there still a diagnostic gap, which in turn
often leads physicians to either over-prescribe Abx (the
"Just-in-case-approach"), or under-prescribe Abx (the
"Wait-and-see-approach") (Little, P. S. and I. Williamson 1994;
Little, P. 2005; Spiro, D. M. and K. Y. Tay, et al. 2006), both of
which have far reaching health and financial consequences.
[0008] Accordingly, a need exists for a rapid method that
accurately differentiates between bacterial, viral, mixed and
non-infectious disease patients that addresses these
challenges.
[0009] WO 2013/117746 teaches signatures and determinants for
distinguishing between a bacterial and viral infection.
[0010] Additional Background art includes Kfir et al., PLOS One,
March 18, DOI:10.1371/journal.pone.0120012, 2015.
SUMMARY OF THE INVENTION
[0011] According to one aspect of the present invention there is
provided a method of determining an infection type in a subject
comprising measuring the concentration of a first determinant
selected from the group consisting of the determinants which are
set forth in Table 1 and a second determinant selected from the
group of the determinants which are set forth in Table 2 in a
sample derived from the subject, wherein the concentration is
indicative of the infection type.
[0012] According to one aspect of the present invention there is
provided a method of determining an infection type in a subject
comprising measuring the concentration of at least two determinants
which are set forth in Table 1 in a sample derived from the
subject, wherein the concentration is indicative of the infection
type.
[0013] According to one aspect of the present invention there is
provided a method of distinguishing between a bacterial or mixed
infection, and a viral infection in a subject comprising:
[0014] (a) measuring the concentration of a first determinant
selected from the group consisting of the determinants which are
set forth in Table 1 and a second determinant selected from the
group of the determinants which are set forth in Table 2 in a
sample derived from the subject;
[0015] (b) applying a pre-determined mathematical function on the
concentrations of the determinants to compute a score;
[0016] (c) comparing the score to a predetermined reference
value.
[0017] According to one aspect of the present invention there is
provided a method of distinguishing between a bacterial or mixed
infection, and a viral infection in a subject comprising:
[0018] (a) measuring the concentration of at least two determinants
which are set forth in Table 1 in a sample derived from the
subject;
[0019] (b) applying a pre-determined mathematical function on the
concentrations of the determinants to compute a score;
[0020] (c) comparing the score to a predetermined reference
value.
[0021] According to one aspect of the present invention there is
provided a method of determining an infection type in a child,
comprising measuring the concentration of the determinant neopterin
and/or the determinant NGAL in a sample derived from the child,
wherein the concentration is indicative of the infection type.
[0022] According to one aspect of the present invention there is
provided a method of determining an infection type in an adult,
comprising measuring the concentration of the determinant
osteopontin in a sample derived from the adult, and at least one of
the determinants set forth in Table 2, wherein the concentration is
indicative of the infection type.
[0023] According to one aspect of the present invention there is
provided a kit comprising a plurality of determinant detection
reagents that specifically detect a first determinant selected from
the group consisting of the determinants which are set forth in
Table 1 and a second determinant selected from the group of the
determinants which are set forth in Table 2.
[0024] According to one aspect of the present invention there is
provided a kit comprising a plurality of detection reagents that
specifically detect at least two determinants which are set forth
in Table 1.
[0025] According to some embodiments, the first determinant is
selected from the group consisting of a1 Acid Glycoprotein,
Adiponectin, Angiogenin, Angiopoietinl, Angiopoietin2, APRIL, BAFF,
BDNF, CD 23, CD14, CD142, CD27, CD95, Clusterin, Complement factor
D, Corin, CXCL13, Cystatin C, Dkk1, E Cadherin, E Selectin,
Endostatin, Fetuin A, GCP2, GDF15, ICAM1, IGFBP3, IL18, IL19,
Leptin, Leptin R, LIGHT, MBL, MIF, MMP2, MMP3, MMP7, MMP8,
Myeloperoxidase, Neopterin, NGAL, Osteopontin, Osteoprotegerin, P
Selectin, PCSK9, Pentraxin3, Pro Cathepsin B, Progranulin,
ProMMP10, Prostaglandin E2, RBP4, Resistin, SLPI, Substance P,
TFPI, TGF B1, Thrombospondin2, Tie2, uPAR, VCAM1, VEGF C and
Vitamin D Binding Protein.
[0026] According to some embodiments, the first determinant is
selected from the group consisting of NGAL, Resistin, MMP8,
Pentraxin3, E Selectin, MMP7, Myeloperoxidase, Osteopontin, PCSK9,
Pro Cathepsin B, a1 Acid Glycoprotein, GDF15, Progranulin,
Adiponectin, Clusterin, Corin, Neopterin, Cystatin C, CD27, E
Cadherin, Complement factor D, IGFBP3, GCP2, RBP4, CD14 and
ProMMP10.
[0027] According to some embodiments, the first determinant is
NGAL, MMP8 or neopterin.
[0028] According to some embodiments, the first determinant is a
polypeptide.
[0029] According to some embodiments, at least one of the at least
two determinants is selected from the group consisting of a1 Acid
Glycoprotein, Adiponectin, Angiogenin, Angiopoietinl,
Angiopoietin2, APRIL, BAFF, BDNF, CD 23, CD14, CD142, CD27, CD95,
Clusterin, Complement factor D, Corin, CXCL13, Cystatin C, Dkk1, E
Cadherin, E Selectin, Endostatin, Fetuin A, GCP2, GDF15, ICAM1,
IGFBP3, IL18, IL19, Leptin, Leptin R, LIGHT, MBL, MIF, MMP2, MMP3,
MMP7, MMP8, Myeloperoxidase, Neopterin, NGAL, Osteopontin,
Osteoprotegerin, P Selectin, PCSK9, Pentraxin3, Pro Cathepsin B,
Progranulin, ProMMP10, Prostaglandin E2, RBP4, Resistin, SLPI,
Substance P, TFPI, TGF B1, Thrombospondin2, Tie2, uPAR, VCAM1, VEGF
C and Vitamin D Binding Protein.
[0030] According to some embodiments, at least one of the at least
two determinants is selected from the group consisting of NGAL,
Resistin, MMP8, Pentraxin3, E Selectin, MMP7, Myeloperoxidase,
Osteopontin, PCSK9, Pro Cathepsin B, a1 Acid Glycoprotein, GDF15,
Progranulin, Adiponectin, Clusterin, Corin, Neopterin, Cystatin C,
CD27, E Cadherin, Complement factor D, IGFBP3, GCP2, RBP4, CD14 and
ProMMP10.
[0031] According to some embodiments, at least one of the at least
two determinants is NGAL, MMP8 or neopterin.
[0032] According to some embodiments, at least one of the at least
two determinants is a polypeptide.
[0033] According to some embodiments, at least two determinants are
polypeptides.
[0034] According to some embodiments, the determinant of Table 1 is
selected from the group consisting of a1 Acid Glycoprotein,
Adiponectin, Angiogenin, Angiopoietinl, Angiopoietin2, APRIL, BAFF,
BDNF, CD 23, CD14, CD142, CD27, CD95, Clusterin, Complement factor
D, Corin, CXCL13, Cystatin C, Dkk1, E Cadherin, E Selectin,
Endostatin, Fetuin A, GCP2, GDF15, ICAM1, IGFBP3, IL18, IL19,
Leptin, Leptin R, LIGHT, MBL, MIF, MMP2, MMP3, MP7, MMP8,
Myeloperoxidase, Neopterin, NGAL, Osteopontin, Osteoprotegerin, P
Selectin, PCSK9, Pentraxin3, Pro Cathepsin B, Progranulin,
ProMMP10, Prostaglandin E2, RBP4, Resistin, SLPI, Substance P,
TFPI, TGF B1, Thrombospondin2, Tie2, uPAR, VCAM1, VEGF C and
Vitamin D Binding Protein.
[0035] According to some embodiments, the determinant of Table 1 is
selected from the group consisting of NGAL, Resistin, MMP8,
Pentraxin3, E Selectin, MMP7, Myeloperoxidase, Osteopontin, PCSK9,
Pro Cathepsin B, a1 Acid Glycoprotein, GDF15, Progranulin,
Adiponectin, Clusterin, Corin, Neopterin, Cystatin C, CD27, E
Cadherin, Complement factor D, IGFBP3, GCP2, RBP4, CD14 and
ProMMP10.
[0036] According to some embodiments, the determinant of Table 1 is
selected from the group consisting of NGAL, MMP8 and Neopterin.
[0037] According to some embodiments, the second determinant is
selected from the group consisting of CRP, TRAIL and IP-10.
[0038] According to some embodiments, the determinants
comprise:
[0039] (i) CRP and NGAL;
[0040] (ii) CRP and MMP8;
[0041] (iii) CRP and neopterin;
[0042] (iv) TRAIL and NGAL;
[0043] (v) TRAIL and MMP8;
[0044] (vi) TRAIL and neopterin;
[0045] (vii) IP10 and NGAL;
[0046] (viii) IP10 and MMP8; or
[0047] (ix) IP10 and neopterin; or
[0048] (x) Neopterin and PCT; or
[0049] (xi) NGAL and PCT.
[0050] According to some embodiments, the at least two determinants
are:
[0051] (i) NGAL and MMP8;
[0052] (ii) NGAL and neopterin; or
[0053] (iii) neopterin and MMP8
[0054] According to some embodiments, the second determinant is
TRAIL.
[0055] According to some embodiments, the concentration of the
TRAIL is higher than a pre-determined threshold value, a bacterial
infection is ruled out for the subject.
[0056] According to some embodiments, the concentration of the
TRAIL is higher than a pre-determined threshold value, a viral
infection is ruled in for the subject.
[0057] According to some embodiments, the method further comprises
measuring the concentration of CRP and/or IP-10.
[0058] According to some embodiments, the method further comprises
determining the concentration of at least one of the determinants
set forth in Table 2.
[0059] According to some embodiments, no more than two determinants
are measured.
[0060] According to some embodiments, no more than three
determinants are measured.
[0061] According to some embodiments, no more than four
determinants are measured.
[0062] According to some embodiments, the sample is whole blood or
a fraction thereof.
[0063] According to some embodiments, the blood fraction sample
comprises cells selected from the group consisting of lymphocytes,
monocytes and granulocytes.
[0064] According to some embodiments, the blood fraction sample
comprises serum or plasma.
[0065] According to some embodiments, the concentration of the
determinant is determined electrophoretically or
immunochemically.
[0066] According to some embodiments, the immunochemical detection
is by flow cytometry, radioimmunoassay, immunofluorescence assay or
by an enzyme-linked immunosorbent assay.
[0067] According to some embodiments, the concentration of the
determinant is measured within about 24 hours after the sample is
obtained.
[0068] According to some embodiments, the concentration of TRAIL is
measured in a sample that was stored at 12.degree. C. or lower,
wherein the storage begins less than 24 hours after the sample is
obtained.
[0069] According to some embodiments, the determinant of Table 1 is
set forth in Table 5.
[0070] According to some embodiments, the determinant set forth in
Table 5 is selected from the group consisting of NGAL, neopterin,
and osteopontin.
[0071] According to some embodiments, the method further comprises
age normalization of the determinant concentration.
[0072] According to some embodiments, the method further comprises
stratifying the subject according to age and wherein the threshold
is an appropriate age dependent threshold.
[0073] According to some embodiments, at least one of the
determinants is a polypeptide.
[0074] According to some embodiments, the detection reagent is an
antibody or fragment thereof.
[0075] According to some embodiments, the kit comprises antibodies
that detect no more than 10 determinants.
[0076] Unless otherwise defined, all technical and/or scientific
terms used herein have the same meaning as commonly understood by
one of ordinary skill in the art to which the invention pertains.
Although methods and materials similar or equivalent to those
described herein can be used in the practice or testing of
embodiments of the invention, exemplary methods and/or materials
are described below. In case of conflict, the patent specification,
including definitions, will control. In addition, the materials,
methods, and examples are illustrative only and are not intended to
be necessarily limiting.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0077] Some embodiments of the invention are herein described, by
way of example only, with reference to the accompanying drawings.
With specific reference now to the drawings in detail, it is
stressed that the particulars shown are by way of example and for
purposes of illustrative discussion of embodiments of the
invention. In this regard, the description taken with the drawings
makes apparent to those skilled in the art how embodiments of the
invention may be practiced.
[0078] In the drawings:
[0079] FIG. 1: Clinical study workflow.
[0080] FIG. 2: Distribution of age and gender of the patients
enrolled in the clinical study (N=122).
[0081] FIG. 3: Distribution of physiological systems of the
patients enrolled in the clinical study.
[0082] FIG. 4: Distribution of major clinical syndromes of the
patients enrolled in the clinical study.
[0083] FIG. 5: Distribution of maximal body temperatures of the
patients enrolled in the clinical study.
[0084] FIG. 6: Distribution of time from initiation of symptoms of
the patients enrolled in the clinical study.
[0085] FIG. 7: Examples of determinants that differentiate between
bacterial versus viral infected subjects.
[0086] FIGS. 8A-C. Examples of determinants which expression
patterns in bacterial and viral patients differ between children
and adults (A) Osteopontin; (B) NGAL; (C) Neopterin. Med=medians of
bacterial and viral infected patients; mean=means.+-.standard
deviation of bacterial and viral infected patients; RS p=Wilcoxon
ranksum P-value.
[0087] FIG. 9: Examples of expression patterns of pairs of
determinants in bacterial (red) and viral (blue) infected
subjects.
[0088] FIG. 10: Classification accuracy in terms of AUC of viral
versus bacterial infected patients attained for pairs of
determinants using a logistic regression model. Hot and cold colors
indicate pairs of determinants whose combined classification
accuracy is high or low respectively, as indicated in the
legend.
[0089] FIG. 11: Classification accuracy in terms of MCC of viral
versus bacterial infected patients attained for pairs of
determinants using a logistic regression model. Hot and cold colors
indicate pairs of determinants whose combined classification
accuracy is high or low respectively, as indicated in the
legend.
[0090] FIG. 12: Some determinant combinations exhibit an improved
diagnostic accuracy (in terms of AUC) compared to that of the
corresponding individual determinants, whereas other combinations
exhibit a reduced accuracy. The change in classification accuracy
(dAUC) for the determinants described in Table 4 (according to the
serial numbers) is computed as follows: AUCi,j--max(AUCi, AUCj),
where AUCi and AUCj correspond to the AUC obtained for determinant
i and j individually and AUCi,j is obtained for the pair. Hot and
cold colors indicate pairs of determinants whose combined
classification accuracy compared to the individual determinant
accuracy is higher and lower respectively.
[0091] FIG. 13: Some determinant combinations exhibit an improved
diagnostic accuracy (in terms of MCC) compared to that of the
corresponding individual determinants, whereas other combinations
exhibit a reduced accuracy. The change in classification accuracy
(dMCC) for the determinants described in Table 4 (according to the
serial numbers) is computed as follows: MCCi,j--max(MCCi, MCCj),
where MCCi and MCCj correspond to the AUC obtained for determinant
i and j individually and MCCi,j is obtained for the pair. Hot and
cold colors indicate pairs of determinants whose combined
classification accuracy compared to the individual determinant
accuracy is higher and lower respectively.
[0092] FIGS. 14A-B: The levels of additional biomarkers can be
combined with CRP to improve overall diagnostic performance.
Routinely used CRP cutoffs (20 .mu.g/ml and 80 .mu.g/ml) are marked
by red lines. (A) NGAL. An example of NGAL cutoff (150 ng/ml) is
marked by a blue line. (B) Neopterin. An example of Neopterin
cutoff (4 pg/ml) is marked by a blue line.
[0093] FIGS. 15A-B: The levels of additional biomarkers can be
combined with TRAIL to improve overall diagnostic performance. An
example of TRAIL cutoff (70 pg/ml) is marked by a red line. (A)
NGAL. An example of NGAL cutoff (150 ng/ml) is marked by a blue
line. (B) Neopterin. An example of Neopterin cutoff (4 pg/ml) is
marked by a blue line.
[0094] FIGS. 16A-B: The levels of additional biomarkers can be
combined with IP-10 to improve overall diagnostic performance. An
example of IP-10 cutoff (500 pg/ml) is marked by a red line. (A)
NGAL. An example of NGAL cutoff (150 ng/ml) is marked by a blue
line. (B) Neopterin. An example of Neopterin cutoff (4 pg/ml) is
marked by a blue line.
[0095] FIGS. 17A-B: Additional biomarkers can be combined with the
CRP-TRAIL-IP-10 signature to attain higher sensitivity (and lower
specificity) when distinguishing between bacterial and viral
infected children. Viral, bacterial, and equivocal immune scores
generated by the CRP-TRAIL-IP-10 signature are marked by blue, red
and gray areas respectively (Viral<35, Equivocal 35-65,
Bacterial >65). (A) NGAL. An example of NGAL cutoff (150 ng/ml)
is marked by a blue line. (B) Neopterin. An example of Neopterin
cutoff (4 pg/ml) is marked by a blue line.
[0096] FIGS. 18A-B: Additional biomarkers can be combined with the
CRP-TRAIL-IP-10 signature to attain higher sensitivity (and lower
specificity) when distinguishing between bacterial and viral
infected adults. Viral, bacterial, and equivocal immune scores
generated by the CRP-TRAIL-IP-10 signature are marked by blue, red
and gray areas respectively (Viral<35, Equivocal 35-65,
Bacterial >65). (A) NGAL. An example of NGAL cutoff (150 ng/ml)
is marked by a blue line. (B) Neopterin. An example of Neopterin
cutoff (4 pg/ml) is marked by a blue line.
DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION
[0097] The present invention, in some embodiments thereof, relates
to the identification of signatures and determinants associated
with bacterial, viral and mixed (i.e., bacterial and viral
co-infections) infections. More specifically it was discovered that
certain determinants are differentially expressed in a
statistically significant manner in subjects with bacteria, viral
or mixed (i.e., bacterial and viral co-infections) as well as
non-infectious disease and healthy subjects.
[0098] Before explaining at least one embodiment of the invention
in detail, it is to be understood that the invention is not
necessarily limited in its application to the details set forth in
the following description or exemplified by the Examples. The
invention is capable of other embodiments or of being practiced or
carried out in various ways.
[0099] Methods of distinguishing between bacterial and viral
infections have been disclosed in International Patent Application
WO2013/117746, to the present inventors. Seeking to expand the
number of determinants that can aid in accurate diagnosis, the
present inventors have now carried out additional clinical
experiments and have identified other determinants that can be used
for this aim.
[0100] Furthermore, the present inventors have now shown that
analysis of some of these determinants in combination with
previously disclosed determinants, or previously disclosed
determinant combinations, improve the sensitivity of the diagnostic
test, in some cases at a cost of reduced specificity.
[0101] Correct identification of bacterial patients is of high
importance as these patients require antibiotic treatment and in
some cases more aggressive management (hospitalization, additional
diagnostic tests etc). Misclassification of bacterial patients
increases the chance of morbidity and mortality. Therefore,
increasing the sensitivity of a biomarker or diagnostic test that
distinguishes between bacterial and viral infections may be
desired, even though specificity may be reduced.
[0102] Whilst further reducing the present invention to practice,
the present inventors have now found that for particular
determinants, the threshold level for distinguishing between the
different types of infections is age dependent. Thus, the present
inventors conclude that for those determinants it is important to
take into account the age of the tested subject.
[0103] Thus, according to a first aspect of the present invention
there is provided a method for determining an infection type in a
subject comprising measuring the concentration of a first
determinant selected from the group consisting of the determinants
which are set forth in Table 1 and a second determinant selected
from the group of the determinants which are set forth in Table 2
in a sample derived from the subject, wherein the concentration is
indicative of the infection type.
[0104] According to another aspect of the present invention there
is provided a method of determining an infection type in a subject
comprising measuring the concentration of only one determinant
which is set forth in Table 1 in a subject derived sample, wherein
the concentration is indicative of the infection type.
[0105] According to still another aspect of the present invention
there is provided a method of determining an infection type in a
subject comprising measuring the concentration of at least two
determinants which are set forth in Table 1 in a sample derived
from the subject, wherein the concentration is indicative of the
infection type. In one aspect of the invention, these determinants
include at least one that is set forth in Table 1 and at least one
that is set forth in Table 2. In another aspect of the invention,
these determinants include at least two that are set forth in Table
1.
TABLE-US-00001 TABLE 1 DETERMINANT RefSeq DNA sequence RefSeq
proteins 4-1BB Ligand/TNFSF9 NC_000019.10, NP_003802.1 Tumor
Necrosis Factor NT_011295.12, (Ligand) Superfamily, NC_018930.2
Member 9 4-1BB/TNFRSF9 NC_000001.11, NP_001552.2 tumor necrosis
factor receptor NT_032977.10, superfamily, member 9 NC_018912.2
a1-Acid Glycoprotein NC_000009.12, NP_000598.2 ORM2 (orosomucoid 2)
NC_018920.2, NT_008470.20 ACE/CD143 NC_000017.11, NP_000780.1,
angiotensin I converting NT_010783.16, NP_001171528.1 enzyme
NC_018928.2 NP_690043.1 INHBA/Inhibin Beta A NC_000007.14
NP_002183.1 NC_018918.2 NT_007819.18 Adiponectin/Acrp30
NC_000003.12, NP_001171271.1, ADIPOQ (adiponectin, C1Q
NT_005612.17, NP_004788.1 and collagen domain NC_018914.2
containing) a-Fetoprotein/AFP NC_000004.12, NP_001125.1
alpha-fetoprotein NT_016354.20, NC_018915.2 AgRP/Agouti-related
Protein NC_000016.10, NP_001129.1 agouti related neuropeptide
NT_010498.16, NC_018927.2 AKT1 NC_000014.9, NP_001014431.1 v-akt
murine thymoma viral NT_026437.13, NP_001014432.1 oncogene homolog
1 NC_018925.2 NP_005154.2 Angiogenin/ANG NC_000014.9,
NP_001091046.1 angiogenin, ribonuclease, NC_018925.2, NP_001136.1
RNase A family, 5 NT_026437.13 Angiopoietin-1 NC_000008.11,
NP_001137.2 ANGPT1 NT_008046.17, NP_001186788.1 NC_018919.2
Angiopoietin-2 NC_000008.11, NP_001112359.1 ANGPT2 NC_018919.2,
NP_001112360.1 NT_023736.18 NP_001138.1 Angiopoietin-like 3
NC_000001.11, NP_055310.1 ANGPTL3 NC_018912.2, NT_032977.10 APP
NC_000021.9, NP_000475.1, amyloid beta (A4) precursor NC_018932.2,
NP_001129488.1 protein NT_011512.12 NP_001129601.1, NP_001129602.1
NP_001129603.1 NP_001191230.1 NP_001191231.1 NP_001191232.1,
NP_958816.1 NP_958817.1 APRIL/TNFSF13 NC_000017.11, NP_001185551.1
tumor necrosis factor NC_018928.2, NP_001185552.1 superfamily
member 13 NT_010718.17 NP_001185553.1 NP_003799.1 NP_742084.1
NP_742085.1 BAFF/TNFSF13B NC_000013.11, NP_001139117.1 tumor
necrosis factor NT_009952.15, NP_006564.1 superfamily member 13b
NC_018924.2 BAFFR/TNFRSF13C NC_000022.11, NP_443177.1 tumor
necrosis factor receptor NC_018933.2, superfamily member 13C
NT_011520.13 BCMA/TNFRSF17 NC_000016.10, NP_001183.2 tumor necrosis
factor receptor NC_018927.2, superfamily member 17 NT_010393.17
BDNF NC_000011.10, NP_001137277.1 brain-derived neurotrophic
NT_009237.19, NP_001137278.1 factor NC_018922.2 NP_001137279.1
NP_001137280.1 NP_001137281.1 NP_001137282.1 NP_001137283.1
NP_001137284.1 NP_001137285.1 NP_001137286.1 NP_001137288.1
NP_001700.2 NP_733927.1 NP_733928.1 NP_733929.1 NP_733930.1
NP_733931.1 CTNNB1/Beta-catenin NC_000003.12, NP_001091679.1
catenin beta 1 NT_022517.19, NP_001091680.1 NC_018914.2 NP_001895.1
BMP-2 NC_000020.11, NP_001191.1 bone morphogenetic protein 2
NT_011387.9, NC_018931.2 BMP-4 NC_000014.9, NP_001193.2 bone
morphogenetic protein 4 NT_026437.13, NP_570911.2 NP_570912.2
NC_018925.2 BMP-7 NC_000020.11, NP_001710.1 bone morphogenetic
protein 7 NC_018931.2, NT_011362.11 Carbonic Anhydrase IX/CA9
NC_000009.12, NP_001207.2 NT_008413.19, NC_018920.2 Cathepsin V
NC_000009.12, NP_001188504.1 CTSV NC_018920.2, NP_001324.2
NT_008470.20 CD14 NC_000005.10, NP_000582.1 CD14 molecule
NC_018916.2, NP_001035110.1 NT_029289.12 NP_001167575.1
NP_001167576.1 CD23/FCER2 NC_000019.10 NP_001193948.2 Fc fragment
of IgE, low NT_011295.12 NP_001207429.1 affinity II, receptor for
NC_018930.2 NP_001993.2 CD27 Ligand/TNFSF7 NC_000019.10,
NP_001243.1 CD70 molecule NC_018930.2, NT_011295.12 CD27/TNFRSF7
NC_000012.12, NP_001233.1 CD27 molecule NT_009759.17, NC_018923.2
CD30 Ligand/TNFSF8 NC_000009.12, NP_001235.1 tumor necrosis factor
NC_018920.2, NP_001239219.1 superfamily member 8 NT_008470.20
CD32/Fcg RII NC_000001.11, NP_001002273.1 FCGR2A NT_004487.20,
NP_001002274.1 Fc fragment of IgG, low NC_018912.2 NP_001002275.1
affinity IIa, receptor NP_001177757.1 FCGR2B NP_003992.3 Fc
fragment of IgG, low affinity IIb, receptor Chemerin/RARRES2
NC_000007.14, NP_002880.1 retinoic acid receptor NC_018918.2,
responder (tazarotene NT_007933.16 induced) 2 CLU/Clusterin
NC_000008.11, NP_001822.3 NT_167187.2, NC_018919.2 CNTF
NC_000011.10, NP_000605.1 ciliary neurotrophic factor NC_018922.2,
NT_167190.2 F3/Coagulation Factor NC_000001.11, NP_001171567.1
III/Tissue Factor/CD142 NC_018912.2, NP_001984.1 coagulation factor
III NT_032977.10 (thromboplastin, tissue factor) CFD/Complement
Factor NC_000019.10, NP_001919.2 D/Adipsin NC_018930.2,
NT_011295.12, NT_187622.1 Corin NC_000004.12, NP_001265514.1 corin,
serine peptidase NC_018915.2, NP_001265515.1 NT_006238.12
NP_006578.2 CREB NC_000002.12, NP_004370.1 NP_604391.1 cAMP
responsive element NT_005403.18, binding protein 1 (CREB1)
NC_018913.2 CXCL13/BLC/BCA-1 NC_000004.12, NP_006410.1 chemokine
(C--X--C motif) NT_016354.20, ligand 13 NC_018915.2 CXCL3
NC_000004.12, NP_002081.2 chemokine (C--X--C motif) NC_018915.2,
ligand 3 NT_016354.20 CXCL6/GCP-2 NC_000004.12, NP_002984.1
chemokine (C--X--C motif) NC_018915.2, ligand 6 NT_016354.20
CXCL9/MIG NC_000004.12, NP_002407.1 chemokine (C--X--C motif)
NC_018915.2, ligand 9 NT_016354.20 Cystatin C NC_000020.11,
NP_000090.1 CST3 NC_018931.2, NT_011387.9 NP_001275543.1
DCR3/TNFRSF6B NC_000020.11, NP_003814.1 tumor necrosis factor
receptor NC_018931.2, superfamily member 6b NT_011362.11 Dkk-1
NC_000010.11, NP_036374.1 dickkopf WNT signaling NC_018921.2,
pathway inhibitor 1 NT_030059.14 DLL1 NC_000006.12, NP_005609.3
delta-like 1 (Drosophila) NT_025741.16, NT_187553.1, NC_018917.2
DPPIV/CD26 NC_000002.12, NP_001926.2 dipeptidyl-peptidase 4
NT_005403.18, NC_018913.2 DR3/TNFRSF25 NC_000001.11, NP_001034753.1
tumor necrosis factor receptor NC_018912.2, NP_003781.1 superfamily
member 25 NT_032977.10 NP_683866.1 NP_683867.1 NP_683868.1
NP_683871.1 DR6/TNFRSF21 NC_000006.12, NP_055267.1 tumor necrosis
factor receptor NT_007592.16, superfamily member 21 NC_018917.2
E-Cadherin/CDH1 NC_000016.10, NP_004351.1 cadherin 1, type 1
NT_010498.16, NC_018927.2 EDA NC_000023.11, NP_001005609.1
ectodysplasin A NT_011651.18, NP_001005610.2 NC_018934.2
NP_001005612.2 NP_001005613.1 NP_001390.1 EDA2R/TNFRSF27
NC_000023.11, NP_001186616.1 ectodysplasin A2 receptor
NT_011651.18, NP_001229239.1 NC_018934.2 NP_068555.1
EDA-A1/Ectodysplasin A NC_000023.11, NP_001005609.1 NT_011651.18,
NP_001005610.2 NC_018934.2 NP_001005612.2 NP_001005613.1
NP_001390.1 EDAR NC_000002.12, NP_071731.1 ectodysplasin A receptor
NT_005403.18, NC_018913.2 EG-VEGF/PK1 NC_000001.11, NP_115790.1
PROK1 NC_018912.2, prokineticin 1 NT_032977.10 Endoglin/CD105
NC_000009.12, NP_000109.1, ENG NC_018920.2, NP_001108225.1,
NT_008470.20 NP_001265067.1 Endostatin/COL18A1 NC_000021.9,
NP_085059.2, NP_569711.2, collagen, type XVIII, alpha 1
NC_018932.2, NP_569712.2 NT_011512.12 Endothelin-1/ET-1
NC_000006.12, NP_001161791.1, EDN1 NT_007592.16, NP_001946.3
NC_018917.2 Endothelin-2 NC_000001.11, NP_001289198.1, EDN2
NC_018912.2, NP_001947.1 NT_032977.10 Endothelin-3 NC_000020.11,
NP_001289384.1, EDN3 NT_011362.11, NP_001289385.1, NC_018931.2
NP_996915.1, NP_996916.1, NP_996917.1 EPCR NC_000020.11,
NP_006395.2 PROCR NT_011362.11, protein C receptor, NC_018931.2
endothelial ErbB2/Her2 NC_000017.11, NP_001005862.1, erb-b2
receptor tyrosine NC_018928.2, NP_001276865.1, kinase 2
NT_010783.16 NP_001276866.1, NP_001276867.1, NP_004439.2 ErbB3/Her3
NC_000012.12, NP_001005915.1, erb-b2 receptor tyrosine NC_018923.2,
NP_001973.2 kinase 3 NT_029419.13 Erythropoietin/EPO NC_000007.14,
NP_000790.2 NC_018918.2, NT_007933.16 E-Selectin/CD62E
NC_000001.11, NP_000441.2 SELE NC_018912.2, NT_004487.20 Fas
Ligand/TNFSF6 NC_000001.11, NP_000630.1, FASLG NC_018912.2,
NP_001289675.1 TNF superfamily, member 6 NT_004487.20
Fas/TNFRSF6/CD95 NC_000010.11 NP_000034.1 NP_690610.1 Fas cell
surface death NT_030059.14 NP_690611.1 receptor NC_018921.2 Fetuin
A NC_000003.12, NP_001613.2 AHSG NC_018914.2,
alpha-2-HS-glycoprotein NT_005612.17 FGF acidic (FGF1)
NC_000005.10, NP_000791.1, fibroblast growth factor 1 NT_029289.12,
NP_001138364.1, (acidic) NC_018916.2 NP_001138406.1,
NP_001138407.1,
NP_001244134.1, NP_001244135.1, NP_001244136.1, NP_001244137.1,
NP_001244138.1, NP_001244139.1, NP_001244140.1, NP_001244141.1,
NP_149127.1, NP_149128.1 FGF-19 NC_000011.10, NP_005108.1
fibroblast growth factor 19 NC_018922.2, NT_167190.2 FGF-21
NC_000019.10, NP_061986.1 fibroblast growth factor 21 NT_011109.17,
NC_018930.2 Follistatin NC_000005.10, NP_006341.1, NP_037541.1 FST
NT_034772.7, NC_018916.2 FRS2 NC_000012.12, NP_001036020.1,
fibroblast growth factor NC_018923.2, NP_001265280.1, receptor
substrate 2 NT_029419.13 NP_001265282.1, NP_001265283.1,
NP_001265284.1, NP_001265285.1, NP_001265286.1, NP_006645.3 Gas6
NC_000013.11, NP_000811.1 growth arrest specific 6 NC_018924.2,
NT_024498.13 GDF-15 NC_000019.10, NP_004855.2 growth
differentiation factor NC_018930.2, 15 NT_011295.12 GITR
Ligand/TNFSF18 NC_000001.11, NP_005083.2 tumor necrosis factor
NC_018912.2, superfamily member 18 NT_004487.20 GITR/TNFRSF18
NC_000001.11, NP_004186.1, NP_683699.1, tumor necrosis factor
receptor NC_018912.2, NP_683700.1 superfamily member 18
NT_032977.10 Granzyme A NC_000005.10, NP_006135.1 GZMA NC_018916.2,
NT_034772.7 Granzyme B NC_000014.9, NP_004122.2 GZMB NT_026437.13,
NC_018925.2 Granzyme H NC_000014.9, NP_001257709.1, GZMH
NT_026437.13, NP_001257710.1, NC_018925.2 NP_219491.1 Granzyme K
NC_000005.10, NP_002095.1 GZMK NC_018916.2, NT_034772.7 Growth
Hormone 1/GH1 NC_000017.11, NP_000506.2, NP_072053.1, NT_010783.16,
NP_072054.1 NC_018928.2 Growth Hormone 2/GH2 NC_000017.11
NP_002050.1 NP_072050.1 NC_018928.2 NP_072051.1 NP_072052.1
NT_010783.16 GSK-3a NC_000019.10, NP_063937.2 glycogen synthase
kinase NC_018930.2, NP_001139628.1, 3 alpha NT_011109.17
NP_002084.2 GSK-3b NC_000003.12, glycogen synthase kinase 3
NT_005612.17, beta NC_018914.2 APRIL/TNFSF13 NC_000017.11
NP_001185551.1 tumor necrosis factor NC_018928.2 NP_001185552.1
superfamily member 13 NT_010718.17 NP_001185553.1 NP_003799.1
NP_742084.1 NP_742085.1 CD134/OX40/TNFRSF4 NC_000001.11 NP_003318.1
tumor necrosis factor receptor NT_032977.10 superfamily member 4
NC_018912.2 CD137/4-1BB/TNFRSF9 NC_000001.11 NP_001552.2 tumor
necrosis factor receptor NT_032977.10 superfamily member 9
NC_018912.2 TWEAK/TNFSF12 NC_000017.11 NP_003800.1 tumor necrosis
factor NC_018928.2 superfamily member 12 NT_010718.17 HGF
NC_000007.14, NP_000592.3, hepatocyte growth factor NT_007933.16,
NP_001010931.1, NC_018918.2 NP_001010932.1, NP_001010933.1,
NP_001010934.1 HGF R NC_000007.14 NP_000236.2 HGFR/MET NT_007933.16
NP_001120972.1 NC_018918.2 HIF-1a NC_000014.9, NP_001230013.1,
hypoxia inducible factor 1, NC_018925.2, NP_001521.1, NP_851397.1
alpha subunit NT_026437.13 (basic helix-loop-helix transcription
factor) Histone H2AX NC_000011.10, NP_002096.1 H2AFX NC_018922.2,
NT_033899.9 H2A histonefamily member X HSPB1 NC_000007.14,
NP_001531.1 heat shock protein family B NC_018918.2, (small) member
1 NT_007933.16 HSPB2 NC_000011.10, NP_001532.1 heat shock protein
family B NC_018922.2, NT_033899.9 (small) member 2 HSPB3
NC_000005.10, NP_006299.1 heat shock protein family B NC_018916.2,
NT_034772.7 (small) member 3 HVEM/TNFRSF14 NC_000001.11,
NP_001284534.1, tumor necrosis factor receptor NT_032977.10,
NP_003811.2 superfamily, member 14 NT_187515.1, NC_018912.2
ICAM-1/CD54 NC_000019.10, NP_000192.2 intercellular adhesion
NC_018930.2, molecule 1 NT_011295.12 IFNB NC_000009.12, NP_002167.1
interferon, beta 1, fibroblast NC_018920.2, NT_008413.19 IFNW1
NC_000009.12, NP_002168.1 interferon, omega 1 NC_018920.2,
NT_008413.19 IGFBP-3 NC_000007.14, NP_000589.2, insulin like growth
factor NC_018918.2, NP_001013416.1 binding protein 3 NT_007819.18
IGF-I NC_000012.12, NP_000609.1, insulin like growth factor 1
NT_029419.13, NP_001104753.1, NC_018923.2 NP_001104754.1,
NP_001104755.1 IkB-alpha/NFKBIA NC_000014.9, NP_065390.1 nuclear
factor of kappa light NC_018925.2, polypeptide NT_026437.13 gene
enhancer in B-cells inhibitor, alpha IL-1 (IL1B) NC_000002.12,
NP_000567.1 interleukin 1 beta NT_005403.18, NC_018913.2 IL-17A
NC_000006.12, NP_002181.1 interleukin 17A NC_018917.2, NT_007592.16
IL-17F NC_000006.12, NP_443104.1 interleukin 17F NT_007592.16,
NC_018917.2 IL-18/IL-1F4 NC_000011.10, NP_001230140.1, interleukin
18 NT_033899.9, NC_018922.2 NP_001553.1 IL-19 NC_000001.11,
NP_037503.2, NP_715639.1 interleukin 19 NT_004487.20, NC_018912.2
IL-1A/IL1F1/IL1 NC_000002.12, NP_000566.3 interleukin 1 alpha
NC_018913.2, NT_005403.18 IL-22 NC_000012.12, NP_065386.1
interleukin 22 NC_018923.2, NT_029419.13 KGF/FGF-7 NC_000015.10,
NP_002000.1 fibroblast growth factor 7 NC_018926.2, NT_010194.18
Leptin R (Leptin Receptor) NC_000001.11, NP_001003679.1, LEPR
NC_018912.2, NP_001003680.1, NT_032977.10 NP_001185616.1,
NP_001185617.1, NP_001185618.1, NP_002294.2 Leptin/OB NC_000007.14,
NP_000221.1 LEP NT_007933.16, NC_018918.2 LIGHT/TNFSF14
NC_000019.10, NP_003798.2, NP_742011.2 tumor necrosis factor
NT_011295.12, superfamily member 14 NC_018930.2 Lipocalin-2/NGAL
NC_000009.12, NP_005555.2 LCN2 NC_018920.2, NT_008470.20 LOX-1/OLR1
NC_000012.12, NP_001166103.1, oxidized low density NT_009714.18,
NP_001166104.1, lipoprotein (lectin-like) NC_018923.2 NP_002534.1
receptor 1 LRG/LRG1 NC_000019.10, NP_443204.1 leucine-rich alpha-2-
NC_018930.2, glycoprotein 1 NT_011295.12 Lymphotoxin beta/TNFSF3
NC_000006.12, NP_002332.1, NP_033666.1 LTB NC_018917.2,
NT_007592.16, NT_113891.3, NT_167244.2, NT_167245.2, NT_167246.2,
NT_167247.2, NT_167248.2, NT_167249.2 MAPK8 NC_000010.11,
NP_001265476.1, mitogen-activated protein NT_030059.14,
NP_001265477.1, kinase 8 NC_018921.2 NP_002741.1, NP_620634.1,
NP_620637.1 MBL NC_000010.11, NP_000233.1 mannose-binding lectin
NT_030059.14, (protein C) 2, soluble NC_018921.2 MEK1/MAP2K1
NC_000015.10 NP_002746.1 mitogen-activated protein NT_010194.18
kinase kinase 1 NC_018926.2 MEK2/MAP2K2 NC_000019.10 NP_109587.1
mitogen-activated protein NT_011295.12 kinase kinase 2 NC_018930.2
MIF NC_000022.11, NP_002406.1 macrophage migration NC_018933.2,
inhibitory factor NT_011520.13, (glycosylation-inhibiting
NT_187633.1 factor) MMP-1 NC_000011.10, NP_001139410.1, matrix
metallopeptidase 1 NC_018922.2, NT_033899.9 NP_002412.1 MMP-13
NC_000011.10, NP_002418.1 matrix metallopeptidase 13 NC_018922.2,
NT_033899.9, MMP-2 NC_000016.10, NP_001121363.1, matrix
metallopeptidase 2 NC_018927.2, NP_001289437.1, NT_010498.16
NP_001289438.1, NP_001289439.1, NP_004521.1 MMP-3 NC_000011.10,
NP_002413.1 matrix metallopeptidase 3 NC_018922.2, NT_033899.9
MMP-7 NC_000011.10, NP_002414.1 matrix metallopeptidase 7
NC_018922.2, NT_033899.9 MMP-8 NC_000011.10, NP_001291370.1, matrix
metallopeptidase 8 NT_033899.9, NC_018922.2 NP_001291371.1,
NP_002415.1 Myeloperoxidase/MPO NC_000017.11, NP_000241.1
NT_010783.16, NC_018928.2 NAPSA NC_000019.10, NP_004842.1 napsin A
aspartic peptidase NT_011109.17, NC_018930.2 NGFB NC_000001.11,
NP_002497.2 nerve growth factor (beta NT_032977.10, polypeptide)
NC_018912.2 NGFR/TNFRSF16 NC_000017.11, NP_002498.1 nerve growth
factor receptor NC_018928.2, NT_010783.16 NT-3/Ntf3 NC_000012.12,
NP_001096124.1, neurotrophin 3 NT_009759.17, NP_002518.1
NC_018923.2 NT-4/NTF4 NC_000019.10, NP_006170.1 neurotrophin 4
NT_011109.17, NC_018930.2 OSM/Oncostatin M NC_000022.11,
NP_065391.1 NT_011520.13, NC_018933.2 Osteopontin/OPN/SPP1
NC_000004.12, NP_000573.1, secreted phosphoprotein 1 NC_018915.2,
NP_001035147.1, NT_016354.20 NP_001035149.1, NP_001238758.1,
NP_001238759.1 Osteoprotegerin/TNFRSF11B NC_000008.11, NP_002537.3
tumor necrosis factor receptor NC_018919.2, superfamily member 11b
NT_008046.17 OX40/TNFRSF4 NC_000001.11, NP_003318.1 tumor necrosis
factor receptor NT_032977.10, superfamily member 4 NC_018912.2
OX40L/TNFSF4 NC_000001.11, NP_001284491.1, tumor necrosis factor
NT_004487.20, NP_003317.1 superfamily member 4 NC_018912.2
p38/MAPK14 NC_000006.12, NP_001306.1, NP_620581.1,
mitogen-activated protein NT_007592.16, NP_620582.1, NP_620583.1
kinase 14 NC_018917.2 P70 S6 Kinase Alpha/ NC_000017.11
NP_001258971.1 P70S6K1/RPS6KB1 NT_010783.16 NP_001258972.1
ribosomal protein S6 kinase, NC_018928.2 NP_001258973.1
70 kDa, polypeptide 1 NP_001258989.1 NP_003152.1
Pappalysin-1/PAPP-A NC_000009.12, NP_002572.2 pregnancy-associated
plasma NT_008470.20, protein A, pappalysin 1 NC_018920.2 Pentraxin
3/TSG-14/PTX3 NC_000003.12, NP_002843.2 NC_018914.2, NT_005612.17
Periostin/OSF-2/POSTN NC_000013.11, NP_001129406.1, periostin,
osteoblast specific NT_024524.15, NP_001129407.1, factor
NC_018924.2 NP_001129408.1, NP_001273594.1, NP_001273595.1,
NP_001273596.1, NP_006466.2 PI3/Elafin NC_000020.11, NP_002629.1
peptidase inhibitor 3, skin- NC_018931.2, derived NT_011362.11 PIGF
NC_000002.12, NP_002634.1, NP_775097.1 phosphatidylinositol glycan
NT_022184.16, anchor biosynthesis class F NC_018913.2
Pref-1/DLK-1/FA1 NC_000014.9, NP_003827.3 delta-like 1 homolog
NC_018925.2, (Drosophila) NT_026437.13 Pro-Cathepsin B NC_000008.11
cathepsin B/CTSB NT_077531.5 NC_018919.2 Progranulin/GRN
NC_000017.11, NP_002078.1 granulin NT_010783.16, NC_018928.2
Pro-MMP-10 (Stromelysin-2/ NC_000011.10 MMP10) NC_018922.2
NT_033899.9 matrix metallopeptidase 10 Proprotein Convertase
NC_000001.11, NP_777596.2 9/PCSK9 NT_032977.10, proprotein
convertase NC_018912.2 subtilisin/kexin type 9
P-Selectin/CD62P/SELP NC_000001.11, NP_002996.2 selectin P
NT_004487.20, NC_018912.2 RANK/TNFRSF11A NC_000018.10,
NP_001257878.1, tumor necrosis factor receptor NT_010966.15,
NP_001257879.1, superfamily member 11a NC_018929.2 NP_001257880.1,
NP_001265197.1, NP_003830.1 RBP4 NC_000010.11, NP_006735.2 retinol
binding protein 4 NC_018921.2, NT_030059.14 Relaxin-2/RLN2
NC_000009.12, NP_005050.2, NP_604390.1 Relaxin 2 NT_008413.19,
NC_018920.2 RELT/TNFRSF19L NC_000011.10, NP_116260.2, NP_689408.1
RELT tumor necrosis factor NT_167190.2, NC_018922.2 receptor RETN
NC_000019.10, NP_001180303.1, Resistin NC_018930.2, NP_065148.1
NT_011295.12 CD14 NC_000005.10, NP_000582.1, CD14 molecule
NC_018916.2, NP_001035110.1, NT_029289.12 NP_001167575.1,
NP_001167576.1 KIT/SCFR/c-kit NC_000004.12, NP_000213.1, v-kit
Hardy-Zuckerman 4 NT_022853.16, NP_001087241.1 feline sarcoma viral
oncogene NC_018915.2 homolog SERPINE1/Serpin E1/PAI-1 NC_000007.14,
NP_000593.1 serpin peptidase inhibitor, NC_018918.2, clade E
(nexin, plasminogen NT_007933.16 activator inhibitor type 1),
member 1 SLPI NC_000020.11, NP_003055.1 secretory leukocyte
peptidase NC_018931.2, inhibitor NT_011362.11 ST2/IL1RL1
NC_000002.12, NP_001269337.1, interleukin 1 receptor-like 1
NT_005403.18, NP_003847.2, NP_057316.3 NC_018913.2 STAT2
NC_000012.12, NP_005410.1, NP_938146.1 signal transducer and
NT_029419.13, activator of transcription 2 NC_018923.2 STAT3
NC_000017.11, NP_003141.2, NP_644805.1, signal transducer and
NT_010783.16, NP_998827.1 activator of transcription 3 NC_018928.2
(acute-phase response factor) STAT4 NC_000002.12, NP_001230764.1,
signal transducer and NT_005403.18, NP_003142.1 activator of
transcription 4 NC_018913.2 STAT5A NC_000017.11, NP_001275647.1,
signal transducer and NT_010783.16, NP_001275648.1, activator of
transcription 5A NC_018928.2 NP_001275649.1, NP_003143.2 STAT5B
NC_000017.11, NP_036580.2 signal transducer and NT_010783.16,
activator of transcription 5B NC_018928.2 STAT6 NC_000012.12,
NP_001171549.1, signal transducer and NT_029419.13, NP_001171550.1,
activator of transcription 6, NC_018923.2 NP_001171551.1,
interleukin-4 induced NP_001171552.1, NP_003144.3 TAC1/Substance P
NC_000007.14, NP_003173.1, NP_054702.1, tachykinin precursor 1
NC_018918.2, NP_054703.1, NT_007933.16 NP_054704.1 SFTPD/Surfactant
Protein D NC_000010.11, NP_003010.4 NT_030059.14, NC_018921.2
Survivin/BIRC5 NC_000017.11, NP_001012270.1, baculoviral IAP repeat
NT_010783.16, NP_001012271.1, containing 5 NC_018928.2 NP_001159.2
TACI/TNFRSF13B NC_000017.11, NP_036584.1 tumor necrosis factor
receptor NC_018928.2, superfamily member 13B NT_010718.17 TFPI
NC_000002.12, NP_001027452.1, tissue factor pathway NT_005403.18,
NP_006278.1 inhibitor NC_018913.2 TfR/Transferrin Receptor
NC_000003.12, NP_001121620.1, NT_005612.17, NP_003225.2
NC_018914.2, TGFB1 (TGF-b1) NC_000019.10, NP_000651.3 transforming
growth factor NT_011109.17, beta 1 NC_018930.2 TGFB2/TGF-Beta2
(TGF-b2) NC_000001.11, NP_001129071.1, transforming growth factor
NC_018912.2, NP_003229.1 beta 2 NT_004487.20 THBS2/Thrombospondin-2
NC_000006.12, NP_003238.2 NC_018917.2, NT_025741.16 Tie-1/TIE1
NC_000001.11, NP_001240286.1, tyrosine kinase with NT_032977.10,
NP_005415.1 immunoglobulin-like and NC_018912.2 EGF-like domains 1
TEK/Tie-2 NC_000009.12, NP_000450.2, TEK tyrosine kinase,
NT_008413.19, NP_001277006.1, endothelial NC_018920.2
NP_001277007.1 TIMP4/TIMP-4 NC_000003.12, NP_003247.1 TIMP
metallopeptidase NC_018914.2, inhibitor 4 NT_022517.19 TL1A/TNFSF15
NC_000009.12, NP_001191273.1, tumor necrosis factor NC_018920.2,
NP_005109.2 superfamily member 15 NT_008470.20 LTBR/TNFRSF3
NC_000012.12, NP_001257916.1, lymphotoxin beta receptor
NT_009759.17, NP_002333.1 NC_018923.2 MTOR NC_000001.11 NP_004949.1
mechanistic target of NT_032977.10 rapamycin (serine/threonine
NC_018912.2 kinase) PLAT/TPA NC_000008.11, NP_000921.1, NP_127509.1
plasminogen activator, tissue NC_018919.2, NT_167187.2
TRAIL-R1/TNFRSF10A NC_000008.11, NP_003835.3 tumor necrosis factor
receptor NC_018919.2, NT_167187.2 superfamily member 10a
TRAIL-R2/TNFRSF10B NC_000008.11, NP_003833.4, NP_671716.2 tumor
necrosis factor receptor NT_167187.2, NC_018919.2 superfamily
member 10b TRAILR3/TNFRSF10C NC_000008.11, NP_003832.2 tumor
necrosis factor receptor NC_018919.2, NT_167187.2 superfamily
member 10c, decoy without an intracellular domain TRAILR4/TNFRSF10D
NC_000008.11, NP_003831.2 tumor necrosis factor receptor
NC_018919.2, NT_167187.2 superfamily member 10d, decoy with
truncated death domain TRANCE/TNFSF11 NC_000013.11, NP_003692.1,
NP_143026.1 tumor necrosis factor NT_024524.15, superfamily member
11 NC_018924.2 NTRK1/TrkA NC_000001.11, NP_001007793.1,
neurotrophic tyrosine kinase, NC_018912.2, NP_001012331.1,
receptor, type 1 NT_004487.20 NP_002520.2 TROY/TNFRSF19
NC_000013.11, NP_001191387.1, tumor necrosis factor receptor
NT_024524.15, NP_001191388.1, superfamily member 19 NC_018924.2
NP_061117.2, NP_683760.1 TWEAK/TNFSF12 NC_000017.11, NP_003800.1
tumor necrosis factor NC_018928.2, superfamily member 12
NT_010718.17 TWEAKR/TNFRSF12A NC_000016.10, NP_057723.1 tumor
necrosis factor receptor NC_018927.2, superfamily member 12A
NT_010393.17 PLAUR/Upar NC_000019.10, NP_001005376.1, plasminogen
activator, NT_011109.17, NP_001005377.1, urokinase receptor
NC_018930.2 NP_001287966.1, NP_002650.1 VCAM1/CD106 NC_000001.11,
NP_001069.1, vascular cell adhesion NC_018912.2, NP_001186763.1,
molecule 1 NT_032977.10 NP_542413.1 VEGFC NC_000004.12, NP_005420.1
vascular endothelial growth NC_018915.2, factor C NT_016354.20,
FIGF/VEGF-D NC_000023.11, NP_004460.1 c-fos induced growth factor
NC_018934.2, NT_167197.2 (vascular endothelial growth factor D)
GC/Vitamin D Binding NC_000004.12, NP_000574.2, Protein
NT_016354.20, NP_001191235.1, group-specific component NC_018915.2
NP_001191236.1 (vitamin D binding protein) Neopterin NA NA cGMP NA
NA Leukotriene NA NA Cotisol NA NA Hyaloyronan NA NA Prostaglandin
E2 NA NA Prostaglandin NA NA Testosterone NA NA
TABLE-US-00002 TABLE 2 CRP TRAIL IP-10 IL1R/IL1R1/IL1RA
Procalcitonin (PCT) SAA/SAA1 TREM1 TREM2 RSAD2 MX1
[0106] In some cases, the determinants which are gene products are
identified based on the official letter abbreviation or gene symbol
assigned by the international Human Genome Organization Naming
Committee (HGNC).
[0107] In some embodiments the level of additional parameters may
be analyzed such as absolute Neutrophil count (ANC), ALC, Neu (%),
Lymphocyte percentage (Lym (%)), Monocyte percentage (Mono (%)),
Maximal temperature, Time from symptoms, Age, Creatinine (Cr),
Potassium (K), Pulse and Urea.
[0108] In other embodiments, the level of different parameters may
be analyzed, such as those selected from the group consisting of:
ARG1, ARPC2, ATP6V0B, BILI (BILIRUBIN), BRI3BP, CCL19-MIP3B, CES1,
CORO1A, EOS(%), HERC5, IFI6, IFIT3, KIAA0082, LIPT1, LRDD, MCP-2,
NA (Sodium), PARP9, PTEN, QARS, RAB13, RPL34, SART3, TRIM22, UBE2N,
WBC (Whole Blood Count), XAF1 and ZBP1.
[0109] In still other embodiment, the level of traditional
laboratory risk factors and clinical parameters are also measured.
These factors and parameters are further described herein
below.
[0110] Additional determinants which may be measured together with
those disclosed herein are provided in International Patent
Application WO2013/117746, the contents of which is incorporated
herein by reference and International Patent Application
IL2015/050823, the contents of which are incorporated herein by
reference.
[0111] The present invention, in some embodiments thereof: (i)
enables accurate differentiation between a broad range of bacterial
versus viral infections; (ii) enables rapid diagnosis (within
minutes); (iii) avoids the "false positive" identification of
non-pathogenic bacteria that are part of the body's natural flora,
(iv) allows for accurate differentiation between mixed and pure
viral infections and (v) allows diagnosis in cases where the
pathogen is inaccessible.
[0112] To address the clinical challenge of mixed infection
diagnosis and treatment, some aspects of the present invention
include a method for differentiating between mixed infections
(which require Abx treatment despite the presence of a virus) and
pure viral infections (which do not require Abx treatment).
[0113] Some aspects of the present invention also address the
challenge of "false-positive" diagnostics due to non-pathogenic
strains of bacteria that are part of the body's natural flora. This
is achieved by measuring biomarkers derived from the host rather
than the pathogen.
[0114] Another aspect of the present invention enables the
diagnosis of different infections, which is invariant to the
presence or absence of colonizers (e.g. bacteria and viruses that
are part of the natural flora). This addresses one of the major
challenges in infectious disease diagnostics today:
"false-positives" due to colonizers.
[0115] Importantly, some aspects of the current invention do not
require direct access to the pathogen, because the immune system
circulates in the entire body, thereby facilitating diagnosis in
cases in which the pathogen is inaccessible.
[0116] Another aspect of the present invention is the fraction in
which the biomarkers are measured, which affects the ease by which
the assay can be performed in the clinical settings, and especially
the point-of-care. For example, it is easier to measure proteins in
the serum or plasma fraction compared to nucleic acids or
intracellular proteins in the leukocytes fraction (the latter
requires an additional experimental step in which leukocytes are
isolated from the whole blood sample, washed and lysed).
Accordingly, some aspects of the present invention also describe
serum and plasma based protein signatures that are easily
measurable using various immunoassays available in clinical
settings.
[0117] Other aspects of the invention provide methods for
identifying subjects who have an infection by the detection of
determinants associated with an infection, including those subjects
who are asymptomatic for the infection. These signatures and
determinants are also useful for monitoring subjects undergoing
treatments and therapies for infection, and for selecting or
modifying diagnostics, therapies and treatments that would be
efficacious in subjects having an infection.
[0118] Exemplary determinants measured in the present invention are
described herein below.
[0119] CRP: C-reactive protein; additional aliases of CRP include
without limitation RP11-419N10.4 and PTX1.
[0120] An exemplary amino acid sequence of human CRP is set forth
below in SEQ ID NO: 1.
[0121] TRAIL: The protein encoded by this gene is a cytokine that
belongs to the tumor necrosis factor (TNF) ligand family. The
present invention contemplates measuring either the soluble and/or
the membrane form of this protein. In one embodiment, only the
soluble form of this protein is measured. Additional names of the
gene include without limitations APO2L, TNF-related
apoptosis-inducing ligand, TNFSF10 and CD253. This protein binds to
several members of the TNF receptor superfamily such as
TNFRSF10A/TRAILR1, TNFRSF10B/TRAILR2, TNFRSF10C/TRAILR3,
TNFRSF10D/TRAILR4, and possibly also to TNFRSF11B/OPG.
[0122] Exemplary amino acid sequences of TRAIL are set forth in SEQ
ID NOs: 2 or 3.
[0123] IP10: This gene encodes a chemokine of the CXC subfamily and
ligand for the receptor CXCR3. Additional names of the gene include
without limitations: CXCL10, Gamma-IP10, INP10 and chemokine (C-X-C
motif) ligand 10.
[0124] An exemplary amino acid sequence of human IP10 is set forth
in SEQ ID NO: 4.
[0125] IL1RA: The protein encoded by this gene is a cytokine
receptor that belongs to the interleukin 1 receptor family.
Additional names of the gene include without limitations: CD121A,
IL-1RT1, p80, CD121a antigen, CD121A, IL1R and IL1ra.
[0126] PCT: Procalcitonin (PCT) is a peptide precursor of the
hormone calcitonin, the latter being involved with calcium
homeostasis.
[0127] TREM1: Triggering receptor expressed on myeloid cells 1;
additional aliases of TREM1 are CD354 and TREM-1.
[0128] RSAD2: Radical S-adenosyl methionine domain containing 2;
additional aliases of RSAD2 include without limitation
2510004L01Rik, cig33, cig5 and vig1.
[0129] MX1/MXA: myxovirus (influenza virus) resistance 1;
additional aliases of MX1 include without limitation IFI-78K,
IFI78, MX and MxA.
[0130] TRAILR3/TNFRSF10C: The protein encoded by this gene is a
member of the TNF-receptor superfamily.
[0131] Exemplary amino acid sequences of this protein are set forth
in SEQ ID NOs: 5 or 6.
[0132] TRAILR4/TNFRSF10D: The protein encoded by this gene is a
member of the TNF-receptor superfamily.
[0133] Exemplary amino acid sequences of this protein are set forth
in SEQ ID NOs: 7 or 8.
[0134] TRAIL-R1/TNFRSF10A: The protein encoded by this gene is a
member of the TNF-receptor superfamily. Exemplary amino acid
sequences of this protein are set forth in SEQ ID NOs: 9, 10 or
11.
[0135] TRAIL-R2/TNFRSF10B: The protein encoded by this gene is a
member of the TNF-receptor superfamily, and contains an
intracellular death domain. Exemplary amino acid sequences of this
protein are set forth in SEQ ID NOs; 12, 13 or 14.
[0136] NGAL: Neutrophil gelatinase-associated lipocalin (NGAL) is
also known as Lipocalin-2 (LCN2), also known as oncogene 24p3. An
exemplary amino acid sequence of NGAL is set forth in SEQ ID NO:
15.
[0137] MMP8: Matrix metallproteinase 8 (MMP8) is a collagen
cleaving enzyme. Exemplary amino acid sequences of MMP8 are set
forth in SEQ ID NOs: 16-18.
[0138] Neopterin: Neopterin is the catabolic product of guanosine
triphosphate, a purine nucleotide. Neopterin belongs to the
chemical group known as pteridines.
[0139] Cortisol: Cortisol is a steroid hormone, more specifically a
glucocorticoid, which is produced by the zona fasciculata of the
adrenal cortex. It is released in response to stress and a low
level of blood glucose.
Definitions
[0140] As used herein, the term "determinant" refers to a
polypeptide or chemical agent produced in the body which can serve
as a marker for infection and/or infection type. In a particular
embodiment, the determinant is not an RNA molecule.
[0141] In one embodiment, the determinant is a polypeptide.
[0142] In another embodiment, the determinant is a hormone.
[0143] In another embodiment, the determinant is a second
messenger.
[0144] In still another embodiment, the determinant is a
metabolite.
[0145] According to a particular embodiment, the determinants are
soluble or secreted and are present outside the cellular interior
in different body fluids such as serum, plasma, urine, CSF, sputum,
sweat, stool, seminal fluid, etc.
[0146] "Traditional laboratory risk factors" encompass biomarkers
isolated or derived from subject samples and which are currently
evaluated in the clinical laboratory and used in traditional global
risk assessment algorithms, such as absolute neutrophil count
(abbreviated ANC), absolute lymphocyte count (abbreviated ALC),
white blood count (abbreviated WBC), neutrophil % (defined as the
fraction of white blood cells that are neutrophils and abbreviated
Neu (%)), lymphocyte % (defined as the fraction of white blood
cells that are lymphocytes and abbreviated Lym (%)), monocyte %
(defined as the fraction of white blood cells that are monocytes
and abbreviated Mon (%)), Sodium (abbreviated Na), Potassium
(abbreviated K), Bilirubin (abbreviated Bili).
[0147] "Clinical parameters" encompass all non-sample or
non-analyte biomarkers of subject health status or other
characteristics, such as, without limitation, age (Age), ethnicity
(RACE), gender (Sex), core body temperature (abbreviated
"temperature"), maximal core body temperature since initial
appearance of symptoms (abbreviated "maximal temperature"), time
from initial appearance of symptoms (abbreviated "time from
symptoms") or family history (abbreviated FamHX).
[0148] An "Infection Reference Expression Profile," is a set of
values associated with two or more determinants resulting from
evaluation of a biological sample (or population or set of
samples).
[0149] A "subject with non-infectious disease" is one whose disease
is not caused by an infectious disease agent (e.g. bacteria or
virus). An "acute infection" is characterized by rapid onset of
disease, a relatively brief period of symptoms, and resolution
within days.
[0150] A "chronic infection" is an infection that develops slowly
and lasts a long time. Viruses that may cause a chronic infection
include Hepatitis C and HIV. One difference between acute and
chronic infection is that during acute infection the immune system
often produces IgM+ antibodies against the infectious agent,
whereas the chronic phase of the infection is usually
characteristic of IgM-/IgG+ antibodies. In addition, acute
infections cause immune mediated necrotic processes while chronic
infections often cause inflammatory mediated fibrotic processes and
scaring (e.g. Hepatitis C in the liver). Thus, acute and chronic
infections may elicit different underlying immunological
mechanisms.
[0151] By infection type is meant to include bacterial infections,
mixed infections, viral infections, no infection, infectious or
non-infectious.
[0152] By "ruling in" an infection it is meant that the subject has
that type of infection.
[0153] By "ruling out" an infection it is meant that the subject
does not have that type of infection.
[0154] The "natural flora", or "colonizers" refers to
microorganisms, such as bacteria or viruses, that may be present in
healthy a-symptomatic subjects and in sick subjects.
[0155] An "anti-viral treatment" includes the administration of a
compound, drug, regimen or an action that when performed by a
subject with a viral infection can contribute to the subject's
recovery from the infection or to a relief from symptoms. Examples
of anti-viral treatments include without limitation the
administration of the following drugs: oseltamivir, RNAi
antivirals, monoclonal antibody respigams, zanamivir, and
neuriminidase blocking agents.
[0156] "TP" is true positive, means positive test result that
accurately reflects the tested-for activity. For example in the
context of the present invention a TP, is for example but not
limited to, truly classifying a bacterial infection as such.
[0157] "TN" is true negative, means negative test result that
accurately reflects the tested-for activity. For example in the
context of the present invention a TN, is for example but not
limited to, truly classifying a viral infection as such.
[0158] "FN" is false negative, means a result that appears negative
but fails to reveal a situation. For example in the context of the
present invention a FN, is for example but not limited to, falsely
classifying a bacterial infection as a viral infection.
[0159] "FP" is false positive, means test result that is
erroneously classified in a positive category. For example in the
context of the present invention a FP, is for example but not
limited to, falsely classifying a viral infection as a bacterial
infection.
[0160] "Sensitivity" is calculated by TP/(TP+FN) or the true
positive fraction of disease subjects.
[0161] "Specificity" is calculated by TN/(TN+FP) or the true
negative fraction of non-disease or normal subjects.
[0162] "Total accuracy" is calculated by (TN+TP)/(TN+FP+TP+FN).
[0163] "Positive predictive value" or "PPV" is calculated by
TP/(TP+FP) or the true positive fraction of all positive test
results. It is inherently impacted by the prevalence of the disease
and pre-test probability of the population intended to be
tested.
[0164] "Negative predictive value" or "NPV" is calculated by
TN/(TN+FN) or the true negative fraction of all negative test
results. It also is inherently impacted by the prevalence of the
disease and pre-test probability of the population intended to be
tested. See, e.g., O'Marcaigh A S, Jacobson R M, "Estimating The
Predictive Value Of A Diagnostic Test, How To Prevent Misleading Or
Confusing Results," Clin. Ped. 1993, 32(8): 485-491, which
discusses specificity, sensitivity, and positive and negative
predictive values of a test, e.g., a clinical diagnostic test.
[0165] "MCC" (Mathwes Correlation coefficient) is calculated as
follows: MCC=(TP*TN-FP*FN)/{(TP+FN)*(TP+FP)*(TN+FP)*(TN+FN)} 0.5
where TP, FP, TN, FN are true-positives, false-positives,
true-negatives, and false-negatives, respectively. Note that MCC
values range between -1 to +1, indicating completely wrong and
perfect classification, respectively. An MCC of 0 indicates random
classification. MCC has been shown to be a useful for combining
sensitivity and specificity into a single metric (Baldi, Brunak et
al. 2000). It is also useful for measuring and optimizing
classification accuracy in cases of unbalanced class sizes (Baldi,
Brunak et al. 2000).
[0166] Often, for binary disease state classification approaches
using a continuous diagnostic test measurement, the sensitivity and
specificity is summarized by a Receiver Operating Characteristics
(ROC) curve according to Pepe et al., "Limitations of the Odds
Ratio in Gauging the Performance of a Diagnostic, Prognostic, or
Screening Marker," Am. J. Epidemiol 2004, 159 (9): 882-890, and
summarized by the Area Under the Curve (AUC) or c-statistic, an
indicator that allows representation of the sensitivity and
specificity of a test, assay, or method over the entire range of
test (or assay) cut points with just a single value. See also,
e.g., Shultz, "Clinical Interpretation Of Laboratory Procedures,"
chapter 14 in Teitz, Fundamentals of Clinical Chemistry, Burtis and
Ashwood (eds.), 4.sup.th edition 1996, W.B. Saunders Company, pages
192-199; and Zweig et al., "ROC Curve Analysis: An Example Showing
The Relationships Among Serum Lipid And Apolipoprotein
Concentrations In Identifying Subjects With Coronory Artery
Disease," Clin. Chem., 1992, 38(8): 1425-1428. An alternative
approach using likelihood functions, odds ratios, information
theory, predictive values, calibration (including goodness-of-fit),
and reclassification measurements is summarized according to Cook,
"Use and Misuse of the Receiver Operating Characteristic Curve in
Risk Prediction," Circulation 2007, 115: 928-935.
[0167] "Accuracy" refers to the degree of conformity of a measured
or calculated quantity (a test reported value) to its actual (or
true) value. Clinical accuracy relates to the proportion of true
outcomes (true positives (TP) or true negatives (TN) versus
misclassified outcomes (false positives (FP) or false negatives
(FN)), and may be stated as a sensitivity, specificity, positive
predictive values (PPV) or negative predictive values (NPV),
Matheus correlation coefficient (MCC), or as a likelihood, odds
ratio, Receiver Operating Characteristic (ROC) curve, Area Under
the Curve (AUC) among other measures.
[0168] A "formula," "algorithm," or "model" is any mathematical
equation, algorithmic, analytical or programmed process, or
statistical technique that takes one or more continuous or
categorical inputs (herein called "parameters") and calculates an
output value, sometimes referred to as an "index" or "index value".
Non-limiting examples of "formulas" include sums, ratios, and
regression operators, such as coefficients or exponents, biomarker
value transformations and normalizations (including, without
limitation, those normalization schemes based on
clinical-determinants, such as gender, age, or ethnicity), rules
and guidelines, statistical classification models, and neural
networks trained on historical populations. Of particular use in
combining determinants are linear and non-linear equations and
statistical classification analyses to determine the relationship
between levels of determinants detected in a subject sample and the
subject's probability of having an infection or a certain type of
infection. In panel and combination construction, of particular
interest are structural and syntactic statistical classification
algorithms, and methods of index construction, utilizing pattern
recognition features, including established techniques such as
cross-correlation, Principal Components Analysis (PCA), factor
rotation, Logistic Regression (LogReg), Linear Discriminant
Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA),
Support Vector Machines (SVM), Random Forest (RF), Recursive
Partitioning Tree (RPART), as well as other related decision tree
classification techniques, Shrunken Centroids (SC), StepAIC,
Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks,
Bayesian Networks, and Hidden Markov Models, among others. Other
techniques may be used in survival and time to event hazard
analysis, including Cox, Weibull, Kaplan-Meier and Greenwood models
well known to those of skill in the art. Many of these techniques
are useful either combined with a determinant selection technique,
such as forward selection, backwards selection, or stepwise
selection, complete enumeration of all potential panels of a given
size, genetic algorithms, or they may themselves include biomarker
selection methodologies in their own technique. These may be
coupled with information criteria, such as Akaike's Information
Criterion (AIC) or Bayes Information Criterion (BIC), in order to
quantify the tradeoff between additional biomarkers and model
improvement, and to aid in minimizing overfit. The resulting
predictive models may be validated in other studies, or
cross-validated in the study they were originally trained in, using
such techniques as Bootstrap, Leave-One-Out (LOO) and 10-Fold
cross-validation (10-Fold CV). At various steps, false discovery
rates may be estimated by value permutation according to techniques
known in the art. A "health economic utility function" is a formula
that is derived from a combination of the expected probability of a
range of clinical outcomes in an idealized applicable patient
population, both before and after the introduction of a diagnostic
or therapeutic intervention into the standard of care. It
encompasses estimates of the accuracy, effectiveness and
performance characteristics of such intervention, and a cost and/or
value measurement (a utility) associated with each outcome, which
may be derived from actual health system costs of care (services,
supplies, devices and drugs, etc.) and/or as an estimated
acceptable value per quality adjusted life year (QALY) resulting in
each outcome. The sum, across all predicted outcomes, of the
product of the predicted population size for an outcome multiplied
by the respective outcome's expected utility is the total health
economic utility of a given standard of care. The difference
between (i) the total health economic utility calculated for the
standard of care with the intervention versus (ii) the total health
economic utility for the standard of care without the intervention
results in an overall measure of the health economic cost or value
of the intervention. This may itself be divided amongst the entire
patient group being analyzed (or solely amongst the intervention
group) to arrive at a cost per unit intervention, and to guide such
decisions as market positioning, pricing, and assumptions of health
system acceptance. Such health economic utility functions are
commonly used to compare the cost-effectiveness of the
intervention, but may also be transformed to estimate the
acceptable value per QALY the health care system is willing to pay,
or the acceptable cost-effective clinical performance
characteristics required of a new intervention.
[0169] For diagnostic (or prognostic) interventions of the
invention, as each outcome (which in a disease classifying
diagnostic test may be a TP, FP, TN, or FN) bears a different cost,
a health economic utility function may preferentially favor
sensitivity over specificity, or PPV over NPV based on the clinical
situation and individual outcome costs and value, and thus provides
another measure of health economic performance and value which may
be different from more direct clinical or analytical performance
measures. These different measurements and relative trade-offs
generally will converge only in the case of a perfect test, with
zero error rate (a.k.a., zero predicted subject outcome
misclassifications or FP and FN), which all performance measures
will favor over imperfection, but to differing degrees.
[0170] "Measuring" or "measurement," or alternatively "detecting"
or "detection," means assessing the presence, absence, quantity or
amount (which can be an effective amount) of either a given
substance within a clinical or subject-derived sample, including
the derivation of qualitative or quantitative concentration levels
of such substances, or otherwise evaluating the values or
categorization of a subject's non-analyte clinical parameters or
clinical-determinants.
[0171] "Analytical accuracy" refers to the reproducibility and
predictability of the measurement process itself, and may be
summarized in such measurements as coefficients of variation (CV),
Pearson correlation, and tests of concordance and calibration of
the same samples or controls with different times, users, equipment
and/or reagents. These and other considerations in evaluating new
biomarkers are also summarized in Vasan, 2006.
[0172] "Performance" is a term that relates to the overall
usefulness and quality of a diagnostic or prognostic test,
including, among others, clinical and analytical accuracy, other
analytical and process characteristics, such as use characteristics
(e.g., stability, ease of use), health economic value, and relative
costs of components of the test. Any of these factors may be the
source of superior performance and thus usefulness of the test, and
may be measured by appropriate "performance metrics," such as AUC
and MCC, time to result, shelf life, etc. as relevant.
[0173] A "sample" in the context of the present invention is a
biological sample isolated from a subject and can include, by way
of example and not limitation, whole blood, serum, plasma, saliva,
mucus, breath, urine, CSF, sputum, sweat, stool, hair, seminal
fluid, biopsy, rhinorrhea, tissue biopsy, cytological sample,
platelets, reticulocytes, leukocytes, epithelial cells, or whole
blood cells.
[0174] According to a particular embodiment the sample is a serum
sample.
[0175] By "statistically significant", it is meant that the
alteration is greater than what might be expected to happen by
chance alone (which could be a "false positive"). Statistical
significance can be determined by any method known in the art.
Commonly used measures of significance include the p-value, which
presents the probability of obtaining a result at least as extreme
as a given data point, assuming the data point was the result of
chance alone. A result is often considered highly significant at a
p-value of 0.05 or less.
[0176] A "subject" in the context of the present invention may be a
mammal (e.g. human dog, cat, horse, cow, sheep, pig, goat).
According to another embodiment, the subject is a bird (e.g.
chicken, turkey, duck, goose. According to a particular embodiment,
the subject is a human. A subject can be male or female. A subject
can be one who has been previously diagnosed or identified as
having an infection, and optionally has already undergone, or is
undergoing, a therapeutic intervention for the infection.
Alternatively, a subject can also be one who has not been
previously diagnosed as having an infection. For example, a subject
can be one who exhibits one or more risk factors for having an
infection.
[0177] In the context of the present invention the following
abbreviations may be used: Antibiotics (Abx), Adverse Event (AE),
Arbitrary Units (A.U.), Complete Blood Count (CBC), Case Report
Form (CRF), Chest X-Ray (CXR), Electronic Case Report Form (eCRF),
Food and Drug Administration (FDA), Good Clinical Practice (GCP),
Gastrointestinal (GI), Gastroenteritis (GE), International
Conference on Harmonization (ICH), Infectious Disease (ID), In
vitro diagnostics (IVD), Lower Respiratory Tract Infection (LRTI),
Myocardial infarction (MI), Polymerase chain reaction (PCR),
Per-oss (P.O), Per-rectum (P.R), Standard of Care (SoC), Standard
Operating Procedure (SOP), Urinary Tract Infection (UTI), Upper
Respiratory Tract Infection (URTI).
[0178] Methods and Uses of the Invention
[0179] The methods disclosed herein are used to identify subjects
with an infection or a specific infection type. By type of
infection it is meant to include bacterial infections, viral
infections, mixed infections, no infection (i.e., non-infectious).
More specifically, some methods of the invention are used to
distinguish subjects having a bacterial infection, a viral
infection, a mixed infection (i.e., bacterial and viral
co-infection), patients with a non-infectious disease and healthy
individuals. Some methods of the present invention can also be used
to monitor or select a treatment regimen for a subject who has a an
infection, and to screen subjects who have not been previously
diagnosed as having an infection, such as subjects who exhibit risk
factors developing an infection. Some methods of the present
invention are used to identify and/or diagnose subjects who are
asymptomatic for an infection. "Asymptomatic" means not exhibiting
the traditional signs and symptoms.
[0180] The term "Gram-positive bacteria" are bacteria that are
stained dark blue by Gram staining. Gram-positive organisms are
able to retain the crystal violet stain because of the high amount
of peptidoglycan in the cell wall.
[0181] The term "Gram-negative bacteria" are bacteria that do not
retain the crystal violet dye in the Gram staining protocol.
[0182] The term "Atypical bacteria" are bacteria that do not fall
into one of the classical "Gram" groups. They are usually, though
not always, intracellular bacterial pathogens. They include,
without limitations, Mycoplasmas spp., Legionella spp. Rickettsiae
spp., and Chlamydiae spp.
[0183] As used herein, infection is meant to include any infectious
agent of viral or bacterial origin. The bacterial infection may be
the result of gram-positive, gram-negative bacteria or atypical
bacteria.
[0184] A subject having an infection is identified by measuring the
amounts (including the presence or absence) of an effective number
(which can be one or more) of determinants in a subject-derived
sample. A clinically significant alteration in the level of the
determinant is determined. Alternatively, the amounts are compared
to a reference value. Alterations in the amounts and patterns of
expression determinants in the subject sample compared to the
reference value are then identified. In various embodiments, two,
three, four, five, six, seven, eight, nine, ten or more
determinants are measured. In various embodiments not more than
two, no more than three, no more than four determinants are
measured.
[0185] In some embodiments, the combination of determinants
comprise measurements of a first determinant which is set forth in
Table 1 and a second determinant which is set forth in Table 2.
[0186] According to a particular embodiment, the first determinant
is a1 Acid Glycoprotein, Adiponectin, Angiogenin, Angiopoietinl,
Angiopoietin2, APRIL, BAFF, BDNF, CD 23, CD14, CD142, CD27, CD95,
Clusterin, Complement factor D, Corin, CXCL13, Cystatin C, Dkk1, E
Cadherin, E Selectin, Endostatin, Fetuin A, GCP2, GDF15, ICAM1,
IGFBP3, IL18, IL19, Leptin, Leptin R, LIGHT, MBL, MIF, MMP2,
[0187] MMP3, MMP7, MMP8, Myeloperoxidase, Neopterin, NGAL,
Osteopontin, Osteoprotegerin, P Selectin, PCSK9, Pentraxin3, Pro
Cathepsin B, Progranulin, ProMMP10, Prostaglandin E2, RBP4,
Resistin, SLPI, Substance P, TFPI, TGF B1, Thrombospondin2, Tie2,
uPAR, VCAM1, VEGF C or Vitamin D Binding Protein. In another
embodiment, the first determinant is NGAL, Resistin, MMP8,
Pentraxin3, E Selectin, MMP7, Myeloperoxidase, Osteopontin, PCSK9,
Pro Cathepsin B, a1 Acid Glycoprotein, GDF15, Progranulin,
Adiponectin, Clusterin, Corin, Neopterin, Cystatin C, CD27, E
Cadherin, Complement factor D, IGFBP3, GCP2, RBP4, CD14 or
ProMMP10.
[0188] In yet another embodiment, the first determinant is NGAL,
MMP8 or Neopterin.
[0189] According to another embodiment, the second determinant is
CRP, TRAIL or IP-10.
[0190] In one embodiment, the present inventors contemplate
analyzing no more than two determinants to distinguish between
bacterial and viral infections.
[0191] Exemplary pairs include, but are not limited to CRP and
NGAL; CRP and MMP8; CRP and Neopterin; TRAIL and NGAL; TRAIL and
MMP8 TRAIL and Neopterin; IP-10 and NGAL; IP-10 and MMP8; IP-10 and
Neopterin; IL1R and NGAL; IL1R and MMP8; IL1R and Neopterin; PCT
and NGAL; PCT and MMP8; PCT and Neopterin; SAA and NGAL; SAA and
MMP8; SAA and Neopterin. TREM1 and NGAL; TREM1 and MMP8; TREM1 and
Neopterin; TREM2 and NGAL; TREM2 and MMP8; TREM2 and Neopterin; MX1
and NGAL; MX1 and MMP8; MX1 and Neopterin; RSAD2 and NGAL; RSAD2
and MMP8; RSAD2 and Neopterin.
[0192] Additional contemplated exemplary pairs include CRP and
TRAILR3/TNFRSF10C, CRP and TRAILR4/TNFRSF10D, CRP and
TRAIL-R1/TNFRSF10A and CRP and TRAIL-R2/TNFRSF10B.
[0193] Other exemplary pairs include TRAIL and TRAILR3/TNFRSF10C;
TRAIL and TRAILR4/TNFRSF10D; TRAIL and TRAIL-R1/TNFRSF10A; and
TRAIL and TRAIL-R2/TNFRSF10B.
[0194] Other exemplary pairs include IP10 and TRAILR3/TNFRSF10C,
IP10 and TRAILR4/TNFRSF10D, IP10 and TRAIL-R1/TNFRSF10A and IP10
and TRAIL-R2/TNFRSF10B.
[0195] Other exemplary pairs include Neopterin and PCT; or NGAL and
PCT.
[0196] Other exemplary pairs include IL1-Ra and TRAILR3/TNFRSF10C,
IL1-Ra and TRAILR4/TNFRSF10D, IL1-Ra and TRAIL-R1/TNFRSF10A, and
IL1-Ra and TRAIL-R2/TNFRSF10B.
[0197] Other exemplary pairs include PCT and TRAILR3/TNFRSF10C, PCT
and TRAILR4/TNFRSF10D, PCT and TRAIL-R1/TNFRSF10A and PCT and
TRAIL-R2/TNFRSF10B.
[0198] Other exemplary pairs include sTREM and TRAILR3/TNFRSF10C,
sTREM and TRAILR4/TNFRSF10D, sTREM and TRAIL-R1/TNFRSF10A and,
sTREM and TRAIL-R2/TNFRSF10B.
[0199] Other exemplary pairs include RSAD2 and TRAILR3/TNFRSF10C,
RSAD2 and TRAILR4/TNFRSF10D, RSAD2 and TRAIL-R1/TNFRSF10A, and
RSAD2 and TRAIL-R2/TNFRSF10B.
[0200] Other exemplary pairs include MX1 and TRAILR3/TNFRSF10C, MX1
and TRAIL-R1/TNFRSF10A, MX1 and TRAIL-R2/TNFRSF10B, and MX1 and
TRAILR4/TNFRSF10D.
[0201] Additional pairs contemplated by the present inventors
include TRAIL and MX1; TRAIL and RSAD2; TRAIL and sTREM; and TRAIL
and IL1-Ra.
[0202] Exemplary pairs include CRP and NGAL, CRP and a1-Acid
Glycoprotein/ORM1, CRP and IL18, CRP and CXCL6, CRP and MBL, CRP
and OSM/Oncostatin M, CRP and TNFSF14 and CRP and CD14.
[0203] Other exemplary pairs include TRAIL and NGAL, TRAIL and
a1-Acid Glycoprotein/ORM1, TRAIL and IL18, TRAIL and CXCL6, TRAIL
and MBL, TRAIL and OSM/Oncostatin M, TRAIL and TNFSF14 and TRAIL
and CD14.
[0204] Other exemplary pairs include IP10 and NGAL, IP10 and
a1-Acid Glycoprotein/ORM1, IP10 and IL18, IP10 and CXCL6, IP10 and
MBL, IP10 and OSM/Oncostatin M, IP10 and TNFSF14 and IP10 and
CD14.
[0205] Other exemplary pairs include IL1-Ra and NGAL, IL1-Ra and
a1-Acid Glycoprotein/ORM1, IL1-Ra and IL18, IL1-Ra and CXCL6,
IL1-Ra and MBL, IL1-Ra and OSM/Oncostatin M, IL1-Ra and TNFSF14 and
IL1-Ra and CD14.
[0206] Other exemplary pairs include PCT and NGAL, PCT and a1-Acid
Glycoprotein/ORM1, PCT and IL18, PCT and CXCL6, PCT and MBL, PCT
and OSM/Oncostatin M, PCT and TNFSF14 and PCT and CD14.
[0207] Other exemplary pairs include sTREM and NGAL, sTREM and
a1-Acid Glycoprotein/ORM1, sTREM and IL18, sTREM and CXCL6, sTREM
and MBL, sTREM and OSM/Oncostatin M, sTREM and TNFSF14 and sTREM
and CD14.
[0208] Other exemplary pairs include RSAD2 and NGAL, RSAD2 and
a1-Acid Glycoprotein/ORM1, RSAD2 and IL18, RSAD2 and CXCL6, RSAD2
and MBL, RSAD2 and OSM/Oncostatin M, RSAD2 and TNFSF14 and RSAD2
and CD14.
[0209] Other exemplary pairs include MX1 and NGAL, MX1 and a1-Acid
Glycoprotein/ORM1, MX1 and IL18, MX1 and CXCL6, MX1 and MBL, MX1
and OSM/Oncostatin M, MX1 and TNFSF14 and MX1 and CD14.
[0210] It will be appreciated that 2, 3, 4 or more determinants
from group 2 may be measured together with at least 1 determinant
from group 1.
[0211] Thus for example TRAIL and CRP may be measured together with
neopterin; TRAIL and CRP may be measured together with NGAL; TRAIL
and CRP may be measured together with MMP8; TRAILR3/TNFRSF10C;
TRAIL and CRP may be measured together with TRAILR4/TNFRSF10D;
TRAIL and CRP may be measured together with TRAIL-R1/TNFRSF10A; and
TRAIL and CRP may be measured together with TRAIL-R2/TNFRSF10B.
[0212] Alternatively, TRAIL and IP10 may be measured together with
neopterin; TRAIL and IP10 may be measured together with NGAL; TRAIL
and IP10 may be measured together with MMP8; TRAIL and IP10 may be
measured together with TRAILR3/TNFRSF10C; TRAIL and IP10 may be
measured together with TRAILR4/TNFRSF10D; TRAIL and IP10 may be
measured together with TRAIL-R1/TNFRSF10A; and TRAIL and IP10 may
be measured together with TRAIL-R2/TNFRSF10B.
[0213] Alternatively, CRP and IP10 may be measured together with
neopterin; CRP and IP10 may be measured together with NGAL; CRP and
IP10 may be measured together with MMP8; CRP and IP10 may be
measured together with TRAILR3/TNFRSF10C; CRP and IP10 may be
measured together with TRAIL-R1/TNFRSF10A; and CRP and IP10 may be
measured together with TRAIL-R2/TNFRSF10B.
[0214] Thus for example TRAIL and CRP may be measured together with
NGAL; TRAIL and CRP may be measured together with a1-Acid
Glycoprotein/ORM1; TRAIL and CRP may be measured together with
IL18; TRAIL and CRP may be measured together with CXCL6; TRAIL and
CRP may be measured together with MBL; TRAIL and CRP may be
measured together with OSM/Oncostatin M; TRAIL and CRP may be
measured together with TNFSF14 and TRAIL and CRP may be measured
together with CD14.
[0215] Alternatively TRAIL and IP10 may be measured together with
NGAL; TRAIL and IP10 may be measured together with a1-Acid
Glycoprotein/ORM1; TRAIL and IP10 may be measured together with
IL18; TRAIL and IP10 may be measured together with CXCL6; TRAIL and
IP10 may be measured together with MBL; TRAIL and IP10 may be
measured together with OSM/Oncostatin M; TRAIL and IP10 may be
measured together with TNFSF14 and TRAIL and IP10 may be measured
together with CD14.
[0216] Alternatively CRP and IP10 may be measured together with
NGAL, CRP and IP10 may be measured together with a1-Acid
Glycoprotein/ORM1; CRP and IP10 may be measured together with IL18;
CRP and IP10 may be measured together with CXCL6; CRP and IP10 may
be measured together with MBL; CRP and IP10 may be measured
together with OSM/Oncostatin M; CRP and IP10 may be measured
together with TNFSF14 and CRP and IP10 may be measured together
with CD14.
[0217] According to another embodiment 3 proteins from Table 2 are
measured with at least one determinant in Table 1. Exemplary
combinations include TRAIL, CRP and IP10 may be measured together
with NGAL; TRAIL, CRP and IP10 may be measured together with
neopterin; TRAIL, CRP and IP10 may be measured together with MMP8;
TRAIL, CRP and IP10 may be measured together with
TRAILR3/TNFRSF10C; TRAIL, CRP and IP10 may be measured together
with TRAILR4/TNFRSF10D; TRAIL, CRP and IP10 may be measured
together with TRAIL-R1/TNFRSF10A; TRAIL, CRP and IP10 may be
measured together with TRAIL-R2/TNFRSF10B.
[0218] Exemplary combinations include TRAIL, CRP and IP10 may be
measured together with NGAL; TRAIL, CRP and IP10 may be measured
together with a1-Acid Glycoprotein/ORM1; TRAIL, CRP and IP10 may be
measured together with IL18; TRAIL, CRP and IP10 may be measured
together with CXCL6; TRAIL, CRP and IP10 may be measured together
with MBL; TRAIL, CRP and IP10 may be measured together with
OSM/Oncostatin M; TRAIL, CRP and IP10 may be measured together with
TNFSF14; and TRAIL, CRP and IP10 may be measured together with
CD14.
[0219] In other embodiments, the combination of determinants
comprise measurements of at least two determinants which are set
forth in Table 1.
[0220] According to a particular embodiment, at least one of the
determinants in Table 1 is MMP-8, NGAL, or neopterin. Other
contemplated combinations include TRAILR3/TNFRSF10C,
TRAILR4/TNFRSF10D, TRAIL-R1/TNFRSF10A, TRAIL-R2/TNFRSF10B.
[0221] According to yet another embodiment, both the determinants
in Table 1 are selected from the group consisting of:
[0222] MMP-8, NGAL, neopterin, TRAILR3/TNFRSF10C,
TRAILR4/TNFRSF10D, TRAIL-R1/TNFRSF10A and TRAIL-R2/TNFRSF10B.
[0223] Particular combinations include MMP-8 and NGAL; MMP-8 and
neopterin or NGAL and neopterin.
[0224] In some embodiments, the determinant measurements further
comprise measurements of one or more clinical-determinants selected
from the group consisting of ANC, ALC, Neu (%), Lym (%), Mono (%),
Maximal temperature, Time from symptoms, Age, Creatinine (Cr),
Potassium (K), Pulse and Urea.
[0225] In some embodiments, the determinants or
clinical-determinants further comprise measurements of one or more
polypeptide or clinical-determinants selected from the group
consisting of ARG1, ARPC2, ATP6V0B, BILI (Bilirubin), BRI3BP,
CCL19-MIP3B, CES1, CORO1A, EOS(%), HERC5, IFI6, IFIT3, KIAA0082,
LIPT1, LRDD, MCP-2, NA (Sodium), PARP9, PTEN, QARS, RAB13, RPL34,
SART3, TRIM22, UBE2N, WBC (Whole Blood Count), XAF1 and ZBP1.
[0226] In various aspects the method distinguishes a virally
infected subject from either a subject with non-infectious disease
or a healthy subject; a bacterially infected subject, from either a
subject with non-infectious disease or a healthy subject; a subject
with an infectious disease from either a subject with an
non-infectious disease or a healthy subject; a bacterially infected
subject from a virally infected subject; a mixed infected subject
from a virally infected subject; a mixed infected subject from a
bacterially infected subject and a bacterially or mixed infected
and subject from a virally infected subject.
[0227] In one aspect the method distinguishes a bacterially
infected subject from a virally infected subject by measuring a
first determinant set forth in Table 1 and a second determinant as
set forth in Table 2.
[0228] Exemplary pairs are provided herein above.
[0229] In one aspect the method distinguishes a bacterially
infected subject from a virally infected subject by measuring at
least two determinants set forth in Table 1.
[0230] Exemplary pairs are provided herein above.
[0231] In another aspect the method distinguishes between a
bacterial or mixed infected subject and a virally infected subject
by measuring a first determinant set forth in Table 1 and a second
determinant as set forth in Table 2.
[0232] Exemplary pairs are provided herein above.
[0233] In another aspect the method distinguishes between a
bacterial or mixed infected subject and a virally infected subject
by measuring at least two determinants set forth in Table 1.
[0234] Exemplary pairs of determinants from Table 1 include NGAL
and MMP8; NGAL and Neopterin; and MMP8 and Neopterin.
[0235] In another aspect the method distinguishes between a subject
with an infectious disease and a subject with a non-infectious
disease or a healthy subject by measuring a first determinant set
forth in Table 1 and a second determinant as set forth in Table
2.
[0236] Exemplary pairs are provided herein above.
[0237] In another aspect the method distinguishes between a subject
with an infectious disease and a subject with a non-infectious
disease or a healthy subject by measuring at least two determinants
set forth in Table 1.
[0238] Exemplary pairs are provided herein above.
[0239] In specific embodiments the invention includes determining
if a subject does not have a bacterial infection (i.e. ruling out a
bacterial infection).
[0240] For example, a bacterial infection may be ruled out if the
polypeptide concentration of TRAIL determined is higher than a
pre-determined first threshold value. Optionally, the method
further includes determining if a subject has a viral infection
(i.e., ruling in a viral infection). A viral infection is rule in
if the polypeptide concentration of TRAIL is higher than a
pre-determined second threshold value.
[0241] In another specific embodiment the invention includes
determining if a subject does not have a viral infection (i.e.
ruling out a viral infection). A viral infection is ruled out if
the polypeptide concentration of TRAIL determined is lower than a
pre-determined first threshold value. Optionally, the method
further includes determining if a subject has a bacterial infection
(i.e., ruling in a bacterial infection). A bacterial infection is
rule in if the polypeptide concentration of TRAIL is lower than a
pre-determined second threshold value.
[0242] Indicative levels of some exemplary determinants which
correspond to particular infection types are set forth in Table
3.
TABLE-US-00003 TABLE 3 Determinant Bacterial Viral Fas/TNFRSF6 +
+++ Fas Ligand/TNFSF6 + +++ TWEAK/TNFSF12 + +++ 4-1BB/TNFRSF9 + +++
OX40/TNFRSF4 + +++ CD30 Ligand/TNFSF8 + +++ TRANCE/TNFSF11 + +++
GITR Ligand/TNFSF18 + +++ GITR/TNFRSF18 + +++ DCR3/TNFRSF6B + +++
HVEM/TNFRSF14 + +++ TWEAKR/TNFRSF12A + +++ TACI/TNFRSF13B + +++
BCMA/TNFRSF17 + +++ NGFR/TNFRSF16 + +++ DR6/TNFRSF21 + +++
RANK/TNFRSF11A + +++ EDA2R/TNFRSF27 + +++ RELT/TNFRSF19L + +++
OX40L/TNFSF4 + +++ TNFRSF3 + +++ 4-1BB Ligand/TNFSF9 + +++
DR3/TNFRSF25 + +++ TL1A/TNFSF15 + +++ TROY/TNFRSF19 + +++
BAFFR/TNFRSF13C + +++ CD27 Ligand/TNFSF7 + +++ TRAILR3/TNFRSF10C -
+++ TRAILR4/TNFRSF10D - +++ TRAIL-R1/TNFRSF10A - +++
TRAIL-R2/TNFRSF10B - +++
[0243] For TRAILR3/TNFRSF10C, a bacterial infection may be ruled in
if the polypeptide concentration is below a predetermined level. A
bacterial infection may be ruled out if the polypeptide
concentration is above a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is above a
predetermined level.
[0244] For TRAILR4/TNFRSF10D, a bacterial infection may be ruled in
if the polypeptide concentration is below a predetermined level. A
bacterial infection may be ruled out if the polypeptide
concentration is above a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is above a
predetermined level.
[0245] For TRAIL-R1/TNFRSF10A, a bacterial infection may be ruled
in if the polypeptide concentration is below a predetermined level.
A bacterial infection may be ruled out if the polypeptide
concentration is above a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is above a
predetermined level.
[0246] For TRAIL-R2/TNFRSF10B, a bacterial infection may be ruled
in if the polypeptide concentration is below a predetermined level.
A bacterial infection may be ruled out if the polypeptide
concentration is above a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is above a
predetermined level.
[0247] For MMP8, a bacterial infection may be ruled in if the
polypeptide concentration is above a predetermined level. A
bacterial infection may be ruled out if the polypeptide
concentration is below a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is below a
predetermined level.
[0248] For NGAL, a bacterial infection may be ruled in if the
polypeptide concentration is above a predetermined level. A
bacterial infection may be ruled out if the polypeptide
concentration is below a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is below a
predetermined level.
[0249] For neopterin, a bacterial infection may be ruled in if the
concentration thereof is below a predetermined level. A bacterial
infection may be ruled out if the concentration thereof is above a
predetermined level. A viral infection may be ruled in if the
concentration thereof is above a predetermined level.
[0250] For a1-Acid Glycoprotein/ORM1, a bacterial infection may be
ruled in if the polypeptide concentration is above a predetermined
level. A bacterial infection may be ruled out if the polypeptide
concentration is below a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is above a first
predetermined level and below a second predetermined level.
[0251] For IL18, a bacterial infection may be ruled in if the
polypeptide concentration is above a predetermined level. A
bacterial infection may be ruled out if the polypeptide
concentration is below a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is above a first
predetermined level and below a second predetermined level.
[0252] For CXCL6, a bacterial infection may be ruled in if the
polypeptide concentration is above a predetermined level. A
bacterial infection may be ruled out if the polypeptide
concentration is below a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is above a first
predetermined level and below a second predetermined level.
[0253] For MBL, a bacterial infection may be ruled in if the
polypeptide concentration is above a predetermined level. A
bacterial infection may be ruled out if the polypeptide
concentration is below a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is above a first
predetermined level and below a second predetermined level.
[0254] For OSM/Oncostatin M, a bacterial infection may be ruled in
if the polypeptide concentration is above a predetermined level. A
bacterial infection may be ruled out if the polypeptide
concentration is below a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is above a first
predetermined level and below a second predetermined level.
[0255] For TNFSF14, a bacterial infection may be ruled in if the
polypeptide concentration is below a predetermined level. A
bacterial infection may be ruled out if the polypeptide
concentration is above a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is above a
predetermined level.
[0256] For CD14. a bacterial infection may be ruled in if the
polypeptide concentration is above a predetermined level. A
bacterial infection may be ruled out if the polypeptide
concentration is below a predetermined level. A viral infection may
be ruled in if the polypeptide concentration is above a first
predetermined level and below a second predetermined level.
[0257] For example, a subject may be diagnosed as having a viral
infection when the determinant levels of TRAIL, IP-10, Progranulin,
Adiponectin, Clusterin, Corn, Neopterin, Cystatin C, CD27, E
Cadherin, Complement factor D, IGFBP3, GCP2, RBP4, CD14 and/or
ProMMP10, are at least 5%, 10%, 20%, 30%, 40%, 50%, 100%, 200%,
300%, or 400% higher than a bacterially-infected subject reference
value.
[0258] For example, a subject may be diagnosed as having a
bacterial infection when the polypeptide levels of CRP, NGAL,
Resistin, MMP8, Pentraxin3, IL1R, E Selectin, MMP7,
Myeloperoxidase, Osteopontin, PCSK9, Pro Cathepsin B, a1 Acid
Glycoprotein, GDF15, are at least 5%, 10%, 20%, 30%, 40%, 50%,
100%, 200%, 300%, or 400% higher than a virally-infected subject
reference value.
[0259] For example a subject may be diagnosed as having a bacterial
infection when the determinant levels of TRAIL, IP-10, Progranulin,
Adiponectin, Clusterin, Corin, Neopterin, Cystatin C, CD27, E
Cadherin, Complement factor D, IGFBP3, GCP2, RBP4, CD14, ProMMP10
are 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, 5%, 1% or less of
a virally-infected subject or a healthy subject reference
value.
[0260] For example a subject may be diagnosed as having a viral
infection when the polypeptide levels of CRP, NGAL, Resistin, MMP8,
Pentraxin3, IL1R, E Selectin, MMP7, Myeloperoxidase, Osteopontin,
PCSK9, Pro Cathepsin B, a1 Acid Glycoprotein, GDF15, are 90%, 80%,
70%, 60%, 50%, 40%, 30%, 20%, 10%, 5%, 1% or less than a
bacterially-infected subject or a healthy subject reference
value.
[0261] In other embodiments the invention includes a method of
distinguishing between a bacterial infection and a viral infection
in a subject by measuring the polypeptide concentration of TRAIL
and NGAL in a subject derived sample, applying a pre-determined
mathematical function on the concentrations of TRAIL and NGAL to
compute a score and comparing the score to a predetermined
reference value. Optionally, one or more of CRP, PCT or IP10 is
also measured. These additional measurements may be integrated into
the score.
[0262] In other embodiments the invention includes a method of
distinguishing between a bacterial infection and a viral infection
in a subject by measuring the polypeptide concentration of TRAIL
and MMP-8 in a subject derived sample, applying a pre-determined
mathematical function on the concentrations of TRAIL and MMP-8 to
compute a score and comparing the score to a predetermined
reference value. Optionally, one or more of CRP, NGAL, PCT or IP10
is also measured. These additional measurements may be integrated
into the score.
[0263] In other embodiments the invention includes a method of
distinguishing between a bacterial infection and a viral infection
in a subject by measuring the concentration of TRAIL and neopterin
in a subject derived sample, applying a pre-determined mathematical
function on the concentrations of TRAIL and neopterin to compute a
score and comparing the score to a predetermined reference value.
Optionally, one or more of CRP, NGAL, PCT or IP10 is also measured.
These additional measurements may be integrated into the score.
[0264] In other embodiments the invention includes a method of
distinguishing between a bacterial infection and a viral infection
in a subject by measuring the polypeptide concentration of TRAIL
and TRAILR3/TNFRSF10C in a subject derived sample, applying a
pre-determined mathematical function on the concentrations of TRAIL
and TRAILR3/TNFRSF10C to compute a score and comparing the score to
a predetermined reference value. Optionally, one or more of CRP or
IP10, NGAL, PCT is also measured. These additional measurements may
be integrated into the score.
[0265] In another embodiment, the invention provides a method of
distinguishing between a bacterial or mixed infection, and a viral
infection in a subject by measuring the polypeptide concentration
of TRAIL and TRAILR3/TNFRSF10C in a subject derived sample,
applying a pre-determined mathematical function on the
concentrations of TRAIL and TRAILR3/TNFRSF10C to compute a score
and comparing the score to a predetermined reference value.
Optionally, one or more of CRP, NGAL, PCT or IP10 is also measured.
These additional measurements may be integrated into the score.
[0266] In other embodiments the invention includes a method of
distinguishing between a bacterial infection and a viral infection
in a subject by measuring the polypeptide concentration of TRAIL
and TRAIL-R1/TNFRSF10A in a subject derived sample, applying a
pre-determined mathematical function on the concentrations of TRAIL
and TRAIL-R1/TNFRSF10A to compute a score and comparing the score
to a predetermined reference value. Optionally, one or more of CRP,
NGAL, PCT or IP10 is measured. These additional measurements may be
integrated into the score.
[0267] In another embodiment, the invention provides a method of
distinguishing between a bacterial or mixed infection, and a viral
infection in a subject by measuring the polypeptide concentration
of TRAIL and TRAIL-R1/TNFRSF10A in a subject derived sample,
applying a pre-determined mathematical function on the
concentrations of TRAIL and TRAIL-R1/TNFRSF10A to compute a score
and comparing the score to a predetermined reference value.
Optionally, one or more of CRP, NGAL, PCT or IP10 is also measured.
These additional measurements may be integrated into the score.
[0268] In other embodiments the invention includes a method of
distinguishing between a bacterial infection and a viral infection
in a subject by measuring the polypeptide concentration of TRAIL
and TRAILR4/TNFRSF10D in a subject derived sample, applying a
pre-determined mathematical function on the concentrations of TRAIL
and TRAILR4/TNFRSF10D to compute a score and comparing the score to
a predetermined reference value. Optionally, one or more of CRP,
NGAL, PCT or IP10 is measured. These additional measurements may be
integrated into the score.
[0269] In another embodiment, the invention provides a method of
distinguishing between a bacterial or mixed infection, and a viral
infection in a subject by measuring the polypeptide concentration
of TRAIL and TRAILR4/TNFRSF10D in a subject derived sample,
applying a pre-determined mathematical function on the
concentrations of TRAIL and TRAILR4/TNFRSF10D to compute a score
and comparing the score to a predetermined reference value.
Optionally, one or more CRP, NGAL, PCT or IP10 is also measured.
These additional measurements may be integrated into the score.
[0270] In other embodiments the invention includes a method of
distinguishing between a bacterial infection and a viral infection
in a subject by measuring the polypeptide concentration of TRAIL
and TRAIL-R2/TNFRSF10B in a subject derived sample, applying a
pre-determined mathematical function on the concentrations of TRAIL
and TRAIL-R2/TNFRSF10B to compute a score and comparing the score
to a predetermined reference value. Optionally, one or more of CRP,
NGAL, PCT or IP10 is also measured. These additional measurements
may be integrated into the score.
[0271] In another embodiment, the invention provides a method of
distinguishing between a bacterial or mixed infection, and a viral
infection in a subject by measuring the polypeptide concentration
of TRAIL and TRAIL-R2/TNFRSF10B in a subject derived sample,
applying a pre-determined mathematical function on the
concentrations of TRAIL and TRAIL-R2/TNFRSF10B to compute a score
and comparing the score to a predetermined reference value.
Optionally, one or more CRP, NGAL, PCT or IP10 is also measured.
These additional measurements may be integrated into the score.
[0272] In another embodiment, the invention provides a method of
distinguishing between a bacterial or mixed infection, and a viral
infection in a subject by measuring the polypeptide concentration
of TRAIL, IP10, CRP and at least one of neopterin, MMP8 or NGAL in
a subject derived sample, applying a pre-determined mathematical
function on the concentrations of each to compute a score and
comparing the score to a predetermined reference value. Further
information on generating pre-determined mathematical functions in
general and for CRP, IP10 and TRAIL in particular are provided in
International Patent Application IL2015/050823, the contents of
which are incorporated herein by reference.
[0273] A reference value can be relative to a number or value
derived from population studies, including without limitation, such
subjects having the same infection, subject having the same or
similar age range, subjects in the same or similar ethnic group, or
relative to the starting sample of a subject undergoing treatment
for an infection. Such reference values can be derived from
statistical analyses and/or risk prediction data of populations
obtained from mathematical algorithms and computed indices of
infection. Reference determinant indices can also be constructed
and used using algorithms and other methods of statistical and
structural classification.
[0274] In one embodiment of the present invention, the reference
value is the amount (i.e. level) of determinants in a control
sample derived from one or more subjects who do not have an
infection (i.e., healthy, and or non-infectious individuals). In a
further embodiment, such subjects are monitored and/or periodically
retested for a diagnostically relevant period of time
("longitudinal studies") following such test to verify continued
absence of infection. Such period of time may be one day, two days,
two to five days, five days, five to ten days, ten days, or ten or
more days from the initial testing date for determination of the
reference value. Furthermore, retrospective measurement of
determinants in properly banked historical subject samples may be
used in establishing these reference values, thus shortening the
study time required.
[0275] A reference value can also comprise the amounts of
determinants derived from subjects who show an improvement as a
result of treatments and/or therapies for the infection. A
reference value can also comprise the amounts of determinants
derived from subjects who have confirmed infection by known
techniques.
[0276] An example of a bacterially infected reference value index
value is the mean or median concentrations of that determinant in a
statistically significant number of subjects having been diagnosed
as having a bacterial infection.
[0277] An example of a virally infected reference value is the mean
or median concentrations of that determinant in a statistically
significant number of subjects having been diagnosed as having a
viral infection.
[0278] Exemplary bacterial and viral reference values are provided
in Table 4 for each of the determinants (presented as the mean
and/or the median).
[0279] In another embodiment, the reference value is an index value
or a baseline value. An index value or baseline value is a
composite sample of an effective amount of determinants from one or
more subjects who do not have an infection. A baseline value can
also comprise the amounts of determinants in a sample derived from
a subject who has shown an improvement in treatments or therapies
for the infection. In this embodiment, to make comparisons to the
subject-derived sample, the amounts of determinants are similarly
calculated and compared to the index value. Optionally, subjects
identified as having an infection, are chosen to receive a
therapeutic regimen to slow the progression or eliminate the
infection.
[0280] Additionally, the amount of the determinant can be measured
in a test sample and compared to the "normal control level,"
utilizing techniques such as reference limits, discrimination
limits, or risk defining thresholds to define cutoff points and
abnormal values. The "normal control level" means the level of one
or more determinants or combined determinant indices typically
found in a subject not suffering from an infection. Such normal
control level and cutoff points may vary based on whether a
determinant is used alone or in a formula combining with other
determinants into an index. Alternatively, the normal control level
can be a database of determinant patterns from previously tested
subjects.
[0281] The effectiveness of a treatment regimen can be monitored by
detecting a determinant in an effective amount (which may be one or
more) of samples obtained from a subject over time and comparing
the amount of determinants detected. For example, a first sample
can be obtained prior to the subject receiving treatment and one or
more subsequent samples are taken after or during treatment of the
subject.
[0282] For example, the methods of the invention can be used to
discriminate between bacterial, viral and mixed infections (i.e.
bacterial and viral co-infections.) This will allow patients to be
stratified and treated accordingly.
[0283] In a specific embodiment of the invention a treatment
recommendation (i.e., selecting a treatment regimen) for a subject
is provided by identifying the type infection (i.e., bacterial,
viral, mixed infection or no infection) in the subject according to
the method of any of the disclosed methods and recommending that
the subject receive an antibiotic treatment if the subject is
identified as having bacterial infection or a mixed infection; or
an anti-viral treatment is if the subject is identified as having a
viral infection.
[0284] In another embodiment, the methods of the invention can be
used to prompt additional targeted diagnosis such as pathogen
specific PCRs, chest-X-ray, cultures etc. For example, a diagnosis
that indicates a viral infection according to embodiments of this
invention, may prompt the usage of additional viral specific
multiplex-PCRs, whereas a diagnosis that indicates a bacterial
infection according to embodiments of this invention may prompt the
usage of a bacterial specific multiplex-PCR. Thus, one can reduce
the costs of unwarranted expensive diagnostics.
[0285] In a specific embodiment, a diagnostic test recommendation
for a subject is provided by identifying the infection type (i.e.,
bacterial, viral, mixed infection or no infection) in the subject
according to any of the disclosed methods and recommending a test
to determine the source of the bacterial infection if the subject
is identified as having a bacterial infection or a mixed infection;
or a test to determine the source of the viral infection if the
subject is identified as having a viral infection.
[0286] Some aspects of the present invention also comprise a kit
with a detection reagent that binds to one or more determinant.
Also provided by the invention is an array of detection reagents,
e.g., antibodies that can bind to one or more determinants. In one
embodiment, the kit contains antibodies that bind at least one
determinant which appears in Table 1 and at least one polypeptide
which appears in Table 2. In another embodiment, the kit contains
antibodies that bind at least two determinants which appears in
Table 1.
[0287] According to an exemplary embodiment, the kit (or array)
does not detect more than 2 determinants, does not detect more than
3 determinants, does not detect more than 4 determinants, does not
detect more than 5 determinants.
[0288] Thus, the kit may comprise antibodies which specifically
recognize two different determinants, three different determinants,
four different determinants, five different determinants, six
different determinants, seven different determinants, eight
different determinants, nine different determinants or ten or more
different determinants. Preferably, the kit does not contain
antibodies which recognize more than 20 different determinants, 30
different determinants, 40 different determinants, 50 different
determinants, 100 different determinants or 200 different
determinants.
[0289] Preferably, the concentration of the determinants is
measured within about 24 hours after sample is obtained.
Alternatively, the concentration of the polypeptide-determinant is
measured in a sample that was stored at 12.degree. C. or lower,
when storage begins less than 24 hours after the sample is
obtained.
[0290] In some embodiments the sample could have been stored in
either room temperature, 4.degree. C., -20.degree. C. or
-80.degree. C. before measurement is performed.
[0291] In some embodiments the sample could have been stored for 1,
2, 3, 4, 5, 10, 12, 15, 20 or 24 hours before measurement is
performed.
[0292] In some embodiments the sample may be stored for less than 5
minutes, 10 minutes, 20 minutes, 30 minutes, 45 minutes or 60
minutes before measurement is performed.
[0293] In some embodiments the sample is collected in a serum
separator tube (SST). Following collection, the sample may be left
at room temperature for at least 5, 10, 12, 15, 20, 25 30 minutes
to allow blood clotting and then centrifuged for about 5-30 minutes
(e.g. at least 5, 10, 12, 15, 20, 25, or 30 minutes) at
1200.times.g or at about 3000 RPM.
[0294] According to a specific embodiment, the kit comprises
antibodies for detection of CRP and TRAILR3/TNFRSF10C, CRP and
TRAILR4/TNFRSF10D, CRP and TRAIL-R1/TNFRSF10A, CRP and
TRAIL-R2/TNFRSF10B, CRP and NGAL, CRP and neopterin or CRP and
MMP8.
[0295] According to a specific embodiment, the kit comprises
antibodies for detection of TRAIL and TRAILR3/TNFRSF10C, TRAIL and
TRAILR4/TNFRSF10D, TRAIL and TRAIL-R1/TNFRSF10A, TRAIL and
TRAIL-R2/TNFRSF10B, TRAIL and NGAL, TRAIL and neopterin or TRAIL
and MMP8.
[0296] According to a specific embodiment, the kit comprises
antibodies for detection of IP10 and TRAILR3/TNFRSF10C, IP10 and
TRAILR4/TNFRSF10D, IP10 and TRAIL-R1/TNFRSF10A, IP10 and
TRAIL-R2/TNFRSF10B, IP10 and neopterin, IP10 and
[0297] NGAL or IP10 and MMP8.
[0298] According to a specific embodiment, the kit comprises
antibodies for detection of IL1-Ra and TRAILR3/TNFRSF10C, IL1-Ra
and TRAILR4/TNFRSF10D, IL1-Ra and TRAIL-R1/TNFRSF10A, IL1-Ra and
TRAIL-R2/TNFRSF10B, IL1-Ra and neopterin, IL1Ra and NGAL or IL1Ra
and MMP8.
[0299] According to a specific embodiment, the kit comprises
antibodies for detection of PCT and TRAILR3/TNFRSF10C, PCT and
TRAILR4/TNFRSF10D, PCT and TRAIL-R1/TNFRSF10A, PCT and
TRAIL-R2/TNFRSF10B, PCT and neopterin, PCT and NGAL or PCT and
MMP8.
[0300] According to a specific embodiment, the kit comprises
antibodies for detection of sTREM and TRAILR3/TNFRSF10C, sTREM and
TRAILR4/TNFRSF10D, sTREM and TRAIL-R1/TNFRSF10A, sTREM and
TRAIL-R2/TNFRSF10B, sTREM and neopterin, sTREM and NGAL or sTREM
and MMP8.
[0301] According to a specific embodiment, the kit comprises
antibodies for detection of RSAD2 and TRAILR3/TNFRSF10C, RSAD2 and
TRAILR4/TNFRSF10D, RSAD2 and TRAIL-R1/TNFRSF10A, RSAD2 and
TRAIL-R2/TNFRSF10B, RSAD2 and neopterin, RSAD2 and NGAL or RSAD2
and MMP8.
[0302] According to a specific embodiment, the kit comprises
antibodies for detection of MX1 and TRAILR3/TNFRSF10C, MX1 and
TRAILR4/TNFRSF10D, MX1 and TRAIL-R1/TNFRSF10A, MX1 and
TRAIL-R2/TNFRSF10B, MX1 and neopterin, MX1 and NGAL or MX1 and
MMP8.
[0303] According to a specific embodiment, the kit comprises
antibodies for detection of TRAIL and MX1; TRAIL and RSAD2; TRAIL
and sTREM; and TRAIL and IL1-Ra.
[0304] According to a specific embodiment, the kit comprises
antibodies for detection of TRAIL, CRP and TRAILR3/TNFRSF10C;
TRAIL, CRP and TRAILR4/TNFRSF10D; TRAIL, CRP and
TRAIL-R1/TNFRSF10A; TRAIL, CRP and TRAIL-R2/TNFRSF10B, TRAIL, CRP
and neopterin; TRAIL, CRP and NGAL; or TRAIL, CRP and MMP8.
[0305] According to a specific embodiment, the kit comprises
antibodies for detection of TRAIL, IP10 and TRAILR3/TNFRSF10C;
TRAIL, IP10 and TRAILR4/TNFRSF10D; TRAIL, IP10 and
TRAIL-R1/TNFRSF10A; TRAIL, IP10 and TRAIL-R2/TNFRSF10B; TRAIL, IP10
and neopterin; TRAIL, IP10 and NGAL; or TRAIL, IP10 and MMP8.
[0306] According to a specific embodiment, the kit comprises
antibodies for detection of CRP, IP10 and TRAILR3/TNFRSF10C, CRP,
IP10 and with TRAILR4/TNFRSF10D; CRP, IP10 and TRAIL-R1/TNFRSF10A;
CRP, IP10 and TRAIL-R2/TNFRSF10B; CRP, IP10 and neopterin: CRP,
IP10 and NGAL; or CRP, IP10 and MMP8.
[0307] According to a specific embodiment, the kit comprises
antibodies for detection of TRAIL, CRP, IP10 and TRAILR3/TNFRSF10C;
TRAIL, CRP, IP10 and TRAILR4/TNFRSF10D; TRAIL, CRP, IP10 and
TRAIL-R1/TNFRSF10A; TRAIL, CRP, IP10 and TRAIL-R2/TNFRSF10B; TRAIL,
CRP, IP10 and neopterin; TRAIL, CRP, IP10 and NGAL; or TRAIL, CRP,
IP10 and MMP8.
[0308] According to a specific embodiment, the kit comprises
antibodies for detection of CRP and NGAL, CRP and a1-Acid
Glycoprotein/ORM1, CRP and IL18, CRP and CXCL6, CRP and MBL, CRP
and OSM/Oncostatin M, CRP and TNFSF14 or CRP and CD14.
[0309] According to a specific embodiment, the kit comprises
antibodies for detection of TRAIL and NGAL, TRAIL and a1-Acid
Glycoprotein/ORM1, TRAIL and IL18, TRAIL and CXCL6, TRAIL and MBL,
TRAIL and OSM/Oncostatin M, TRAIL and TNFSF14 or TRAIL and
CD14.
[0310] According to a specific embodiment, the kit comprises
antibodies for detection of IP10 and NGAL, IP10 and a1-Acid
Glycoprotein/ORM1, IP10 and IL18, IP10 and CXCL6, IP10 and MBL,
IP10 and OSM/Oncostatin M, IP10 and TNFSF14 or IP10 and CD14.
[0311] According to a specific embodiment, the kit comprises
antibodies for detection of IL1-Ra and NGAL, IL1-Ra and a1-Acid
Glycoprotein/ORM1, IL1-Ra and IL18, IL1-Ra and CXCL6, IL1-Ra and
MBL, IL1-Ra and OSM/Oncostatin M, IL1-Ra and TNFSF14 or IL1-Ra and
CD14.
[0312] According to a specific embodiment, the kit comprises
antibodies for detection of PCT and NGAL, PCT and a1-Acid
Glycoprotein/ORM1, PCT and IL18, PCT and CXCL6, PCT and MBL, PCT
and OSM/Oncostatin M, PCT and TNFSF14 or PCT and CD14.
[0313] According to a specific embodiment, the kit comprises
antibodies for detection of sTREM and NGAL, sTREM and a1-Acid
Glycoprotein/ORM1, sTREM and IL18, sTREM and CXCL6, sTREM and MBL,
sTREM and OSM/Oncostatin M, sTREM and TNFSF14 or sTREM and
CD14.
[0314] According to a specific embodiment, the kit comprises
antibodies for detection of RSAD2 and NGAL, RSAD2 and a1-Acid
Glycoprotein/ORM1, RSAD2 and IL18, RSAD2 and CXCL6, RSAD2 and MBL,
RSAD2 and OSM/Oncostatin M, RSAD2 and TNFSF14 or RSAD2 and
CD14.
[0315] According to a specific embodiment, the kit comprises
antibodies for detection of MX1 and NGAL, MX1 and a1-Acid
Glycoprotein/ORM1, MX1 and IL18, MX1 and CXCL6, MX1 and MBL, MX1
and OSM/Oncostatin M, MX1 and TNFSF14 or MX1 and CD14.
[0316] According to a specific embodiment, the kit comprises
antibodies for detection of TRAIL, CRP and NGAL; TRAIL, CRP and
a1-Acid Glycoprotein/ORM1; TRAIL, CRP or IL18; TRAIL, CRP or CXCL6;
TRAIL, CRP or MBL; TRAIL, CRP or OSM/Oncostatin M; TRAIL, CRP or
TNFSF14, TRAIL, CRP or CD14.
[0317] According to a specific embodiment, the kit comprises
antibodies for detection of TRAIL, IP10 and NGAL; TRAIL, IP10 and
a1-Acid Glycoprotein/ORM1; TRAIL, IP10 and IL18; TRAIL, IP10 and
CXCL6; TRAIL, IP10 and MBL; TRAIL, IP10 and OSM/Oncostatin M;
TRAIL, IP10 and TNFSF14; or TRAIL, IP10 and CD14.
[0318] According to a specific embodiment, the kit comprises
antibodies for detection of CRP, IP10 and NGAL, CRP, IP10 and with
a1-Acid Glycoprotein/ORM1; CRP, IP10 and IL18; CRP, IP10 and CXCL6;
CRP, IP10 and MBL; CRP, IP10 and OSM/Oncostatin M; CRP, IP10 and
TNFSF14 or CRP, IP10 and CD14.
[0319] According to a specific embodiment, the kit comprises
antibodies for detection of TRAIL, CRP, IP10 and NGAL; TRAIL, CRP,
IP10 and a1-Acid Glycoprotein/ORM1; TRAIL, CRP, IP10 and IL18;
TRAIL, CRP, IP10 and CXCL6; TRAIL, CRP, IP10 and MBL; TRAIL, CRP,
IP10 and OSM/Oncostatin M; TRAIL, CRP, IP10 and TNFSF14; or TRAIL,
CRP, IP10 and CD14.
[0320] In other embodiments, the kit comprises antibodies for
detection of at least two polypeptide determinants which are set
forth in Table 2.
[0321] According to a particular embodiment, at least one of the
antibodies in the kit recognizes TRAILR3/TNFRSF10C.
[0322] According to a particular embodiment, at least one of the
antibodies in the kit recognizes TRAILR4/TNFRSF10D.
[0323] According to a particular embodiment, at least one of the
antibodies in the kit recognizes TRAIL-R1/TNFRSF10A.
[0324] According to a particular embodiment, at least one of the
antibodies in the kit recognizes TRAIL-R2/TNFRSF10B.
[0325] According to a particular embodiment, at least one of the
antibodies in the kit recognizes TRAILR3/TNFRSF10C,
TRAILR4/TNFRSF10D, TRAIL-R1/TNFRSF10A, TRAIL-R2/TNFRSF10B or TRAIL
(membrane form).
[0326] According to a particular embodiment, at least one of the
antibodies in the kit recognizes neopterin.
[0327] According to a particular embodiment, at least one of the
antibodies in the kit recognizes MMP8.
[0328] According to a particular embodiment, at least one of the
antibodies in the kit recognizes NGAL.
[0329] Some aspects of the present invention can also be used to
screen patient or 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, or for
the collection of epidemiological data. Insurance companies (e.g.,
health, life or disability) may screen applicants in the process of
determining coverage or pricing, or existing clients for possible
intervention. Data collected in such population screens,
particularly when tied to any clinical progression to conditions
like infection, will be of value in the operations of, for example,
health maintenance organizations, public health programs and
insurance companies. 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, improved insurance operation, etc. See,
for example, U.S. Patent Application No. 2002/0038227; U.S. Patent
Application No. US 2004/0122296; U.S. Patent Application No. US
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.
[0330] A machine-readable storage medium can comprise a data
storage material encoded with machine readable data or data arrays
which, when using a machine programmed with instructions for using
the data, is capable of use for a variety of purposes. Measurements
of effective amounts of the biomarkers of the invention and/or the
resulting evaluation of risk from those biomarkers can be
implemented in computer programs executing on programmable
computers, comprising, inter alia, a processor, a data storage
system (including volatile and non-volatile memory and/or storage
elements), at least one input device, and at least one output
device. Program code can be applied to input data to perform the
functions described above and generate output information. The
output information can be applied to one or more output devices,
according to methods known in the art. The computer may be, for
example, a personal computer, microcomputer, or workstation of
conventional design.
[0331] Each program can be implemented in a high level procedural
or object oriented programming language to communicate with a
computer system. However, the programs can be implemented in
assembly or machine language, if desired. The language can be a
compiled or interpreted language. Each such computer program can be
stored on a storage media or device (e.g., ROM or magnetic diskette
or others as defined elsewhere in this disclosure) readable by a
general or special purpose programmable computer, for configuring
and operating the computer when the storage media or device is read
by the computer to perform the procedures described herein. The
health-related data management system used in some aspects of the
invention may also be considered to be implemented as a
computer-readable storage medium, configured with a computer
program, where the storage medium so configured causes a computer
to operate in a specific and predefined manner to perform various
functions described herein.
[0332] The determinants of the present invention, in some
embodiments thereof, can be used to generate a "reference
determinant profile" of those subjects who do not have an
infection. The determinants disclosed herein can also be used to
generate a "subject determinant profile" taken from subjects who
have an infection. The subject determinant profiles can be compared
to a reference determinant profile to diagnose or identify subjects
with an infection. The subject determinant profile of different
infection types can be compared to diagnose or identify the type of
infection. The reference and subject determinant profiles of the
present invention, in some embodiments thereof, can be contained in
a machine-readable medium, such as but not limited to, analog tapes
like those readable by a VCR, CD-ROM, DVD-ROM, USB flash media,
among others. Such machine-readable media can also contain
additional test results, such as, without limitation, measurements
of clinical parameters and traditional laboratory risk factors.
Alternatively or additionally, the machine-readable media can also
comprise subject information such as medical history and any
relevant family history. The machine-readable media can also
contain information relating to other disease-risk algorithms and
computed indices such as those described herein.
[0333] Performance and Accuracy Measures of the Invention.
[0334] The performance and thus absolute and relative clinical
usefulness of the invention may be assessed in multiple ways as
noted above. Amongst the various assessments of performance, some
aspects of the invention are intended to provide accuracy in
clinical diagnosis and prognosis. The accuracy of a diagnostic or
prognostic test, assay, or method concerns the ability of the test,
assay, or method to distinguish between subjects having an
infection is based on whether the subjects have, a "significant
alteration" (e.g., clinically significant and diagnostically
significant) in the levels of a determinant. By "effective amount"
it is meant that the measurement of an appropriate number of
determinants (which may be one or more) to produce a "significant
alteration" (e.g. level of expression or activity of a determinant)
that is different than the predetermined cut-off point (or
threshold value) for that determinant (s) and therefore indicates
that the subject has an infection for which the determinant (s) is
a determinant. The difference in the level of determinant is
preferably statistically significant. As noted below, and without
any limitation of the invention, achieving statistical
significance, and thus the preferred analytical, diagnostic, and
clinical accuracy, may require that combinations of several
determinants be used together in panels and combined with
mathematical algorithms in order to achieve a statistically
significant determinant index.
[0335] In the categorical diagnosis of a disease state, changing
the cut point or threshold value of a test (or assay) usually
changes the sensitivity and specificity, but in a qualitatively
inverse relationship. Therefore, in assessing the accuracy and
usefulness of a proposed medical test, assay, or method for
assessing a subject's condition, one should always take both
sensitivity and specificity into account and be mindful of what the
cut point is at which the sensitivity and specificity are being
reported because sensitivity and specificity may vary significantly
over the range of cut points. One way to achieve this is by using
the MCC metric, which depends upon both sensitivity and
specificity. Use of statistics such as AUC, encompassing all
potential cut point values, is preferred for most categorical risk
measures when using some aspects of the invention, while for
continuous risk measures, statistics of goodness-of-fit and
calibration to observed results or other gold standards, are
preferred.
[0336] By predetermined level of predictability it is meant that
the method provides an acceptable level of clinical or diagnostic
accuracy. Using such statistics, an "acceptable degree of
diagnostic accuracy", is herein defined as a test or assay (such as
the test used in some aspects of the invention for determining the
clinically significant presence of determinants, which thereby
indicates the presence an infection type) in which the AUC (area
under the ROC curve for the test or assay) is at least 0.60,
desirably at least 0.65, more desirably at least 0.70, preferably
at least 0.75, more preferably at least 0.80, and most preferably
at least 0.85.
[0337] By a "very high degree of diagnostic accuracy", it is meant
a test or assay in which the AUC (area under the ROC curve for the
test or assay) is at least 0.75, 0.80, desirably at least 0.85,
more desirably at least 0.875, preferably at least 0.90, more
preferably at least 0.925, and most preferably at least 0.95.
[0338] Alternatively, the methods predict the presence or absence
of an infection or response to therapy with at least 75% total
accuracy, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or
greater total accuracy.
[0339] Alternatively, the methods predict the presence of a
bacterial infection or response to therapy with at least 75%
sensitivity, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or
greater sensitivity.
[0340] Alternatively, the methods predict the presence of a viral
infection or response to therapy with at least 75% specificity,
more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater
specificity. Alternatively, the methods predict the presence or
absence of an infection or response to therapy with an MCC larger
than 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8., 0.9 or 1.0.
[0341] The predictive value of any test depends on the sensitivity
and specificity of the test, and on the prevalence of the condition
in the population being tested. This notion, based on Bayes'
theorem, provides that the greater the likelihood that the
condition being screened for is present in an individual or in the
population (pre-test probability), the greater the validity of a
positive test and the greater the likelihood that the result is a
true positive. Thus, the problem with using a test in any
population where there is a low likelihood of the condition being
present is that a positive result has limited value (i.e., more
likely to be a false positive). Similarly, in populations at very
high risk, a negative test result is more likely to be a false
negative.
[0342] As a result, ROC and AUC can be misleading as to the
clinical utility of a test in low disease prevalence tested
populations (defined as those with less than 1% rate of occurrences
(incidence) per annum, or less than 10% cumulative prevalence over
a specified time horizon).
[0343] A health economic utility function is an yet another means
of measuring the performance and clinical value of a given test,
consisting of weighting the potential categorical test outcomes
based on actual measures of clinical and economic value for each.
Health economic performance is closely related to accuracy, as a
health economic utility function specifically assigns an economic
value for the benefits of correct classification and the costs of
misclassification of tested subjects. As a performance measure, it
is not unusual to require a test to achieve a level of performance
which results in an increase in health economic value per test
(prior to testing costs) in excess of the target price of the
test.
[0344] In general, alternative methods of determining diagnostic
accuracy are commonly used for continuous measures, when a disease
category has not yet been clearly defined by the relevant medical
societies and practice of medicine, where thresholds for
therapeutic use are not yet established, or where there is no
existing gold standard for diagnosis of the pre-disease. For
continuous measures of risk, measures of diagnostic accuracy for a
calculated index are typically based on curve fit and calibration
between the predicted continuous value and the actual observed
values (or a historical index calculated value) and utilize
measures such as R squared, Hosmer-Lemeshow P-value statistics and
confidence intervals. It is not unusual for predicted values using
such algorithms to be reported including a confidence interval
(usually 90% or 95% CI) based on a historical observed cohort's
predictions, as in the test for risk of future breast cancer
recurrence commercialized by Genomic Health, Inc. (Redwood City,
Calif.).
[0345] In general, by defining the degree of diagnostic accuracy,
i.e., cut 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 determinants of the
invention allows for one of skill in the art to use the
determinants to identify, diagnose, or prognose subjects with a
pre-determined level of predictability and performance.
[0346] Furthermore, other unlisted biomarkers will be very highly
correlated with the determinants (for the purpose of this
application, any two variables will be considered to be "very
highly correlated" when they have a Coefficient of Determination
(R.sup.2) of 0.5 or greater). Some aspects of the present invention
encompass such functional and statistical equivalents to the
aforementioned determinants. Furthermore, the statistical utility
of such additional determinants is substantially dependent on the
cross-correlation between multiple biomarkers and any new
biomarkers will often be required to operate within a panel in
order to elaborate the meaning of the underlying biology.
[0347] One or more of the listed determinants can be detected in
the practice of the present invention, in some embodiments thereof.
For example, two (2), three (3), four (4), five (5), ten (10),
fifteen (15), twenty (20), forty (40), or more determinants can be
detected.
[0348] In some aspects, all determinants listed herein can be
detected. Preferred ranges from which the number of determinants
can be detected include ranges bounded by any minimum selected from
between one and, particularly two, three, four, five, six, seven,
eight, nine ten, twenty, or forty. Particularly preferred ranges
include two to five (2-5), two to ten (2-10), two to twenty (2-20),
or two to forty (2-40).
[0349] Construction of Determinant Panels
[0350] Groupings of determinants can be included in "panels", also
called "determinant-signatures", "determinant signatures", or
"multi-determinant signatures." A "panel" within the context of the
present invention means a group of biomarkers (whether they are
determinants, clinical parameters, or traditional laboratory risk
factors) that includes one or more determinants. A panel can also
comprise additional biomarkers, e.g., clinical parameters,
traditional laboratory risk factors, known to be present or
associated with infection, in combination with a selected group of
the determinants listed herein.
[0351] As noted above, many of the individual determinants,
clinical parameters, and traditional laboratory risk factors
listed, when used alone and not as a member of a multi-biomarker
panel of determinants, have little or no clinical use in reliably
distinguishing individual normal subjects, subjects at risk for
having an infection (e.g., bacterial, viral or co-infection), and
thus cannot reliably be used alone in classifying any subject
between those three states. Even where there are statistically
significant differences in their mean measurements in each of these
populations, as commonly occurs in studies which are sufficiently
powered, such biomarkers may remain limited in their applicability
to an individual subject, and contribute little to diagnostic or
prognostic predictions for that subject. A common measure of
statistical significance is the p-value, which indicates the
probability that an observation has arisen by chance alone;
preferably, such p-values are 0.05 or less, representing a 5% or
less chance that the observation of interest arose by chance. Such
p-values depend significantly on the power of the study
performed.
[0352] Despite this individual determinant performance, and the
general performance of formulas combining only the traditional
clinical parameters and few traditional laboratory risk factors,
the present inventors have noted that certain specific combinations
of two or more determinants can also be used as multi-biomarker
panels comprising combinations of determinants that are known to be
involved in one or more physiological or biological pathways, and
that such information can be combined and made clinically useful
through the use of various formulae, including statistical
classification algorithms and others, combining and in many cases
extending the performance characteristics of the combination beyond
that of the individual determinants. These specific combinations
show an acceptable level of diagnostic accuracy, and, when
sufficient information from multiple determinants is combined in a
trained formula, they often reliably achieve a high level of
diagnostic accuracy transportable from one population to
another.
[0353] The general concept of how two less specific or lower
performing determinants are combined into novel and more useful
combinations for the intended indications, is a key aspect of some
embodiments of the invention. Multiple biomarkers can yield better
performance than the individual components when proper mathematical
and clinical algorithms are used; this is often evident in both
sensitivity and specificity, and results in a greater AUC or MCC.
Secondly, there is often novel unperceived information in the
existing biomarkers, as such was necessary in order to achieve
through the new formula an improved level of sensitivity or
specificity. This hidden information may hold true even for
biomarkers which are generally regarded to have suboptimal clinical
performance on their own. In fact, the suboptimal performance in
terms of high false positive rates on a single biomarker measured
alone may very well be an indicator that some important additional
information is contained within the biomarker results--information
which would not be elucidated absent the combination with a second
biomarker and a mathematical formula.
[0354] Several statistical and modeling algorithms known in the art
can be used to both assist in determinant selection choices and
optimize the algorithms combining these choices. Statistical tools
such as factor and cross-biomarker correlation/covariance analyses
allow more rationale approaches to panel construction. Mathematical
clustering and classification tree showing the Euclidean
standardized distance between the determinants can be
advantageously used. Pathway informed seeding of such statistical
classification techniques also may be employed, as may rational
approaches based on the selection of individual determinants based
on their participation across in particular pathways or
physiological functions.
[0355] Ultimately, formula such as statistical classification
algorithms can be directly used to both select determinants and to
generate and train the optimal formula necessary to combine the
results from multiple determinants into a single index. Often,
techniques such as forward (from zero potential explanatory
parameters) and backwards selection (from all available potential
explanatory parameters) are used, and information criteria, such as
AIC or BIC, are used to quantify the tradeoff between the
performance and diagnostic accuracy of the panel and the number of
determinants used. The position of the individual determinant on a
forward or backwards selected panel can be closely related to its
provision of incremental information content for the algorithm, so
the order of contribution is highly dependent on the other
constituent determinants in the panel.
[0356] Construction of Clinical Algorithms
[0357] Any formula may be used to combine determinant results into
indices useful in the practice of the invention. As indicated
above, and without limitation, such indices may indicate, among the
various other indications, the probability, likelihood, absolute or
relative risk, time to or rate of conversion from one to another
disease states, or make predictions of future biomarker
measurements of infection. This may be for a specific time period
or horizon, or for remaining lifetime risk, or simply be provided
as an index relative to another reference subject population.
[0358] Although various preferred formula are described here,
several other model and formula types beyond those mentioned herein
and in the definitions above are well known to one skilled in the
art. The actual model type or formula used may itself be selected
from the field of potential models based on the performance and
diagnostic accuracy characteristics of its results in a training
population. The specifics of the formula itself may commonly be
derived from determinant results in the relevant training
population. Amongst other uses, such formula may be intended to map
the feature space derived from one or more determinant inputs to a
set of subject classes (e.g. useful in predicting class membership
of subjects as normal, having an infection), to derive an
estimation of a probability function of risk using a Bayesian
approach, or to estimate the class-conditional probabilities, then
use Bayes' rule to produce the class probability function as in the
previous case.
[0359] Preferred formulas include the broad class of statistical
classification algorithms, and in particular the use of
discriminant analysis. The goal of discriminant analysis is to
predict class membership from a previously identified set of
features. In the case of linear discriminant analysis (LDA), the
linear combination of features is identified that maximizes the
separation among groups by some criteria. Features can be
identified for LDA using an eigengene based approach with different
thresholds (ELDA) or a stepping algorithm based on a multivariate
analysis of variance (MANOVA). Forward, backward, and stepwise
algorithms can be performed that minimize the probability of no
separation based on the Hotelling-Lawley statistic.
[0360] Eigengene-based Linear Discriminant Analysis (ELDA) is a
feature selection technique developed by Shen et al. (2006). The
formula selects features (e.g. biomarkers) in a multivariate
framework using a modified eigen analysis to identify features
associated with the most important eigenvectors. "Important" is
defined as those eigenvectors that explain the most variance in the
differences among samples that are trying to be classified relative
to some threshold.
[0361] A support vector machine (SVM) is a classification formula
that attempts to find a hyperplane that separates two classes. This
hyperplane contains support vectors, data points that are exactly
the margin distance away from the hyperplane. In the likely event
that no separating hyperplane exists in the current dimensions of
the data, the dimensionality is expanded greatly by projecting the
data into larger dimensions by taking non-linear functions of the
original variables (Venables and Ripley, 2002). Although not
required, filtering of features for SVM often improves prediction.
Features (e.g., biomarkers) can be identified for a support vector
machine using a non-parametric Kruskal-Wallis (KW) test to select
the best univariate features. A random forest (RF, Breiman, 2001)
or recursive partitioning (RPART, Breiman et al., 1984) can also be
used separately or in combination to identify biomarker
combinations that are most important. Both KW and RF require that a
number of features be selected from the total. RPART creates a
single classification tree using a subset of available
biomarkers.
[0362] Other formula may be used in order to pre-process the
results of individual determinant measurements into more valuable
forms of information, prior to their presentation to the predictive
formula. Most notably, normalization of biomarker results, using
either common mathematical transformations such as logarithmic or
logistic functions, as normal or other distribution positions, in
reference to a population's mean values, etc. are all well known to
those skilled in the art. Of particular interest are a set of
normalizations based on clinical-determinants such as time from
symptoms, gender, race, or sex, where specific formula are used
solely on subjects within a class or continuously combining a
clinical-determinants as an input. In other cases, analyte-based
biomarkers can be combined into calculated variables which are
subsequently presented to a formula.
[0363] In addition to the individual parameter values of one
subject potentially being normalized, an overall predictive formula
for all subjects, or any known class of subjects, may itself be
recalibrated or otherwise adjusted based on adjustment for a
population's expected prevalence and mean biomarker parameter
values, according to the technique outlined in D'Agostino et al.,
(2001) JAMA 286:180-187, or other similar normalization and
recalibration techniques. Such epidemiological adjustment
statistics may be captured, confirmed, improved and updated
continuously through a registry of past data presented to the
model, which may be machine readable or otherwise, or occasionally
through the retrospective query of stored samples or reference to
historical studies of such parameters and statistics. Additional
examples that may be the subject of formula recalibration or other
adjustments include statistics used in studies by Pepe, M. S. et
al., 2004 on the limitations of odds ratios; Cook, N. R., 2007
relating to ROC curves. Finally, the numeric result of a classifier
formula itself may be transformed post-processing by its reference
to an actual clinical population and study results and observed
endpoints, in order to calibrate to absolute risk and provide
confidence intervals for varying numeric results of the classifier
or risk formula.
[0364] Some determinants may exhibit trends that depends on the
patient age (e.g. the population baseline may rise or fall as a
function of age). One can use a `Age dependent normalization or
stratification` scheme to adjust for age related differences.
Performing age dependent normalization, stratification or distinct
mathematical formulas can be used to improve the accuracy of
determinants for differentiating between different types of
infections. For example, one skilled in the art can generate a
function that fits the population mean levels of each determinant
as function of age and use it to normalize the determinant of
individual subjects levels across different ages. Another example
is to stratify subjects according to their age and determine age
specific thresholds or index values for each age group
independently.
[0365] According to a particular embodiment, the set of
normalizations, stratification or distinct mathematical formulas
are based on age. As shown in FIGS. 8A-C, particular determinants
show an age dependent level of expression which further relate to
infection type. These include neopterin, NGAL and osteopontin.
Other determinants which show an age dependent level of expression
during infection type are set forth in Table 5 herein below. Thus,
the present invention contemplates different determinant thresholds
depending on the age of the subject.
[0366] In one embodiment, there are different thresholds,
normalizations or stratification if the subject is an adult (e.g.
older than 18, 21, or 22 years) another if the subject is a child
(e.g. younger than 18, 21 or 22 years).
[0367] In another embodiment, there are different thresholds,
normalizations or stratification if the subject is an adult (e.g.
older than 18, 21, or 22 years) another if the subject is an
adolescent between 12 and 21 years, another if the subject is a
child (between 2 and 12 years), another if the subject is an infant
29 days to less than 2 years of age, another if the subject is
neonates (birth through the first 28 days of life).
[0368] In other embodiments, there are different thresholds,
normalizations or stratification for a subject who is older than
70, 65, 60, 55, 50, 40, 30, 22, 21, 18, 12, 2, 1 year or older than
3, 2 and/or 1 month.
[0369] In other embodiments, there are different thresholds,
normalizations or stratification for a subject who is younger than
70, 65, 60, 55, 50, 40, 30, 22, 21, 18, 12, 2, 1 year or older than
3, 2 and/or 1 month.
[0370] In specific embodiments the invention includes ruling out a
bacterial infection in an adult subject if the polypeptide
concentration of NGAL is lower than about 150, 140, 125, 100, 75,
50, 25 or even 10 ng/ml. Optionally, the method further includes
ruling in a viral infection in the adult subject if the polypeptide
concentration of NGAL is lower than 100, 90, 75, 50, 25 or even 10
ng/ml.
[0371] The invention may also include ruling out a viral infection
in an adult subject if the polypeptide concentration of NGAL is
higher than about 90, 100, 125, 150 or even 200 ng/ml. Optionally,
the method further ruling in a bacterial infection in the adult
subject if the polypeptide concentration of NGAL is higher than
125, 150, 175, 200, 250, 300, 400 or even 500 ng/ml.
[0372] In specific embodiments the invention includes ruling out a
bacterial infection in an adolescent subject if the polypeptide
concentration of NGAL is lower than about 150, 140, 125, 100, 75,
50, 25 or even 10 ng/ml. Optionally, the method further includes
ruling in a viral infection in the adolescent subject if the
polypeptide concentration of NGAL is lower than 100, 90, 75, 50, 25
or even 10 ng/ml.
[0373] The invention may also include ruling out a viral infection
in an adolescent subject if the polypeptide concentration of NGAL
is higher than about 90, 100, 125, 150 or even 200 ng/ml.
Optionally, the method further ruling in a bacterial infection in
the adolescent subject if the polypeptide concentration of NGAL is
higher than 125, 150, 175, 200, 250, 300, 400 or even 500
ng/ml.
[0374] In specific embodiments the invention includes ruling out a
bacterial infection in a child subject if the polypeptide
concentration of NGAL is lower than about 150, 140, 125, 100, 75,
50, 25 or even 10 ng/ml. Optionally, the method further includes
ruling in a viral infection in the child subject if the polypeptide
concentration of NGAL is lower than 100, 90, 75, 50, 25 or even 10
ng/ml.
[0375] The invention may also include ruling out a viral infection
in an child subject if the polypeptide concentration of NGAL is
higher than about 90, 100, 125, 150 or even 200 ng/ml. Optionally,
the method further ruling in a bacterial infection in the child
subject if the polypeptide concentration of NGAL is higher than
125, 150, 175, 200, 250, 300, 400 or even 500 ng/ml.
[0376] In specific embodiments the invention includes ruling out a
bacterial infection in an infant subject if the polypeptide
concentration of NGAL is lower than about 150, 140, 125, 100, 75,
50, 25 or even 10 ng/ml. Optionally, the method further includes
ruling in a viral infection in the infant subject if the
polypeptide concentration of NGAL is lower than 100, 90, 75, 50, 25
or even 10 ng/ml.
[0377] The invention may also include ruling out a viral infection
in an infant subject if the polypeptide concentration of NGAL is
higher than about 90, 100, 125, 150 or even 200 ng/ml. Optionally,
the method further ruling in a bacterial infection in the infant
subject if the polypeptide concentration of NGAL is higher than
125, 150, 175, 200, 250, 300, 400 or even 500 ng/ml.
[0378] In specific embodiments the invention includes ruling out a
bacterial infection in an adult subject if the concentration of
Neopterin is higher than about 4, 5, 6, 7, 8, 10, 15, 20, 50, or
even 100 pg/ml. Optionally, the method further includes ruling in a
viral infection in the adult subject if the concentration of
Neopterin is higher than about 4, 5, 6, 7, 8, 10, 15, 20, 50, or
even 100 pg/ml.
[0379] The invention may also include ruling out a viral infection
in an adult subject if the concentration of Neopterin is lower than
about 7, 6, 5, 4, 3, 2 or even 1 pg/ml. Optionally, the method
further ruling in a bacterial infection in the adult subject if the
concentration of Neopterin is lower than about 7, 6, 5, 4, 3, 2 or
even 1 pg/ml.
[0380] In specific embodiments the invention includes ruling out a
bacterial infection in an adolescent subject if the concentration
of Neopterin is higher than about 4, 5, 6, 7, 8, 10, 15, 20, 50, or
even 100 pg/ml. Optionally, the method further includes ruling in a
viral infection in the adolescent subject if the concentration of
Neopterin is higher than about 4, 5, 6, 7, 8, 10, 15, 20, 50, or
even 100 pg/ml.
[0381] The invention may also include ruling out a viral infection
in an adolescent subject if the concentration of Neopterin is lower
than about 7, 6, 5, 4, 3, 2 or even 1 pg/ml. Optionally, the method
further ruling in a bacterial infection in the adolescent subject
if the concentration of Neopterin is lower than about 7, 6, 5, 4,
3, 2 or even 1 pg/ml.
[0382] In specific embodiments the invention includes ruling out a
bacterial infection in a child subject if the concentration of
Neopterin is higher than about 4, 5, 6, 7, 8, 10, 15, 20, 50, or
even 100 pg/ml. Optionally, the method further includes ruling in a
viral infection in the child subject if the concentration of
Neopterin is higher than about 4, 5, 6, 7, 8, 10, 15, 20, 50, or
even 100 pg/ml.
[0383] The invention may also include ruling out a viral infection
in a child subject if the concentration of Neopterin is lower than
about 7, 6, 5, 4, 3, 2 or even 1 pg/ml.
[0384] Optionally, the method further ruling in a bacterial
infection in the child subject if the concentration of Neopterin is
lower than about 7, 6, 5, 4, 3, 2 or even 1 pg/ml.
[0385] In specific embodiments the invention includes ruling out a
bacterial infection in an infant subject if the concentration of
Neopterin is higher than about 4, 5, 6, 7, 8, 10, 15, 20, 50, or
even 100 pg/ml. Optionally, the method further includes ruling in a
viral infection in the infant subject if the concentration of
Neopterin is higher than about 4, 5, 6, 7, 8, 10, 15, 20, 50, or
even 100 pg/ml.
[0386] The invention may also include ruling out a viral infection
in an infant subject if the concentration of Neopterin is lower
than about 7, 6, 5, 4, 3, 2 or even 1 pg/ml. Optionally, the method
further ruling in a bacterial infection in the infant subject if
the concentration of Neopterin is lower than about 7, 6, 5, 4, 3, 2
or even 1 pg/ml.
[0387] Measurement of Determinants
[0388] The actual measurement of levels or amounts of the
determinants can be determined at the protein or polypeptide level
using any method known in the art.
[0389] For example, by measuring the levels of polypeptide encoded
by the gene products described herein, or subcellular localization
or activities thereof. Such methods are well known in the art and
include, e.g., immunoassays based on antibodies to proteins,
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 marker genes according to the
activity of each protein analyzed.
[0390] The determinants can be detected in any suitable manner, but
are typically detected by contacting a sample from the subject with
an antibody, which binds the determinant and then detecting the
presence or absence of a reaction product. The antibody may be
monoclonal, polyclonal, chimeric, or a fragment of the foregoing,
as discussed in detail above, and the step of detecting the
reaction product may be carried out with any suitable immunoassay.
The sample from the subject is typically a biological sample as
described above, and may be the same sample of biological sample
used to conduct the method described above.
[0391] In one embodiment, the antibody which specifically binds the
determinant is attached (either directly or indirectly) to a signal
producing label, including but not limited to a radioactive label,
an enzymatic label, a hapten, a reporter dye or a fluorescent
label.
[0392] Immunoassays carried out in accordance with some embodiments
of the present invention may be homogeneous assays or heterogeneous
assays. In a homogeneous assay the immunological reaction usually
involves the specific antibody (e.g., anti-determinant antibody), a
labeled analyte, and the sample of interest. The signal arising
from the label is modified, directly or indirectly, upon the
binding of the antibody to the labeled analyte. Both the
immunological reaction and detection of the extent thereof can be
carried out in a homogeneous solution. Immunochemical labels, which
may be employed, include free radicals, radioisotopes, fluorescent
dyes, enzymes, bacteriophages, or coenzymes.
[0393] In a heterogeneous assay approach, the reagents are usually
the sample, the antibody, and means for producing a detectable
signal. Samples as described above may 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
specimen suspected of containing the antigen in a liquid phase. The
support is then separated from the liquid phase and either the
support phase or the liquid phase is examined for a detectable
signal employing means for producing such signal. The signal is
related to the presence of the analyte in the sample. Means for
producing a detectable signal include 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 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 antigen in the test sample.
Examples of suitable immunoassays are oligonucleotides,
immunoblotting, immunofluorescence methods, immunoprecipitation,
chemiluminescence methods, electrochemiluminescence (ECL) or
enzyme-linked immunoassays.
[0394] Those skilled in the art will be familiar with numerous
specific immunoassay formats and variations thereof which may be
useful for carrying out the method disclosed herein. See generally
E. Maggio, Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton,
Fla.); see also U.S. Pat. No. 4,727,022 to Skold et al., titled
"Methods for Modulating Ligand-Receptor Interactions and their
Application," U.S. Pat. No. 4,659,678 to Forrest et al., titled
"Immunoassay of Antigens," U.S. Pat. No. 4,376,110 to David et al.,
titled "Immunometric Assays Using Monoclonal Antibodies," U.S. Pat.
No. 4,275,149 to Litman et al., titled "Macromolecular Environment
Control in Specific Receptor Assays," U.S. Pat. No. 4,233,402 to
Maggio et al., titled "Reagents and Method Employing Channeling,"
and U.S. Pat. No. 4,230,767 to Boguslaski et al., titled
"Heterogenous Specific Binding Assay Employing a Coenzyme as
Label." The determinant can also be detected with antibodies using
flow cytometry. Those skilled in the art will be familiar with flow
cytometric techniques which may be useful in carrying out the
methods disclosed herein (Shapiro 2005). These include, without
limitation, Cytokine Bead Array (Becton Dickinson) and Luminex
technology.
[0395] 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
may likewise be conjugated to detectable labels or groups such as
radiolabels (e.g., .sup.35S, .sup.125I, .sup.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.
[0396] Antibodies can also be useful for detecting
post-translational modifications of determinant proteins,
polypeptides, mutations, and polymorphisms, such as tyrosine
phosphorylation, threonine phosphorylation, serine phosphorylation,
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 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) (Wirth U.
and Muller D. 2002).
[0397] For determinant-proteins, polypeptides, mutations, and
polymorphisms known to have enzymatic activity, the activities can
be determined in vitro using enzyme assays known in the art. Such
assays include, without limitation, kinase assays, phosphatase
assays, reductase assays, among many others. Modulation of the
kinetics of enzyme activities can be determined by measuring the
rate constant K.sub.M using known algorithms, such as the Hill
plot, Michaelis-Menten equation, linear regression plots such as
Lineweaver-Burk analysis, and Scatchard plot.
[0398] 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. In
this regard, other DETERMINANT 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
poly-amino carboxylic acid, Fluo series, Fura-2A, Rhod-2, the
ratiometric calcium indicator Indo-1, among others. Other
determinant metabolites can be similarly detected using reagents
that are specifically designed or tailored to detect such
metabolites.
[0399] Kits
[0400] Some aspects of the invention also include a
determinant-detection reagent, or antibodies packaged together in
the form of a kit. The kit may contain in separate containers an
antibody (either already bound to a solid matrix or packaged
separately with reagents for binding them to the matrix), control
formulations (positive and/or negative), and/or a detectable label
such as fluorescein, green fluorescent protein, rhodamine, cyanine
dyes, Alexa dyes, luciferase, radiolabels, among others. The
detectable label may be attached to a secondary antibody which
binds to the Fc portion of the antibody which recognizes the
determinant. Instructions (e.g., written, tape, VCR, CD-ROM, etc.)
for carrying out the assay may be included in the kit. The assay
may for example be in the form of a sandwich ELISA as known in the
art.
[0401] For example, determinant detection reagents can be
immobilized on a solid matrix such as a porous strip to form at
least one determinant detection site. The measurement or detection
region of the porous strip may include a plurality of sites. A test
strip may 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 may
contain different amounts of immobilized detection reagents, 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 determinants present in the sample. The
detection sites may be configured in any suitably detectable shape
and are typically in the shape of a bar or dot spanning the width
of a test strip.
[0402] Suitable sources for antibodies for the detection of
determinants include commercially available sources such as, for
example, Abazyme, Abnova, AssayPro, Affinity Biologicals,
AntibodyShop, Aviva bioscience, Biogenesis, Biosense Laboratories,
Calbiochem, Cell Sciences, Chemicon International, Chemokine,
Clontech, Cytolab, DAKO, Diagnostic BioSystems, eBioscience,
Endocrine Technologies, Enzo Biochem, Eurogentec, Fusion
Antibodies, Genesis Biotech, GloboZymes, Haematologic Technologies,
Immunodetect, Immunodiagnostik, Immunometrics, Immunostar,
Immunovision, Biogenex, Invitrogen, Jackson ImmunoResearch
Laboratory, KMI Diagnostics, Koma Biotech, LabFrontier Life Science
Institute, Lee Laboratories, Lifescreen, Maine Biotechnology
Services, Mediclone, MicroPharm Ltd., ModiQuest, Molecular
Innovations, Molecular Probes, Neoclone, Neuromics, New England
Biolabs, Novocastra, Novus Biologicals, Oncogene Research Products,
Orbigen, Oxford Biotechnology, Panvera, PerkinElmer Life Sciences,
Pharmingen, Phoenix Pharmaceuticals, Pierce Chemical Company,
Polymun Scientific, Polysiences, Inc., Promega Corporation,
Proteogenix, Protos Immunoresearch, QED Biosciences, Inc., R&D
Systems, Repligen, Research Diagnostics, Roboscreen, Santa Cruz
Biotechnology, Seikagaku America, Serological Corporation, Serotec,
SigmaAldrich, StemCell Technologies, Synaptic Systems GmbH,
Technopharm, Terra Nova Biotechnology, TiterMax, Trillium
Diagnostics, Upstate Biotechnology, US Biological, Vector
Laboratories, Wako Pure Chemical Industries, and Zeptometrix.
However, the skilled artisan can routinely make antibodies, against
any of the polypeptide determinants described herein.
[0403] Another Company from which antibodies may be obtained is
RnD.
[0404] We note that the fraction in which the polypeptide
determinants reside affects the ease by which the assay can be
performed at the clinical setting. For example, in the clinical
setting, especially the point-of-care, it is often easier to
measure determinants that are present in the serum or plasma
fraction compared to intracellular determinants within the
leukocytes fraction. This is because the latter requires an
additional experimental step in which leukocytes are isolated from
the whole blood sample, washed and lysed.
[0405] Examples of "Monoclonal antibodies for measuring TRAIL",
include without limitation: Mouse, Monoclonal (55B709-3) IgG;
Mouse, Monoclonal (2E5) IgG1; Mouse, Monoclonal (2E05) IgG1; Mouse,
Monoclonal (M912292) IgG1 kappa; Mouse, Monoclonal (IIIF6) IgG2b;
Mouse, Monoclonal (2E1-1B9) IgG1; Mouse, Monoclonal (RIK-2) IgG1,
kappa; Mouse, Monoclonal M181 IgG1; Mouse, Monoclonal VI10E IgG2b;
Mouse, Monoclonal MAB375 IgG1; Mouse, Monoclonal MAB687 IgG1;
Mouse, Monoclonal HS501 IgG1; Mouse, Monoclonal clone 75411.11
Mouse IgG1; Mouse, Monoclonal T8175-50 IgG; Mouse, Monoclonal
2B2.108 IgG1; Mouse, Monoclonal B-T24 IgG1; Mouse, Monoclonal
55B709.3 IgG1; Mouse, Monoclonal D3 IgG1; Goat, Monoclonal C19 IgG;
Rabbit, Monoclonal H257 IgG; Mouse, Monoclonal 500-M49 IgG; Mouse,
Monoclonal 05-607 IgG; Mouse, Monoclonal B-T24 IgG1; Rat,
Monoclonal (N2B2), IgG2a, kappa; Mouse, Monoclonal (1A7-2B7), IgG1;
Mouse, Monoclonal (55B709.3), IgG and Mouse, Monoclonal B-S23*
IgG1.
[0406] Soluble TRAIL and membrane TRAIL can be distinguished by
using different measuring techniques and samples. For example,
Soluble TRAL can be measured without limitation in cell free
samples such as serum or plasma. Membrane TRAIL can be measured in
samples that contain cells using cell based assays including
without limitation flow cytometry, ELISA, and other
immunoassays.
[0407] Examples of "Monoclonal antibodies for measuring CRP",
include without limitation: Mouse, Monoclonal (108-2A2); Mouse,
Monoclonal (108-7G41D2); Mouse, Monoclonal (12D-2C-36), IgG1;
Mouse, Monoclonal (1G1), IgG1; Mouse, Monoclonal (5A9), IgG2a
kappa; Mouse, Monoclonal (63F4), IgG1; Mouse, Monoclonal (67A1),
IgG1; Mouse, Monoclonal (8B-5E), IgG1; Mouse, Monoclonal (B893M),
IgG2b, lambda; Mouse, Monoclonal (C1), IgG2b; Mouse, Monoclonal
(C11F2), IgG; Mouse, Monoclonal (C2), IgG1; Mouse, Monoclonal (C3),
IgG1; Mouse, Monoclonal (C4), IgG1; Mouse, Monoclonal (C5), IgG2a;
Mouse, Monoclonal (C6), IgG2a; Mouse, Monoclonal (C7), IgG1; Mouse,
Monoclonal (CRP103), IgG2b; Mouse, Monoclonal (CRP11), IgG1; Mouse,
Monoclonal (CRP135), IgG1; Mouse, Monoclonal (CRP169), IgG2a;
Mouse, Monoclonal (CRP30), IgG1; Mouse, Monoclonal (CRP36), IgG2a;
Rabbit, Monoclonal (EPR283Y), IgG; Mouse, Monoclonal (KT39), IgG2b;
Mouse, Monoclonal (N-a), IgG1; Mouse, Monoclonal (N1G1), IgG1;
Monoclonal (P5A9AT); Mouse, Monoclonal (S5G1), IgG1; Mouse,
Monoclonal (SB78c), IgG1; Mouse, Monoclonal (SB78d), IgG1 and
Rabbit, Monoclonal (Y284), IgG.
[0408] Polyclonal antibodies for measuring determinants include
without limitation antibodies that were produced from sera by
active immunization of one or more of the following: Rabbit, Goat,
Sheep, Chicken, Duck, Guinea Pig, Mouse, Donkey, Camel, Rat and
Horse.
[0409] Examples of detection agents, include without limitation:
scFv, dsFv, Fab, sVH, F(ab')2, Cyclic peptides, Haptamers, A
single-domain antibody, Fab fragments, Single-chain variable
fragments, Affibody molecules, Affilins, Nanofitins, Anticalins,
Avimers, DARPins, Kunitz domains, Fynomers and Monobody.
[0410] As used herein the term "about" refers to .+-.10%.
[0411] The terms "comprises", "comprising", "includes",
"including", "having" and their conjugates mean "including but not
limited to".
[0412] The term "consisting of" means "including and limited
to".
[0413] The term "consisting essentially of" means that the
composition, method or structure may include additional
ingredients, steps and/or parts, but only if the additional
ingredients, steps and/or parts do not materially alter the basic
and novel characteristics of the claimed composition, method or
structure.
[0414] As used herein, the singular form "a", "an" and "the"
include plural references unless the context clearly dictates
otherwise. For example, the term "a compound" or "at least one
compound" may include a plurality of compounds, including mixtures
thereof.
[0415] Throughout this application, various embodiments of this
invention may be presented in a range format. It should be
understood that the description in range format is merely for
convenience and brevity and should not be construed as an
inflexible limitation on the scope of the invention. Accordingly,
the description of a range should be considered to have
specifically disclosed all the possible subranges as well as
individual numerical values within that range. For example,
description of a range such as from 1 to 6 should be considered to
have specifically disclosed subranges such as from 1 to 3, from 1
to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as
well as individual numbers within that range, for example, 1, 2, 3,
4, 5, and 6. This applies regardless of the breadth of the
range.
[0416] Whenever a numerical range is indicated herein, it is meant
to include any cited numeral (fractional or integral) within the
indicated range. The phrases "ranging/ranges between" a first
indicate number and a second indicate number and "ranging/ranges
from" a first indicate number "to" a second indicate number are
used herein interchangeably and are meant to include the first and
second indicated numbers and all the fractional and integral
numerals therebetween.
[0417] As used herein the term "method" refers to manners, means,
techniques and procedures for accomplishing a given task including,
but not limited to, those manners, means, techniques and procedures
either known to, or readily developed from known manners, means,
techniques and procedures by practitioners of the chemical,
pharmacological, biological, biochemical and medical arts.
[0418] As used herein, the term "treating" includes abrogating,
substantially inhibiting, slowing or reversing the progression of a
condition, substantially ameliorating clinical or aesthetical
symptoms of a condition or substantially preventing the appearance
of clinical or aesthetical symptoms of a condition.
[0419] It is appreciated that certain features of the invention,
which are, for clarity, described in the context of separate
embodiments, may also be provided in combination in a single
embodiment. Conversely, various features of the invention, which
are, for brevity, described in the context of a single embodiment,
may also be provided separately or in any suitable subcombination
or as suitable in any other described embodiment of the invention.
Certain features described in the context of various embodiments
are not to be considered essential features of those embodiments,
unless the embodiment is inoperative without those elements.
[0420] Although the invention has been described in conjunction
with specific embodiments thereof, it is evident that many
alternatives, modifications and variations will be apparent to
those skilled in the art. Accordingly, it is intended to embrace
all such alternatives, modifications and variations that fall
within the spirit and broad scope of the appended claims.
[0421] All publications, patents and patent applications mentioned
in this specification are herein incorporated in their entirety by
reference into the specification, to the same extent as if each
individual publication, patent or patent application was
specifically and individually indicated to be incorporated herein
by reference. In addition, citation or identification of any
reference in this application shall not be construed as an
admission that such reference is available as prior art to the
present invention. To the extent that section headings are used,
they should not be construed as necessarily limiting.
[0422] It is appreciated that certain features of the invention,
which are, for clarity, described in the context of separate
embodiments, may also be provided in combination in a single
embodiment. Conversely, various features of the invention, which
are, for brevity, described in the context of a single embodiment,
may also be provided separately or in any suitable subcombination
or as suitable in any other described embodiment of the invention.
Certain features described in the context of various embodiments
are not to be considered essential features of those embodiments,
unless the embodiment is inoperative without those elements.
[0423] Various embodiments and aspects of the present invention as
delineated hereinabove and as claimed in the claims section below
find experimental support in the following examples.
EXAMPLES
[0424] Reference is now made to the following examples, which
together with the above descriptions illustrate some embodiments of
the invention in a non limiting fashion.
[0425] Generally, the nomenclature used herein and the laboratory
procedures utilized in the present invention include molecular,
biochemical, microbiological and recombinant DNA techniques. Such
techniques are thoroughly explained in the literature. See, for
example, "Molecular Cloning: A laboratory Manual" Sambrook et al.,
(1989); "Current Protocols in Molecular Biology" Volumes I-III
Ausubel, R. M., ed. (1994); Ausubel et al., "Current Protocols in
Molecular Biology", John Wiley and Sons, Baltimore, Md. (1989);
Perbal, "A Practical Guide to Molecular Cloning", John Wiley &
Sons, New York (1988); Watson et al., "Recombinant DNA", Scientific
American Books, New York; Birren et al. (eds) "Genome Analysis: A
Laboratory Manual Series", Vols. 1-4, Cold Spring Harbor Laboratory
Press, New York (1998); methodologies as set forth in U.S. Pat.
Nos. 4,666,828; 4,683,202; 4,801,531; 5,192,659 and 5,272,057;
"Cell Biology: A Laboratory Handbook", Volumes I-III Cellis, J. E.,
ed. (1994); "Culture of Animal Cells--A Manual of Basic Technique"
by Freshney, Wiley-Liss, N. Y. (1994), Third Edition; "Current
Protocols in Immunology" Volumes I-III Coligan J. E., ed. (1994);
Stites et al. (eds), "Basic and Clinical Immunology" (8th Edition),
Appleton & Lange, Norwalk, Conn. (1994); Mishell and Shiigi
(eds), "Selected Methods in Cellular Immunology", W. H. Freeman and
Co., New York (1980); available immunoassays are extensively
described in the patent and scientific literature, see, for
example, U.S. Pat. Nos. 3,791,932; 3,839,153; 3,850,752; 3,850,578;
3,853,987; 3,867,517; 3,879,262; 3,901,654; 3,935,074; 3,984,533;
3,996,345; 4,034,074; 4,098,876; 4,879,219; 5,011,771 and
5,281,521; "Oligonucleotide Synthesis" Gait, M. J., ed. (1984);
"Nucleic Acid Hybridization" Hames, B. D., and Higgins S. J., eds.
(1985); "Transcription and Translation" Hames, B. D., and Higgins
S. J., eds. (1984); "Animal Cell Culture" Freshney, R. I., ed.
(1986); "Immobilized Cells and Enzymes" IRL Press, (1986); "A
Practical Guide to Molecular Cloning" Perbal, B., (1984) and
"Methods in Enzymology" Vol. 1-317, Academic Press; "PCR Protocols:
A Guide To Methods And Applications", Academic Press, San Diego,
Calif. (1990); Marshak et al., "Strategies for Protein Purification
and Characterization--A Laboratory Course Manual" CSHL Press
(1996); all of which are incorporated by reference as if fully set
forth herein. Other general references are provided throughout this
document. The procedures therein are believed to be well known in
the art and are provided for the convenience of the reader. All the
information contained therein is incorporated herein by
reference.
Example 1
Identifying Host-Proteome Signatures for Distinguishing Between
Acute Bacterial and Viral Infections
[0426] Patients were recruited as part of a multi-center,
observational, prospective clinical study with the aim to develop
and test a host proteins-signature for the purpose of rapid and
accurate diagnosis of patients with viral and bacterial
diseases.
[0427] Methods
[0428] Patient Recruitment:
[0429] A total of 122 patients were recruited of whom 111 had a
suspected infectious disease and 11 had a non-infectious disease
(control group). Informed consent was obtained from each
participant or legal guardian, as applicable. Inclusion criteria
for the infectious disease cohort included: clinical suspicion of
an acute infectious disease, peak fever >37.5.degree. C. since
symptoms onset, and duration of symptoms .ltoreq.12 days. Inclusion
criteria for the control group included: clinical impression of a
non-infectious disease (e.g. trauma, stroke and myocardial
infarction), or healthy subjects. Exclusion criteria included:
evidence of any episode of acute infectious disease in the two
weeks preceding enrollment; diagnosed congenital immune deficiency;
current treatment with immunosuppressive or immunomodulatory
therapy; active malignancy, proven or suspected human
immunodeficiency virus (HIV)-1, hepatitis B virus (HBV), or
hepatitis C virus (HCV) infection. Importantly, in order to enable
broad generalization, antibiotic treatment at enrollment did not
cause exclusion from the study. An overview of study workflow is
depicted in FIG. 1.
[0430] Enrollment Process and Data Collection:
[0431] For each patient, the following baseline variables were
recorded: demographics, physical examination, medical history (e.g.
main complaints, underlying diseases, chronically-administered
medications, comorbidities, time of symptom onset, and peak
temperature), complete blood count (CBC) obtained at enrollment,
and chemistry panel (e.g. creatinine, urea, electrolytes, and liver
enzymes). A nasal swab was obtained from each patient for further
microbiological investigation, and a blood sample was obtained for
protein screening and validation. Additional samples were obtained
as deemed appropriate by the physician (e.g. urine and stool
samples in cases of suspected urinary tract infection [UTI], and
gastroenteritis [GI] respectively). Radiological tests were
obtained at the discretion of the physician (e.g. chest X-ray for
suspected lower respiratory tract infection [LRTI]). All
information was recorded in a custom electronic case report form
(eCRF).
[0432] Establishing the Reference Standard:
[0433] Currently, no single reference standard exists for
determining bacterial and viral infections in a wide range of
clinical syndromes. Therefore, a rigorous reference standard was
created following recommendations of the Standards for Reporting of
Diagnostic Accuracy (STARD) (Bossuyt et al. 2003). First, a
thorough clinical and microbiological investigation was performed
for each patient as described above. Then, all the data collected
throughout the disease course was reviewed by a panel of up to
three physicians that assigned one of the following diagnostic
labels to each patient: (i) bacterial; (ii) viral; (iii) no
apparent infectious disease or healthy (controls); and (iv)
indeterminate. Importantly, the panel members were blinded to the
labeling of their peers to prevent group pressure or influential
personality bias as recommended by NHS-HTA (Rutjes et al. 2007),
and to the results of the host-proteins measurements.
[0434] Samples, Procedures and Sample Processing:
[0435] Venous blood samples were stored at 4.degree. C. for up to 5
hours, subsequently fractionated into plasma, serum and total
leukocytes, and stored at -80.degree. C. Nasal swabs and stool
samples were stored at 4.degree. C. for up to 72 hours and
subsequently transported to a certified service laboratory for
multiplex PCRs. Host-determinants were measured using enzyme-linked
immunosorbent-assay (ELISA).
[0436] Statistical Analysis
[0437] Primary analysis was based on area under the receiver
operating curve (AUC), Matthews correlation coefficient (MCC),
sensitivity, specificity, total accuracy. positive predictive value
(PPV), and negative predictive value (NPV). These measures are
defined as follows:
Sensitivity = TP TP + FN ##EQU00001## Specificity = TN TN + TP
##EQU00001.2## total accuracy = TP + TN TP + FN + TN + FP
##EQU00001.3## PPV = TP TP + FP = sensitivity prevalence
sensitivity prevalence + ( 1 - specificity ) ( 1 - prevalence )
##EQU00001.4## NPV = TN TN + FN = specificity ( 1 - prevalence )
specificty ( 1 - prevalence ) + ( 1 - sensitivity ) ( prevalence )
##EQU00001.5## MCC = TP .times. TN - FP .times. FN ( TP - FP ) ( TP
+ FN ) ( TN + FP ) ( TN + FN ) ##EQU00001.6##
P, N, TP, FP, TN, FN are positives, negatives, true-positives,
false-positives, true-negatives, and false-negatives, respectively.
Unless mentioned otherwise, positives and negatives refer to
patients with bacterial and viral infections, respectively.
[0438] Results
[0439] Patients Characteristics:
[0440] The studied group of pediatric patients included 62 females
(51%) and 60 males (49%) aged 3 months to 79 years. The patients
presented with a variety of clinical syndromes affecting different
physiological systems (e.g., respiratory, urinal, central nervous
system, systemic). Detailed characterization of studied patients is
depicted in FIGS. 2-6.
[0441] Single DETERMINANTS can Distinguish Between Bacterial (or
Mixed) and Viral Patients:
[0442] The expression profiles of multiple DETERMINANTS measured in
serum samples obtained from the described acute infection patients
were studied (FIG. 7). Based on these measurements, a classifier
was developed for distinguishing between bacterial and viral
patients using logistic regression. It was further calculated for
these determinants the measures of accuracy in distinguishing
between bacterial and viral patients including AUC, MCC, total
accuracy, sensitivity, specificity and Wilcoxon ranksum P-value
(Table 4).
TABLE-US-00004 TABLE 4 Concen- Total Sen- Speci- ranksum Mean Mean
tration Number Feature #1 AUC MCC accuracy sitivity ficity PPV NPV
P-value bacterial viral units 1 a1 Acid 0.61 -0.31 0.649 0.71 0.60
0.60 0.71 0.046554 3086569.8 3019442.3 ng/ml Glycoprotein 2
Adiponectin 0.69 0.32 0.662 0.74 0.60 0.61 0.73 0.010312 7918.4
13110.0 ng/ml 3 Angiogenin 0.58 0.16 0.581 0.56 0.60 0.54 0.62
0.34813 300216.0 328195.0 pg/ml 4 Angiopoietin1 0.57 0.11 0.575
0.50 0.64 0.55 0.60 0.44214 110144.2 98354.7 ml 5 Angiopoietin2
0.52 0.03 0.541 0.65 0.45 0.50 0.60 0.72039 3991.3 3283.8 pg/ml 6
APRIL 0.46 -0.05 0.554 0.56 0.55 0.51 0.60 0.23495 1.5 1.9 ng/ml 7
BAFF 0.57 -0.15 0.575 0.55 0.60 0.53 0.62 0.26059 2365.1 2444.3
pg/ml 8 BDNF 0.52 -0.11 0.63 0.52 0.73 0.61 0.64 0.11424 15035
15219 pg/ml 9 CD 23 0.52 -0.16 0.622 0.56 0.68 0.59 0.64 0.34258
4645.3 4795.3 pg/ml 10 CD14 0.60 0.24 0.618 0.67 0.58 0.59 0.66
0.26342 1762487.4 1841178.6 pg/ml 11 CD142 0.57 0.11 0.528 0.74
0.32 0.51 0.57 0.45032 35.7 37.0 pg/ml 12 CD27 0.66 0.17 0.622 0.65
0.60 0.58 0.67 0.027295 104 129 U/ml 13 CD95 0.40 -0.19 0.635 0.62
0.65 0.60 0.67 0.041434 5004.5 4744.0 pg/ml 14 Clusterin 0.69 0.30
0.689 0.63 0.74 0.69 0.69 0.009075 198900.7 285325.2 pg/ml 15
Complement 0.62 0.34 0.662 0.77 0.58 0.61 0.74 0.10033 2155.0
2526.1 ng/ml factor D 16 Corin 0.68 0.19 0.645 0.58 0.70 0.64 0.65
0.010336 1171.5 1647.9 pg/ml 17 CRP 0.82 0.60 0.82 0.76 0.86 0.79
0.84 7.02E-08 111.7 32.7 .mu.g/ml 18 CXCL13 0.57 0.09 0.514 0.77
0.30 0.48 0.60 0.82826 175.7 151.9 pg/ml 19 Cystatin C 0.67 0.24
0.632 0.72 0.55 0.59 0.69 0.034257 1411 1600 ng/ml 20 Dkk1 0.57
0.19 0.486 0.71 0.30 0.46 0.55 0.53284 2564.2 2788.0 ng/ml 21 E
Cadherin 0.64 0.18 0.452 0.27 0.62 0.38 0.49 0.09171 56.7 63.9
ng/ml 22 E Selectin 0.67 0.32 0.658 0.68 0.64 0.62 0.69 0.032367
81.4 64.2 ng/ml 23 Endostatin 0.48 -0.12 0.592 0.58 0.60 0.57 0.62
0.11029 101.9 101.4 ng/ml 24 Fetuin A 0.54 -0.18 0.622 0.57 0.67
0.61 0.63 0.16914 1712799.6 1836541.5 ng/ml 25 GCP2 0.61 0.11 0.635
0.53 0.73 0.62 0.64 0.16503 222.0 257.1 pg/ml 26 GDF15 0.60 0.20
0.5 0.47 0.53 0.46 0.54 0.4674 1376.3 1081.2 pg/ml 27 ICAM1 0.54
0.07 0.526 0.50 0.55 0.50 0.55 1 1320.6 1131.4 ng/ml 28 IGFBP3 0.62
0.22 0.622 0.71 0.55 0.57 0.69 0.10259 2390.6 2759.9 ng/ml 29 IL18
0.53 -0.27 0.654 0.55 0.75 0.68 0.64 0.007719 619.2 624.1 pg/ml 30
IL19 0.50 -0.01 0.527 0.41 0.63 0.48 0.56 0.95242 123 117 pg/ml 31
IL1R 0.69 0.29 0.662 0.56 0.75 0.66 0.67 0.0197 37.2 23.4 pg/ml 32
IP-10 0.75 0.25 0.73 0.73 0.73 0.65 0.80 4.84E-05 428.6 870.6 pg/ml
33 Leptin 0.50 -0.13 0.608 0.56 0.65 0.58 0.63 0.18755 7140.4
6613.1 pg/ml 34 Leptin R 0.53 -0.10 0.622 0.47 0.75 0.62 0.63
0.081707 37.8 38.4 ng/ml 35 LIGHT 0.59 0.17 0.459 0.38 0.53 0.41
0.50 0.30281 188.2 169.7 pg/ml 36 MBL 0.52 -0.21 0.635 0.56 0.70
0.61 0.65 0.033964 1390.6 1337.6 ng/ml 37 MIF 0.51 -0.15 0.649 0.65
0.65 0.61 0.68 0.06286 109.4 117.7 ng/ml 38 MMP2 0.51 -0.02 0.539
0.58 0.50 0.51 0.57 0.50887 143.9 150.9 ng/ml 39 MMP3 0.55 -0.07
0.595 0.77 0.44 0.55 0.68 0.42303 11.7 14.4 ng/ml 40 MMP7 0.66 0.21
0.625 0.64 0.62 0.58 0.67 0.03001 3.8 2.8 ng/ml 41 MMP8 0.74 0.43
0.73 0.74 0.72 0.70 0.76 0.001119 80.0 40.3 ng/ml 42 Myelo- 0.65
0.05 0.486 0.54 0.44 0.46 0.52 0.63381 610.2 549.1 ng/ml peroxidase
43 Neopterin 0.68 0.26 0.703 0.65 0.75 0.69 0.71 0.009685 4.6 7.2
pg/ml 44 NGAL 0.77 0.34 0.74 0.85 0.64 0.67 0.83 0.000159 144.1
92.2 ng/ml 45 Osteopontin 0.63 0.19 0.611 0.60 0.62 0.60 0.62
0.15573 191.7 161.7 ng/ml 46 Osteoprotegerin 0.59 0.17 0.595 0.53
0.65 0.56 0.62 0.46408 94.8 32.7 pg/ml 47 P Selectin 0.52 -0.27
0.632 0.67 0.60 0.60 0.67 0.028534 82.9 80.0 ng/ml 48 PCSK9 0.63
0.21 0.605 0.64 0.58 0.58 0.64 0.070271 563.6 483.0 ng/ml 49
Pentraxin3 0.72 0.37 0.681 0.60 0.76 0.70 0.67 0.012377 8.9 5.6
ng/ml 50 Pro Cathepsin B 0.62 0.17 0.644 0.62 0.67 0.62 0.67
0.11253 106.0 83.2 pg/ml 51 Progranulin 0.75 0.44 0.726 0.79 0.67
0.68 0.79 0.000646 94.9 163.5 pg/ml 52 ProMMP10 0.60 0.11 0.608
0.83 0.41 0.56 0.73 0.15611 1604.8 2049.9 pg/ml 53 Prostaglandin
0.48 -0.26 0.676 0.65 0.70 0.65 0.70 0.025815 30 30 pg/ml E2 54
RBP4 0.61 0.19 0.608 0.65 0.58 0.56 0.66 0.19492 12112.3 14761.5
ng/ml 55 Resistin 0.76 0.34 0.716 0.71 0.73 0.69 0.74 0.002105 26.5
14.7 ng/ml 56 SLPI 0.55 -0.10 0.645 0.72 0.58 0.61 0.70 0.081415
41716.5 42982.4 pg/ml 57 Substance P 0.46 -0.28 0.658 0.59 0.72
0.65 0.67 0.028153 67 72 pg/ml 58 TFPI 0.54 0.00 0.556 0.54 0.57
0.54 0.57 0.49547 29307.8 27608.6 pg/ml 59 TGF B1 0.53 0.03 0.566
0.81 0.35 0.53 0.67 0.47924 303 295 pg/ml 60 Thrombo- 0.44 -0.29
0.662 0.59 0.73 0.65 0.67 0.001689 45.3 41.7 ng/ml spondin2 61 Tie2
0.57 0.23 0.622 0.53 0.70 0.60 0.64 0.62933 22.7 21.0 ng/ml 62
TRAIL 0.95 0.75 0.901 0.89 0.91 0.87 0.92 5.67E-15 49.5 182.9 pg/ml
63 uPAR 0.50 -0.04 0.554 0.50 0.60 0.52 0.59 0.47073 4875.5 4144.9
pg/ml 64 VCAM1 0.45 -0.06 0.658 0.56 0.75 0.67 0.65 0.02302 1228.0
1202.4 ng/ml 65 VEGF C 0.54 -0.03 0.554 0.41 0.68 0.52 0.57 0.48079
8292.3 8632.7 pg/ml 66 Vitamin D 0.53 -0.31 0.667 0.58 0.75 0.69
0.65 0.002578 185488.3 183031.6 pg/ml Binding Protein
Determinants Whose Accuracy for Distinguishing Between Bacterial
(or Mixed) and Viral Infection Differs Between Children and
Adults:
[0443] For each of the examined determinants, the present inventors
performed measurements for both children (3 months-18 years) and
adults (older than 18 years), and monitored the change in
performance when distinguishing between bacterial and viral
infections. Most of the determinants did not show an age dependent
performance. However, the performance of some of the studied
determinants was significantly dependent on age.
[0444] Examples of determinants for which expression patterns in
bacterial (or mixed) and viral patients differ between children and
adults are summarized in Table 5.
TABLE-US-00005 TABLE 5 AUC AUC AUC Delta AUC Protein All Adults
Children (adults vs children) Myeloperoxidase 0.65 0.78 0.43 0.35
Osteopontin 0.63 0.84 0.54 0.30 Complement factor D 0.62 0.83 0.59
0.25 PCSK9 0.63 0.76 0.52 0.24 IGFBP3 0.62 0.44 0.67 0.22 GDF15
0.60 0.69 0.47 0.22 Osteoprotegerin 0.59 0.71 0.51 0.21 Neopterin
0.68 0.57 0.76 0.19 SLPI 0.55 0.75 0.56 0.19 Progranulin 0.75 0.64
0.82 0.18 Adiponectin 0.69 0.59 0.76 0.18 E Cadherin 0.64 0.54 0.71
0.18 ICAM1 0.54 0.77 0.59 0.17 CXCL13 0.57 0.70 0.53 0.17 CD95 0.40
0.58 0.73 0.15 LIGHT 0.59 0.68 0.53 0.15 Angiopoietin1 0.57 0.52
0.65 0.13 Resistin 0.76 0.84 0.71 0.13 Angiogenin 0.58 0.55 0.68
0.12 Pro Cathepsin B 0.62 0.68 0.58 0.10 IL19 0.50 0.69 0.60 0.10
NGAL 0.77 0.71 0.81 0.09 BAFF 0.57 0.58 0.66 0.08 RBP4 0.61 0.69
0.61 0.08 MMP2 0.51 0.71 0.63 0.08 CD27 0.66 0.71 0.65 0.07 GCP2
0.61 0.65 0.59 0.06 Clusterin 0.69 0.68 0.74 0.06 Cystatin C 0.67
0.63 0.69 0.06
[0445] For example, Osteopontin is a highly distinctive marker in
adults but not in children (FIG. 8A), while NGAL and Neopterin are
much more distinctive markers in children than in adults (FIG.
8B-C). For Neopterin, besides the changes in differential
expression patterns, the actual cutoffs were changed as well as
there was a shift up in Neopterin expression levels in adults (FIG.
8C; mean Neopterin levels in bacterial and viral patients were 5.9
and 8.1 pg/ml respectively in adults and only 2.1 and 5.4 pg/ml
respectively in children). Importantly, the differences between
accuracy levels in children and adults can lead to a reduced
accuracy when considering the entire population (comprised of both
children and adults) as it masks the differential expression of one
of the age groups. Accordingly, for the determinants presented in
Table 5, the AUC of a specific determinant for a specific age group
(AUC Adults or AUC Children) was up to 0.33 higher than for the
entire population (AUC All; see for example CD95 in which AUC
children is 0.73 compared to an AUC of 0.4 for the entire
population).
[0446] Combining Different Determinants to Increase Diagnostic
Accuracy:
[0447] Next, the present inventors tested whether combining several
determinants can improve diagnostic accuracy of single
determinants. They used a linear logistic regression to develop a
classifier for each pair of determinants (2145 combinations) and
evaluated its ability to distinguish between bacterial (or mixed)
and viral patients. FIG. 9 includes examples of scatter plots of
pairs of determinants that differentiate between bacterial (red) vs
viral (blue) infected subjects.
[0448] FIGS. 10 and 11 present the classification accuracy in terms
of AUC and MCC (respectively) of viral versus bacterial infected
patients attained for pairs of determinants using a logistic
regression model.
[0449] Table 6 presents examples of pairs that demonstrated high
accuracy improvement as calculated by the difference in AUC of the
pair compared to the AUC of the single determinant (out of the same
pair) with the highest AUC (delta AUC). Combining pairs of
determinants generated an increase of up to 0.18 in AUC (when
comparing AUC of single vs. pairs of determinants; e.g., combined
AUC of 0.87 compared to AUC of 0.69 of the best single) and up to
0.27 in MCC (when comparing MCC of single vs. pairs of
determinants; e.g., combined MCC of 0.61 compared to MCC of 0.34 of
the best single), to generate highly discriminative combinations
(AUCs between 0.75-0.96, average AUC 0.90, when testing the pairs
summarized in Table 6).
TABLE-US-00006 TABLE 6 Feature Feature Combined Delta Delta Sen-
Specfi- #1 #2 AUC_1 AUC_2 model AUC AUC MCC_1 MCC_2 MCC MCC
sitivity city Adiponectin Osteopontin 0.69 0.63 0.87 0.18 0.32 0.19
0.55 0.23 0.77 0.84 Progranulin Resistin 0.75 0.76 0.92 0.16 0.44
0.34 0.63 0.20 0.92 0.83 Pentraxin3 Progranulin 0.72 0.75 0.87 0.13
0.37 0.44 0.45 0.01 0.85 0.67 ProMMP10 Resistin 0.60 0.76 0.88 0.12
0.11 0.34 0.46 0.11 0.93 0.77 IL1R Progranulin 0.69 0.75 0.87 0.12
0.29 0.44 0.49 0.05 0.92 0.72 CRP NGAL 0.82 0.77 0.92 0.10 0.60
0.34 0.61 0.01 0.82 0.85 E Cadherin Resistin 0.64 0.76 0.86 0.10
0.18 0.34 0.61 0.27 0.73 0.89 CRP Progranulin 0.82 0.75 0.91 0.10
0.60 0.44 0.64 0.04 0.82 0.87 CD27 NGAL 0.66 0.77 0.86 0.10 0.17
0.34 0.45 0.11 0.89 0.71 IL19 NGAL 0.50 0.77 0.86 0.09 -0.01 0.34
0.58 0.24 0.77 0.82 Corin NGAL 0.68 0.77 0.86 0.09 0.19 0.34 0.55
0.21 0.78 0.83 Angiopoietin1 CRP 0.57 0.82 0.91 0.09 0.11 0.60 0.64
0.04 0.88 0.87 CRP E Selectin 0.82 0.67 0.91 0.09 0.60 0.32 0.56
-0.04 0.77 0.85 CRP Substance P 0.82 0.46 0.90 0.09 0.60 -0.28 0.70
0.10 0.82 0.90 CRP MMP7 0.82 0.66 0.90 0.08 0.60 0.21 0.72 0.12
0.85 0.87 CRP Pro Cathepsin B 0.82 0.62 0.90 0.08 0.60 0.17 0.67
0.07 0.79 0.90 CRP E Cadherin 0.82 0.64 0.90 0.08 0.60 0.18 0.67
0.07 0.82 0.87 BDNF CRP 0.52 0.82 0.90 0.08 -0.11 0.60 0.59 -0.02
0.82 0.83 BAFF CRP 0.57 0.82 0.89 0.07 -0.15 0.60 0.64 0.04 0.76
0.88 CRP MMP8 0.82 0.74 0.89 0.07 0.60 0.43 0.57 -0.04 0.80 0.80
NGAL Resistin 0.77 0.76 0.83 0.07 0.34 0.34 0.60 0.26 0.77 0.88
Angiogenin CRP 0.58 0.82 0.88 0.06 0.16 0.60 0.59 -0.01 0.88 0.75
NGAL PCSK9 0.77 0.63 0.83 0.06 0.34 0.21 0.53 0.19 0.78 0.77 NGAL
Vitamin D 0.77 0.53 0.82 0.06 0.34 -0.31 0.40 0.06 0.89 0.71
Binding Protein CRP Neopterin 0.82 0.68 0.88 0.06 0.60 0.26 0.46
-0.15 0.88 0.70 NGAL SLPI 0.77 0.55 0.82 0.06 0.34 -0.10 0.38 0.05
0.78 0.77 CRP Resistin 0.82 0.76 0.87 0.06 0.60 0.34 0.51 -0.09
0.82 0.78 NGAL RBP4 0.77 0.61 0.82 0.05 0.34 0.19 0.45 0.11 0.81
0.71 NGAL Tie2 0.77 0.57 0.82 0.05 0.34 0.23 0.49 0.15 0.73 0.88
CRP Pentraxin3 0.82 0.72 0.87 0.05 0.60 0.37 0.58 -0.02 0.74 0.87
NGAL Progranulin 0.77 0.75 0.81 0.05 0.34 0.44 0.47 0.03 0.85 0.71
NGAL ProMMP10 0.77 0.60 0.81 0.05 0.34 0.11 0.44 0.10 0.70 0.77 CRP
MMP3 0.82 0.55 0.86 0.05 0.60 -0.07 0.62 0.02 0.74 0.87 CRP
ProMMP10 0.82 0.60 0.86 0.05 0.60 0.11 0.57 -0.04 0.74 0.82 NGAL a1
Acid 0.77 0.61 0.81 0.05 0.34 -0.31 0.45 0.11 0.85 0.77
Glycoprotein NGAL TGF B1 0.77 0.53 0.81 0.04 0.34 0.03 0.35 0.01
0.78 0.72 NGAL Osteoprotegerin 0.77 0.59 0.81 0.04 0.34 0.17 0.49
0.15 0.77 0.77 NGAL VCAM1 0.77 0.45 0.81 0.04 0.34 -0.06 0.38 0.05
0.78 0.77 CRP IP-10 0.82 0.75 0.86 0.04 0.60 0.25 0.59 -0.02 0.71
0.86 NGAL uPAR 0.77 0.50 0.80 0.04 0.34 -0.04 0.41 0.07 0.77 0.77
CRP TFPI 0.82 0.54 0.85 0.04 0.60 0.00 0.53 -0.07 0.77 0.81 NGAL
Neopterin 0.77 0.68 0.80 0.04 0.34 0.26 0.45 0.11 0.85 0.71 NGAL
Prostatandin E2 0.77 0.48 0.80 0.04 0.34 -0.26 0.33 -0.01 0.81 0.72
CRP Cystatin C 0.82 0.67 0.85 0.04 0.60 0.24 0.55 -0.05 0.69 0.88
NGAL P Selectin 0.77 0.52 0.80 0.04 0.34 -0.27 0.38 0.05 0.82 0.77
NGAL Pentraxin3 0.77 0.72 0.80 0.04 0.34 0.37 0.58 0.22 0.74 0.87
IP-10 NGAL 0.75 0.77 0.80 0.03 0.25 0.34 0.49 0.15 0.82 0.69 CRP
Complement 0.82 0.62 0.85 0.03 0.60 0.34 0.59 -0.01 0.74 0.93
factor D CRP Myeloperoxidase 0.82 0.65 0.85 0.03 0.60 0.05 0.59
-0.01 0.77 0.82 NGAL Osteopontin 0.77 0.63 0.79 0.03 0.34 0.19 0.44
0.10 0.85 0.67 NGAL Substance P 0.77 0.46 0.79 0.03 0.34 -0.28 0.33
-0.01 0.82 0.66 NGAL Thrombospondin2 0.77 0.44 0.79 0.03 0.34 -0.29
0.41 0.07 0.85 0.65 NGAL VEGF C 0.77 0.54 0.79 0.03 0.34 -0.03 0.46
0.12 0.69 0.78 Angiopoietin1 TRAIL 0.57 0.95 0.96 0.01 0.11 0.75
0.78 0.03 0.91 0.95 E Cadherin TRAIL 0.64 0.95 0.96 0.01 0.18 0.75
0.78 0.03 0.91 0.87 Progranulin TRAIL 0.75 0.95 0.96 0.01 0.44 0.75
0.81 0.06 0.91 0.90 CD14 TRAIL 0.60 0.95 0.95 0.01 0.24 0.75 0.75
0.00 0.86 0.90 NGAL Pro Cathepsin B 0.77 0.62 0.77 0.00 0.34 0.17
0.25 -0.09 0.88 0.58 Pro Cathepsin B TRAIL 0.62 0.95 0.95 0.00 0.17
0.75 0.73 -0.02 0.91 0.85 Myeloperoxidase TRAIL 0.65 0.95 0.95 0.00
0.05 0.75 0.74 -0.01 0.89 0.87 Endostatin TRAIL 0.48 0.95 0.95 0.00
-0.12 0.75 0.77 0.02 0.86 0.93 MMP8 TRAIL 0.74 0.95 0.95 0.00 0.43
0.75 0.71 -0.04 0.89 0.90 NGAL TRAIL 0.77 0.95 0.95 0.00 0.34 0.75
0.71 -0.04 0.85 0.95 PCSK9 TRAIL 0.63 0.95 0.95 0.00 0.21 0.75 0.72
-0.03 0.86 0.93 IL18 TRAIL 0.53 0.95 0.95 0.00 -0.27 0.75 0.75 0.00
0.90 0.88 IP-10 TRAIL 0.75 0.95 0.95 0.00 0.25 0.75 0.73 -0.02 0.87
0.92 MMP3 TRAIL 0.55 0.95 0.95 0.00 -0.07 0.75 0.68 -0.07 0.89 0.87
TGF B1 TRAIL 0.53 0.95 0.95 0.00 0.03 0.75 0.74 -0.01 0.92 0.85
TRAIL VCAM1 0.95 0.45 0.95 0.00 0.75 -0.06 0.74 -0.01 0.89 0.88
ICAM1 TRAIL 0.54 0.95 0.95 0.00 0.07 0.75 0.77 0.02 0.94 0.85 MMP2
TRAIL 0.51 0.95 0.95 0.00 -0.02 0.75 0.74 -0.01 0.86 0.95 P
Selectin TRAIL 0.52 0.95 0.95 0.00 -0.27 0.75 0.74 -0.01 0.86 0.93
ProMMP 10 TRAIL 0.60 0.95 0.95 0.00 0.11 0.75 0.71 -0.04 0.89 0.87
TRAIL Vitamin D 0.95 0.53 0.95 0.00 0.75 -0.31 0.77 0.02 0.87 0.95
Binding Protein TRAIL a1 Acid 0.95 0.61 0.95 0.00 0.75 -0.31 0.77
0.02 0.85 0.95 Glycoprotein CRP TRAIL 0.82 0.95 0.95 0.00 0.60 0.75
0.77 0.02 0.91 0.91 CXCL13 TRAIL 0.57 0.95 0.95 0.00 0.09 0.75 0.71
-0.04 0.82 0.95 E Selectin TRAIL 0.67 0.95 0.95 0.00 0.32 0.75 0.68
-0.06 0.82 0.85 Adiponectin TRAIL 0.69 0.95 0.95 0.00 0.32 0.75
0.76 0.01 0.91 0.88 Clusterin TRAIL 0.69 0.95 0.95 0.00 0.30 0.75
0.76 0.01 0.89 0.90 RBP4 TRAIL 0.61 0.95 0.95 0.00 0.19 0.75 0.74
-0.01 0.85 0.93 SLPI TRAIL 0.55 0.95 0.95 0.00 -0.10 0.75 0.66
-0.09 0.86 0.93 Substance P TRAIL 0.46 0.95 0.95 0.00 -0.28 0.75
0.71 -0.04 0.82 0.92 CD 23 TRAIL 0.52 0.95 0.94 0.00 -0.16 0.75
0.68 -0.07 0.88 0.90 CD27 TRAIL 0.66 0.95 0.94 0.00 0.17 0.75 0.74
-0.01 0.85 0.93 CD95 TRAIL 0.40 0.95 0.94 0.00 -0.19 0.75 0.71
-0.04 0.85 0.90 Cystatin C TRAIL 0.67 0.95 0.94 0.00 0.24 0.75 0.74
-0.01 0.89 0.90 Fetuin A TRAIL 0.54 0.95 0.94 0.00 -0.18 0.75 0.71
-0.04 0.86 0.95 Leptin R TRAIL 0.53 0.95 0.94 0.00 -0.10 0.75 0.77
0.02 0.85 0.95 TRAIL uPAR 0.95 0.50 0.94 0.00 0.75 -0.04 0.74 -0.01
0.91 0.88 Angiogenin TRAIL 0.58 0.95 0.94 -0.01 0.16 0.75 0.76 0.01
0.85 0.95 APRIL TRAIL 0.46 0.95 0.94 -0.01 -0.05 0.75 0.72 -0.03
0.91 0.85 Corin TRAIL 0.68 0.95 0.94 -0.01 0.19 0.75 0.74 -0.01
0.89 0.95 GDF15 TRAIL 0.60 0.95 0.94 -0.01 0.20 0.75 0.76 0.01 0.91
0.88 IGFBP3 TRAIL 0.62 0.95 0.94 -0.01 0.22 0.75 0.73 -0.02 0.85
0.95 IL19 TRAIL 0.50 0.95 0.94 -0.01 -0.01 0.75 0.74 -0.01 0.88
0.85 Leptin TRAIL 0.50 0.95 0.94 -0.01 -0.13 0.75 0.79 0.04 0.85
0.95 LIGHT TRAIL 0.59 0.95 0.94 -0.01 0.17 0.75 0.68 -0.07 0.85
0.93 MBL TRAIL 0.52 0.95 0.94 -0.01 -0.21 0.75 0.73 -0.02 0.88 0.90
MMP7 TRAIL 0.66 0.95 0.94 -0.01 0.21 0.75 0.70 -0.05 0.88 0.87
Neopterin TRAIL 0.68 0.95 0.94 -0.01 0.26 0.75 0.71 -0.04 0.85 0.90
Prostaglandin E2 TRAIL 0.48 0.95 0.94 -0.01 -0.26 0.75 0.71 -0.04
0.88 0.88 Resistin TRAIL 0.76 0.95 0.94 -0.01 0.34 0.75 0.76 0.01
0.91 0.88 TRAIL Thrombo- 0.95 0.44 0.94 -0.01 0.75 -0.29 0.74 -0.01
0.91 0.85 spondin2 BAFF TRAIL 0.57 0.95 0.94 -0.01 -0.15 0.75 0.73
-0.02 0.88 0.88 BDNF TRAIL 0.52 0.95 0.94 -0.01 -0.11 0.75 0.71
-0.04 0.85 0.95 Dkk1 TRAIL 0.57 0.95 0.94 -0.01 0.19 0.75 0.71
-0.04 0.85 0.93 TRAIL Tie2 0.95 0.57 0.94 -0.01 0.75 0.23 0.77 0.02
0.85 0.95 GCP2 TRAIL 0.61 0.95 0.94 -0.01 0.11 0.75 0.68 -0.07 0.88
0.90 MIF TRAIL 0.51 0.95 0.94 -0.01 -0.15 0.75 0.73 -0.02 0.88 0.90
Osteoprotegerin TRAIL 0.59 0.95 0.94 -0.01 0.17 0.75 0.76 0.01 0.88
0.90 TRAIL VEGF C 0.95 0.54 0.94 -0.01 0.75 -0.03 0.73 -0.02 0.88
0.88 Angiopoietin2 TRAIL 0.52 0.95 0.94 -0.01 0.03 0.75 0.68 -0.07
0.88 0.88 Complement TRAIL 0.62 0.95 0.94 -0.01 0.34 0.75 0.74
-0.01 0.91 0.90 factor D IL1R TRAIL 0.69 0.95 0.94 -0.01 0.29 0.75
0.71 -0.04 0.88 0.88 Osteopontin TRAIL 0.63 0.95 0.94 -0.01 0.19
0.75 0.78 0.03 0.89 0.89 Pentraxin 3 TRAIL 0.72 0.95 0.94 -0.01
0.37 0.75 0.78 0.03 0.91 0.89 TFPI TRAIL 0.54 0.95 0.94 -0.01 0.00
0.75 0.72 -0.02 0.89 0.92 CD142 TRAIL 0.57 0.95 0.93 -0.01 0.11
0.75 0.72 -0.02 0.89 0.92 NGAL TFPI 0.77 0.54 0.75 -0.02 0.34 0.00
0.28 -0.06 0.82 0.67
[0450] It is noted that some determinant combinations exhibited an
improved diagnostic accuracy (in terms of AUC or MCC) compared to
that of the corresponding individual determinants, whereas other
combinations exhibit a reduced accuracy (FIGS. 12-13).
[0451] FIGS. 14-16 and Tables 7-8 demonstrate the ability of NGAL
and Neopterin to increase the sensitivity of CRP, TRAIL, and IP-10
using selected cutoffs. For example, combining NGAL (at a selected
cutoff of 150 ng/ml) increased the sensitivity of CRP (at the
routinely used cutoff of 80 .mu.g/ml) from 0.59 to 0.74 (25%
increase; Table 7). In this analysis a patient is classified as
having a bacterial infection if his CRP levels were higher than 80
.mu.g/ml OR his NGAL levels were higher than 150 ng/ml. Similarly,
combining Neopterin (at a selected cutoff of 4 pg/ml) increased the
sensitivity of CRP (at the routinely used cutoff of 80 .mu.g/ml)
from 0.56 to 0.88 (57% increase; Table 8). In this analysis a
patient is classified as having a bacterial infection if his CRP
levels were higher than 80 .mu.g/ml or his Neopterin levels were
lower than 4 pg/ml.
[0452] Table 7 provides data illustrating that levels of NGAL can
be combined with other biomarkers to improve overall diagnostic
performance (N=66). A patient was classified as having a bacterial
infection in the following cases (according to the evaluated
determinant): if his CRP levels were higher than the indicated
cutoff (20 .mu.g/ml or 80 .mu.g/ml) OR his NGAL levels were higher
than 150 ng/ml; if his TRAIL levels were lower than the indicated
cutoff (70 pg/ml) OR his NGAL levels were higher than 150 ng/ml; if
his IP-10 levels were lower than the indicated cutoff (500 pg/ml)
OR his NGAL levels were higher than 150 ng/ml; if his
CRP-TRAIL-IP-10 signature score was higher than 65 OR his NGAL
levels were higher than 150 ng/ml.
TABLE-US-00007 TABLE 7 Sensitivity Specificity Sensitivity
Specificity (biomarker + (biomarker + (bio- (bio- NGAL NGAL marker
marker 150 ng/ml 150 ng/ml Delta Delta Biomarker alone) alone)
cutoff) cutoff) sensitivity specificity CRP 0.94 0.63 1.00 0.56
0.06 -0.06 (20 .mu.g/ml) CRP 0.59 0.97 0.74 0.84 0.15 -0.13 (80
.mu.g/ml) TRAIL 0.71 0.97 0.79 0.81 0.09 -0.16 (70 pg/ml) IP-10
0.79 0.66 0.88 0.63 0.09 -0.03 (500 pg/ml) CRP + 0.83 1.00 0.90
0.89 0.07 -0.11 TRAIL + IP-10 signature
Table 8 below provides data showing that the levels of Neopterin
can be combined with other biomarkers to improve overall diagnostic
performance (N=74). A patient was classified as having a bacterial
infection in the following cases (according to the evaluated
determinant): if his CRP levels were higher than the indicated
cutoff (20 .mu.g/ml or 80 .mu.g/ml) OR his Neopterin levels were
lower than 4 pg/ml; if his TRAIL levels were lower than the
indicated cutoff (70 pg/ml) OR his Neopterin levels were lower than
4 pg/ml; if his IP-10 levels were lower than the indicated cutoff
(500 pg/ml) OR his Neopterin levels were lower than 4 pg/ml; if his
CRP-TRAIL-IP-10 signature score was higher than 65 OR his Neopterin
levels were lower than 4 pg/ml.
TABLE-US-00008 TABLE 8 Sensitivity Specificity (biomarker +
(biomarker + Sensitivity Specificity Neopterin Neopterin (biomarker
(biomarker 4 pg/ml 4 pg/ml Delta Delta Biomarker alone) alone)
cutoff) cutoff) sensitivity specificity CRP 0.82 0.58 1.00 0.38
0.18 -0.20 (20 .mu.g/ml) CRP 0.56 0.95 0.88 0.68 0.32 -0.28 (80
.mu.g/ml) TRAIL 0.74 0.95 0.88 0.70 0.15 -0.25 (70 pg/ml) IP-10
0.76 0.68 0.79 0.58 0.03 -0.10 (500 pg/ml) CRP + 0.79 0.92 0.94
0.68 0.15 -0.25 TRAIL + IP-10 signature
[0453] FIGS. 17-18 demonstrate the ability of NGAL and Neopterin to
increase the sensitivity (at the expense of specificity) of the
CRP, TRAIL, and IP-10 signature, using selected cutoff in children
but not in adults (consistent with the findings described in FIG.
8).
[0454] Next, the present inventors evaluated the diagnostic
accuracy of triplets of determinants. They developed a linear
logistic regression classifier for each triplet (45,760
combinations) and further calculated for these triplets the
measures of accuracy in distinguishing between bacterial (or mixed)
and viral patients including AUC, MCC, total accuracy, sensitivity,
specificity and Wilcoxon ranksum P-value. Table 9 presents
different determinant triplets with very high accuracy levels (AUCs
between 0.89-0.99, average AUC 0.95) when tested on 111 infectious
disease patients.
TABLE-US-00009 TABLE 9 Total Feature Feature Feature ranksum accu-
Sensi- Spec- #1 #2 #3 AUC P-value MCC racy tivity ificity
Angiogenin CRP Progranulin 0.99 2.39E-05 0.76 0.91 0.92 0.89 CRP
Complement NGAL 0.99 3.52E-05 0.76 0.88 0.89 0.88 factor D CRP
Osteopontin Pro 0.98 4.35E-06 0.69 0.86 0.89 0.80 Cathepsin B CRP
Pentraxin3 Pro 0.98 3.38E-06 0.69 0.88 0.82 1.00 Cathepsin B CRP
Pentraxin3 Progranulin 0.98 4.35E-06 0.70 0.86 0.82 0.93 APRIL E
Cadherin TRAIL 0.98 6.08E-06 0.73 0.86 0.85 0.89 Angiopoietin1 TGF
B1 TRAIL 0.98 1.21E-06 0.77 0.89 0.93 0.83 CD 23 E Cadherin TRAIL
0.98 6.08E-06 0.62 0.84 0.81 0.89 CD14 E Cadherin TRAIL 0.98
1.02E-05 0.59 0.84 0.82 0.89 CD95 E Cadherin TRAIL 0.98 3.86E-06
0.67 0.89 0.81 1.00 CRP Complement Progranulin 0.98 5.71E-07 0.77
0.91 0.92 0.89 factor D Dkk1 E Cadherin TRAIL 0.98 7.62E-06 0.63
0.86 0.81 0.94 E Cadherin Endostatin TRAIL 0.98 9.13E-06 0.64 0.84
0.82 0.89 E Cadherin IL18 TRAIL 0.98 9.13E-06 0.53 0.82 0.82 0.83 E
Cadherin Leptin TRAIL 0.98 1.32E-05 0.58 0.84 0.81 0.89 E Cadherin
RBP4 TRAIL 0.98 6.38E-05 0.67 0.84 0.89 0.78 E Cadherin TRAIL
Thrombo- 0.98 6.08E-06 0.57 0.84 0.81 0.89 spondin2 E Cadherin
TRAIL Tie2 0.98 7.62E-06 0.67 0.84 0.81 0.89 E Cadherin TRAIL
Vitamin D 0.98 3.40E-06 0.54 0.89 0.82 1.00 Binding Protein E
Cadherin TRAIL uPAR 0.98 1.06E-05 0.58 0.84 0.77 0.94 Angiogenin E
Cadherin TRAIL 0.98 6.81E-06 0.62 0.84 0.81 0.89 Angiopoietin1 CD
23 TRAIL 0.98 2.72E-06 0.67 0.89 0.89 0.89 Angiopoietin1 Endostatin
TRAIL 0.98 2.71E-06 0.68 0.87 0.89 0.83 Angiopoietin1 IGFBP3 TRAIL
0.98 1.92E-06 0.72 0.89 0.89 0.89 Angiopoietin1 P Selectin TRAIL
0.98 3.25E-07 0.86 0.96 0.96 0.94 Angiopoietin2 E Cadherin TRAIL
0.98 1.19E-05 0.57 0.82 0.81 0.83 E Cadherin GCP2 TRAIL 0.98
6.81E-06 0.72 0.86 0.89 0.83 E Cadherin GDF15 TRAIL 0.98 3.44E-06
0.62 0.89 0.81 1.00 E Cadherin IGFBP3 TRAIL 0.98 3.86E-06 0.67 0.89
0.85 0.94 E Cadherin MMP2 TRAIL 0.98 4.33E-05 0.68 0.84 0.82 0.89 E
Cadherin Neopterin TRAIL 0.98 7.62E-06 0.58 0.89 0.81 1.00 E
Cadherin SLPI TRAIL 0.98 7.36E-06 0.54 0.87 0.78 1.00 Angiopoietin1
IL18 TRAIL 0.98 2.42E-06 0.58 0.87 0.85 0.89 Angiopoietin1 MMP2
TRAIL 0.98 6.43E-05 0.64 0.87 0.89 0.83 Angiopoietin1 TRAIL VCAM1
0.98 3.80E-06 0.72 0.87 0.89 0.83 CD95 CRP NGAL 0.98 1.97E-06 0.72
0.86 0.85 0.88 Corin E Cadherin TRAIL 0.98 3.04E-06 0.63 0.89 0.85
0.94 Cystatin C E Cadherin TRAIL 0.98 1.72E-06 0.77 0.89 0.89 0.89
E Cadherin TGF B1 TRAIL 0.98 8.20E-06 0.59 0.84 0.78 0.94
Progranulin TRAIL Vitamin D 0.98 3.80E-06 0.63 0.84 0.82 0.89
Binding Protein APRIL Angiopoietin1 TRAIL 0.98 1.19E-06 0.72 0.89
0.89 0.89 APRIL Progranulin TRAIL 0.98 3.06E-06 0.72 0.86 0.89 0.83
Angiopoietin1 GDF15 TRAIL 0.98 3.06E-06 0.67 0.89 0.89 0.89
Angiopoietin1 TRAIL Thrombo- 0.98 2.72E-06 0.62 0.86 0.85 0.89
spondin2 Angiopoietin1 TRAIL Tie2 0.98 2.72E-06 0.72 0.89 0.92 0.83
Angiopoietin1 TRAIL a1 Acid 0.98 6.08E-06 0.57 0.84 0.81 0.89
Glycoprotein CD 23 Progranulin TRAIL 0.98 8.51E-06 0.62 0.82 0.77
0.89 CRP Leptin R Progranulin 0.98 9.32E-07 0.77 0.89 0.89 0.89 CRP
Osteo- Progranulin 0.98 3.06E-06 0.67 0.89 0.85 0.94 protegerin E
Cadherin IL19 TRAIL 0.98 7.62E-06 0.63 0.84 0.81 0.89 E Cadherin
Resistin TRAIL 0.98 1.92E-06 0.77 0.91 0.92 0.89 E Cadherin TRAIL
a1 Acid 0.98 6.81E-06 0.67 0.86 0.85 0.89 Glycoprotein
Angiopoietin1 Cystatin C TRAIL 0.98 2.42E-06 0.72 0.87 0.85 0.89
Angiopoietin1 SLPI TRAIL 0.98 3.40E-06 0.58 0.87 0.82 0.94
Angiopoietin1 TRAIL Vitamin D 0.98 1.72E-06 0.63 0.89 0.85 0.94
Binding Protein CD142 Progranulin TRAIL 0.98 4.94E-06 0.80 0.91
0.89 0.93 CRP LIGHT NGAL 0.98 4.14E-05 0.66 0.84 0.81 0.88 CRP NGAL
uPAR 0.98 8.19E-07 0.77 0.91 0.92 0.88 E Cadherin ICAM1 TRAIL 0.98
6.60E-06 0.68 0.84 0.85 0.83 E Cadherin P Selectin TRAIL 0.98
2.16E-06 0.77 0.89 0.89 0.89 MMP2 Progranulin TRAIL 0.98 3.92E-05
0.63 0.84 0.85 0.83 P Selectin Progranulin TRAIL 0.98 8.51E-07 0.68
0.91 0.96 0.83 Pentraxin3 Progranulin TRAIL 0.98 7.16E-06 0.70 0.88
0.85 0.93 Progranulin SLPI TRAIL 0.98 5.92E-06 0.54 0.84 0.82 0.89
Angiogenin Angiopoietin1 TRAIL 0.97 2.72E-06 0.72 0.86 0.85 0.89
Angiopoietin1 CD95 TRAIL 0.97 2.42E-06 0.67 0.86 0.85 0.89
Angiopoietin1 IL19 TRAIL 0.97 4.33E-06 0.72 0.89 0.92 0.83
Angiopoietin1 Leptin TRAIL 0.97 4.85E-06 0.67 0.86 0.85 0.89
Angiopoietin1 MBL TRAIL 0.97 1.34E-06 0.72 0.91 0.89 0.94
Angiopoietin1 RBP4 TRAIL 0.97 2.80E-05 0.67 0.86 0.89 0.83
Angiopoietin1 Resistin TRAIL 0.97 1.34E-06 0.81 0.93 0.96 0.89 CD95
Progranulin TRAIL 0.97 4.85E-06 0.62 0.84 0.81 0.89 CRP Progranulin
Resistin 0.97 2.16E-06 0.67 0.86 0.89 0.83 CXCL13 E Cadherin TRAIL
0.97 6.08E-06 0.62 0.86 0.81 0.94 Dkk1 Progranulin TRAIL 0.97
6.81E-06 0.53 0.84 0.81 0.89 E Cadherin Leptin R TRAIL 0.97
3.86E-06 0.77 0.89 0.89 0.89 E Cadherin MIF TRAIL 0.97 9.32E-07
0.82 0.91 0.89 0.94 E Cadherin TRAIL VEGF C 0.97 3.86E-06 0.65 0.86
0.85 0.89 Pro Prostaglandin TRAIL 0.97 5.43E-06 0.77 0.91 0.89 0.94
Cathepsin B E2 Angiopoietin1 Corin TRAIL 0.97 1.72E-06 0.63 0.89
0.89 0.89 Angiopoietin1 PCSK9 TRAIL 0.97 1.72E-06 0.72 0.89 0.89
0.89 CRP IL19 NGAL 0.97 9.30E-07 0.66 0.91 0.85 1.00 Endostatin
Progranulin TRAIL 0.97 1.02E-05 0.63 0.84 0.85 0.83 PCSK9
Progranulin TRAIL 0.97 4.75E-06 0.72 0.87 0.93 0.78 Progranulin TGF
B1 TRAIL 0.97 6.60E-06 0.59 0.84 0.82 0.89 Angiogenin Progranulin
TRAIL 0.97 7.62E-06 0.57 0.84 0.81 0.89 Angiopoietin1 Angiopoietin2
TRAIL 0.97 3.86E-06 0.67 0.86 0.85 0.89 Angiopoietin1 Dkk1 TRAIL
0.97 1.92E-06 0.67 0.89 0.85 0.94 Angiopoietin1 GCP2 TRAIL 0.97
2.72E-06 0.67 0.86 0.89 0.83 Angiopoietin1 LIGHT TRAIL 0.97
1.19E-06 0.72 0.89 0.85 0.94 Angiopoietin1 Leptin R TRAIL 0.97
1.92E-06 0.76 0.89 0.92 0.83 Angiopoietin1 Neopterin TRAIL 0.97
3.06E-06 0.62 0.84 0.81 0.89 Angiopoietin1 TRAIL uPAR 0.97 3.06E-06
0.57 0.86 0.85 0.89 Angiopoietin2 Progranulin TRAIL 0.97 1.83E-05
0.62 0.84 0.81 0.89 CD27 E Cadherin TRAIL 0.97 6.08E-06 0.52 0.86
0.81 0.94 CRP E Cadherin Resistin 0.97 7.62E-06 0.67 0.86 0.89 0.83
CRP Neopterin Pro 0.97 4.33E-06 0.73 0.91 0.92 0.89 Cathepsin B
GDF15 Progranulin TRAIL 0.97 6.08E-06 0.72 0.86 0.89 0.83 IGFBP3
Progranulin TRAIL 0.97 4.85E-06 0.57 0.86 0.85 0.89 Progranulin
RBP4 TRAIL 0.97 3.83E-05 0.67 0.84 0.81 0.89 Progranulin Resistin
TRAIL 0.97 6.81E-06 0.81 0.93 0.92 0.94 Progranulin TRAIL Tie2 0.97
8.51E-06 0.62 0.84 0.85 0.83 Angiopoietin1 CD14 TRAIL 0.97 1.93E-06
0.63 0.87 0.85 0.89 BDNF CRP IL19 0.97 1.70E-06 0.72 0.89 0.92 0.84
BDNF CRP VEGF C 0.97 2.28E-05 0.82 0.91 0.88 0.94 CRP E Selectin
Neopterin 0.97 1.04E-05 0.76 0.91 0.92 0.88 Corin Progranulin TRAIL
0.97 3.40E-06 0.67 0.87 0.82 0.94 E Cadherin PCSK9 TRAIL 0.97
8.20E-06 0.55 0.84 0.78 0.94 E Cadherin TRAIL VCAM1 0.97 4.33E-05
0.64 0.82 0.78 0.89 Adiponectin Angiopoietin1 TRAIL 0.97 4.33E-06
0.67 0.89 0.89 0.89 Angiopoietin1 MIF TRAIL 0.97 9.32E-07 0.76 0.89
0.89 0.89 Angiopoietin1 Pro TRAIL 0.97 9.43E-11 0.80 0.90 0.91 0.90
Cathepsin B Angiopoietin1 TRAIL VEGF C 0.97 1.19E-06 0.81 0.91 0.89
0.94 Angiopoietin2 Pro TRAIL 0.97 1.18E-05 0.67 0.86 0.89 0.83
Cathepsin B CRP E Cadherin Neopterin 0.97 9.51E-06 0.61 0.89 0.89
0.89 CRP E Selectin Pentraxin3 0.97 4.35E-06 0.70 0.86 0.82 0.93
CRP MMP7 Neopterin 0.97 6.44E-06 0.58 0.86 0.88 0.83 CRP NGAL
Pentraxin3 0.97 9.15E-06 0.70 0.88 0.85 0.93 CRP Neopterin
Substance P 0.97 1.70E-06 0.61 0.89 0.89 0.89 CRP Pro Resistin 0.97
4.85E-06 0.67 0.86 0.81 0.94 Cathepsin B E Cadherin MBL TRAIL 0.97
2.16E-06 0.77 0.91 0.89 0.94 Progranulin Prostaglandin TRAIL 0.97
2.72E-06 0.72 0.89 0.89 0.89 E2 Progranulin TFPI TRAIL 0.97
6.33E-06 0.75 0.88 0.85 0.93 Progranulin TRAIL Thrombo- 0.97
5.43E-06 0.62 0.84 0.81 0.89 spondin2 Progranulin TRAIL a1 Acid
0.97 5.77E-05 0.62 0.84 0.85 0.83 Glycoprotein LIGHT NGAL TRAIL
0.97 1.16E-05 0.76 0.91 0.92 0.88 Angiopoietin1 CRP Neopterin 0.97
2.72E-06 0.61 0.89 0.89 0.89 CRP Cystatin C NGAL 0.97 1.82E-06 0.71
0.86 0.85 0.88 CRP NGAL Resistin 0.97 1.83E-05 0.81 0.91 0.92 0.88
IL19 NGAL TRAIL 0.97 0.00044 0.61 0.86 0.85 0.88 CRP Neopterin
Progranulin 0.97 6.08E-06 0.67 0.84 0.81 0.89 Angiogenin CRP NGAL
0.96 5.16E-06 0.72 0.86 0.85 0.88 CRP Dkk1 NGAL 0.96 3.72E-05 0.56
0.86 0.81 0.94 CRP MIF NGAL 0.96 2.84E-06 0.66 0.86 0.85 0.88 E
Cadherin NGAL TRAIL 0.96 2.97E-10 0.76 0.92 0.88 0.95 Angiopoietin1
NGAL TRAIL 0.96 2.04E-10 0.75 0.92 0.88 0.95 CRP NGAL TFPI 0.96
7.16E-06 0.66 0.86 0.82 0.93 NGAL Progranulin TRAIL 0.96 9.60E-10
0.80 0.92 0.91 0.92 CRP NGAL Neopterin 0.96 3.61E-06 0.62 0.86 0.89
0.82 Complement NGAL TRAIL 0.96 1.83E-05 0.71 0.88 0.85 0.94 factor
D CD27 CRP NGAL 0.96 9.25E-06 0.66 0.86 0.89 0.82 CRP MBL NGAL 0.96
4.07E-06 0.62 0.88 0.92 0.82 CRP NGAL Osteo- 0.96 8.24E-06 0.62
0.81 0.77 0.88 protegerin Neopterin Progranulin TRAIL 0.96 1.19E-05
0.57 0.82 0.77 0.89 CRP NGAL Osteopontin 0.96 1.67E-05 0.56 0.83
0.85 0.80 Angiopoietin2 CRP NGAL 0.96 4.58E-06 0.66 0.86 0.89 0.82
CRP Corin NGAL 0.96 3.40E-06 0.68 0.87 0.89 0.83 CRP GCP2 NGAL 0.96
5.81E-06 0.53 0.84 0.89 0.77 Corin NGAL TRAIL 0.96 1.26E-05 0.68
0.87 0.85 0.89 CD142 CRP NGAL 0.96 8.10E-06 0.60 0.83 0.82 0.87 CRP
IL18 NGAL 0.96 4.18E-05 0.57 0.84 0.89 0.77 APRIL CRP NGAL 0.96
5.81E-06 0.57 0.81 0.81 0.82 CRP CXCL13 NGAL 0.96 7.34E-06 0.61
0.84 0.85 0.82 CRP Leptin NGAL 0.96 2.55E-05 0.53 0.81 0.77 0.88
CRP NGAL RBP4 0.96 1.63E-05 0.57 0.86 0.92 0.77 CRP NGAL VEGF C
0.96 3.86E-06 0.62 0.84 0.81 0.89 Neopterin Pro TRAIL 0.96 1.83E-05
0.63 0.86 0.81 0.94 Cathepsin B CRP Neopterin ProMMP10 0.95
4.66E-06 0.61 0.91 0.96 0.82 CRP NGAL TRAIL 0.95 6.20E-09 0.64 0.84
0.88 0.80 CRP Endostatin NGAL 0.95 2.92E-06 0.63 0.86 0.89 0.82
CD95 NGAL TRAIL 0.95 1.46E-05 0.66 0.91 0.89 0.94 Cystatin C NGAL
TRAIL 0.95 3.76E-05 0.57 0.82 0.78 0.88 CRP MMP3 Neopterin 0.95
3.69E-06 0.61 0.86 0.85 0.88 CRP MMP8 Neopterin 0.95 9.21E-06 0.57
0.82 0.78 0.88 CRP Myelo- Neopterin 0.95 7.35E-06 0.51 0.89 0.93
0.82 peroxidase NGAL Prostaglandin TRAIL 0.95 2.80E-05 0.62 0.86
0.85 0.89 E2 CRP NGAL a1 Acid 0.95 1.04E-05 0.57 0.84 0.89 0.77
Glycoprotein Clusterin NGAL TRAIL 0.95 0.000101 0.71 0.86 0.89 0.82
Myelo- NGAL TRAIL 0.95 2.59E-09 0.68 0.89 0.88 0.90 peroxidase NGAL
Pro TRAIL 0.95 2.87E-09 0.69 0.86 0.82 0.90 Cathepsin B MMP8 NGAL
TRAIL 0.95 7.69E-09 0.64 0.90 0.85 0.95 NGAL ProMMP10 TRAIL 0.95
4.48E-09 0.67 0.88 0.85 0.90 NGAL TRAIL VEGF C 0.95 3.45E-05 0.72
0.86 0.89 0.83 Adiponectin CRP NGAL 0.95 9.25E-06 0.57 0.86 0.92
0.77 CRP GDF15 NGAL 0.95 9.25E-06 0.57 0.84 0.89 0.77 CRP IL1R NGAL
0.95 5.81E-06 0.57 0.81 0.81 0.82 E Selectin NGAL TRAIL 0.95
3.89E-09 0.66 0.89 0.85 0.92 IP-10 NGAL TRAIL 0.95 3.18E-09 0.68
0.89 0.82 0.95 Leptin NGAL TRAIL 0.95 0.000204 0.62 0.81 0.81 0.82
NGAL P Selectin TRAIL 0.95 3.76E-05 0.71 0.86 0.89 0.82 CRP Leptin
R NGAL 0.95 5.43E-06 0.58 0.82 0.73 0.94 CRP NGAL Prostaglandin
0.95 4.33E-06 0.53 0.82 0.77 0.89 E2 CRP Clusterin NGAL 0.95
5.16E-06 0.53 0.84 0.89 0.77 CRP Fetuin A NGAL 0.95 5.81E-06 0.53
0.86 0.92 0.77 CRP IGFBP3 NGAL 0.95 1.16E-05 0.57 0.86 0.92 0.77
CXCL13 NGAL TRAIL 0.95 4.38E-05 0.56 0.81 0.77 0.88 MMP2 NGAL TRAIL
0.95 0.00088 0.62 0.84 0.89 0.77 NGAL Pentraxin3 TRAIL 0.95
7.37E-05 0.70 0.86 0.85 0.87 NGAL RBP4 TRAIL 0.95 0.000364 0.71
0.86 0.89 0.82 NGAL TRAIL VCAM1 0.95 0.000273 0.57 0.80 0.82 0.77
CD14 Neopterin TRAIL 0.95 2.74E-09 0.65 0.88 0.88 0.88 CXCL13
Neopterin TRAIL 0.95 1.70E-09 0.65 0.88 0.85 0.90 MMP8 Neopterin
TRAIL 0.95 0.00017 0.57 0.82 0.78 0.88 Leptin R NGAL TRAIL 0.94
1.64E-05 0.62 0.86 0.85 0.89 MMP3 NGAL TRAIL 0.94 3.91E-09 0.72
0.89 0.82 0.95 NGAL TGF B1 TRAIL 0.94 5.82E-05 0.72 0.87 0.93 0.78
Neopterin PCSK9 TRAIL 0.94 4.10E-09 0.65 0.85 0.85 0.85 CRP MMP3
NGAL 0.94 2.25E-09 0.64 0.85 0.94 0.77 Angiopoietin2 NGAL TRAIL
0.94 0.000152 0.66 0.86 0.92 0.77 BAFF NGAL TRAIL 0.94 6.85E-09
0.64 0.90 0.84 0.95 CRP NGAL P Selectin 0.94 4.15E-06 0.55 0.86
0.93 0.77 CRP NGAL Thrombo- 0.94 5.81E-06 0.53 0.86 0.92 0.77
spondin2 CRP NGAL Tie2 0.94 7.34E-06 0.53 0.81 0.85 0.77 GCP2 NGAL
TRAIL 0.94 3.94E-05 0.71 0.86 0.89 0.82 IL18 NGAL TRAIL 0.94
5.13E-05 0.62 0.84 0.85 0.82 MMP7 NGAL TRAIL 0.94 1.20E-08 0.63
0.90 0.84 0.95 NGAL Osteo- TRAIL 0.94 7.43E-05 0.71 0.86 0.89 0.82
protegerin NGAL PCSK9 TRAIL 0.94 6.97E-05 0.62 0.84 0.85 0.82 NGAL
Substance P TRAIL 0.94 2.44E-08 0.70 0.86 0.88 0.84
APRIL Neopterin TRAIL 0.94 7.18E-09 0.66 0.85 0.85 0.85 Adiponectin
Neopterin TRAIL 0.94 1.05E-09 0.73 0.88 0.88 0.88 Leptin R
Neopterin TRAIL 0.94 2.77E-09 0.67 0.88 0.85 0.90 Myelo- Neopterin
TRAIL 0.94 0.00014 0.57 0.82 0.85 0.77 peroxidase Neopterin TRAIL
VCAM1 0.94 2.39E-09 0.73 0.88 0.88 0.88 BDNF NGAL TRAIL 0.94
7.88E-09 0.66 0.90 0.84 0.95 CD 23 NGAL TRAIL 0.94 4.70E-05 0.67
0.86 0.89 0.83 Angiopoietin2 Neopterin TRAIL 0.94 6.99E-09 0.62
0.84 0.82 0.85 CRP Neopterin TRAIL 0.94 5.19E-09 0.78 0.92 0.91
0.93 Clusterin Neopterin TRAIL 0.94 1.61E-09 0.70 0.88 0.85 0.90
Fetuin A Neopterin TRAIL 0.94 4.16E-09 0.68 0.85 0.85 0.85
Neopterin TRAIL Thrombo- 0.94 8.51E-09 0.65 0.86 0.82 0.90 spondin2
CRP ICAM1 NGAL 0.94 1.03E-05 0.53 0.82 0.85 0.77 Adiponectin NGAL
TRAIL 0.94 0.000124 0.71 0.86 0.89 0.82 Endostatin NGAL TRAIL 0.94
0.000104 0.71 0.86 0.89 0.82 ICAM1 NGAL TRAIL 0.94 4.17E-05 0.62
0.84 0.85 0.82 IGFBP3 NGAL TRAIL 0.94 6.03E-05 0.61 0.86 0.92 0.77
NGAL Resistin TRAIL 0.94 9.14E-05 0.66 0.86 0.89 0.82 NGAL SLPI
TRAIL 0.94 0.000127 0.62 0.84 0.85 0.82 NGAL TFPI TRAIL 0.94
0.000238 0.59 0.81 0.78 0.87 NGAL TRAIL Thrombo- 0.94 6.03E-05 0.66
0.86 0.89 0.82 spondin2 NGAL TRAIL Vitamin D 0.94 7.71E-05 0.66
0.86 0.89 0.82 Binding Protein NGAL TRAIL a1 Acid 0.94 0.000112
0.61 0.86 0.85 0.88 Glycoprotein BAFF CRP Neopterin 0.94 7.53E-06
0.60 0.82 0.80 0.84 CD142 Neopterin TRAIL 0.94 1.11E-08 0.72 0.91
0.87 0.94 Endostatin Neopterin TRAIL 0.94 2.74E-09 0.65 0.88 0.85
0.90 ICAM1 Neopterin TRAIL 0.94 1.29E-09 0.73 0.88 0.88 0.88 MMP2
Neopterin TRAIL 0.94 2.09E-09 0.68 0.86 0.85 0.88 Neopterin Osteo-
TRAIL 0.94 1.93E-09 0.63 0.87 0.82 0.90 protegerin Neopterin P
Selectin TRAIL 0.94 1.59E-09 0.75 0.89 0.88 0.90 Neopterin TFPI
TRAIL 0.94 2.11E-08 0.72 0.87 0.87 0.88 CD 23 CRP NGAL 0.94
4.85E-06 0.53 0.82 0.81 0.83 Angiogenin Neopterin TRAIL 0.94
2.09E-09 0.65 0.88 0.85 0.90 Leptin Neopterin TRAIL 0.94 1.59E-09
0.68 0.88 0.85 0.90 MBL Neopterin TRAIL 0.94 1.93E-09 0.76 0.88
0.88 0.88 Neopterin Pentraxin3 TRAIL 0.94 7.93E-08 0.72 0.87 0.90
0.84 Neopterin RBP4 TRAIL 0.94 1.21E-09 0.71 0.88 0.85 0.90
Neopterin TRAIL a1 Acid 0.94 1.13E-09 0.74 0.87 0.85 0.88
Glycoprotein CD14 CRP NGAL 0.94 8.23E-06 0.53 0.86 0.93 0.77 CRP
MMP2 NGAL 0.94 1.99E-05 0.53 0.84 0.89 0.77 APRIL NGAL TRAIL 0.94
4.88E-05 0.71 0.86 0.89 0.82 Angiogenin NGAL TRAIL 0.94 0.000124
0.71 0.86 0.89 0.82 CRP NGAL Vitamin D 0.94 6.56E-06 0.52 0.86 0.96
0.71 Binding Protein MBL NGAL TRAIL 0.94 3.53E-05 0.71 0.91 0.89
0.94 NGAL TRAIL Tie2 0.94 6.69E-05 0.71 0.86 0.89 0.82 NGAL TRAIL
uPAR 0.94 0.000112 0.66 0.84 0.85 0.82 BDNF CRP Neopterin 0.94
2.71E-06 0.60 0.84 0.88 0.79 CD95 Neopterin TRAIL 0.94 3.14E-09
0.65 0.85 0.85 0.85 Dkk1 Neopterin TRAIL 0.94 4.10E-09 0.70 0.86
0.85 0.88 IL18 Neopterin TRAIL 0.94 1.29E-09 0.68 0.87 0.88 0.85
IL1R Neopterin TRAIL 0.94 5.01E-09 0.68 0.86 0.88 0.85 MIF
Neopterin TRAIL 0.94 1.95E-09 0.67 0.88 0.85 0.90 Neopterin
ProMMP10 TRAIL 0.94 5.69E-05 0.57 0.84 0.85 0.82 Neopterin Resistin
TRAIL 0.94 6.12E-09 0.65 0.88 0.85 0.90 Neopterin SLPI TRAIL 0.94
6.12E-09 0.68 0.88 0.85 0.90 Neopterin TRAIL Tie2 0.94 2.24E-09
0.68 0.88 0.85 0.90 Neopterin TRAIL VEGF C 0.94 4.18E-09 0.66 0.85
0.82 0.87 Neopterin TRAIL uPAR 0.94 3.84E-09 0.65 0.86 0.82 0.90
CD142 NGAL TRAIL 0.94 0.000127 0.64 0.83 0.85 0.80 CRP NGAL TGF B1
0.94 4.78E-05 0.50 0.80 0.85 0.72 CD 23 Neopterin TRAIL 0.94
4.48E-09 0.67 0.88 0.85 0.90 CD27 Neopterin TRAIL 0.94 3.07E-09
0.65 0.87 0.82 0.90 GCP2 Neopterin TRAIL 0.94 3.14E-09 0.70 0.86
0.85 0.88 GDF15 Neopterin TRAIL 0.94 4.39E-09 0.65 0.86 0.82 0.90
IGFBP3 Neopterin TRAIL 0.94 2.56E-09 0.67 0.88 0.85 0.90 IL19
Neopterin TRAIL 0.94 5.36E-09 0.70 0.85 0.85 0.85 Neopterin
Osteopontin TRAIL 0.94 1.16E-07 0.72 0.89 0.87 0.91 Neopterin
Prostaglandin TRAIL 0.94 3.91E-09 0.67 0.85 0.91 0.80 E2 Neopterin
Substance TRAIL 0.94 5.76E-05 0.53 0.82 0.81 0.83 P Neopterin TRAIL
Vitamin D 0.94 1.05E-09 0.68 0.88 0.85 0.90 Binding Protein CRP
NGAL SLPI 0.94 1.28E-05 0.53 0.84 0.89 0.77 CRP NGAL VCAM1 0.94
1.28E-05 0.53 0.86 0.93 0.77 Dkkl NGAL TRAIL 0.94 6.03E-05 0.61
0.84 0.85 0.82 Fetuin A NGAL TRAIL 0.94 0.000152 0.66 0.86 0.89
0.82 GDF15 NGAL TRAIL 0.94 0.000101 0.66 0.84 0.85 0.82 MIF NGAL
TRAIL 0.94 2.84E-05 0.66 0.86 0.85 0.88 LIGHT Neopterin TRAIL 0.94
2.56E-09 0.67 0.88 0.85 0.90 CRP NGAL Substance P 0.94 1.54E-09
0.61 0.85 0.91 0.79 NGAL Osteopontin TRAIL 0.94 4.22E-05 0.70 0.88
0.85 0.93 Corin Neopterin TRAIL 0.94 3.18E-09 0.70 0.88 0.85 0.90
Neopterin TGF B1 TRAIL 0.94 7.19E-09 0.64 0.85 0.82 0.87 CD27 NGAL
TRAIL 0.93 0.000168 0.66 0.84 0.85 0.82 Complement Neopterin TRAIL
0.93 2.69E-09 0.68 0.88 0.85 0.90 factor D Cystatin C Neopterin
TRAIL 0.93 3.14E-09 0.71 0.86 0.85 0.88 CRP IP-10 NGAL 0.93
3.64E-09 0.61 0.82 0.79 0.85 BAFF Neopterin TRAIL 0.93 9.24E-05
0.53 0.80 0.80 0.79 IP-10 Neopterin TRAIL 0.93 2.69E-09 0.63 0.87
0.82 0.90 CD14 NGAL TRAIL 0.93 0.000115 0.57 0.82 0.74 0.94 CRP
NGAL PCSK9 0.93 8.23E-06 0.53 0.84 0.89 0.77 IL1R NGAL TRAIL 0.93
0.000168 0.57 0.79 0.81 0.77 NGAL Neopterin TRAIL 0.93 0.000124
0.51 0.79 0.73 0.88 E Selectin Neopterin TRAIL 0.93 0.000137 0.41
0.81 0.77 0.88 MMP3 Neopterin TRAIL 0.93 5.13E-05 0.52 0.82 0.82
0.82 CRP MMP7 NGAL 0.93 2.59E-08 0.68 0.84 0.81 0.87 CRP NGAL
Progranulin 0.93 3.24E-08 0.58 0.80 0.82 0.79 BAFF CRP NGAL 0.93
2.91E-08 0.63 0.81 0.81 0.82 CRP NGAL ProMMP10 0.93 1.31E-08 0.55
0.82 0.85 0.80 CRP NGAL Pro 0.93 5.31E-09 0.64 0.83 0.85 0.82
Cathepsin B CRP Myelo- NGAL 0.93 2.36E-08 0.58 0.81 0.82 0.80
peroxidase NGAL Progranulin Resistin 0.93 4.61E-05 0.67 0.86 0.84
0.88 BDNF Neopterin TRAIL 0.92 6.22E-05 0.64 0.82 0.80 0.84 MMP7
Neopterin TRAIL 0.92 0.000573 0.51 0.81 0.83 0.78 CRP E Cadherin
NGAL 0.92 1.71E-07 0.63 0.82 0.79 0.84 CRP MMP8 NGAL 0.92 1.31E-08
0.61 0.82 0.85 0.80 CRP E Selectin NGAL 0.92 1.12E-08 0.61 0.81
0.77 0.85 Angiopoietin1 CRP NGAL 0.92 8.49E-09 0.69 0.86 0.82 0.90
Neopterin Progranulin Resistin 0.92 9.51E-06 0.63 0.89 0.92 0.83
BDNF CRP NGAL 0.92 1.58E-08 0.60 0.80 0.84 0.76 CRP Neopterin
Pentraxin3 0.91 5.41E-07 0.50 0.79 0.90 0.69 CRP Neopterin TFPI
0.91 4.69E-07 0.56 0.78 0.77 0.78 Corin NGAL Vitamin D 0.91
0.000127 0.38 0.77 0.74 0.82 Binding Protein CRP Neopterin
Osteopontin 0.91 1.17E-06 0.60 0.79 0.74 0.84 CD27 IL1R NGAL 0.91
0.000137 0.58 0.79 0.77 0.82 IL1R IP-10 NGAL 0.91 8.25E-05 0.62
0.81 0.81 0.82 Complement NGAL Vitamin D 0.91 8.25E-05 0.76 0.88
0.89 0.88 factor D Binding Protein CD27 LIGHT NGAL 0.91 0.000101
0.57 0.81 0.77 0.88 CD142 CRP Neopterin 0.91 4.06E-07 0.62 0.84
0.77 0.91 Adiponectin CRP Neopterin 0.90 6.15E-08 0.61 0.81 0.76
0.85 CRP Neopterin Osteo- 0.90 5.68E-08 0.56 0.84 0.88 0.80
protegerin Corin NGAL ProMMP10 0.90 0.000226 0.49 0.80 0.77 0.83
NGAL Pentraxin3 Progranulin 0.90 0.000682 0.41 0.78 0.85 0.67
Cystatin C IL1R NGAL 0.90 0.000101 0.58 0.84 0.85 0.82 CRP
Neopterin Resistin 0.90 1.54E-07 0.56 0.82 0.85 0.80 Corin MMP8
NGAL 0.90 0.000226 0.58 0.82 0.81 0.83 CRP Corin Neopterin 0.90
1.39E-07 0.61 0.79 0.76 0.82 CRP IP-10 Neopterin 0.90 1.17E-07 0.51
0.78 0.79 0.78 Angiogenin CRP Neopterin 0.90 1.07E-07 0.61 0.81
0.82 0.80 Angiopoietin2 CRP Neopterin 0.90 1.73E-07 0.45 0.78 0.88
0.70 Complement IP-10 NGAL 0.89 0.000364 0.53 0.81 0.77 0.88 factor
D Corin NGAL Pentraxin3 0.89 0.000375 0.62 0.82 0.84 0.79 LIGHT
NGAL Vitamin D 0.89 0.000204 0.66 0.84 0.85 0.82 Binding Protein
CRP Complement Neopterin 0.89 8.66E-08 0.56 0.82 0.79 0.85 factor D
CD27 MMP7 NGAL 0.89 0.001991 0.50 0.78 0.74 0.82 CD27 NGAL
Pentraxin3 0.89 0.001565 0.56 0.79 0.79 0.79 E Cadherin Neopterin
Resistin 0.89 0.000226 0.60 0.82 0.89 0.72 IL1R Neopterin
Progranulin 0.89 0.000187 0.49 0.77 0.77 0.78 Adiponectin Neopterin
Osteopontin 0.89 2.46E-06 0.52 0.79 0.73 0.84 Adiponectin LIGHT
NGAL 0.89 0.000331 0.49 0.81 0.85 0.77 CD27 Corin NGAL 0.89 0.00053
0.42 0.74 0.69 0.82 Complement IL1R NGAL 0.89 9.14E-05 0.41 0.81
0.85 0.77 factor D IL19 IP-10 NGAL 0.89 0.000185 0.47 0.77 0.69
0.88 IL19 NGAL Resistin 0.89 6.03E-05 0.58 0.79 0.77 0.82 IP-10
NGAL Pentraxin3 0.89 0.001134 0.60 0.81 0.82 0.80 CRP Neopterin
VCAM1 0.89 3.96E-07 0.45 0.81 0.70 0.90
Sequence CWU 1
1
181224PRTHomo sapiens 1Met 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 2187PRTArtificial Sequenceamino acid sequences of soluble TRAIL
2Thr Ser Glu Glu Thr Ile Ser Thr Val Gln Glu Lys Gln Gln Asn Ile 1
5 10 15 Ser Pro Leu Val Arg Glu Arg Gly Pro Gln Arg Val Ala Ala His
Ile 20 25 30 Thr Gly Thr Arg Gly Arg Ser Asn Thr Leu Ser Ser Pro
Asn Ser Lys 35 40 45 Asn Glu Lys Ala Leu Gly Arg Lys Ile Asn Ser
Trp Glu Ser Ser Arg 50 55 60 Ser Gly His Ser Phe Leu Ser Asn Leu
His Leu Arg Asn Gly Glu Leu 65 70 75 80 Val Ile His Glu Lys Gly Phe
Tyr Tyr Ile Tyr Ser Gln Thr Tyr Phe 85 90 95 Arg Phe Gln Glu Glu
Ile Lys Glu Asn Thr Lys Asn Asp Lys Gln Met 100 105 110 Val Gln Tyr
Ile Tyr Lys Tyr Thr Ser Tyr Pro Asp Pro Ile Leu Leu 115 120 125 Met
Lys Ser Ala Arg Asn Ser Cys Trp Ser Lys Asp Ala Glu Tyr Gly 130 135
140 Leu Tyr Ser Ile Tyr Gln Gly Gly Ile Phe Glu Leu Lys Glu Asn Asp
145 150 155 160 Arg Ile Phe Val Ser Val Thr Asn Glu His Leu Ile Asp
Met Asp His 165 170 175 Glu Ala Ser Phe Phe Gly Ala Phe Leu Val Gly
180 185 3 168PRTArtificial Sequenceamino acid sequences of soluble
TRAIL 3Val Arg Glu Arg Gly Pro Gln Arg Val Ala Ala His Ile Thr Gly
Thr 1 5 10 15 Arg Gly Arg Ser Asn Thr Leu Ser Ser Pro Asn Ser Lys
Asn Glu Lys 20 25 30 Ala Leu Gly Arg Lys Ile Asn Ser Trp Glu Ser
Ser Arg Ser Gly His 35 40 45 Ser Phe Leu Ser Asn Leu His Leu Arg
Asn Gly Glu Leu Val Ile His 50 55 60 Glu Lys Gly Phe Tyr Tyr Ile
Tyr Ser Gln Thr Tyr Phe Arg Phe Gln 65 70 75 80 Glu Glu Ile Lys Glu
Asn Thr Lys Asn Asp Lys Gln Met Val Gln Tyr 85 90 95 Ile Tyr Lys
Tyr Thr Ser Tyr Pro Asp Pro Ile Leu Leu Met Lys Ser 100 105 110 Ala
Arg Asn Ser Cys Trp Ser Lys Asp Ala Glu Tyr Gly Leu Tyr Ser 115 120
125 Ile Tyr Gln Gly Gly Ile Phe Glu Leu Lys Glu Asn Asp Arg Ile Phe
130 135 140 Val Ser Val Thr Asn Glu His Leu Ile Asp Met Asp His Glu
Ala Ser 145 150 155 160 Phe Phe Gly Ala Phe Leu Val Gly 165
498PRTHomo sapiens 4 Met Asn Gln Thr Ala Ile Leu Ile Cys Cys Leu
Ile Phe Leu Thr Leu 1 5 10 15 Ser Gly Ile Gln Gly Val Pro Leu Ser
Arg Thr Val Arg Cys Thr Cys 20 25 30 Ile Ser Ile Ser Asn Gln Pro
Val Asn Pro Arg Ser Leu Glu Lys Leu 35 40 45 Glu Ile Ile Pro Ala
Ser Gln Phe Cys Pro Arg Val Glu Ile Ile Ala 50 55 60 Thr Met Lys
Lys Lys Gly Glu Lys Arg Cys Leu Asn Pro Glu Ser Lys 65 70 75 80 Ala
Ile Lys Asn Leu Leu Lys Ala Val Ser Lys Glu Arg Ser Lys Arg 85 90
95 Ser Pro 5258PRTArtificial Sequenceamino acid sequences of
TRAILR3/ TNFRSF10C 5Ala Arg Ile Pro Lys Thr Leu Lys Phe Val Val Val
Ile Val Ala Val 1 5 10 15 Leu Leu Pro Val Leu Ala Tyr Ser Ala Thr
Thr Ala Arg Gln Glu Glu 20 25 30 Val Pro Gln Gln Thr Val Ala Pro
Gln Gln Gln Arg His Ser Phe Lys 35 40 45 Gly Glu Glu Cys Pro Ala
Gly Ser His Arg Ser Glu His Thr Gly Ala 50 55 60 Cys Asn Pro Cys
Thr Glu Gly Val Asp Tyr Thr Asn Ala Ser Asn Asn 65 70 75 80 Glu Pro
Ser Cys Phe Pro Cys Thr Val Cys Lys Ser Asp Gln Lys His 85 90 95
Lys Ser Ser Cys Thr Met Thr Arg Asp Thr Val Cys Gln Cys Lys Glu 100
105 110 Gly Thr Phe Arg Asn Glu Asn Ser Pro Glu Met Cys Arg Lys Cys
Ser 115 120 125 Arg Cys Pro Ser Gly Glu Val Gln Val Ser Asn Cys Thr
Ser Trp Asp 130 135 140 Asp Ile Gln Cys Val Glu Glu Phe Gly Ala Asn
Ala Thr Val Glu Thr 145 150 155 160 Pro Ala Ala Glu Glu Thr Met Asn
Thr Ser Pro Gly Thr Pro Ala Pro 165 170 175 Ala Ala Glu Glu Thr Met
Asn Thr Ser Pro Gly Thr Pro Ala Pro Ala 180 185 190 Ala Glu Glu Thr
Met Thr Thr Ser Pro Gly Thr Pro Ala Pro Ala Ala 195 200 205 Glu Glu
Thr Met Thr Thr Ser Pro Gly Thr Pro Ala Pro Ala Ala Glu 210 215 220
Glu Thr Met Thr Thr Ser Pro Gly Thr Pro Ala Ser Ser His Tyr Leu 225
230 235 240 Ser Cys Thr Ile Val Gly Ile Ile Val Leu Ile Val Leu Leu
Ile Val 245 250 255 Phe Val 6259PRTArtificial Sequenceamino acid
sequences of TRAILR3/ TNFRSF10C 6Met Ala Arg Ile Pro Lys Thr Leu
Lys Phe Val Val Val Ile Val Ala 1 5 10 15 Val Leu Leu Pro Val Leu
Ala Tyr Ser Ala Thr Thr Ala Arg Gln Glu 20 25 30 Glu Val Pro Gln
Gln Thr Val Ala Pro Gln Gln Gln Arg His Ser Phe 35 40 45 Lys Gly
Glu Glu Cys Pro Ala Gly Ser His Arg Ser Glu His Thr Gly 50 55 60
Ala Cys Asn Pro Cys Thr Glu Gly Val Asp Tyr Thr Asn Ala Ser Asn 65
70 75 80 Asn Glu Pro Ser Cys Phe Pro Cys Thr Val Cys Lys Ser Asp
Gln Lys 85 90 95 His Lys Ser Ser Cys Thr Met Thr Arg Asp Thr Val
Cys Gln Cys Lys 100 105 110 Glu Gly Thr Phe Arg Asn Glu Asn Ser Pro
Glu Met Cys Arg Lys Cys 115 120 125 Ser Arg Cys Pro Ser Gly Glu Val
Gln Val Ser Asn Cys Thr Ser Trp 130 135 140 Asp Asp Ile Gln Cys Val
Glu Glu Phe Gly Ala Asn Ala Thr Val Glu 145 150 155 160 Thr Pro Ala
Ala Glu Glu Thr Met Asn Thr Ser Pro Gly Thr Pro Ala 165 170 175 Pro
Ala Ala Glu Glu Thr Met Asn Thr Ser Pro Gly Thr Pro Ala Pro 180 185
190 Ala Ala Glu Glu Thr Met Thr Thr Ser Pro Gly Thr Pro Ala Pro Ala
195 200 205 Ala Glu Glu Thr Met Thr Thr Ser Pro Gly Thr Pro Ala Pro
Ala Ala 210 215 220 Glu Glu Thr Met Ile Thr Ser Pro Gly Thr Pro Ala
Ser Ser His Tyr 225 230 235 240 Leu Ser Cys Thr Ile Val Gly Ile Ile
Val Leu Ile Val Leu Leu Ile 245 250 255 Val Phe Val
7385PRTArtificial Sequenceamino acid sequences of TRAILR4/
TNFRSF10D 7Met Gly Leu Trp Gly Gln Ser Val Pro Thr Ala Ser Ser Ala
Arg Ala 1 5 10 15 Gly Arg Tyr Pro Gly Ala Arg Thr Ala Ser Gly Thr
Arg Pro Trp Leu 20 25 30 Leu Asp Pro Lys Ile Leu Lys Phe Val Val
Phe Ile Val Ala Val Leu 35 40 45 Leu Pro Val Arg Val Asp Ser Ala
Thr Ile Pro Arg Gln Asp Glu Val 50 55 60 Pro Gln Gln Thr Val Ala
Pro Gln Gln Gln Arg Arg Ser Leu Lys Glu 65 70 75 80 Glu Glu Cys Pro
Ala Gly Ser His Arg Ser Glu Tyr Thr Gly Ala Cys 85 90 95 Asn Pro
Cys Thr Glu Gly Val Asp Tyr Thr Ile Ala Ser Asn Asn Leu 100 105 110
Pro Ser Cys Leu Leu Cys Thr Val Cys Lys Ser Gly Gln Thr Asn Lys 115
120 125 Ser Ser Cys Thr Thr Thr Arg Asp Thr Val Cys Gln Cys Glu Lys
Gly 130 135 140 Ser Phe Gln Asp Lys Asn Ser Pro Glu Met Cys Arg Thr
Cys Arg Thr 145 150 155 160 Gly Cys Pro Arg Gly Met Val Lys Val Ser
Asn Cys Thr Pro Arg Ser 165 170 175 Asp Ile Lys Cys Lys Asn Glu Ser
Ala Ala Ser Ser Gly Lys Thr Pro 180 185 190 Ala Ala Glu Glu Thr Val
Thr Thr Ile Leu Gly Met Leu Ala Ser Pro 195 200 205 Tyr His Tyr Leu
Ile Ile Ile Val Val Leu Val Ile Ile Leu Ala Val 210 215 220 Val Val
Val Gly Phe Ser Cys Arg Lys Lys Phe Ile Ser Tyr Leu Lys 225 230 235
240 Gly Ile Cys Ser Gly Gly Gly Gly Gly Pro Glu Arg Val His Arg Val
245 250 255 Leu Phe Arg Arg Arg Ser Cys Pro Ser Arg Val Pro Gly Ala
Glu Asp 260 265 270 Asn Ala Arg Asn Glu Thr Leu Ser Asn Arg Tyr Leu
Gln Pro Thr Gln 275 280 285 Val Ser Glu Gln Glu Ile Gln Gly Gln Glu
Leu Ala Glu Leu Thr Gly 290 295 300 Val Thr Val Glu Ser Pro Glu Glu
Pro Gln Arg Leu Leu Glu Gln Ala 305 310 315 320 Glu Ala Glu Gly Cys
Gln Arg Arg Arg Leu Leu Val Pro Val Asn Asp 325 330 335 Ala Asp Ser
Ala Asp Ile Ser Thr Leu Leu Asp Ala Ser Ala Thr Leu 340 345 350 Glu
Glu Gly His Ala Lys Glu Thr Ile Gln Asp Gln Leu Val Gly Ser 355 360
365 Glu Lys Leu Phe Tyr Glu Glu Asp Glu Ala Gly Ser Ala Thr Ser Cys
370 375 380 Leu 385 8386PRTArtificial Sequenceamino acid sequences
of TRAILR4/ TNFRSF10D 8Met Gly Leu Trp Gly Gln Ser Val Pro Thr Ala
Ser Ser Ala Arg Ala 1 5 10 15 Gly Arg Tyr Pro Gly Ala Arg Thr Ala
Ser Gly Thr Arg Pro Trp Leu 20 25 30 Leu Asp Pro Lys Ile Leu Lys
Phe Val Val Phe Ile Val Ala Val Leu 35 40 45 Leu Pro Val Arg Val
Asp Ser Ala Thr Ile Pro Arg Gln Asp Glu Val 50 55 60 Pro Gln Gln
Thr Val Ala Pro Gln Gln Gln Arg Arg Ser Leu Lys Glu 65 70 75 80 Glu
Glu Cys Pro Ala Gly Ser His Arg Ser Glu Tyr Thr Gly Ala Cys 85 90
95 Asn Pro Cys Thr Glu Gly Val Asp Tyr Thr Ile Ala Ser Asn Asn Leu
100 105 110 Pro Ser Cys Leu Leu Cys Thr Val Cys Lys Ser Gly Gln Thr
Asn Lys 115 120 125 Ser Ser Cys Thr Thr Thr Arg Asp Thr Val Cys Gln
Cys Glu Lys Gly 130 135 140 Ser Phe Gln Asp Lys Asn Ser Pro Glu Met
Cys Arg Thr Cys Arg Thr 145 150 155 160 Gly Cys Pro Arg Gly Met Val
Lys Val Ser Asn Cys Thr Pro Arg Ser 165 170 175 Asp Ile Lys Cys Lys
Asn Glu Ser Ala Ala Ser Ser Thr Gly Lys Thr 180 185 190 Pro Ala Ala
Glu Glu Thr Val Thr Thr Ile Leu Gly Met Leu Ala Ser 195 200 205 Pro
Tyr His Tyr Leu Ile Ile Ile Val Val Leu Val Ile Ile Leu Ala 210 215
220 Val Val Val Val Gly Phe Ser Cys Arg Lys Lys Phe Ile Ser Tyr Leu
225 230 235 240 Lys Gly Ile Cys Ser Gly Gly Gly Gly Gly Pro Glu Arg
Val His Arg 245 250 255 Val Leu Phe Arg Arg Arg Ser Cys Pro Ser Arg
Val Pro Gly Ala Glu 260 265 270 Asp Asn Ala Arg Asn Glu Thr Leu Ser
Asn Arg Tyr Leu Gln Pro Thr 275 280 285 Gln Val Ser Glu Gln Glu Ile
Gln Gly Gln Glu Leu Ala Glu Leu Thr 290 295 300 Gly Val Thr Val Glu
Leu Pro Glu Glu Pro Gln Arg Leu Leu Glu Gln 305 310 315 320 Ala Glu
Ala Glu Gly Cys Gln Arg Arg Arg Leu Leu Val Pro Val Asn 325 330 335
Asp Ala Asp Ser Ala Asp Ile Ser Thr Leu Leu Asp Ala Ser Ala Thr 340
345 350 Leu Glu Glu Gly His Ala Lys Glu Thr Ile Gln Asp Gln Leu Val
Gly 355 360 365 Ser Glu Lys Leu Phe Tyr Glu Glu Asp Glu Ala Gly Ser
Ala Thr Ser 370 375 380 Cys Leu 385 9468PRTArtificial Sequenceamino
acid sequences of TRAIL-R1/ TNFRSF10A 9Met Ala Pro Pro Pro Ala Arg
Val His Leu Gly Ala Phe Leu Ala Val 1 5 10 15 Thr Pro Asn Pro Gly
Ser Ala Ala Ser Gly Thr Glu Ala Ala Ala Ala 20 25 30 Thr Pro Ser
Lys Val Trp Gly Ser Ser Ala Gly Arg Ile Glu Pro Arg 35 40 45 Gly
Gly Gly Arg Gly Ala Leu Pro Thr Ser Met Gly Gln His Gly Pro 50 55
60 Ser Ala Arg Ala Arg Ala Gly Arg Ala Pro Gly Pro Arg Pro Ala Arg
65 70 75 80 Glu Ala Ser Pro Arg Leu Arg Val His Lys Thr Phe Lys Phe
Val Val 85 90 95 Val Gly Val Leu Leu Gln Val Val Pro Ser Ser Ala
Ala Thr Ile Lys 100 105 110 Leu His Asp Gln Ser Ile Gly Thr Gln Gln
Trp Glu His Ser Pro Leu 115 120 125 Gly Glu Leu Cys Pro Pro Gly Ser
His Arg Ser Glu His Pro Gly Ala 130 135 140 Cys Asn Arg Cys Thr Glu
Gly Val Gly Tyr Thr Asn Ala Ser Asn Asn 145 150 155 160 Leu Phe Ala
Cys Leu Pro Cys Thr Ala Cys Lys Ser Asp Glu Glu Glu 165 170 175 Arg
Ser Pro Cys Thr Thr Thr Arg Asn Thr Ala Cys Gln Cys Lys Pro 180 185
190 Gly Thr Phe Arg Asn Asp Asn Ser Ala Glu Met Cys Arg Lys Cys Ser
195 200 205 Arg Gly Cys Pro Arg Gly Met Val Lys Val Lys Asp Cys Thr
Pro Trp 210 215 220 Ser Asp Ile Glu Cys Val His Lys Glu Ser Gly Asn
Gly His Asn Ile 225 230 235 240 Trp Val Ile Leu Val Val Thr Leu Val
Val Pro Leu Leu Leu Val Ala 245 250 255 Val Leu Ile Val Cys Cys Cys
Ile Gly Ser Gly Cys Gly Gly Asp Pro 260 265 270 Lys Cys
Met Asp Arg Val Cys Phe Trp Arg Leu Gly Leu Leu Arg Gly 275 280 285
Pro Gly Ala Glu Asp Asn Ala His Asn Glu Ile Leu Ser Asn Ala Asp 290
295 300 Ser Leu Ser Thr Phe Val Ser Glu Gln Gln Met Glu Ser Gln Glu
Pro 305 310 315 320 Ala Asp Leu Thr Gly Val Thr Val Gln Ser Pro Gly
Glu Ala Gln Cys 325 330 335 Leu Leu Gly Pro Ala Glu Ala Glu Gly Ser
Gln Arg Arg Arg Leu Leu 340 345 350 Val Pro Ala Asn Gly Ala Asp Pro
Thr Glu Thr Leu Met Leu Phe Phe 355 360 365 Asp Lys Phe Ala Asn Ile
Val Pro Phe Asp Ser Trp Asp Gln Leu Met 370 375 380 Arg Gln Leu Asp
Leu Thr Lys Asn Glu Ile Asp Val Val Arg Ala Gly 385 390 395 400 Thr
Ala Gly Pro Gly Asp Ala Leu Tyr Ala Met Leu Met Lys Trp Val 405 410
415 Asn Lys Thr Gly Arg Asn Ala Ser Ile His Thr Leu Leu Asp Ala Leu
420 425 430 Glu Arg Met Glu Glu Arg His Ala Arg Glu Lys Ile Gln Asp
Leu Leu 435 440 445 Val Asp Ser Gly Lys Phe Ile Tyr Leu Glu Asp Gly
Thr Gly Ser Ala 450 455 460 Val Ser Leu Glu 465 10468PRTArtificial
Sequenceamino acid sequences of TRAIL-R1/ TNFRSF10A 10Met Ala Pro
Pro Pro Ala Arg Val His Leu Gly Ala Phe Leu Ala Val 1 5 10 15 Thr
Pro Asn Pro Gly Ser Ala Ala Ser Gly Thr Glu Ala Ala Ala Ala 20 25
30 Thr Pro Ser Lys Val Trp Gly Ser Ser Ala Gly Arg Ile Glu Pro Arg
35 40 45 Gly Gly Gly Arg Gly Ala Leu Pro Thr Ser Met Gly Gln His
Gly Pro 50 55 60 Ser Ala Arg Ala Arg Ala Gly Arg Ala Pro Gly Pro
Arg Pro Ala Arg 65 70 75 80 Glu Ala Ser Pro Arg Leu Arg Val His Lys
Thr Phe Lys Phe Val Val 85 90 95 Val Gly Val Leu Leu Gln Val Val
Pro Ser Ser Ala Ala Thr Ile Lys 100 105 110 Leu His Asp Gln Ser Ile
Gly Thr Gln Gln Trp Glu His Ser Pro Leu 115 120 125 Gly Glu Leu Cys
Pro Pro Gly Ser His Arg Ser Glu His Pro Gly Ala 130 135 140 Cys Asn
Arg Cys Thr Glu Gly Val Gly Tyr Thr Asn Ala Ser Asn Asn 145 150 155
160 Leu Phe Ala Cys Leu Pro Cys Thr Ala Cys Lys Ser Asp Glu Glu Glu
165 170 175 Arg Ser Pro Cys Thr Thr Thr Arg Asn Thr Ala Cys Gln Cys
Lys Pro 180 185 190 Gly Thr Phe Arg Asn Asp Asn Ser Ala Glu Met Cys
Arg Lys Cys Ser 195 200 205 Arg Gly Cys Pro Arg Gly Met Val Lys Val
Lys Asp Cys Thr Pro Trp 210 215 220 Ser Asp Ile Glu Cys Val His Lys
Glu Ser Gly Asn Gly His Asn Ile 225 230 235 240 Trp Val Ile Leu Val
Val Thr Leu Val Val Pro Leu Leu Leu Val Ala 245 250 255 Val Leu Ile
Val Cys Cys Cys Ile Gly Ser Gly Cys Gly Gly Asp Pro 260 265 270 Lys
Cys Met Asp Arg Val Cys Phe Trp Arg Leu Gly Leu Leu Arg Gly 275 280
285 Pro Gly Ala Glu Asp Asn Ala His Asn Glu Ile Leu Ser Asn Ala Asp
290 295 300 Ser Leu Ser Thr Phe Val Ser Glu Gln Gln Met Glu Ser Gln
Glu Pro 305 310 315 320 Ala Asp Leu Thr Gly Val Thr Val Gln Ser Pro
Gly Glu Ala Gln Cys 325 330 335 Leu Leu Gly Pro Ala Glu Ala Glu Gly
Ser Gln Arg Arg Arg Leu Leu 340 345 350 Val Pro Ala Asn Gly Ala Asp
Pro Thr Glu Thr Leu Met Leu Phe Phe 355 360 365 Asp Lys Phe Ala Asn
Ile Val Pro Phe Asp Ser Trp Asp Gln Leu Met 370 375 380 Arg Gln Leu
Asp Leu Thr Lys Asn Glu Ile Asp Val Val Arg Ala Gly 385 390 395 400
Thr Ala Gly Pro Gly Asp Ala Leu Tyr Ala Met Leu Met Lys Trp Val 405
410 415 Asn Lys Thr Gly Arg Asn Ala Ser Ile His Thr Leu Leu Asp Ala
Leu 420 425 430 Glu Arg Met Glu Glu Arg His Ala Arg Glu Lys Ile Gln
Asp Leu Leu 435 440 445 Val Asp Ser Gly Lys Phe Ile Tyr Leu Glu Asp
Gly Thr Gly Ser Ala 450 455 460 Val Ser Leu Glu 465
11310PRTArtificial Sequenceamino acid sequences of TRAIL-R1/
TNFRSF10A 11Met Ala Pro Pro Pro Ala Arg Val His Leu Ala Cys Lys Ser
Asp Glu 1 5 10 15 Glu Glu Arg Ser Pro Cys Thr Thr Thr Arg Asn Thr
Ala Cys Gln Cys 20 25 30 Lys Pro Gly Thr Phe Arg Asn Asp Asn Ser
Ala Glu Met Cys Arg Lys 35 40 45 Cys Ser Arg Gly Cys Pro Arg Gly
Met Val Lys Val Lys Asp Cys Thr 50 55 60 Pro Trp Ser Asp Ile Glu
Cys Val His Lys Glu Ser Gly Asn Gly His 65 70 75 80 Asn Ile Trp Val
Ile Leu Val Val Thr Leu Val Val Pro Leu Leu Leu 85 90 95 Val Ala
Val Leu Ile Val Cys Cys Cys Ile Gly Ser Gly Cys Gly Gly 100 105 110
Asp Pro Lys Cys Met Asp Arg Val Cys Phe Trp Arg Leu Gly Leu Leu 115
120 125 Arg Gly Pro Gly Ala Glu Asp Asn Ala His Asn Glu Ile Leu Ser
Asn 130 135 140 Ala Asp Ser Leu Ser Thr Phe Val Ser Glu Gln Gln Met
Glu Ser Gln 145 150 155 160 Glu Pro Ala Asp Leu Thr Gly Val Thr Val
Gln Ser Pro Gly Glu Ala 165 170 175 Gln Cys Leu Leu Gly Pro Ala Glu
Ala Glu Gly Ser Gln Arg Arg Arg 180 185 190 Leu Leu Val Pro Ala Asn
Gly Ala Asp Pro Thr Glu Thr Leu Met Leu 195 200 205 Phe Phe Asp Lys
Phe Ala Asn Ile Val Pro Phe Asp Ser Trp Asp Gln 210 215 220 Leu Met
Arg Gln Leu Asp Leu Thr Lys Asn Glu Ile Asp Val Val Arg 225 230 235
240 Ala Gly Thr Ala Gly Pro Gly Asp Ala Leu Tyr Ala Met Leu Met Lys
245 250 255 Trp Val Asn Lys Thr Gly Arg Asn Ala Ser Ile His Thr Leu
Leu Asp 260 265 270 Ala Leu Glu Arg Met Glu Glu Arg His Ala Arg Glu
Lys Ile Gln Asp 275 280 285 Leu Leu Val Asp Ser Gly Lys Phe Ile Tyr
Leu Glu Asp Gly Thr Gly 290 295 300 Ser Ala Val Ser Leu Glu 305 310
12440PRTArtificial Sequenceamino acid sequences of TRAIL-R2/
TNFRSF10B 12Met Glu Gln Arg Gly Gln Asn Ala Pro Ala Ala Ser Gly Ala
Arg Lys 1 5 10 15 Arg His Gly Pro Gly Pro Arg Glu Ala Arg Gly Ala
Arg Pro Gly Pro 20 25 30 Arg Val Pro Lys Thr Leu Val Leu Val Val
Ala Ala Val Leu Leu Leu 35 40 45 Val Ser Ala Glu Ser Ala Leu Ile
Thr Gln Gln Asp Leu Ala Pro Gln 50 55 60 Gln Arg Ala Ala Pro Gln
Gln Lys Arg Ser Ser Pro Ser Glu Gly Leu 65 70 75 80 Cys Pro Pro Gly
His His Ile Ser Glu Asp Gly Arg Asp Cys Ile Ser 85 90 95 Cys Lys
Tyr Gly Gln Asp Tyr Ser Thr His Trp Asn Asp Leu Leu Phe 100 105 110
Cys Leu Arg Cys Thr Arg Cys Asp Ser Gly Glu Val Glu Leu Ser Pro 115
120 125 Cys Thr Thr Thr Arg Asn Thr Val Cys Gln Cys Glu Glu Gly Thr
Phe 130 135 140 Arg Glu Glu Asp Ser Pro Glu Met Cys Arg Lys Cys Arg
Thr Gly Cys 145 150 155 160 Pro Arg Gly Met Val Lys Val Gly Asp Cys
Thr Pro Trp Ser Asp Ile 165 170 175 Glu Cys Val His Lys Glu Ser Gly
Thr Lys His Ser Gly Glu Val Pro 180 185 190 Ala Val Glu Glu Thr Val
Thr Ser Ser Pro Gly Thr Pro Ala Ser Pro 195 200 205 Cys Ser Leu Ser
Gly Ile Ile Ile Gly Val Thr Val Ala Ala Val Val 210 215 220 Leu Ile
Val Ala Val Phe Val Cys Lys Ser Leu Leu Trp Lys Lys Val 225 230 235
240 Leu Pro Tyr Leu Lys Gly Ile Cys Ser Gly Gly Gly Gly Asp Pro Glu
245 250 255 Arg Val Asp Arg Ser Ser Gln Arg Pro Gly Ala Glu Asp Asn
Val Leu 260 265 270 Asn Glu Ile Val Ser Ile Leu Gln Pro Thr Gln Val
Pro Glu Gln Glu 275 280 285 Met Glu Val Gln Glu Pro Ala Glu Pro Thr
Gly Val Asn Met Leu Ser 290 295 300 Pro Gly Glu Ser Glu His Leu Leu
Glu Pro Ala Glu Ala Glu Arg Ser 305 310 315 320 Gln Arg Arg Arg Leu
Leu Val Pro Ala Asn Glu Gly Asp Pro Thr Glu 325 330 335 Thr Leu Arg
Gln Cys Phe Asp Asp Phe Ala Asp Leu Val Pro Phe Asp 340 345 350 Ser
Trp Glu Pro Leu Met Arg Lys Leu Gly Leu Met Asp Asn Glu Ile 355 360
365 Lys Val Ala Lys Ala Glu Ala Ala Gly His Arg Asp Thr Leu Tyr Thr
370 375 380 Met Leu Ile Lys Trp Val Asn Lys Thr Gly Arg Asp Ala Ser
Val His 385 390 395 400 Thr Leu Leu Asp Ala Leu Glu Thr Leu Gly Glu
Arg Leu Ala Lys Gln 405 410 415 Lys Ile Glu Asp His Leu Leu Ser Ser
Gly Lys Phe Met Tyr Leu Glu 420 425 430 Gly Asn Ala Asp Ser Ala Met
Ser 435 440 13440PRTArtificial Sequenceamino acid sequences of
TRAIL-R2/ TNFRSF10B 13Met Glu Gln Arg Gly Gln Asn Ala Pro Ala Ala
Ser Gly Ala Arg Lys 1 5 10 15 Arg His Gly Pro Gly Pro Arg Glu Ala
Arg Gly Ala Arg Pro Gly Pro 20 25 30 Arg Val Pro Lys Thr Leu Val
Leu Val Val Ala Ala Val Leu Leu Leu 35 40 45 Val Ser Ala Glu Ser
Ala Leu Ile Thr Gln Gln Asp Leu Ala Pro Gln 50 55 60 Gln Arg Ala
Ala Pro Gln Gln Lys Arg Ser Ser Pro Ser Glu Gly Leu 65 70 75 80 Cys
Pro Pro Gly His His Ile Ser Glu Asp Gly Arg Asp Cys Ile Ser 85 90
95 Cys Lys Tyr Gly Gln Asp Tyr Ser Thr His Trp Asn Asp Leu Leu Phe
100 105 110 Cys Leu Arg Cys Thr Arg Cys Asp Ser Gly Glu Val Glu Leu
Ser Pro 115 120 125 Cys Thr Thr Thr Arg Asn Thr Val Cys Gln Cys Glu
Glu Gly Thr Phe 130 135 140 Arg Glu Glu Asp Ser Pro Glu Met Cys Arg
Lys Cys Arg Thr Gly Cys 145 150 155 160 Pro Arg Gly Met Val Lys Val
Gly Asp Cys Thr Pro Trp Ser Asp Ile 165 170 175 Glu Cys Val His Lys
Glu Ser Gly Thr Lys His Ser Gly Glu Val Pro 180 185 190 Ala Val Glu
Glu Thr Val Thr Ser Ser Pro Gly Thr Pro Ala Ser Pro 195 200 205 Cys
Ser Leu Ser Gly Ile Ile Ile Gly Val Thr Val Ala Ala Val Val 210 215
220 Leu Ile Val Ala Val Phe Val Cys Lys Ser Leu Leu Trp Lys Lys Val
225 230 235 240 Leu Pro Tyr Leu Lys Gly Ile Cys Ser Gly Gly Gly Gly
Asp Pro Glu 245 250 255 Arg Val Asp Arg Ser Ser Gln Arg Pro Gly Ala
Glu Asp Asn Val Leu 260 265 270 Asn Glu Ile Val Ser Ile Leu Gln Pro
Thr Gln Val Pro Glu Gln Glu 275 280 285 Met Glu Val Gln Glu Pro Ala
Glu Pro Thr Gly Val Asn Met Leu Ser 290 295 300 Pro Gly Glu Ser Glu
His Leu Leu Glu Pro Ala Glu Ala Glu Arg Ser 305 310 315 320 Gln Arg
Arg Arg Leu Leu Val Pro Ala Asn Glu Gly Asp Pro Thr Glu 325 330 335
Thr Leu Arg Gln Cys Phe Asp Asp Phe Ala Asp Leu Val Pro Phe Asp 340
345 350 Ser Trp Glu Pro Leu Met Arg Lys Leu Gly Leu Met Asp Asn Glu
Ile 355 360 365 Lys Val Ala Lys Ala Glu Ala Ala Gly His Arg Asp Thr
Leu Tyr Thr 370 375 380 Met Leu Ile Lys Trp Val Asn Lys Thr Gly Arg
Asp Ala Ser Val His 385 390 395 400 Thr Leu Leu Asp Ala Leu Glu Thr
Leu Gly Glu Arg Leu Ala Lys Gln 405 410 415 Lys Ile Glu Asp His Leu
Leu Ser Ser Gly Lys Phe Met Tyr Leu Glu 420 425 430 Gly Asn Ala Asp
Ser Ala Met Ser 435 440 14411PRTArtificial Sequenceamino acid
sequences of TRAIL-R2/ TNFRSF10B 14Met Glu Gln Arg Gly Gln Asn Ala
Pro Ala Ala Ser Gly Ala Arg Lys 1 5 10 15 Arg His Gly Pro Gly Pro
Arg Glu Ala Arg Gly Ala Arg Pro Gly Pro 20 25 30 Arg Val Pro Lys
Thr Leu Val Leu Val Val Ala Ala Val Leu Leu Leu 35 40 45 Val Ser
Ala Glu Ser Ala Leu Ile Thr Gln Gln Asp Leu Ala Pro Gln 50 55 60
Gln Arg Ala Ala Pro Gln Gln Lys Arg Ser Ser Pro Ser Glu Gly Leu 65
70 75 80 Cys Pro Pro Gly His His Ile Ser Glu Asp Gly Arg Asp Cys
Ile Ser 85 90 95 Cys Lys Tyr Gly Gln Asp Tyr Ser Thr His Trp Asn
Asp Leu Leu Phe 100 105 110 Cys Leu Arg Cys Thr Arg Cys Asp Ser Gly
Glu Val Glu Leu Ser Pro 115 120 125 Cys Thr Thr Thr Arg Asn Thr Val
Cys Gln Cys Glu Glu Gly Thr Phe 130 135 140 Arg Glu Glu Asp Ser Pro
Glu Met Cys Arg Lys Cys Arg Thr Gly Cys 145 150 155 160 Pro Arg Gly
Met Val Lys Val Gly Asp Cys Thr Pro Trp Ser Asp Ile 165 170 175 Glu
Cys Val His Lys Glu Ser Gly Ile Ile Ile Gly Val Thr Val Ala 180 185
190 Ala Val Val Leu Ile Val Ala Val Phe Val Cys Lys Ser Leu Leu Trp
195 200 205 Lys Lys Val Leu Pro Tyr Leu Lys Gly Ile Cys Ser Gly Gly
Gly Gly 210 215 220 Asp Pro Glu Arg Val Asp Arg Ser Ser Gln Arg Pro
Gly Ala Glu Asp 225 230 235 240 Asn Val Leu Asn Glu Ile Val Ser Ile
Leu Gln Pro Thr Gln Val Pro 245 250 255 Glu Gln Glu Met Glu Val Gln
Glu Pro Ala Glu Pro Thr Gly Val Asn 260 265 270 Met Leu Ser Pro Gly
Glu Ser Glu His Leu Leu Glu Pro Ala Glu Ala 275 280 285 Glu Arg Ser
Gln Arg Arg Arg Leu Leu Val Pro Ala Asn Glu Gly Asp 290 295 300 Pro
Thr Glu Thr Leu Arg Gln Cys Phe Asp Asp Phe Ala Asp Leu Val 305 310
315 320 Pro Phe Asp Ser Trp Glu Pro Leu Met Arg Lys Leu Gly Leu Met
Asp 325 330 335 Asn Glu Ile Lys Val Ala Lys Ala Glu Ala Ala Gly His
Arg Asp Thr 340 345 350 Leu Tyr Thr Met Leu Ile Lys Trp Val Asn Lys
Thr Gly Arg Asp Ala 355 360 365 Ser Val His Thr Leu Leu Asp Ala Leu
Glu Thr Leu Gly Glu Arg Leu 370 375 380 Ala Lys Gln Lys Ile Glu Asp
His Leu Leu Ser Ser Gly Lys Phe Met 385 390 395 400 Tyr Leu Glu Gly
Asn Ala Asp Ser Ala Met
Ser 405 410 15198PRTArtificial SequenceAmino acid sequence of NGAL
15Met Pro Leu Gly Leu Leu Trp Leu Gly Leu Ala Leu Leu Gly Ala Leu 1
5 10 15 His Ala Gln Ala Gln Asp Ser Thr Ser Asp Leu Ile Pro Ala Pro
Pro 20 25 30 Leu Ser Lys Val Pro Leu Gln Gln Asn Phe Gln Asp Asn
Gln Phe Gln 35 40 45 Gly Lys Trp Tyr Val Val Gly Leu Ala Gly Asn
Ala Ile Leu Arg Glu 50 55 60 Asp Lys Asp Pro Gln Lys Met Tyr Ala
Thr Ile Tyr Glu Leu Lys Glu 65 70 75 80 Asp Lys Ser Tyr Asn Val Thr
Ser Val Leu Phe Arg Lys Lys Lys Cys 85 90 95 Asp Tyr Trp Ile Arg
Thr Phe Val Pro Gly Cys Gln Pro Gly Glu Phe 100 105 110 Thr Leu Gly
Asn Ile Lys Ser Tyr Pro Gly Leu Thr Ser Tyr Leu Val 115 120 125 Arg
Val Val Ser Thr Asn Tyr Asn Gln His Ala Met Val Phe Phe Lys 130 135
140 Lys Val Ser Gln Asn Arg Glu Tyr Phe Lys Ile Thr Leu Tyr Gly Arg
145 150 155 160 Thr Lys Glu Leu Thr Ser Glu Leu Lys Glu Asn Phe Ile
Arg Phe Ser 165 170 175 Lys Ser Leu Gly Leu Pro Glu Asn His Ile Val
Phe Pro Val Pro Ile 180 185 190 Asp Gln Cys Ile Asp Gly 195
16444PRTArtificial SequenceAmino acid sequences of MMP8 16Met Gln
Gln Ile Pro Gln Glu Lys Ser Ile Asn Asp Tyr Leu Glu Lys 1 5 10 15
Phe Tyr Gln Leu Pro Ser Asn Gln Tyr Gln Ser Thr Arg Lys Asn Gly 20
25 30 Thr Asn Val Ile Val Glu Lys Leu Lys Glu Met Gln Arg Phe Phe
Gly 35 40 45 Leu Asn Val Thr Gly Lys Pro Asn Glu Glu Thr Leu Asp
Met Met Lys 50 55 60 Lys Pro Arg Cys Gly Val Pro Asp Ser Gly Gly
Phe Met Leu Thr Pro 65 70 75 80 Gly Asn Pro Lys Trp Glu Arg Thr Asn
Leu Thr Tyr Arg Ile Arg Asn 85 90 95 Tyr Thr Pro Gln Leu Ser Glu
Ala Glu Val Glu Arg Ala Ile Lys Asp 100 105 110 Ala Phe Glu Leu Trp
Ser Val Ala Ser Pro Leu Ile Phe Thr Arg Ile 115 120 125 Ser Gln Gly
Glu Ala Asp Ile Asn Ile Ala Phe Tyr Gln Arg Asp His 130 135 140 Gly
Asp Asn Ser Pro Phe Asp Gly Pro Asn Gly Ile Leu Ala His Ala 145 150
155 160 Phe Gln Pro Gly Gln Gly Ile Gly Gly Asp Ala His Phe Asp Ala
Glu 165 170 175 Glu Thr Trp Thr Asn Thr Ser Ala Asn Tyr Asn Leu Phe
Leu Val Ala 180 185 190 Ala His Glu Phe Gly His Ser Leu Gly Leu Ala
His Ser Ser Asp Pro 195 200 205 Gly Ala Leu Met Tyr Pro Asn Tyr Ala
Phe Arg Glu Thr Ser Asn Tyr 210 215 220 Ser Leu Pro Gln Asp Asp Ile
Asp Gly Ile Gln Ala Ile Tyr Gly Leu 225 230 235 240 Ser Ser Asn Pro
Ile Gln Pro Thr Gly Pro Ser Thr Pro Lys Pro Cys 245 250 255 Asp Pro
Ser Leu Thr Phe Asp Ala Ile Thr Thr Leu Arg Gly Glu Ile 260 265 270
Leu Phe Phe Lys Asp Arg Tyr Phe Trp Arg Arg His Pro Gln Leu Gln 275
280 285 Arg Val Glu Met Asn Phe Ile Ser Leu Phe Trp Pro Ser Leu Pro
Thr 290 295 300 Gly Ile Gln Ala Ala Tyr Glu Asp Phe Asp Arg Asp Leu
Ile Phe Leu 305 310 315 320 Phe Lys Gly Asn Gln Tyr Trp Ala Leu Ser
Gly Tyr Asp Ile Leu Gln 325 330 335 Gly Tyr Pro Lys Asp Ile Ser Asn
Tyr Gly Phe Pro Ser Ser Val Gln 340 345 350 Ala Ile Asp Ala Ala Val
Phe Tyr Arg Ser Lys Thr Tyr Phe Phe Val 355 360 365 Asn Asp Gln Phe
Trp Arg Tyr Asp Asn Gln Arg Gln Phe Met Glu Pro 370 375 380 Gly Tyr
Pro Lys Ser Ile Ser Gly Ala Phe Pro Gly Ile Glu Ser Lys 385 390 395
400 Val Asp Ala Val Phe Gln Gln Glu His Phe Phe His Val Phe Ser Gly
405 410 415 Pro Arg Tyr Tyr Ala Phe Asp Leu Ile Ala Gln Arg Val Thr
Arg Val 420 425 430 Ala Arg Gly Asn Lys Trp Leu Asn Cys Arg Tyr Gly
435 440 17444PRTArtificial SequenceAmino acid sequences of MMP8
17Met Gln Gln Ile Pro Gln Glu Lys Ser Ile Asn Asp Tyr Leu Glu Lys 1
5 10 15 Phe Tyr Gln Leu Pro Ser Asn Gln Tyr Gln Ser Thr Arg Lys Asn
Gly 20 25 30 Thr Asn Val Ile Val Glu Lys Leu Lys Glu Met Gln Arg
Phe Phe Gly 35 40 45 Leu Asn Val Thr Gly Lys Pro Asn Glu Glu Thr
Leu Asp Met Met Lys 50 55 60 Lys Pro Arg Cys Gly Val Pro Asp Ser
Gly Gly Phe Met Leu Thr Pro 65 70 75 80 Gly Asn Pro Lys Trp Glu Arg
Thr Asn Leu Thr Tyr Arg Ile Arg Asn 85 90 95 Tyr Thr Pro Gln Leu
Ser Glu Ala Glu Val Glu Arg Ala Ile Lys Asp 100 105 110 Ala Phe Glu
Leu Trp Ser Val Ala Ser Pro Leu Ile Phe Thr Arg Ile 115 120 125 Ser
Gln Gly Glu Ala Asp Ile Asn Ile Ala Phe Tyr Gln Arg Asp His 130 135
140 Gly Asp Asn Ser Pro Phe Asp Gly Pro Asn Gly Ile Leu Ala His Ala
145 150 155 160 Phe Gln Pro Gly Gln Gly Ile Gly Gly Asp Ala His Phe
Asp Ala Glu 165 170 175 Glu Thr Trp Thr Asn Thr Ser Ala Asn Tyr Asn
Leu Phe Leu Val Ala 180 185 190 Ala His Glu Phe Gly His Ser Leu Gly
Leu Ala His Ser Ser Asp Pro 195 200 205 Gly Ala Leu Met Tyr Pro Asn
Tyr Ala Phe Arg Glu Thr Ser Asn Tyr 210 215 220 Ser Leu Pro Gln Asp
Asp Ile Asp Gly Ile Gln Ala Ile Tyr Gly Leu 225 230 235 240 Ser Ser
Asn Pro Ile Gln Pro Thr Gly Pro Ser Thr Pro Lys Pro Cys 245 250 255
Asp Pro Ser Leu Thr Phe Asp Ala Ile Thr Thr Leu Arg Gly Glu Ile 260
265 270 Leu Phe Phe Lys Asp Arg Tyr Phe Trp Arg Arg His Pro Gln Leu
Gln 275 280 285 Arg Val Glu Met Asn Phe Ile Ser Leu Phe Trp Pro Ser
Leu Pro Thr 290 295 300 Gly Ile Gln Ala Ala Tyr Glu Asp Phe Asp Arg
Asp Leu Ile Phe Leu 305 310 315 320 Phe Lys Gly Asn Gln Tyr Trp Ala
Leu Ser Gly Tyr Asp Ile Leu Gln 325 330 335 Gly Tyr Pro Lys Asp Ile
Ser Asn Tyr Gly Phe Pro Ser Ser Val Gln 340 345 350 Ala Ile Asp Ala
Ala Val Phe Tyr Arg Ser Lys Thr Tyr Phe Phe Val 355 360 365 Asn Asp
Gln Phe Trp Arg Tyr Asp Asn Gln Arg Gln Phe Met Glu Pro 370 375 380
Gly Tyr Pro Lys Ser Ile Ser Gly Ala Phe Pro Gly Ile Glu Ser Lys 385
390 395 400 Val Asp Ala Val Phe Gln Gln Glu His Phe Phe His Val Phe
Ser Gly 405 410 415 Pro Arg Tyr Tyr Ala Phe Asp Leu Ile Ala Gln Arg
Val Thr Arg Val 420 425 430 Ala Arg Gly Asn Lys Trp Leu Asn Cys Arg
Tyr Gly 435 440 18467PRTArtificial SequenceAmino acid sequences of
MMP8 18Met Phe Ser Leu Lys Thr Leu Pro Phe Leu Leu Leu Leu His Val
Gln 1 5 10 15 Ile Ser Lys Ala Phe Pro Val Ser Ser Lys Glu Lys Asn
Thr Lys Thr 20 25 30 Val Gln Asp Tyr Leu Glu Lys Phe Tyr Gln Leu
Pro Ser Asn Gln Tyr 35 40 45 Gln Ser Thr Arg Lys Asn Gly Thr Asn
Val Ile Val Glu Lys Leu Lys 50 55 60 Glu Met Gln Arg Phe Phe Gly
Leu Asn Val Thr Gly Lys Pro Asn Glu 65 70 75 80 Glu Thr Leu Asp Met
Met Lys Lys Pro Arg Cys Gly Val Pro Asp Ser 85 90 95 Gly Gly Phe
Met Leu Thr Pro Gly Asn Pro Lys Trp Glu Arg Thr Asn 100 105 110 Leu
Thr Tyr Arg Ile Arg Asn Tyr Thr Pro Gln Leu Ser Glu Ala Glu 115 120
125 Val Glu Arg Ala Ile Lys Asp Ala Phe Glu Leu Trp Ser Val Ala Ser
130 135 140 Pro Leu Ile Phe Thr Arg Ile Ser Gln Gly Glu Ala Asp Ile
Asn Ile 145 150 155 160 Ala Phe Tyr Gln Arg Asp His Gly Asp Asn Ser
Pro Phe Asp Gly Pro 165 170 175 Asn Gly Ile Leu Ala His Ala Phe Gln
Pro Gly Gln Gly Ile Gly Gly 180 185 190 Asp Ala His Phe Asp Ala Glu
Glu Thr Trp Thr Asn Thr Ser Ala Asn 195 200 205 Tyr Asn Leu Phe Leu
Val Ala Ala His Glu Phe Gly His Ser Leu Gly 210 215 220 Leu Ala His
Ser Ser Asp Pro Gly Ala Leu Met Tyr Pro Asn Tyr Ala 225 230 235 240
Phe Arg Glu Thr Ser Asn Tyr Ser Leu Pro Gln Asp Asp Ile Asp Gly 245
250 255 Ile Gln Ala Ile Tyr Gly Leu Ser Ser Asn Pro Ile Gln Pro Thr
Gly 260 265 270 Pro Ser Thr Pro Lys Pro Cys Asp Pro Ser Leu Thr Phe
Asp Ala Ile 275 280 285 Thr Thr Leu Arg Gly Glu Ile Leu Phe Phe Lys
Asp Arg Tyr Phe Trp 290 295 300 Arg Arg His Pro Gln Leu Gln Arg Val
Glu Met Asn Phe Ile Ser Leu 305 310 315 320 Phe Trp Pro Ser Leu Pro
Thr Gly Ile Gln Ala Ala Tyr Glu Asp Phe 325 330 335 Asp Arg Asp Leu
Ile Phe Leu Phe Lys Gly Asn Gln Tyr Trp Ala Leu 340 345 350 Ser Gly
Tyr Asp Ile Leu Gln Gly Tyr Pro Lys Asp Ile Ser Asn Tyr 355 360 365
Gly Phe Pro Ser Ser Val Gln Ala Ile Asp Ala Ala Val Phe Tyr Arg 370
375 380 Ser Lys Thr Tyr Phe Phe Val Asn Asp Gln Phe Trp Arg Tyr Asp
Asn 385 390 395 400 Gln Arg Gln Phe Met Glu Pro Gly Tyr Pro Lys Ser
Ile Ser Gly Ala 405 410 415 Phe Pro Gly Ile Glu Ser Lys Val Asp Ala
Val Phe Gln Gln Glu His 420 425 430 Phe Phe His Val Phe Ser Gly Pro
Arg Tyr Tyr Ala Phe Asp Leu Ile 435 440 445 Ala Gln Arg Val Thr Arg
Val Ala Arg Gly Asn Lys Trp Leu Asn Cys 450 455 460 Arg Tyr Gly
465
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