U.S. patent application number 13/482432 was filed with the patent office on 2012-09-20 for scd fingerprints.
This patent application is currently assigned to CAMBRIDGE ENTERPRISE LIMITED. Invention is credited to Charles Nicholas Hales, Margaret Hales, Cesar Milstein, Celia Prilleltensky-Milstein, Adrian Woolfson.
Application Number | 20120237500 13/482432 |
Document ID | / |
Family ID | 27791909 |
Filed Date | 2012-09-20 |
United States Patent
Application |
20120237500 |
Kind Code |
A1 |
Milstein; Cesar ; et
al. |
September 20, 2012 |
SCD Fingerprints
Abstract
This invention relates to methods of testing, diagnosing,
monitoring, prognostically stratifying and classifying disease,
disorders and other medical conditions and physiological states
through the detection and analysis of soluble CD antigens in a body
fluid sample.
Inventors: |
Milstein; Cesar; (US)
; Prilleltensky-Milstein; Celia; (Cambridge, GB) ;
Hales; Charles Nicholas; (US) ; Hales; Margaret;
(Cambridge, GB) ; Woolfson; Adrian; (London,
GB) |
Assignee: |
CAMBRIDGE ENTERPRISE
LIMITED
Cambridge
GB
MEDICAL RESEARCH COUNCIL
London
GB
|
Family ID: |
27791909 |
Appl. No.: |
13/482432 |
Filed: |
May 29, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12237915 |
Sep 25, 2008 |
8206907 |
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13482432 |
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12070312 |
Feb 15, 2008 |
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12237915 |
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10506906 |
Jun 27, 2006 |
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PCT/GB03/00974 |
Mar 7, 2003 |
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12070312 |
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Current U.S.
Class: |
424/130.1 ;
506/18 |
Current CPC
Class: |
Y02A 90/10 20180101;
G16H 70/60 20180101; G01N 33/54306 20130101; G01N 33/6845 20130101;
G16H 40/63 20180101; A61P 35/00 20180101; G01N 33/6803 20130101;
G01N 33/68 20130101 |
Class at
Publication: |
424/130.1 ;
506/18 |
International
Class: |
A61K 39/395 20060101
A61K039/395; C40B 40/10 20060101 C40B040/10 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 7, 2002 |
GB |
0205394.0 |
Apr 3, 2002 |
GB |
027746.9 |
Dec 3, 2002 |
GB |
0228195.4 |
Claims
1. A composition comprising a plurality of isolated ligands and a
carrier, wherein said ligands comprise one or more ligands that
specifically bind to a soluble CD (sCD) antigen selected from the
group consisting of: sCD14, sCD30, sCD54, sCD117 and sCD130.
2-4. (canceled)
5. The composition of claim 1, wherein said soluble CD (sCD)
antigen is sCD117.
6. The composition of claim 1, wherein said composition comprises
at least a first and a second soluble CD antigen wherein the first
soluble CD (sCD) antigen is sCD117 and the second soluble CD
antigen is selected from the group consisting of: sCD14, sCD30,
sCD54 and sCD130.
7. The composition of claim 1, wherein said composition comprises
at least a first, a second and a third soluble CD antigen wherein
the first soluble CD (sCD) antigen is sCD117, and the second and
third soluble CD antigen is independently selected from the group
consisting of sCD14, sCD30, sCD54 and sCD130.
8. The composition of claim 1, wherein said composition comprises
at least a first, a second, a third and a fourth soluble CD antigen
wherein the first soluble CD (sCD) antigen is sCD117, and the
second, third and fourth soluble CD antigen is each independently
selected from the group consisting of: sCD14, sCD30, sCD54 and
sCD130.
9. The composition of claim 1, wherein said plurality of isolated
ligands specifically binds sCD117, sCD14, sCD30, sCD54 and
sCD130.
10. The composition of claim 1, wherein said composition further
comprises one or more isolated ligands each of which specifically
binds to a sCD antigen selected from the group consisting of the
soluble isoforms of the CD antigens listed in Table 43.
11. The composition of claim 1, wherein said composition further
comprises one or more isolated ligands that specifically binds to a
sCD antigen selected from the group consisting of the soluble
(secreted or shed) isoforms of the CD antigens listed in Table 44
and/or Table 45.
12. The composition of claim 1, wherein said composition further
comprises a ligand that specifically binds to a soluble Major
Histocompatibility Complex (sMHC) class I antigen.
13-18. (canceled)
19. The composition of claim 1, wherein said composition further
comprises one or more of the following: a ligand which selectively
bind to a cytokine, a chemokine, a gene expression signature and a
micro-RNA expression profile.
20. The composition of claim 1, wherein one or more of said ligands
comprises one or more antibody CDR regions.
21. The composition of claim 20, wherein one or more of said
ligands further comprises a non-immunoglobulin scaffold.
22. The composition of claim 21, wherein said non-immunoglobulin
scaffold is synthetic.
23. The composition of claim 21, where said non-immunoglobulin
scaffold is selected from the group consisting of CTLA4,
fibronectin, lipocallin, Rbp, Bbp ApoD, a natural bacterial
receptor, staphyloccocus A protein (SpA), GroEL, transferrin,
tetranectin, human C-lectin, an AVIMER.TM. and an AFFIBODY.TM.
scaffold.
24. The composition of claim 21, wherein one or more of said
ligands is an antibody.
25. The composition of claim 24, wherein said antibody is selected
from the group consisting of: a monoclonal antibody, an Fv, scFv,
Fab, (Fab)2, an Fd, and a single domain antibody.
26. The composition of claim 1, wherein said plurality of ligands
is bound to a solid support.
27. The composition of claim 26, wherein said solid support is
planar.
28. The composition of claim 26, wherein said solid support is
selected from the group consisting of a bead, a chip, a glass
surface, nitrocellulose, and an ELISA plate.
29. The composition of claim 26, wherein said plurality of ligands
bound to a solid support is formatted as an array.
30-72. (canceled)
73. The composition of claim 1, which additionally comprises a
pharmaceutically acceptable carrier, excipient or stabilizer.
74. A kit comprising the composition of claim 1.
Description
[0001] This application is a CIP of Ser. No. 10/506,906, filed Jun.
27, 2006, which is a 371 national phase application of
PCTGB03/00974 filed Mar. 7, 2003, which claims the benefit of
GB0205394.0 filed Mar. 7, 2002; GB0207746.9 filed Apr. 3, 2002; and
GB0228195.4, filed Dec. 3, 2002. Each of these applications in
their entirety is incorporated by reference herein.
FIELD OF THE INVENTION
[0002] This invention relates to methods of testing, diagnosing,
monitoring, prognostically stratifying and classifying disease,
disorders and other medical conditions and physiological states
through the detection and analysis of soluble CD antigens in a body
fluid sample.
BACKGROUND OF THE INVENTION
[0003] Early, rapid and accurate diagnosis facilitates the timely
and appropriate treatment of diseases, conditions and disorders,
and enables selection of the most appropriate therapeutic
interventions. The diagnosis and staging of diseases often involves
many different diagnostic procedures, which in some cases have the
disadvantages of being invasive, and/or prone to errors both due to
limited sensitivity, and/or specificity, sampling variability, and
technician variability. In the case of invasive testing may result
in morbidity and occasionally even mortality. Genetic based
diagnosis has been developed for a variety of diseases, to assess
the presence, or the predisposition to, likelihood of remission and
achievement of remission, response to therapeutic intervention or
reoccurrence of such a disease. Such tests may also enable
prognostic stratification, so as to determine those patients that
need more aggressive therapeutic interventions and more intensive
monitoring. Although there are several genetic assays available to
identify the presence of gene mutations and chromosomal
abnormalities, including polymerase chain reaction analysis, FISH
and cytogenetic analysis, the identification of specific genetic
changes is not always a direct indicator of a disease or a disorder
and the likely aggressiveness of the underlying pathological
process or indeed its likely responsiveness to therapy and it
cannot thus be relied upon as an accurate prognostic indicator.
However, changes in the overall patterns and/or expression levels
of various genes and their corresponding proteins in a tissue or
body fluid sample relative to a pre-disease-state, other stages of
the disease or relative to negative and/or normal controls, can
also be used to diagnose, stage and monitor disease and disorders.
Such patterns of gene expression or protein expression may also be
useful for prognostic stratification.
[0004] Therefore, there is a pressing need in the art to identify a
differential gene expression pattern of a plurality of genes in a
bodily sample that is reliably indicative of a particular disease,
disorder and condition, or stage thereof, or predilection for.
There is also a pressing need for such a display or fingerprint to
be easily obtained from the patient, test or control individual.
Such a fingerprint or `picture` would be of use in diagnosing,
predicting and/or detecting the presence or absence of a disease,
disorder or condition, in assessing the response to a particular
therapeutic intervention, in predicting the likelihood of a
response to a particular therapeutic intervention or procedure, for
predicting the extent and aggressiveness of any necessary
therapeutic intervention, for the selection of a specific treatment
from a selection of potential of therapeutic interventions, for
prognostic stratification to determine the likely progression of
the disease or disorder, or of disease-free survival with and
without treatment for any individual with a particular disease or a
condition, and in monitoring the progression of a disease process,
and/or the impact of treatment on disease states or conditions.
[0005] Such gene expression patterns though are cumbersome to
produce as they involve the preparation of RNA from a tissue sample
and furthermore gene expression arrays are subject to technical
problems including the fact that such arrays are not optimised for
individual genes and that representation of the mRNA species
population can be adversely influenced by the amplification
procedures that are sometimes necessary if only a small amount of
mRNA is present. There is consequently a need for a method that
enables diagnostic patterns to be derived from body fluids. The
measurement of soluble proteins relased from cells by processes
such as secretion of protein isoforms that are usually cell
membrane associated and the derivation of patterns of such proteins
therein, provides a simple method for diagnosing, predicting and/or
detecting the presence or absence of a disease, disorder or
condition, in assessing the response to a particular therapeutic
intervention, in predicting the likelihood of a response to a
particular therapeutic intervention or procedure, for predicting
the extent and aggressiveness of any necessary therapeutic
intervention, for the selection of a specific treatment from a
selection of potential of therapeutic interventions, for prognostic
stratification to determine the likely progression of the disease
or disorder, or of disease-free survival with and without treatment
for any individual with a particular disease or a condition, and in
monitoring the progression of a disease process, and/or the impact
of treatment on disease states or conditions.
[0006] In some instances where the power of an individual test is
limited, gene expression signatures or patterns may be combined
with protein expression signatures or patterns to derive nested
genomic/proteomic patterns that may be used in diagnosing,
predicting and/or detecting the presence or absence of a disease,
disorder or condition, in assessing the response to a particular
therapeutic intervention, in predicting the likelihood of a
response to a particular therapeutic intervention or procedure, for
predicting the extent and aggressiveness of any necessary
therapeutic intervention, for the selection of a specific treatment
from a selection of potential of therapeutic interventions, for
prognostic stratification to determine the likely progression of
the disease or disorder, or of disease-free survival with and
without treatment for any individual with a particular disease or a
condition, and in monitoring the progression of a disease process,
and/or the impact of treatment on disease states or conditions.
CD Antigens:
[0007] Lymphocytes and other leukocytes express large numbers of
different cell surface antigens that are associated with the cell
surface membrane. This cell membrane anchoring is often achieved
through the presence of a hydrophobic transmembrane domain that
spans the cell membrane although other mechanisms fo cell surface
linkage also exist. The differential expression of such cell
surface associated molecules can be used to identify distinct
leukocyte cellular subsets that perform different functions. These
cell surface molecules or `antigens` are known to serve a broad
range of critically important cellular functions (many of which are
related to immune function) and include: receptors for growth
factors, molecules that mediate cell-to-cell interactions,
receptors for viral adhesion, (such as CD4, CD112 and CD5 155),
immunoglobulins, cell adhesion molecules, mediators of complement
stimulation, enzymes and ion channels. These cell surface antigens
can be identified with monoclonal antibodies or other ligands, each
of which recognises with a varting degree of specificity a
different cell surface antigen (or sub-determinant on any
individual cell surface antigen). An international workshop was
established to derive a systematic nomenclature for the monoclonal
antibodies that recognised antigens present on the cell surface of
human leukocytes (The cluster of differentiation (CD) antigens
defined by the First International Workshop on Human Leukocyte
Differentiation Antigens. Hum Immunol. 1984 September; 11(1):
1-10). As a result of the statistical `cluster analysis` method
used to rationalise and map these monoclonal antibodies to specific
antigens, these molecules came to be known as cluster of
differentiation (CD) antigens, or CD molecules/antigens (Kishimoto
et 20 al., 1996 Proceedings of the Sixth International Workshop and
Conference held in Kobe, Japan. 10-14 Garland Publishing Inc. NY,
USA).
[0008] The discovery of CD antigens and the monoclonal antibody
technology used to define them was a direct result of the work of
one of the inventors of the present application (Dr. Cesar
Milstein) who invented monoclonal antibody technology with his
colleague Georges Kohler (Kohler and Milstein). In their classic
paper (Continuous cultures of fused cells secreting antibody of
defined specificity Nature 1975, Aug. 7, 256 (5517), 495-7) Kohler
and Milstein described how monoclonal antibodies of a single
defined specificity could be produced by the fusion of myeloma
cells with plasma cells. Kohler and Milstein were awarded the Nobel
Prize for Medicine and Physiology for this work. In collaboration
with Andrew McMichael in Oxford, Milstein subsequently raised and
identified monoclonal antibodies to the first non-human (CD4) and
human (CD1) CD antigens (McMichael et al. A human thymocyte antigen
defined by a hybrid myeloma monoclonal antibody, Eur. J. Immunol.
1979 March; 9(3):205-10).
[0009] The criteria necessary to assign a CD status to any given
cell surface leukocyte molecule has changed as a result of
technological advances achieved since the 1970s. At that time,
clustering depended exclusively on the statistical revelation of
similarities in the staining pattern of two or more antibodies that
had been analysed on multiple different tissues and cell lines.
However, presently a CD molecule is additionally also typically
classified on the basis of its molecular characteristics, and
structure (Bernard and Boumsell). A current list of CD antigen
markers as of the last international workshop has been compiled
(Table 43). This list was downloaded from the URL:
hcdm.org/CD1toCD350.htm on Nov. 6, 2007, and is updated at regular
intervals. The number of CD antigens has been increasing
exponentially, but this exponential increase is likely to tail off
eventually as the highly expressed antigens are discovered and only
the rarer, lower-expressing molecules remain to be discovered and
assigned a CD number. Eventually the list of CD antigens should be
complete and this will then encompass all human cell surface
leukocyte differentiation antigens and their homologues in other
mammalian and non-mammalian species.
[0010] It should be noted that although CD antigens were initially
defined and characterised on the basis of the fact that they are
expressed on the cell surface where they are associated with the
cell membrane of human leukocytes, including lymphocytes (e.g., T
cells, B cells), monocytes (e.g., macrophages) and granulocytes
(e.g., neutrophils, eosinophils and basophils), CD antigens have
also been found on the surface of other blood borne cells, such as
stem cells, erythrocytes and megakaryocytes, Furthermore there are
CD antigens that are expressed on the cell surface of cells and
tissues which are not typically part of the immune system, and
include cells from tissues such as the brain, liver, kidney,
epithelial cells, etc. A subset of the cell surface CD antigens
expressed in non-immune tissues are tissue specific CD antigens
that are expressed predominantly in a specific tissue or tissues.
Thus, CD molecules are ubiquitous and are expressed in differing
amounts in every tissue in the body.
[0011] Historically, cell surface CD antigens have been used as
diagnostic markers. Indeed, leukemias are diagnosed on the basis of
cell morphology, the expression of particular cell surface CD
antigens, enzyme activities and cytogenetic abnormalities such as
chromosome translocations. The expression of at least three cell
surface CD antigens on leukaemia cells can be determined using
labelled antibodies to particular CD antigens using flow cytometric
analysis.
[0012] Significantly, however, it has been observed that the CD
antigens usually expressed at the cell surface may also be found as
a soluble (sCD) form that is released into the blood (serum, plasma
or whole blood) and into other body fluids including, for example,
cerebrospinal fluid (CSF), urine, saliva, ascitic fluid, peritoneal
fluid, uveal fluid, synovial fluid, pleural fluid. These CD
molecules can be secreted from cells as a result of "active"
processes such as alternative splicing (Woolfson and Milstein,
PNAS, 91 (14) 6683-6687 (1994)) or by "passive" processes, such as
cell surface shedding. Thus, CD molecules can be found in three
different forms, (i) cell surface (membrane associated) CD
molecules, (ii) secreted (shed or soluble) CD molecules, (sCD)
produced by alternative splicing or other mechanisms and (iii)
intracellular CD molecules (that remain within the cell cytoplasm).
Each of these three classes of CD molecules can be complete
molecules or fragments derived from them as a result of alternative
splicing. These different isoforms may also have differential
post-translational modifications, such as glycosylation.
[0013] Recent studies (see WO 00/39580) have described a system for
the diagnosis of haematological malignancies, whereby
immunoglobulins are immobilized on a solid support and used to
detect cell-surface CD antigen levels, in particular cell-surface
CD antigen levels in samples of whole cells. Using this approach, a
pattern of expression of cell surface bound CD antigens is
generated, which one of the inventors (Dr Adrian Woolfson) and
others have shown to be indicative of the presence of various
defined leukemias in a patient. However, this cell-surface based
system of diagnosis is burdened with several disadvantages that are
also applicable to the diagnosis of diseases and disorders that are
not hematological. First, because the technique is cell-based, it
has the associated disadvantages of having an undesirable amount of
background noise and difficulty in measuring antigen levels
accurately. Such methods furthermore only allow semi-quantitative
determination of the relative densities of sub-populations of cells
of distinct immunophenotypes, indeed absolute quantification using
this method may not be possible, even in principle. Another problem
with this cell-based method is that at equilibrium, the number of
cells captured by the immobilised CD ligand dot, (antibody dot),
depends not only on the affinities of the interactions, but also on
the concentration of the CD ligand, (antibody), on the dot and the
level of expression of the CD antigen on the cell surface. And in
addition to this, there is the issue of the stereochemical
availability and accessibility of the CD ligand, (monoclonal
antibody), immobilized on the nitrocellulose membrane of the CD
antibody array.
[0014] Furthermore, computerized quantification of the cell density
as indicated by the pixel intensity corresponding to each dot of
arrayed antibody depends not only on the number of cells in the
test sample, but also on cell size and morphology. In addition to
all these factors, the absolute requirement for purification of
cells from whole blood, and the possible need to fractionate blood
cells still further, makes such a cell-based approach both labor
intensive and time consuming. Importantly though, a cell-based
approach only provides a pattern of CD antigens expressed on the
cell surface and does not take into account soluble CD antigens
that are secreted from the cell or shed from the cell surface (sCD
antigens). Therefore, there exists a need in the art for a simple
method for diagnosis of a disease, disorder or condition, in which
the limitations of the above described cell-surface based system
are overcome, and for a complete, sensitive and specific profile of
a disease which is obtained from an individual in a reliable and
practical manner.
SUMMARY OF THE INVENTION
[0015] The present inventors have surprisingly found that
particular disease states and disorders can be characterized by
specific patterns of expression levels of a plurality of
shed/soluble/secreted CD antigens (sCD) (as herein defined) derived
from a body fluid sample taken from an individual. That is, the
present inventors have found that a profile or `sCD print` or
`fingerprint` or `barcode` or `pattern` of the levels of a
plurality of sCD antigens correlates with a particular disease or
disorder (such as cancers, autoimmune diseases, cardiovascular
diseases and so on), or a combination of diseases and/or disorders,
or physiological states (such as those induced by administration of
a drug or toxin). By developing fingerprints comprising soluble CD
(sCD) antigens from readily available bodily fluids, the present
inventors have overcome the limitations of diagnostic techniques
using cell surface CD molecules discussed above.
[0016] The present inventors have furthermore surprisingly found
that the sCD profile or `sCD finger print` can comprise one or both
of the following two components: (1) a `stromal` component and (2)
a cellular component. The stromal component represents the
expression level of one or more of a plurality of sCD molecules
expressed in a bodily fluid that reflects the immune system's
homeostasis or `steady state`, which is specific to a particular
disease, disorder or condition. In essence, the composite
expression level of a plurality of immunologically related sCD
molecules produces a fingerprint specific to the particular
physiological state induced by the disease, disorder or condition
of interest. The second component of a sCD profile or sCD
fingerprint, the cellular component, represents the expression
level of one or more of a plurality of sCD molecules expressed in a
bodily fluid, and reflects the secretion or shedding of
tissue-specific soluble CD antigens, e.g., including from the
diseased tissue. Thus, the inventors have designated three types of
sCD fingerprints useful in assessing a disease, disorder or
condition: 1) a stromal sCD fingerprint, 2) a cellular sCD
fingerprint, and 3) a composite of a stromal and a cellular sCD
fingerprint.
[0017] The inventors have found that each of these three types of
sCD fingerprints can be encompassed in a yet broader fingerprint
that further includes a profile of expression levels of one or more
of soluble MHC Class I proteins, cytokines and/or chemokines
specific to a particular disease, disorder or condition.
[0018] Further still, the inventors have described herein that each
of these three types of sCD fingerprints, either alone, or
encompassed within the broader fingerprint described just above,
can further be encompassed in an extended fingerprint that further
includes a gene expression signature and/or a micro-RNA signature.
Thus the following fingerprints may in summary be envisaged: (i) a
sCD `stromal` fingerprint, (ii) a sCD `cellular` or
`tissue-specific` fingerprint, (iii) a composite `stromal` sCD/sMHC
Class I/cytokine/chemokine fingerprint, (iv) a composite `cellular`
sCD/sMHC Class I/cytokine/chemokine fingerprint, and (v) any of the
above combined with a gene expression fingerprint or pattern.
[0019] The characterization of a disease or a condition according
to a "sCD fingerprint" or to a fingerprint that includes a sCD
fingerprint" can be used in many applications, including, but
preferably not limited to: diagnosis, early diagnosis, prognostic
stratification, the predisposition of an individual to a disease or
disorder, the exclusion of a specific disease or disorder, staging
of the severity of a disease or disorder, the detection of early
relapse, defining complete remission, the detection of minimal
residual disease, monitoring the progression of a disease or
disorder, and monitoring the response to therapeutic intervention,
whether medical or surgical.
[0020] In one embodiment, the disease includes, but is not limited
to: an infectious disease, an inflammatory disease, an autoimmune
disease and an oncological disease. In another embodiment, the
infectious disease includes, but is not limited to: hepatitis,
tuberculosis (TB), HIV, meningococcal infection, pneumonia and
necrotizing enterocolitis. In another embodiment, the inflammatory
disease includes, but is not limited to: inflammatory bowel
diseases such as ulcerative colitis and Crohn's disease,
appendicitis, endometriosis and chronic lung disease. In another
embodiment the autoimmune disease includes, but is not limited to:
Multiple sclerosis, uveitis, lupus, vasculitis and Behcet's
disease. In another embodiment, the oncological disease includes,
but is not limited to: haematological malignancies such as Myeloma
(Bence Jones Proteinuria), Lymphoma, Chronic Myeloid Leukaemia
(CML), Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia
(CLL), Acute Lymphocytic Leukemia (ALL), Myelodysplastic syndromes;
bone marrow failure, myelodysplastic syndrome, juvenile
myelomonocytic leukaemia, T-cell chronic lymphocytic leukaemia,
prolymphocytic leukaemia, hairy cell leukaemia, follicular
lymphoma, lymphoplasmocytic immunocytoma, plasma cell leukaemia, T
prolymphocytic leukaemia, mycosis fungicides, large granular
lymphocyte leukaemia, and adult T cell leukaemia. In another
embodiment, the oncological disease includes, but is not limited to
solid tumours such as: colorectal cancer, breast cancer, vulval
cancer, and pancreatic cancer, brain tumours such as glioma,
cervical carcinoma, melanoma, ovarian cancer and prostate
cancer.
[0021] In another embodiment, the disease includes, but is not
limited to, a metabolic disease, a degenerative disease, a
psychological disease, a psychiatric disease, an iatrogenic
disease, a drug or toxin related disorder, a cardiovascular disease
or disorder, a dietary disorder, a disease or disorder resulting
from trauma and an endocrine disease or disorder. In one
embodiment, the metabolic disease includes, but is not limited to,
diabetes, diabetic nephropathy, chronic renal failure (for example
that resulting from diabetic kidney disease), renal transplantation
of a diseased kidney, and liver damage that results from a
metabolic disease such as Wilson's disease. In one embodiment, the
cardiovascular disease includes, but is not limited to deep vein
thrombosis (DVT), pulmonary embolism (PE) or cardiac pathology such
as that resulting from atherosclerosis. In one embodiment, the
psychiatric disease includes, but is not limited to, schizophrenia.
In one embodiment, the dietary disease includes, but is not limited
to, macrocytic anaemia (due to vitamin B12 deficiency). In one
embodiment, the drug related disease includes, but is not limited
to, liver damage resulting from a paracetamol or another drug
overdose.
[0022] Described herein is a composition comprising a collection of
a plurality of isolated ligands, one or more of which specifically
binds a sCD antigen. These isolated ligands can be used to identify
a sCD fingerprint of a sample from an individual with disease or
without disease, or from a test or control individual. In one
embodiment, the plurality of sCD antigens that are shed or secreted
from the cell surface or intracellular compartment as a result of
processes that include, but are not limited to, alternative
splicing, are derived from the entirety or any subgroup of the CD
antigens listed in Table 43. Although this list comprises surface
or membrane-associated CD antigens, it should be clear that the
present invention encompasses the corresponding soluble isoform of
the cell surface associated CD antigens, produced as a result of
shedding, alternative splicing, and/or secretion. As such, each
defined cell surface CD antigen in this list stands as an
ambassador for its soluble counterpart. In another embodiment, a
plurality of sCD antigens includes any grouping of soluble isoforms
of the CD antigens listed in Table 44 and/or Table 45 or subgroup
thereof.
[0023] In yet another embodiment of a composition comprising a
collection of a plurality of isolated ligands that specifically
binds a plurality of corresponding sCD antigens, the subgroup or
plurality of sCD antigens includes one or more or all of the
following soluble CD antigens: sCD14, sCD30, sCD54, sCD117 and
sCD130. In one aspect of this embodiment, the plurality of sCD
antigens includes a sCD antigen of the cellular type (CD117) and
one or more sCD antigens of the `stromal` type (CD14 (LPS
receptor), sCD30 (present on T cells), sCD54 (ICAM-1), and sCD130
(a class 1 cytokine receptor). This composition can be used to
generate a sCD fingerprint that is indicative of or classifies with
a sample obtained from an individual with disease, and a
fingerprint from an individual without disease, such as a sample
taken from a healthy, individual. In one embodiment the disease is
AML.
[0024] In yet another embodiment of a composition comprising a
plurality of isolated ligands that specifically binds a plurality
of corresponding sCD antigens, where the plurality of sCD antigens
includes one or more or all of the following soluble CD antigens:
sCD14, sCD30, sCD54, sCD117 and sCD130, the composition further
comprises a plurality of isolated ligands that specifically binds
to (a) one or more isolated ligands that selectively bind to a
soluble isoform of a major histocompatibility (MHC) class I
antigen, and/or (b) one or more isolated ligands that selectively
bind to a chemokine and/or a cytokine, and/or (c) one or more
isolated ligands that selectively bind to an over-expressed surface
antigen associated with a specific pathology. Like the soluble CD
antigens, soluble MHC class I molecules may be formed as a result
of shedding from the cell surface or by an active process of
secretion. These active processes of secretion include, but are not
limited to, processes of alternative splicing that generate soluble
isoforms of molecules that are also found anchored to the cell
membrane. This composition can be used to generate a fingerprint
reflecting the expression levels of one or more of the above
mentioned sCD antigens and soluble MHC Class I antigens, that is
indicative of/or classifies with a sample obtained from an
individual with disease, and a fingerprint from an individual
without disease, such as a sample form a healthy, individual. In
one embodiment the disease is AML.
[0025] In yet another embodiment of a composition comprising a
plurality of isolated ligands which specifically binds a plurality
of sCD antigens, where the plurality of sCD antigens includes one
or more or all of the following soluble CD antigens: sCD14, sCD30,
sCD54, sCD117 and sCD130, and where the composition optionally
further comprises one or more isolated ligands which specifically
binds to (a) one or more isolated ligands that selectively bind to
a soluble isoform of a major histocompatibility (MHC) class I
antigen, and/or (b) one or more isolated ligands that selectively
bind to a chemokine and/or a cytokine, and/or (c) one or more
isolated ligands that selectively bind to an over-expressed surface
antigen associated with a specific pathology, the composition
further comprises ligands capable of identifying a gene signature
and/or a micro-RNA signature. This composition can be used to
generate a fingerprint reflecting the expression levels of one or
more of the above mentioned sCD antigens and optionally one or more
of soluble MHC Class I antigens, cytokines, chemokines, micro-RNAs
and other genes, that is indicative of or classifies with a sample
obtained from an individual with disease, and a fingerprint from an
individual without disease, such as a sample form a healthy,
individual. In one embodiment the disease is AML.
[0026] As used herein, the terms "gene signature" or "gene
expression profile" or "gene expression fingerprint" are
interchangeable and refer to the pattern of gene expression
modulation in a plurality of genes, including an increase or
decrease of gene expression in a sample from an individual with a
disease or disorder of interest relative to that of a control, e.g.
where the control individual does not have the disease or disorder
of interest, and/or is a healthy individual. For example, for a
plurality of 10 genes, possibly genes 1-6 are reduced in expression
and genes 7-10 are increased in expression in the sample of the
diseased individual relative to the control individual. The profile
or fingerprint of a diseased state will include the relative degree
of increase or decrease of expression of the genes of the set in a
sample when compared to the same sample type from a negative
control, e.g. a control individual without the disease such as a
healthy control. For example, expression of gene 1 may be reduced
by half, gene 2 by 2/3, gene 3 not expressed at all, gene 7 doubled
in expression, gene 10 increased 3 fold in expression, and so on in
response to each of the compounds of the set and relative to the
steady state levels of said genes). In the typical case, the
comparison is between a sample from an individual with disease
versus one without the disease, or a comparison between samples
obtained before and after therapy, or a comparison between
different stages of a disease. The result is a gene expression
profile, or gene expression fingerprint, or expression fingerprint.
The fold increase or decrease in expression can range from up to
0.2 fold, 0.3 fold, 0.4 fold, 0.5 fold, 0.6 fold, 0.7 fold, 0.8
fold, 0.9 fold, up to 1 fold, 1.1 fold, 1.2 fold, 1.3 fold, 1.4
fold, 1.5 fold, 1.6 fold, 1.7 fold, 1.8 fold, 1.9 fold up to 2
fold, 2.1 fold, 2.2 fold, 2.3 fold, 2.4 fold, 2.5 fold, 2.6 fold,
2.7 fold, 2.8 fold, 2.9 fold, up to 3 fold, up to a 4 fold or
more.
[0027] Micro-RNA expression profiles can be used to distinguish a
sample from individual(s) with the disease of interest vs. those
without the disease of interest. Micro-RNAs (miRs) are
naturally-occurring 19 to 25 nucleotide transcripts found in over
one hundred distinct organisms, including fruit flies, nematodes
and humans. The miRs are typically processed from 60- to
70-nucleotide foldback RNA precursor structures, which are
transcribed from the miR gene. The miR precursor processing
reaction requires Dicer RNase III and Argonaute family members
(Sasaki et al. (2003), Genomics 82, 323-330). The miR precursor or
processed miR products are easily detected, and an alteration in
the levels of these molecules within a cell can indicate a
perturbation in the chromosomal region containing the miR gene, as
described in US20060106360.
[0028] In one embodiment, a diagnostic method comprises the
following steps: in a sample obtained from a subject suspected of
having a disease such as AML, the status of one or more miR genes
is evaluated by measuring the level of each miR gene product from
the miR gene in the sample. An alteration in the level of miR gene
product in the sample relative to the level of miR gene product in
a control sample is indicative of the presence of the disease,
(AML) in the subject. In a related embodiment, the invention
provides a method of diagnosing a disease, particularly cancer, and
including AML, in a subject, comprising reverse transcribing total
RNA from a sample from the subject to provide a set of labeled
target oligodeoxynucleotides; hybridizing the target
oligodeoxynucleotides to a microarray comprising micro-RNA-specific
probe oligonucleotides to provide a hybridization profile for the
sample; and comparing the sample hybridization profile to the
hybridization profile generated from a control sample, such as a
healthy person or a person without disease, where an alteration in
the micro-RNA in the subject relative to the control profile is
indicative of the subject either having, or being at risk for
developing, the disease of interest, e.g. AML. The microarray of
micro-RNA-specific probe oligonucleotides preferably comprises
micro-RNA-specific probe oligonucleotides for one or more, or a
substantial portion of the human miRNome, or the full complement of
micro-RNA genes in a cell. The microarray more preferably comprises
at least about 60%, 70%, 80%, 90%, or 95% of the human miRNome.
[0029] A gene signature can be identified or confirmed using many
techniques, including but preferably not limited or confirmed using
the microarray technique. Thus, the gene signature of a plurality
of disease-associated genes can be measured in a bodily sample
using microarray technology. In this method, polynucleotide
sequences of interest are plated, or arrayed, on a microchip
substrate. The arrayed sequences are then hybridized with specific
DNA probes from cells or tissues of interest. Just as in the RT-PCR
method, the source of mRNA typically is total RNA isolated from the
sample, and corresponding normal or `healthy` sample(s).
[0030] In a specific embodiment of the microarray technique, PCR
amplified inserts of cDNA clones are applied to a substrate in a
dense array. Preferably at least 10,000 nucleotide sequences are
applied to the substrate. The microarrayed genes, immobilized on
the microchip at 10,000 elements each, are suitable for
hybridization under stringent conditions. Fluorescently labeled
cDNA probes may be generated through incorporation of fluorescent
nucleotides by reverse transcription of RNA extracted from tissues
of interest. Labeled cDNA probes applied to the chip hybridize with
specificity to each spot of DNA on the array. After stringent
washing to remove non-specifically bound probes, the chip is
scanned by confocal laser microscopy or by another detection
method, such as a CCD camera. Quantitation of hybridization of each
arrayed element allows for assessment of corresponding mRNA
abundance. With dual color fluorescence, separately labeled cDNA
probes generated from two sources of RNA are hybridized pair wise
to the array. The relative abundance of the transcripts from the
two sources corresponding to each specified gene is thus determined
simultaneously. The miniaturized scale of the hybridization affords
a convenient and rapid evaluation of the expression pattern for
large numbers of genes. Such methods have been shown to have the
sensitivity required to detect rare transcripts, which are
expressed at a few copies per cell, and to reproducibly detect at
least approximately two-fold differences in the expression levels
(Schena et al., Proc. Natl. Acad. Sci. USA 93(2):106-149 (1996)).
Microarray analysis can be performed by commercially available
equipment, following manufacturer's protocols, such as by using the
Affymetrix GenChip technology, or Incyte's microarray
technology.
[0031] The development of microarray methods for large-scale
analysis of gene expression makes it possible to obtain gene
signatures specific to a disease, disorder or condition of
interest, often in many cases enabling diagnosis, staging, therapy
and outcome prediction in a variety of diseases, disorders and
conditions.
[0032] In one embodiment, one or more of the ligands used to
capture the soluble CD antigens, the soluble MHC class I molecules,
the chemokines and the cytokines is a protein. In another
embodiment, one or more of the ligands contains one or more
antibody CDR regions, and further comprises an immunoglobulin or
non-immunoglobulin scaffold. In another embodiment, one or more of
the ligands is an antibody. The antibody includes, but is not
limited to, a monoclonal antibody, a polyclonal antibody, an Fv,
scFv, Fab, (Fab).sub.2, an Fd, and a single domain antibody.
[0033] In one embodiment, the composition comprising a collection
of plurality of isolated ligands that bind selectively to a
plurality of sCD antigens and optionally comprising one or more
ligands that selectively bind to one or more soluble MHC class I
antigens, is bound to a solid support, which can be optionally
formatted as an array. The plurality of isolated ligands in the
array preferably is positioned in identifiable areas of the array
and optionally in replicate. Solid supports include, but are not
limited to, nitrocellulose, chips, beads, and silica based
supports. The manner of linking a wide variety of compounds to
various surfaces is well known and is amply illustrated in the
literature. (See also, for example, Immobilized Enzymes, Ichiro
Chibata, Halsted Press, New York, 1978, and Cuatrecasas, J. Biol.
Chem. 1970 June; 245(12):3059-65, the disclosures of which are
incorporated herein by reference).
[0034] Reagents may be applied to the membrane materials in a
variety of ways that are well known in the art. Various `printing`
techniques are suitable for application of liquid reagents to the
membranes, such as micro-syringes, pens using metered pumps, direct
printing, ink-jet printing, air-brush, and contact (or filament)
methods and any of these techniques can be used in the present
context. To facilitate manufacture, the membrane can be treated
with the reagents and then subdivided into smaller portions (for
example small narrow strips each embodying the required
reagent-containing zones) to provide a plurality of identical
carrier units.
[0035] Also described herein are kits comprising a collection of
individual isolated ligands that bind selectively to individual sCD
antigens and optionally comprising kits that contain ligands that
selectively bind soluble MHC class I antigens, cytokines or
chemokines. Although in isolation these kits are able to measure
only individual sCD antigens, soluble MHC Class I molecules,
cytokines or chemokines, if multiple kits are used then the levels
of multiple soluble antigens, be they sCD antigens, soluble MHC
class I antigens, cytokines or chemokines can be measured, so as to
define a pattern in a manner analogous to a chip or bead based
multiplexed method.
[0036] Also described herein are methods of diagnosing or
prognosing or monitoring a disease or disorder, or predicting
response to a therapeutic intervention, or detecting remission or
detecting a relapse of the disease process or determining
sensitivity to a given therapeutic intervention in advance of that
intervention being commenced by analysing the levels of sCD
antigens in a body fluid sample from a test individual, and
comparing them to respective samples from one or more controls,
where the controls can be positive and/or negative controls, and/or
comparing them to databases containing reference fingerprints from
positive and/or negative controls. Negative controls include
healthy individuals, that is individuals with no documented
pathology. Negative controls also includes individuals who do not
have the disease or condition of interest, for example, AML. The
sample can be, but is not limited to, a body fluid sample such as:
whole blood, serum, plasma, saliva, urine, lymphatic fluid,
cerebrospinal fluid, pleural fluid, follicular fluid, seminal
fluid, amniotic fluid, milk, ascites, sputum, tears, perspiration,
mucus, synovial fluid uveal fluid, and peritoneal fluid. The method
covers tissue culture supernatants as well as body fluids. In
another embodiment, the sample can be an in vitro tissue culture
sample from one or more cell lines ie tissue culture supernatants.
The cell lines can be an established cell line, or a cell line from
the subject being tested. One embodiment described herein is a
method of diagnosing or prognosing, or predicting response to a
therapeutic intervention, or detecting minimal residual disease, or
detecting remission or detecting a relapse of the disease process
or determining sensitivity to a given therapeutic intervention in
advance of that intervention being commenced for acute myeloid
leukemia (AML) in a test individual who optionally may have been
previously diagnosed as having leukaemia, where the method
comprises: (a) determining the level of each of a plurality of
soluble CD (sCD) antigens, including one or more of the following
sCD antigens: sCD14, sCD30, sCD54, sCD117 and sCD130 in a serum or
plasma sample from the test individual, and then (b) comparing the
level of each said sCD antigen of step (a) with the level of each
of the sCD antigens in a serum or plasma sample or whole blood
sample taken from control individuals that are either healthy
individuals with no documented pathology or who have one of the
following leukemias: chronic myeloid leukemia (CML), non-Hodgkin's
lymphoma (NHL), chronic lymphocytic leukemia (CLL), where detecting
a statistically significant difference in the level of the sCD
antigens in the comparison of step or defining a unique pattern of
sCD antigen expression using a mathematical algorithm, such as the
application of neural network analysis (b), is indicative of AML in
the test individual.
[0037] Also described herein are methods of diagnosing or
prognosing or monitoring or predicting response to a therapeutic
intervention, or detecting remission or detecting a relapse of the
disease process or determining sensitivity to a given therapeutic
intervention in advance of that intervention being commenced, for
leukemia in a test individual, where the method comprises
determining the level (using a mathematical algorithm such as the
application of neural network analysis, able to discern patterns)
of each of the sCD antigens sCD14, sCD30, sCD54, sCD117 and sCD130,
in a serum/plasma sample from the test individual, and comparing
the level of each sCD antigen with the level of each of the sCD
antigens in a serum/plasma sample from one or more representative
healthy control individuals not having leukemia, where detecting a
statistically significant difference in the level of each of the
sCD antigens in the test individual, or deriving a disease state
specific pattern using a mathematical algorithm such as neural
network analysis, is indicative of leukemia in said test
individual. In a preferred embodiment, the leukemia is acute
myeloid leukemia, (AML), chronic myeloid leukemia (CML),
non-Hodgkin's lymphoma (NHL) or chronic lymphocytic leukemia (CLL).
In a further preferred embodiment, the leukemia is acute myeloid
leukemia (AML).
[0038] Also described herein are methods of diagnosing or
prognosing or predicting the response to a therapeutic
intervention, or detecting remission or detecting a relapse of the
disease process or determining sensitivity to a given therapeutic
intervention in advance of that intervention being commenced, for
acute myeloid leukemia (AML) in a test individual diagnosed as
having leukemia, comprising: (a) determining the level of each of
sCD14, sCD30, sCD54, sCD54, sCD117 and sCD130, in a serum or plasma
sample or whole blood taken from a test individual, (b) comparing
the level of each sCD antigen of step (a) with the level of each of
said sCD antigens in a serum/plasma sample from healthy individuals
or control individuals having a leukemia selected from the group
consisting of, but not limited to: acute myeloid leukemia (AML),
chronic myeloid leukemia (CML), non-Hodgkin's lymphoma (NHL) and
chronic lymphocytic leukemia (CLL), (c) comparing the level of each
of the sCD antigens of step (a) with the level of each of the sCD
antigens in a serum sample or plasma sample or whole blood from
control individuals having AML, (d) determining whether the level
of each of the sCD antigens of step (a) corresponds with the level
of each of the sCD antigens of the control individuals having
either CML, NHL, or CLL, of step b) or healthy individuals, or with
the level of each of said sCD antigens in serum/plasma from said
control individuals having AML of step (b), wherein a determination
in step (d) that said level of each of the sCD antigens of step (a)
corresponds with the level of each of the sCD antigens in serum, or
plasma or whole blood from said control individuals having AML of
step (b) is indicative of AML in the test individual. A fingerprint
or expression pattern comprising the levels of a plurality of sCDs
where the sCD fingerprint represents one or more disease states can
be generated using the above comparisons by means of the
application of pattern recognition algorithms including, but not
limited to genetic algorithms or neural network analysis.
[0039] Also described herein are methods of diagnosing or
prognosing (by the prognostic stratification of patients into
different prognostic groups), or predicting the response to a
therapeutic intervention, or detecting minimal residual disease, or
detecting remission or detecting a relapse of the disease process
or determining sensitivity to a given therapeutic intervention in
advance of that intervention being commenced, leukemia in an
individual, comprising the steps of: (a) determining the level of a
plurality of sCD antigens expressed in a serum, plasma or whole
blood sample obtained from the individual, wherein the plurality of
sCD antigens are soluble isoforms of the CD antigens listed in
Table 43, Table 44, or Table 45, and (b) comparing the level of
each of said plurality of sCD antigens in the serum/plasma sample
according to step (a) with the level of each of said plurality of
sCD antigens in serum/plasma from one or more individuals having
leukemia, (c) comparing the level of each of said five or more sCD
antigens in said blood according to step (a) with the level of each
of said plurality of sCD antigens in blood from one or more
individuals not having leukemia, (d) determining whether the level
of said five or more sCD antigens of step (a) corresponds with the
levels of said plurality of sCD antigens in step (b) as compared
with levels of said plurality of sCD antigens in step (c), wherein
said determination is indicative of said individual of step (a)
having leukemia. The above method can be modified to distinguish
between different subgroups of AML.
[0040] Also described herein are methods of developing a classifier
(Duda 2001) useful for diagnosing or prognosing (by the prognostic
stratification of patients into different prognostic groups), or
predicting response to a therapeutic intervention, or detecting
minimal residual disease, or detecting remission or detecting a
relapse of the disease process or determining sensitivity to a
given therapeutic intervention in advance of that intervention
being commenced, for a leukemia selected from the group consisting
of AML, CML, CLL and NHL, comprising: (a) measuring the level of
sCD antigens selected from the group consisting of the soluble
isoforms of the CD antigens listed in Tables 43, 44 and/or 45, in a
training population wherein said training population is comprised
of two subgroups, a first subgroup diagnosed as having a first
leukemia selected from the group consisting of AML, CML, CLL and
NHL, and a second subgroup diagnosed as having said leukemias other
than said first leukemia, (b) apply one or more mathematical models
to the levels of expression of step (a) to develop one or more
classifiers which differentiate between said first subgroup and
said second subgroup. In one embodiment, the leukemia of the first
group is AML.
[0041] Also described herein are methods of diagnosing or
prognosing (by the prognostic stratification of patients into
different prognostic groups), or detecting minimal residual
disease, or predicting response to a therapeutic intervention, or
detecting remission or detecting a relapse of the disease process
or determining sensitivity to a given therapeutic intervention in
advance of that intervention being commenced of a leukemia in an
individual, comprising determining the level of plurality of sCD
antigens expressed in a serum/plasma sample obtained from said
individual, where said plurality of sCD antigens are selected from
the group consisting of the soluble isoforms of the CD antigens
listed in Table 43, 44 and/or 45, and (b) using the results from
step (a) in combination with a classifier designed to differentiate
samples from an individual having AML from samples from individuals
having CML or CLL or NHL (or controls or differentiation among AML
subgroups) so as to determine a diagnosis with respect to AML (or
specific subgroup).
[0042] In another embodiment of the methods described herein, the
step of determining the level of each of said sCD antigens in the
sample comprises contacting the sample with ligands specific for
the sCD antigens. In one embodiment of the methods and products
described herein, one or more of the ligands specific for the sCD
antigens is an antibody, where each of the antibodies is specific
for one of the sCD antigens. The antibodies include, but are not
limited to a polyclonal antibody, monoclonal antibody, fv, scfv,
dab, fd, fab, and fab'.sub.2.
[0043] In another embodiment, methods of diagnosis based on
analyses of sCD antigens as described herein are used in
combination with one or more other diagnostic methods, including
analysis of patient symptoms and/or presenting complaints.
[0044] In another embodiment, the one or more ligands that
specifically bind an sCD antigen are attached to a surface,
preferably a solid surface. The solid surface includes, but is not
limited to a bead, a chip, a glass surface, nitrocellulose, or an
ELISA plate.
[0045] Detailed embodiments of the above described compositions and
methods are described below.
[0046] One embodiment disclosed herein is a composition having a
plurality of isolated ligands and a carrier, the ligands
encompassing one or more ligands that specifically binds to a
soluble CD (sCD) antigen. The sCD antigen can be any sCD antigen,
including, but preferably but not limited to, a soluble isoform of
a CD antigen listed in Table 43, or one or more of the following
sCD antigens: sCD14, sCD30, sCD54, sCD117 and sCD130. In another
embodiment, the composition comprises a plurality of isolated
ligands and a carrier, where each of the isolated ligands
specifically binds to one of the following soluble CD (sCD)
antigens: sCD14, sCD30, sCD54, sCD117 and sCD130. Another
embodiment described is a composition consisting essentially of a
plurality of isolated ligands and a carrier, where each of the
isolated ligands specifically binds to a soluble CD (sCD) antigen
listed as follows: sCD14, sCD30, sCD54, sCD117 and sCD130. Also
described herein is a composition consisting of a plurality of
isolated ligands and a carrier, where each of the isolated ligands
specifically binds to a soluble CD (sCD) antigen selected from the
group consisting of: sCD14, sCD30, sCD54, sCD117 and sCD130. In any
of the above compositions, (a) the soluble CD (sCD) antigen can be
sCD117, or (b) a first soluble CD (sCD) antigen can be sCD117 and a
second soluble CD antigen can be any of: sCD14, sCD30, sCD54 and
sCD130, or (c) a first soluble CD (sCD) antigen can be sCD117, and
a second and third soluble CD antigen can be any of: sCD14, sCD30,
sCD54 and sCD130, or (d) a first soluble CD (sCD) antigen is
sCD117, and a second, third and fourth soluble CD antigen can be
any of: sCD14, sCD30, sCD54 and sCD130, or (e) a first, second,
third, fourth and fifth soluble antigens are sCD117, sCD14, sCD30,
sCD54 and sCD130, respectively. In any of the above compositions,
the number of sCD antigens can preferably range from two, up to
three, up to four, up to five, up to six, up to seven, up to eight,
up to nine, or up to 10 sCD antigens or more. The sCD antigens can
include any combination or subgrouping of soluble isoforms of the
CD antigens listed in Table 43, and/or Table 44 and/or Table 45. In
another embodiment, the above compositions can further have a
ligand which specifically binds to a soluble Major
Histocompatibility Complex (sMHC) class I antigen. In another
aspect, a composition can consist essentially of a plurality of
isolated ligands and a carrier, where each of the isolated ligands
specifically binds to a soluble CD (sCD) antigen selected from the
group consisting of: sCD14, sCD30, sCD54, sCD117, sCD130 and a
soluble Major Histocompatibility Complex (sMHC) class I antigen. In
another embodiment, a composition consists of a plurality of
isolated ligands and a carrier, where each of the isolated ligands
specifically binds to a soluble CD (sCD) antigen selected from the
group consisting of: sCD14, sCD30, sCD54, sCD117, sCD130 and a
soluble Major Histocompatibility Complex (sMHC) class I antigen. In
another embodiment, any of the above compositions can further
comprise a ligand which selectively bind to a cytokine or to a
chemokine. In another aspect, any of the above compositions, one or
more of the ligands can comprise one or more antibody CDR regions,
which can optionally further comprises a non-immunoglobulin
scaffold which can optionally be synthetic. In one aspect, the
non-immunoglobulin scaffold includes, but preferably is not limited
to CTLA4, fibronectin, lipocallin, Rbp, Bbp ApoD, a natural
bacterial receptor, staphylococcus A protein (SpA), GroEL,
transferrin, tetranectin, human C-lectin, an AVIMER.TM. and/or an
AFFIBODY.TM. scaffold. In any of the above compositions the ligand
can be an antibody. The antibody includes, but preferably is not
limited to a monoclonal antibody, an Fv, scFv, Fab, (Fab)2, an Fd,
and a single domain antibody.
[0047] The ligands of any of the above compositions can be bound to
a solid support, which includes a planar support. The support also
includes, but is not limited to a bead, a chip, a glass surface,
nitrocellulose, and an ELISA plate. In another aspect the plurality
of ligands bound to a solid support is formatted as an array. Any
of the above compositions, or combination of ligands thereof, can
be formulated as a kit. Further, any of the above compositions or
combination of ligands thereof can be used in any of the methods
described herein, including but not limited to the following
methods described below.
[0048] A method of detecting, diagnosing or prognosing acute
myeloid leukemia (AML) in a test individual, the method comprising,
for each of a plurality of sCD antigens, where at least one sCD
antigen is selected from the group consisting of: sCD14, sCD30,
sCD54, sCD117 and sCD130, (a) quantifying a level of expression of
the sCD antigen in a serum/plasma sample of the test individual,
and (b) comparing the level of sCD antigen quantified in step (a)
to a quantified level of control sCD antigen in serum/plasma
samples of control subjects classified as healthy subjects; where a
determination from step (b) that is statistically different from
the levels in the serum/plasma samples of the subjects classified
as healthy subjects, results in a classification of the sCD antigen
expression in the test subject with that of the subjects classified
as having AML.
[0049] A method of detecting, diagnosing or prognosing acute
myeloid leukemia (AML) in a test individual the method comprising,
for each of a plurality of sCD antigens, where at least one sCD
antigen is selected from the group consisting of: sCD14, sCD30,
sCD54, sCD117 and sCD130, (a) quantifying a level of expression of
the sCD antigen in a serum/plasma sample of the test individual,
(b) comparing the level of sCD antigen quantified in step (a) to a
quantified level of control sCD antigen in serum/plasma samples of
control subjects classified as healthy subjects; and (c) comparing
the level of sCD antigen quantified in step (a) to a quantified
level of control sCD antigen in serum/plasma samples of control
subjects classified as having AML; where a determination from steps
(b) and (c) that the level of step (a) is statistically similar to
the levels in the serum/plasma samples of the subjects classified
as having AML, and is statistically different from the levels in
the serum/plasma samples of the subjects classified as healthy
subjects, results in a classification of the sCD antigen expression
in the test subject with that of the subjects classified as having
AML.
[0050] A method of detecting, diagnosing or prognosing acute
myeloid leukemia (AML) in a test individual the method comprising,
for each of a plurality of sCD antigens, where at least one sCD
antigen is selected from the group consisting of: sCD14, sCD30,
sCD54, sCD117 and sCD130, (a) quantifying a level of expression of
the sCD antigen in a serum/plasma sample of the test individual,
(b) comparing the level of sCD antigen quantified in step (a) to a
quantified level of control sCD antigen in serum/plasma samples of
control subjects classified as healthy subjects; and (c) comparing
the level of sCD antigen quantified in step (a) to a quantified
level of control sCD antigen in serum/plasma samples of control
subjects classified as having AML; where a determination from steps
(b) and (c) that the level of step (a) is statistically different
from the levels in the serum/plasma samples of the subjects
classified as having AML and is statistically similar to the levels
in the serum/plasma samples of the subjects classified as healthy
subjects, results in a classification of the sCD antigen expression
in the test subject with that of the subjects who classified as
healthy subjects, and where a determination from steps (b) and (c)
that the level of step (a) is statistically similar to the levels
in the serum/plasma samples of the subjects classified as having
AML, and is statistically different from the levels in the
serum/plasma samples of the subjects classified as healthy
subjects, results in a classification of the sCD antigen expression
in the test subject with that of the subjects classified as having
AML.
[0051] A method of detecting, diagnosing or prognosing acute
myeloid leukemia (AML) in a test individual the method comprising,
for each of a plurality of sCD antigens, where the plurality of sCD
antigens comprises one or more sCD antigens selected from the group
consisting of: sCD14, sCD30, sCD54, sCD117 and sCD130, and where
the plurality of sCD antigens comprises one or more sCD antigens
selected from the group consisting of the soluble isoforms of the
CD antigens listed in Table (43) (a) quantifying a level of
expression of the sCD antigen in a serum/plasma sample of the test
individual, (b) comparing the level of sCD antigen quantified in
step (a) to a quantified level of control sCD antigen in
serum/plasma samples of control subjects classified as healthy
subjects; where a determination from step (b) that is statistically
different from the levels in the serum/plasma samples of the
subjects classified as healthy subjects, results in a
classification of the sCD antigen expression in the test subject
with that of the subjects classified as having AML.
[0052] A method of detecting, diagnosing or prognosing acute
myeloid leukemia (AML) in a test individual the method comprising,
for each of a plurality of sCD antigens, where the plurality of sCD
antigens comprises one or more sCD antigens selected from the group
consisting of: sCD14, sCD30, sCD54, sCD117 and sCD130, and where
the plurality of sCD antigens comprises one or more sCD antigens
selected from the soluble isoforms of the CD antigens listed in
Table 43; (a) quantifying a level of expression of the sCD antigen
in a serum/plasma sample of the test individual, (b) comparing the
level of sCD antigen quantified in step (a) to a quantified level
of control sCD antigen in serum/plasma samples of control subjects
classified as healthy subjects; (c) comparing the level of sCD
antigen quantified in step (a) to a quantified level of control sCD
antigen in serum/plasma samples of control subjects classified as
having AML; where a determination from steps (b) and (c) that the
level of step (a) is statistically similar to the levels in the
serum/plasma samples of the subjects classified as having AML, and
is statistically different from the levels in the serum/plasma
samples of the subjects classified as healthy subjects, results in
a classification of the sCD antigen expression in the test subject
with that of the subjects classified as having AML.
[0053] A method of detecting, diagnosing or prognosing acute
myeloid leukemia (AML) in a test individual the method comprising,
for each of a plurality of sCD antigens, where the plurality of sCD
antigens comprises one or more sCD antigens selected from the group
consisting of: sCD14, sCD30, sCD54, sCD117 and sCD130, and where
the plurality of sCD antigens comprises one or more sCD antigens
selected from the group consisting of the soluble isoforms of the
CD antigens listed in Table 43; (a) quantifying a level of
expression of the sCD antigen in a serum/plasma sample of the test
individual, (b) comparing the level of sCD antigen quantified in
step (a) to a quantified level of control sCD antigen in
serum/plasma samples of control subjects classified as healthy
subjects; and (c) comparing the level of sCD antigen quantified in
step (a) to a quantified level of control sCD antigen in
serum/plasma samples of control subjects classified as having AML;
where a determination from steps (b) and (c) that the level of step
(a) is statistically different from the levels in the serum/plasma
samples of the subjects classified as having AML and is
statistically similar to the levels in the serum/plasma samples of
the subjects classified as healthy subjects, results in a
classification of the sCD antigen expression in the test subject
with that of the subjects who classified as healthy subjects, and
where a determination from steps (b) and (c) that the level of step
(a) is statistically similar to the levels in the serum/plasma
samples of the subjects classified as having AML, and is
statistically different from the levels in the serum/plasma samples
of the subjects classified as healthy subjects, results in a
classification of the antigen expression in the test subject with
that of the subjects classified as having AML.
[0054] A method of detecting, diagnosing or prognosing acute
myeloid leukemia (AML) in a test individual the method comprising,
for each of a plurality of antigens comprising sCD antigens and MHC
Class I antigens, where the sCD antigens comprises one or more sCD
antigens selected from the group consisting of: sCD14, sCD30,
sCD54, sCD117 and sCD130, (a) quantifying a level of expression of
the antigen in a serum/plasma sample of the test individual, (b)
comparing the level of antigen quantified in step (a) to a
quantified level of control antigen in serum/plasma samples of
control subjects classified as healthy subjects; where a
determination from step (b) that is statistically different from
the levels in the serum/plasma samples of the subjects classified
as healthy subjects, results in a classification of the sCD antigen
expression in the test subject with that of the subjects classified
as having AML.
[0055] A method of detecting, diagnosing or prognosing acute
myeloid leukemia (AML) in a test individual the method comprising,
for each of a plurality of antigens comprising sCD antigens and MHC
Class I antigens, where the sCD antigens comprises one or more sCD
antigens selected from the group consisting of sCD14, sCD30, sCD54,
sCD117 and sCD130, (a) quantifying a level of expression of the
antigen in a serum/plasma sample of the test individual, (b)
comparing the level of antigen quantified in step (a) to a
quantified level of control antigen in serum/plasma samples of
control subjects classified as healthy subjects; and (c) comparing
the level of antigen quantified in step (a) to a quantified level
of control antigen in serum/plasma samples of control subjects
classified as having AML; where a determination from steps (b) and
(c) that the level of step (a) is statistically similar to the
levels in the serum samples of the subjects classified as having
AML, and is statistically different from the levels in the
serum/plasma samples of the subjects classified as healthy
subjects, results in a classification of the antigen expression in
the test subject with that of the subjects classified as having
AML.
[0056] A method of detecting, diagnosing or prognosing acute
myeloid leukemia (AML) in a test individual the method comprising,
for each of a plurality of antigens comprising sCD antigens and
soluble MHC Class I antigens, where the sCD antigens comprises one
or more sCD antigens selected from the group consisting of: sCD14,
sCD30, sCD54, sCD117 and sCD130, (a) quantifying a level of
expression of the antigen in a serum/plasma sample of the test
individual, (b) comparing the level of antigen quantified in step
(a) to a quantified level of control antigen in serum samples of
control subjects classified as healthy subjects; and (c) comparing
the level of antigen quantified in step (a) to a quantified level
of control antigen in serum/plasma samples of control subjects
classified as having AML; where a determination from steps (b) and
(c) that the level of step (a) is statistically different from the
levels in the serum/plasma samples of the subjects classified as
having AML and is statistically similar to the levels in the
serum/plasma samples of the subjects classified as healthy
subjects, results in a classification of the antigen expression in
the test subject with that of the subjects who classified as
healthy subjects, and where a determination from steps (b) and (c)
that the level of step (a) is statistically similar to the levels
in the serum/plasma samples of the subjects classified as having
AML, and is statistically different from the levels in the
serum/plasma samples of the subjects classified as healthy
subjects, results in a classification of the antigen expression in
the test subject with that of the subjects classified as having
AML.
[0057] A method of detecting, diagnosing or prognosing acute
myeloid leukemia (AML) in a test individual the method comprising,
for each of a plurality of antigens comprising sCD antigens and MHC
Class I antigens, where the sCD antigens comprises one or more sCD
antigens selected from the group consisting of: sCD14, sCD30,
sCD54, sCD117 and sCD130, and one or more sCD antigens selected
from the soluble isoforms of the CD antigens the listed in Table
43, (a) quantifying a level of expression of the antigen in a
serum/plasma sample of the test individual, and (b) comparing the
level of antigen quantified in step (a) to a quantified level of
control antigen in serum/plasma samples of control subjects
classified as healthy subjects; where a determination from step (b)
that is statistically different from the levels in the serum/plasma
samples of the subjects classified as healthy subjects, results in
a classification of the sCD antigen expression in the test subject
with that of the subjects classified as having AML.
[0058] A method of detecting, diagnosing or prognosing acute
myeloid leukemia (AML) in a test individual the method comprising,
for each of a plurality of antigens comprising sCD antigens and MHC
Class I antigens, where the sCD antigens comprises one or more sCD
antigens selected from the group consisting of: sCD14, sCD30,
sCD54, sCD117 and sCD130, and one or more sCD antigens selected
from the soluble isoforms of the CD antigens listed in Table 43,
(a) quantifying a level of expression of the antigen in a
serum/plasma sample of the test individual, (b) comparing the level
of antigen quantified in step (a) to a quantified level of control
antigen in serum/plasma samples of control subjects classified as
healthy subjects; and (c) comparing the level of antigen quantified
in step (a) to a quantified level of control antigen in
serum/plasma samples of control subjects classified as having AML;
where a determination from steps (b) and (c) that the level of step
(a) is statistically similar to the levels in the serum/plasma
samples of the subjects classified as having AML, and is
statistically different from the levels in the serum/plasma samples
of the subjects classified as healthy subjects, results in a
classification of the antigen expression in the test subject with
that of the subjects classified as having AML.
[0059] A method of detecting, diagnosing or prognosing (by the
prognostic stratification of patients into different prognostic
groups) acute myeloid leukemia (AML) in a test individual the
method comprising, for each of a plurality of antigens comprising
sCD antigens and soluble MHC Class I antigens, where the sCD
antigens comprises one or more sCD antigens selected from the group
consisting of: sCD14, sCD30, sCD54, sCD117 and sCD130, and one or
more sCD antigens selected from the group of soluble isoforms of
the CD antigens listed in Table 43, (a) quantifying a level of
expression of the antigen in a serum/plasma sample of the test
individual, (b) comparing the level of antigen quantified in step
(a) to a quantified level of control antigen in serum/plasma
samples of control subjects classified as healthy subjects; (c)
comparing the level of antigen quantified in step (a) to a
quantified level of control antigen in serum/plasma samples of
control subjects classified as having AML; where a determination
from steps (b) and (c) that the level of step (a) is statistically
different from the levels in the serum/plasma samples of the
subjects classified as having AML and is statistically similar to
the levels in the serum/plasma samples of the subjects classified
as healthy subjects, results in a classification of the antigen
expression in the test subject with that of the subjects who
classified as healthy subjects, and where a determination from
steps (b) and (c) that the level of step (a) is statistically
similar to the levels in the serum/plasma samples of the subjects
classified as having AML, and is statistically different from the
levels in the serum/plasma samples of the subjects classified as
healthy subjects, results in a classification of the antigen
expression in the test subject with that of the subjects classified
as having AML.
[0060] A method of detecting remission of acute myeloid leukemia
(AML) in a test individual suspected of being in remission of acute
leukemia, the method comprising, for each of a plurality of sCD
antigens, where at least one sCD antigen is selected from the group
consisting of: sCD14, sCD30, sCD54, sCD117 and sCD130, (a)
quantifying a level of expression of the sCD antigen in a
serum/plasma sample of the test individual, (b) comparing the level
of sCD antigen quantified in step (a) to a quantified level of
control sCD antigen in serum/plasma samples of control subjects
classified as healthy subjects; and (c) comparing the level of sCD
antigen quantified in step (a) to a quantified level of control sCD
antigen in serum/plasma samples of control subjects classified as
having AML; where a determination from steps (b) and (c) that the
level of step (a) is statistically different from the levels in the
serum/plasma samples of the subjects classified as having AML and
is statistically similar to the levels in the serum/plasma samples
of the subjects classified as healthy subjects, is indicative of
the test individual's being in full remission, and where a
determination from steps (b) and (c) that the level of step (a) is
statistically similar to the levels in the serum/plasma samples of
the subjects classified as having AML, and is statistically
different from the levels in the serum/plasma samples of the
subjects classified as healthy subjects, results in a
classification of the sCD antigen expression in the test subject
with that of the subjects classified as having AML.
[0061] A method of detecting relapse of acute myeloid leukemia
(AML) in a test individual suspected of having a relapse of AML,
the method comprising, for each of a plurality of sCD antigens,
where at least one sCD antigen is selected from the group
consisting of: sCD14, sCD30, sCD54, sCD117 and sCD130, (a)
quantifying a level of expression of the sCD antigen in a
serum/plasma sample of the test individual, (b) comparing the level
of sCD antigen quantified in step (a) to a quantified level of
control sCD antigen in serum/plasma samples of control subjects
classified as healthy subjects; (c) comparing the level of sCD
antigen quantified in step (a) to a quantified level of control sCD
antigen in serum/plasma samples of control subjects classified as
having AML; where a determination from steps (b) and (c) that the
level of step (a) is statistically different from the levels in the
serum/plasma samples of the subjects classified as having AML and
is statistically similar to the levels in the serum/plasma samples
of the subjects classified as healthy subjects, results in a
classification of the sCD antigen expression in the test subject
with that of the subjects classified as not having leukemia, and
where a determination from steps (b) and (c) that the level of step
(a) is statistically similar to the levels in the serum/plasma
samples of the subjects classified as having AML, and is
statistically different from the levels in the serum/plasma samples
of the subjects classified as healthy subjects, is indicative of
the test individual's having a relapse of AML.
[0062] A method of monitoring the disease state of a test
individual undergoing therapy for AML comprising at regular time
points throughout the course of therapy, for each of a plurality of
sCD antigens, where at least one sCD antigen is selected from the
group consisting of: sCD14, sCD30, sCD54, sCD117 and sCD130, (a)
quantifying a level of expression of the sCD antigen in a
serum/plasma sample of the test individual, (b) comparing the level
of sCD antigen quantified in step (a) to a quantified level of
control sCD antigen in serum/plasma samples of control subjects
classified as healthy subjects; and (c) comparing the level of sCD
antigen quantified in step (a) to a quantified level of control sCD
antigen in serum/plasma samples of control subjects classified as
having AML; where a determination from steps (b) and (c) that the
level of step (a) is statistically different from the levels in the
serum/plasma samples of the subjects classified as having AML and
is statistically similar to the levels in the serum/plasma samples
of the subjects classified as healthy subjects, is indicative of
the therapy being effective in the test individual, and where a
determination from steps (b) and (c) that the level of step (a) is
statistically similar to the levels in the serum/plasma samples of
the subjects classified as having AML, and is statistically
different from the levels in the serum/plasma samples of the
subjects classified as healthy subjects, is indicative of the
therapy not being effective in the test individual.
[0063] Embodiments of any of these methods include a plurality of
ligands which bind to two or more sCD antigens, where the sCD
antigens includes a first soluble CD (sCD) antigen is sCD117, or
where a first soluble CD (sCD) antigen is sCD117 and a second
soluble CD antigen is selected from the group consisting of: sCD14,
sCD30, sCD54 and sCD130, or where a first soluble CD (sCD) antigen
is sCD117, and a second and third soluble CD antigen is selected
from the group consisting of: sCD14, sCD30, sCD54 and sCD130, or
where a first soluble CD (sCD) antigen is sCD117, and a second,
third and fourth soluble CD antigen is selected from the group
consisting of: sCD14, sCD30, sCD54 and sCD130, or where a first
soluble CD (sCD) antigen is sCD117, and a second, third, fourth and
fifth soluble CD antigen is selected from the group consisting of:
sCD14, sCD30, sCD54 and sCD130.
[0064] Embodiments of any of these methods include a plurality of
ligands which bind to two or more sCD antigens, where the plurality
of sCD antigens consists of a first soluble CD (sCD) antigen and a
second soluble CD (sCD) antigen, where the first soluble CD (sCD)
antigen is sCD117 and the second soluble CD antigen is selected
from the group consisting of: sCD14, sCD30, sCD54 and sCD130, where
the plurality of sCD antigens consists of a first soluble CD (sCD)
antigen and a second soluble CD (sCD) antigen, and a third soluble
(sCD) antigen, where the first soluble CD (sCD) antigen is sCD117,
and the second soluble CD antigen and the third soluble CD antigen
is selected from the group consisting of: sCD14, sCD30, sCD54 and
sCD130, where the plurality of sCD antigens consists of a first
soluble CD (sCD) antigen and a second soluble CD (sCD) antigen, and
a third soluble (sCD) antigen, and a fourth soluble (sCD) antigen,
where the first soluble CD (sCD) antigen is sCD117, and the second
soluble CD antigen and the third soluble CD antigen and the fourth
soluble antigen is selected from the group consisting of: sCD14,
sCD30, sCD54 and sCD130, where the plurality of sCD antigens
consists of two or more soluble CD (sCD) antigens selected from the
group consisting of: sCD117, sCD14, sCD30, sCD54 and sCD130, where
the plurality of sCD antigens consists of three or more soluble CD
(sCD) antigens selected from the group consisting of: sCD117,
sCD14, sCD30, sCD54 and sCD130, where the plurality of sCD antigens
consists of four or more soluble CD (sCD) antigens selected from
the group consisting of: sCD117, sCD14, sCD30, sCD54 and sCD130,
and where the plurality of sCD antigens consists of the five
soluble CD (sCD) antigens selected from the group consisting of:
sCD117, sCD14, sCD30, sCD54 and sCD130. In one aspect of any of the
above methods of diagnosing AML, the sensitivity is greater than
70%, 75%, 80%, 83% up to 85%. In another aspect of any of the above
methods of diagnosing AML, the specificity is greater than 70%,
75%, 80%, 83%, 85%, 90%, 95% up to and including 99%. In one aspect
of any of the above methods of diagnosing AML, determining the
classification is made through the use of neural networks. Where
determining the level of each of the sCD antigens in the sample
comprises contacting the sample with one or more ligands, where
each of the ligands is specific for one of the sCD antigens, and
measuring the level of each the sCD antigen.
[0065] In another aspect of any of the above methods and
compositions, the ligand is an antibody, and the antibody is
selected from the group consisting of: a polyclonal antibody, a
monoclonal antibody, fv, scfv, dab, fd, fab, and fab'2. In an
embodiment of the above methods, the serum/plasma sample can be
substituted by a bodily fluid such as one selected from the group
consisting of whole blood, plasma, lymphatic fluid, cerebrospinal
fluid, synovial fluid, urine, and saliva. In an embodiment of the
above methods, the level of each sCD antigen in the sample is
determined comprising the following steps: i) contacting the sample
with a first ligand, where the ligand specifically binds a soluble
CD antigen in the sample, ii) detecting the binding of the first
ligand to the sCD antigen, and iii) quantitating the level of the
sCD antigen. The detecting step can be accomplished by any means
including the use of a detecting antibody or fragment or derivative
thereof, which specifically binds its cognate ligand. In one
embodiment of the methods described herein, the ligand is attached
to a surface, such as a bead, a chip, a glass surface,
nitrocellulose or an ELISA plate. In an embodiment of the methods
described herein, a ligand is further comprises a
non-immunoglobulin scaffold which includes but is not limited to
CTLA4, fibronectin, lipocallin, Rbp, Bbp ApoD, a natural bacterial
receptor, staphylococcus A protein (SpA), GroEL, transferrin,
tetranectin, human C-lectin, an AVIMER.TM. and/or an AFFIBODY.TM.
scaffold.
BRIEF DESCRIPTION OF THE DRAWINGS
[0066] FIG. 1. Illustrates one embodiment of the layout of a chip.
Each of the squares represents a well. Each well is 9.times.9 mm in
dimension with a total number of 16 wells per chip.
[0067] FIG. 2. Illustrates one embodiment of a probe layout for two
of the eight samples captured by a single chip. Five sCD antigen
probes, a positive control and an additional negative normal probe
were assessed, leading to a total number of 84 measured probes per
sample distributed over two wells.
[0068] FIG. 3. Flow chart of the full data analysis procedure.
[0069] FIG. 4: Spatial distribution of Neg. Probe relative
fluorescence units (RFUs) for two slides measured on the second
day. The expression level (log 10) is coded where increase in
brightness corresponds to a stronger signal.
[0070] FIG. 5: Spatial distribution of Probe background RFUs in log
10 for two slides measured on the second day. The expression level
(log 10) is coded where an increase in brightness corresponds to a
stronger signal.
[0071] FIG. 6: Raw Standard Curves for all five antigens on both
days. Curves plotted using a LOWESS smoother (degree 1, span
0.5).
[0072] FIG. 7: Iterative weighted least squares (IWLS) mean
extended Standard Curves for all five antigens on both days. Curves
plotted using a LOWESS smoother (degree 1, span 0.5).
[0073] FIG. 8: Iterative weighted least squares (IWLS) mean
extended Standard Curves converted to concentration levels.
[0074] FIG. 9: Displays minusNegByWell.robust classifier input
data. Antigens (probe) from 1 to 5: sCD14, s CD30, sCD54, sCD117,
sgp130. Colours code the five sample classes (AML, CML, CLL, NHL,
and NormMix). FIG. 9 illustrates the summarized intensity values
for the antigens for all 47 samples using minusNegByWell.robust
summarization. It is visually apparent that probe number 4 (sCD117)
separates many of the different classes quite well.
[0075] FIG. 10: Displays minusNegByWell.robust classifier input
data. Antigens (probe) from 1 to 5: sCD14, sCD30, sCD 54, sCD117,
sCD130.
[0076] FIG. 11: Displays Standard Curve on dilutions of CD178
[0077] FIG. 12: Displays Standard Curve on dilutions of CD127
[0078] FIGS. 13A-13E. Displays 2d-scatter plots for all possible
pairings of sCD14, sCD30, sCD 54, sCD117, sCD130.
DETAILED DESCRIPTION OF THE INVENTION
[0079] Due to a large variety of molecular disease mechanisms
affecting the state of the immune system, sCD data provide a highly
focused, disease relevant view, permitting the use of much fewer
measurements for the construction of a generic assay for
diagnostics.
[0080] Modern algorithm methods allow the separation of signal
signatures characteristic of specific diseases in high-dimensional
input sets. The invention described herein is in the application of
the concept of signature analysis in the disease relevant focus of
sCD measurements.
[0081] In the prior art, sCDs have been studied individually. At
most, sCDs have been studied in pairs. Conceptually, these studies
are identical to biomarker studies from the prior art. These
typically take one or two markers and associate them with a
specific disease. This results in a simple binary result. By
contrast, the present relates to the detection and derivation of
sCD fingerprints, that is specific patterns. The methods of the
present invention typically survey at least five different sCD
entities. The resulting pattern formed by combination of these data
points creates a signature or fingerprint for a particular disease
state. Thus, applied to the diagnosis of a patient, at least five
sCDs will be typed. The pattern or fingerprint which these multiple
data points produce may then be used to deduce the diagnosis or
prognosis for that patient. Thus, the diagnostic readout is linked
to the specific pattern or fingerprint composed of at least five
different sCDs, this fingerprint being considered as a single
conceptual entity for the purposes of diagnosis. As soon as one
uses more than a single predictive quantity of sCD antigens, e.g.
five or more, there is no need for a significant change in
expression of an individual sCD antigen to be able to discriminate
disease classes using the joint set.
[0082] In this case, the features are the individual sCDs measured.
It is well understood that the accuracy of a classifier generally
increases with the number of features selected and, depending on
the application domain, cost/benefit tradeoffs need to be
made--efficient choices are certainly not arbitrary. In
applications where features are cheap, such as in microarray
studies, very large feature sets are therefore sometimes applied.
On the other hand, very often sufficient accuracy can already be
achieved for a specific disease domain with just 2-3 features,
sometimes even by a single feature. In principle, several of these
simple classifiers could be arbitrarily combined for assays
supporting multiple disease domains. Besides issues of scaling,
however, this approach would not be well suited for the development
of a generic assay/sCD fingerprinting device as presented in this
invention. Multiple patterns and signatures of specific diseases
can be distilled de novo from large sets of feature candidates by
use of modern machine learning methods, such as advanced factor
analysis and algorithms for class discovery. The present invention
employs sCD sets large enough to support the detection of such
patterns and signatures that reflect the state of the immune
system. For an implementation that demonstrates the benefits of
this approach in the construction of generic disease related
assays, five sCDs or more are preferable: 2-3 features would
realistically only support an accurate prognosis or diagnostics for
a single typical disease domain. Whereas doubling this number
would, in the worst case, support two disease domains by simple
aggregation, the approach of this invention utilizes patterns in a
space of 5 dimensions or more allowing a high-dimensional
representation of immune system states for a superior performance
in the characterization of multiple disease types as required for a
generic assay. Clearly, more demanding applications can be
accommodated by an increase of feature numbers
[0083] Assessment of sCDs, however, provides a unifying focus, even
capturing clinically relevant effects of mechanistically extremely
different disease types in a compact set of variables. While
multiple diseases may affect the same particular sCD feature,
interactions of immune system components reflected in the
multi-dimensional feature set will permit efficient super-linear
scaling by algorithmic separation of independent effects (e.g., by
application of advanced factor analysis). We can hence use a
relatively small number of sCD features for a high-dimensional
representation of immune system states to provide generic
disease-related assays. It is the application of this modern
analytical approach to sCDs, which provide a unifying focus on
immune system relevance, that underpins the present invention and
that creates novel value in its clinical applications.
[0084] Further, the power of methods of the present invention lies
in their capacity to read out against multiple disease states from
only a single fingerprint. This feature cannot be found anywhere in
the prior art. A key point to note is that the sCDs read out by the
present invention may not be unique in their presence or absence or
elevation or depression in a particular disease state. Indeed,
numerous different disease states may possess numerous similar or
identical individual marker results. Clearly, by applying the prior
art techniques of biomarker assay, these disease states could never
be successfully distinguished. However, the present invention
advantageously permits these to be discriminated by the application
of modern high-dimensional data analysis methods. This is due to
the simultaneous analysis of a minimum of five different sCDs in
production of the fingerprint. It is this `parallel processing`
which is both novel and inventive with regard to the state of the
prior art.
[0085] Described herein are compositions and methods used to
characterize a disease, disorder or condition, in an individual by
analyzing the levels of soluble CD (sCD) antigens, and optionally
soluble MHC Class I antigens, cytokines or chemokines, in a sample
from said individual. The analysis of sCD levels in the body fluid
sample can be used in many applications, including, but not limited
to diagnosis, prognosis, predilection toward a specific disease or
disorder, ruling out the presence of a disease or disorder, staging
of the severity of the disease or disorder, monitoring the
progression of the disease or disorder, and monitoring the effect
of treatment or other external influence on the disease. In a
preferred embodiment, the disease, disorder or condition is
leukemia. The analysis of sCD levels in the sample can also be used
to distinguish between a limited number of diseases, as for
example, between different types of leukemia or different subtypes
of leukemia.
Composition
[0086] One embodiment described herein is a composition comprising
a collection of two or more, three or more, four or more, or a
plurality of six or more, seven or more, eight or more, nine or
more, ten or more, eleven or more, twelve or more, thirteen or
more, fourteen or more, fifteen or more, sixteen or more, up to a
plurality of twenty or more, thirty or more, fifty or seventy or
more, one hundred or more, one hundred and fifty or more, two
hundred or more, three hundred or more, three hundred and fifty or
more, up to four or five hundred or more distinct, isolated
ligands, each of which binds specifically to a sCD antigen, some of
which have not yet been defined, and some of which have been
defined but have not yet been assigned a formal CD
nomenclature.
[0087] In one embodiment, the composition of ligands comprises
ligands that bind specifically to a sCD antigen, the sCD antigen
including soluble/shed/secreted isoforms of all the CD antigens
listed in Table 43, or fragments thereof. In another embodiment,
the sCD molecules include the soluble/shed/secreted forms of any
sub-grouping of two or more soluble isoforms of the CD antigens
listed in Table 43, or fragments thereof. In one embodiment, the
sCD antigens include soluble/shed/secreted forms of all the CD
antigens listed in Table 44, or fragments thereof. In another
embodiment, the sCD molecules include the soluble/shed/secreted
forms of any sub-grouping of two or more of the CD antigens listed
in soluble isoforms of the CD antigens listed in Table 44, or
fragments thereof. In one embodiment, the sCD antigens include
soluble/shed/secreted forms of all the CD antigens listed in Table
45, or fragments thereof. In another embodiment, the sCD molecules
include the soluble/shed/secreted forms of any sub-grouping of two
or more soluble isoforms of the CD antigens listed in Table 45, or
fragments thereof. In yet another preferred embodiment, the sCD
antigens include soluble/shed/secreted forms of the following CD
antigens: CD14, CD30, CD54, CD117 and CD130, or fragments thereof.
In another embodiment, the sCD molecules include the
soluble/shed/secreted forms of any sub-grouping of two or more of:
CD14, CD30, CD54, CD117 and CD130, or fragments thereof.
[0088] In another embodiment, the composition of ligands, which
comprises ligands that bind specifically to a sCD antigen as
described above, may also contain ligands that serve as controls
for the assay, including positive and/or negative controls. In one
embodiment, the composition of ligands comprises ligands which bind
specifically to a sCD antigen that are present in replicate, e.g.
in duplicate, or triplicate, or four times in replicate, or five
times in replicate, or six times in replicate, or up to 10, 20, up
to 50 times in replicate.
[0089] The term "sCD antigen" is used interchangeably with the
terms "soluble CD antigen", "shed CD antigen" and "secreted CD
antigen". All four terms represent a soluble isoform of a CD
antigen listed in Table 43, or a fragment thereof, or a spliced or
alternatively spliced CD antigen, where the sCD antigen is located
extracellularly. In one embodiment, an sCD antigen is found soluble
in the serum/plasma and in other body fluids. A sCD molecule can be
generated as the result of a process of alternative splicing
(Woolfson and Milstein, PNAS, 91 (14) 6683-6687 (1994)) or cell
surface shedding, or it can be made recombinantly. Advantageously,
as herein defined, a shed form of sCD is generated by various
mechanisms including, but not limited to, any of those selected
from the group consisting of the following: alternative splicing,
proteolytic cleavage and dissociation. The methods describe herein
also include measurement of a sCD antigen and/or a soluble MHC
class I antigen in a body fluid of an individual who has had been
administered one or more sCD antigens and/or one or more soluble
HEM class I antigens as part of a therapeutic procedure. In one
embodiment, is the detected antigen is a fragment of a CD antigen
or a sCD antigen, where one of the protein determinants or epitopes
on the fragment maintains its ability to specifically bind an
antibody, which specifically binds the respective sCD antigen from
which the fragment is derived.
[0090] The ligand used to recognise the sCD antigen may be any
molecule whether natural or synthetic which specifically binds a
sCD antigen. The ligand may be engineered, for example the protein
gene product of an artificial construct consisting of an expressed
fragment derived from an antibody molecule with its antigen binding
region intact, or the ligand may be a non-protein molecule, or a
protein molecule which is not an antibody, for example a derivative
of an antibody, for example made by introducing antibody binding
regions, e.g. CDRs, into a non antibody scaffolding, as described
below. In one embodiment, the antibody used to recognise the
soluble CD molecule may be monoclonal or may be polyclonal.
[0091] The invention includes methods comprising comparisons of
differences in expression levels between different clinical body
fluid samples or in the case of gene expression analysis
differences in expression levels between different clinical
tissue-derived RNA samples, and thus determining relative levels.
Comparison of expression levels can be done visually or manually,
or can be automated and done by a machine, using, for example,
optical detection means. Subrahmanyam et al., 97 BLOOD 2457 (2001);
Prashar et al., 303 METHODS ENZYMOL. 258 (1999). Hardware and
software for analyzing differential expression of genes are
available, and can be used in practicing the present invention.
See, e.g., GenStat Software and GeneExpress.RTM. GX Explorer.TM.
Training Manual; Baxevanis et al., 7 CURR. OPIN. BIOTECHNOL. 102
(1996).
[0092] Gene symbols written in this application using all capital
letters refer to human genes to which such symbol has been assigned
as its Official Symbol by The Human Genome Organisation (HUGO) Gene
Nomenclature Committee.
[0093] As used herein, "a" or "an" means "at least one" or "one or
more."
[0094] "Diagnosis" generally includes a determination of a
subject's susceptibility to a disease or disorder, a determination
as to whether a subject is presently affected by a disease or
disorder, a prognosis of a subject affected by a disease or
disorder, and therametrics (e.g., monitoring a patient's condition
to provide information as to the effect or efficacy of
therapy).
[0095] "Expression" generally refers to transcriptional or
translational activity of a partial or entire gene,
post-transcriptional or translational activities, e.g., activation
or stabilization of a partial or entire gene, or the presence of
any detectable level of one or more partial or entire transcription
or translation products of a gene.
[0096] "Gene" refers to a polynucleotide sequence that comprises
coding sequences, and optionally control sequences necessary for
the production of a polypeptide or precursor. The polypeptide can
be encoded by a full length coding sequence or by any portion of
the coding sequence. A gene may constitute an uninterrupted coding
sequence or it may include one or more introns, bound by the
appropriate splice junctions. Moreover, a gene may contain one or
more modifications in either the coding or the untranslated regions
that could affect the biological activity or the chemical structure
of the expression product, the rate of expression, or the manner of
expression control. Such modifications include, but are not limited
to, mutations, insertions, deletions, and substitutions of one or
more nucleotides.
[0097] "Gene product" refers to a biomolecule, such as a protein or
mRNA, that is produced when a gene in an organism is transcribed or
translated or post-translationally modified.
[0098] "Hybridization" refers to any process by which a
polynucleotide sequence binds to a complementary sequence through
base pairing. Hybridization conditions can be defined by, for
example, the concentrations of salt or formamide in the
prehybridization and hybridization solutions, or by the
hybridization temperature, and are well known in the art.
Hybridization can occur under conditions of various stringency.
[0099] "Kit" refers to a combination of physical elements, e.g.,
probes, including without limitation specific primers, labeled
nucleotide acid probes, antibodies, protein-capture agent(s),
reagent(s), instruction sheet(s) and other elements useful to
practice the invention. These physical elements can be arranged in
any way suitable for carrying out the invention. For example,
probes can be provided in one or more containers or in an array or
microarray device.
[0100] "Predisposition" or "predilection" to a disease refers to an
individual's susceptibility to such disease. Individuals who are
susceptible are statistically more likely to have a particular
disease than normal/wild type individuals.
[0101] Prognosis" refers to the art or act of foretelling the
course of a disease or disorder. Additionally, the term refers to
the prospect of survival and recovery from a disease or disorder as
anticipated from the usual course or indicated by special features
of the individual's case. Further, the term refers to the art or
act of identifying a disease or disorder from its signs and
symptoms.
[0102] The phrase "binds specifically" or "specifically binds"
refers to the interaction of a ligand to its receptor or binding
moiety, e.g., a CD specific ligand to a sCD antigen, with a Kd
value greater than 1 Molar preferably 10.sup.7 M-1 or greater, more
preferably 10.sup.8 M-1 or greater, and most preferably 10.sup.9
M-1 or greater. Preferably, a CD specific ligand will specifically
bind a target sCD antigen or epitope with an affinity of less than
500 nM, preferably less than 200 nM, and more preferably less than
10 nM, such as less than 500 pM. The binding affinity, Kd rate
constant is defined as K.sub.off/K.sub.on, and can be measured in
many ways well known to one of skill in the art, including
measurement by Scatchard analysis and by surface plasmon resonance.
Standards techniques for surface plasmon resonance (SPR) assays
include Jan Terje Andersen et al. (2006) Eur. J. Immunol.
36:304-3051; Fagerstam (1991) Tech. Protein Chem. 2:65-71; and
Johnsson et al (1991) Anal. Biochem. 198:268-277. The phrase "binds
specifically" or "specifically binds" can also refer to the
interaction of a ligand to its receptor or binding moiety, e.g., a
CD specific ligand to a sCD antigen, in terms of binding with an
affinity that is at least two-fold, 50-fold, 100-fold, or greater
than its affinity for binding to a non-specific antigen (e.g.,
BSA).
[0103] The term "cytokine" is used broadly herein to refer to
soluble glycoproteins that are released by cells of the immune
system and act non-enzymatically through specific receptors to
regulate immune responses. As such, the term "cytokine" as used
herein includes chemokines, interleukins, lymphokines, monokines,
interferons, colony stimulating factors, platelet activating
factors, tumor necrosis factor-.alpha., and receptor associated
proteins, as well as functional fragments thereof.
[0104] Cytokines are well known in the art and include, for
example, endothelial monocyte activating polypeptide II (EMAP-II),
granulocyte-macrophage colony stimulating factor (GM-CSF),
granulocyte-CSF (G-CSF), macrophage-CSF (M-CSF), IL-1, IL-2, IL-3,
IL-4, IL-5, IL-6, IL-10, IL-12, IL-13, etc., the interferons,
including IFN.alpha., IFN.beta. and IFN.gamma., and
TNF-.quadrature., each of which is associated with a particular
biologic, morphologic, or phenotypic alteration in a cell or cell
mechanism.
[0105] The chemokines are further exemplified by the members of the
CXC chemokine (or .alpha.) subfamily, which possess an intervening
amino acid between the first two conserved cysteines; the members
of the CC (or .beta. subfamily, which do not contain such an
intervening amino acid residue; and the C (or .gamma.) chemokines,
which lack the first and third cysteine residues. In general, the
.alpha. chemokine members are active on neutrophils and T
lymphocytes (T cells), and the .beta. chemokines are active on
monocytes, macrophages and T cells. Several members of the .alpha.
and .beta. chemokine sub-families also are active on dendritic
cells, which are migratory cells that exhibit potent
antigen-presenting properties and are thought to participate in the
pathophysiology of many inflammatory diseases (Xu et al., J. Leuk.
Biol., 60:365-71, 1996; and Sozzani et al., J. Immunol.,
159:1993-2000, 1997). A fourth human CX3C-type chemokine,
fractalkine, also has been described (Bazan et al., Nature,
385:640-4, 1997; Imai et al., Cell, 91:521-30, 1997; Mackay, Curr.
Biol. 7:R384-6, 1997). Unlike other chemokines, fractalkine exists
in membrane and soluble forms. The soluble form is a potent
chemoattractant for monocytes and T cells. The cell surface
receptor for this chemokine is termed CX3CR1.
[0106] The .quadrature. chemokines (also known as IL-8) are
exemplified by granulocyte chemotactic protein-2 (GCP-2);
growth-related oncogene-.alpha. (GRO-.alpha.) GRO-.beta., and
GRO-.gamma.; epithelial cell-derived neutrophil activating
peptide-78 (ENA-78); platelet basic protein (PBP); connective
tissue activating peptide III (CTAP III); neutrophil activating
peptide-2 (NAP-2); low affinity platelet factor-4 (LAPF-4);
monokine induced by IFN.gamma. (MIG); platelet factor 4 (PF4);
interferon inducible protein 10 (IP-10); the stromal cell derived
factors SDF-1.alpha., SDF-1.beta., and SDF-2. The .beta. chemokines
are exemplified by the monocyte chemotactic proteins MCP-1, MCP-2,
MCP-3, MCP-4, and MCP-5; the macrophage inhibitory proteins
MIP-1.alpha., MIP-1.beta., MIP-1.gamma., MIP-2, MIP-2.alpha.,
MIP-2.beta., MIP-3.alpha., MIP-3.beta., MIP-4, and MIP-5;
macrophage-derived chemokine (MDC); human chemokine 1 (HCC-1);
LD78.beta.; RANTES; eotaxin 1; eotaxin 2; TARC; SCYA17 and 1-309;
dendritic cell chemokine-1 (DC-CK-1). The .gamma. chemokines are
exemplified by lymphotactin.
[0107] As used herein, "biological sample" or "sample" encompasses
a variety of sample types obtained from an organism, human or
otherwise, that can be used in a diagnostic or monitoring assay.
The definition encompasses blood and other liquid samples of
biological origin, solid tissue samples, such as a biopsy specimen,
or derived tissue cultures or cells, and the progeny thereof. The
definition also includes samples that have been manipulated in any
way after their procurement, such as by treatment with reagents,
solubilization, or enrichment for certain components, such as
proteins or polynucleotides. The term "biological sample"
encompasses a clinical sample, and also includes cells in culture,
cell supernatants, cell lysates, serum, plasma, biological fluid,
and tissue samples. Generally, the sample will be, or be derived
from, peripheral (or circulating) blood. In some cases, the blood
will have been enriched for a macrophage fraction, by using, for
example, glass or plastic adherence. Alternatively, mononuclear
cells may also be purified using Percoll gradients.
[0108] As used herein, the term "antibody," includes, but is not
limited to a polypeptide substantially encoded by an immunoglobulin
gene or immunoglobulin genes, an IgG antibody, an IgM antibody, or
a portion thereof, or fragments thereof, which specifically bind
and recognize an analyte, antigen or antibody. "Antibody" also
includes, but is not limited to, a polypeptide substantially
encoded by an immunoglobulin gene or immunoglobulin genes, or
fragments thereof, which specifically bind and recognize the
antigen-specific binding region (idiotype) of antibodies produced
by a host in response to exposure to the analyte.
[0109] As used herein, the term "antibody," encompasses polyclonal
and monoclonal antibody preparations, as well as preparations
including monoclonal antibodies, polyclonal antibodies, hybrid
antibodies, phage displays, altered antibodies, F(ab')2 fragments,
F(ab) fragments, Fv fragments, single domain antibodies, chimeric
antibodies, humanized antibodies, dual specific antibodies,
bifunctional antibodies, single chain antibodies, and the like, and
functional fragments and multimers thereof, which retain
specificity for an analyte or antigen. For example, an antibody can
include variable regions, or fragments of variable regions, and
multimers thereof, which retain specificity for an analyte or
antigen. See, for example, Paul, Fundamental Immunology, 3rd Ed.,
1993, Raven Press, New York, for antibody structure and
terminology. The antibody or portion thereof, may be derived from
any mammalian species, e.g., from a mouse, goat, sheep, rat, human,
rabbit, or cow antibody. An antibody may be produced synthetically
by methods known in the art, including modification of whole
antibodies or synthesis using recombinant DNA methodologies.
Antibodies may be labelled with detectable moieties by one of skill
in the art. In some embodiments, the antibody that binds to an
entity one wishes to measure (the primary antibody) is not
labelled, but is instead detected by binding of a labelled
secondary antibody that specifically binds to the primary antibody.
In one preferred antibody embodiment, the antibody YTH, or which is
an MHC class I antibody, and binds soluble MHC class I antigen, and
can be used to detect soluble MHC Class I antigens in the methods
and products comprising ligands described herein.
[0110] Techniques for the preparation of antibodies, are, for
example, described in the following reviews and the references
cited therein: Winter & Milstein, (1991) Nature 349:293-299;
Plueckthun (1992) Immunological Reviews 130:151-188; Wright et al.,
(1992) Crit. Rev. Immunol. 12:125-168; Holliger, P. & Winter,
G. (1993) Curr. Op. Biotechn. 4, 446-449; Carter, et al. (1995) J.
Hematother. 4, 463-470; Chester, K. A. & Hawkins, R. E. (1995)
Trends Biotechn. 13, 294-300; Hoogenboom, H. R. (1997) Nature
Biotechnol. 15, 125-126; Fearon, D. (1997) Nature Biotechnol. 15,
618-619; Pluckthun, A. & Pack, P. (1997) Immunotechnology 3,
83-105; Carter, P. & Merchant, A. M. (1997) Curr. Opin.
Biotechnol. 8, 449-454; Holliger, P. & Winter, G. (1997) Cancer
Immunol. Immunother. 45,128-130.
[0111] "Carriers" as used herein include pharmaceutically
acceptable carriers, excipients, or stabilizers which are non-toxic
to the cell or mammal being exposed thereto at the dosages and
concentrations employed. Often the physiologically acceptable
carrier is an aqueous pH buffered solution. Examples of
physiologically acceptable carriers include buffers such as
phosphate, citrate, and other organic acids; antioxidants including
ascorbic acid; low molecular weight (less than about 10 residues)
polypeptide; proteins, such as serum, albumin, gelatin, or
immunoglobulins; hydrophilic polymers such as polyvinylpyrrolidone;
amino acids such as glycine, glutamine, asparagine, arginine or
lysine; monosaccharides, disaccharides, and other carbohydrates
including glucose, mannose, or dextrins; chelating agents such as
EDTA; sugar alcohols such as mannitol or sorbitol; salt-forming
counterions such as sodium; and/or nonionic surfactants such as
TWEEN.TM., polyethylene glycol (PEG), and PLURONICS.TM..
[0112] An "isolated" ligand is one which has been identified and
separated and/or recovered from a component of its natural
environment. Contaminant components of its natural environment are
materials which would interfere with diagnostic or therapeutic uses
for the antibody, and may include enzymes, hormones, and other
proteinaceous or nonproteinaceous solutes. In preferred
embodiments, the ligand will be purified (1) to greater than 95% by
weight of antibody as determined by the Lowry method, and most
preferably more than 99% by weight, (2) to a degree sufficient to
obtain at least 15 residues of N-terminal or internal amino acid
sequence by use of a spinning cup sequenator, or (3) to homogeneity
by SDS-PAGE under reducing or nonreducing conditions using
Coomassie blue or, preferably, silver stain. Isolated antibody
includes the antibody in situ within recombinant cells since at
least one component of the antibody's natural environment will not
be present. Ordinarily, however, isolated ligand will be prepared
by at least one purification step.
[0113] The word "label" when used herein refers to a detectable
compound or composition which is conjugated directly or indirectly
to the antibody so as to generate a "labeled" antibody. The label
may be detectable by itself (e.g. radioisotope labels or
fluorescent labels) or, in the case of an enzymatic label, may
catalyze chemical alteration of a substrate compound or composition
which is detectable.
[0114] By "solid support" is meant a non-aqueous matrix to which
the ligand, e.g., antibody, of the present invention can adhere.
Examples of solid phases encompassed herein include those formed
partially or entirely of glass (e.g., controlled pore glass),
polysaccharides (e.g., agarose), polyacrylamides, polystyrene,
polyvinyl alcohol and silicones. In certain embodiments, depending
on the context, the solid phase can comprise the well of an assay
plate; in others it is a purification column (e.g., an affinity
chromatography column). This term also includes a discontinuous
solid phase of discrete particles, such as those described in U.S.
Pat. No. 4,275,149.
[0115] "Ligand" as used herein is any molecule that is capable of
specifically binding to or reacting with a molecule, the molecule
including, but preferably not limited to a soluble CD antigen, a
soluble MHC Class I antigen and a chemokine. A ligand can be a
peptide molecule or a non-peptide organic molecule, as described in
U.S. Pat. Nos. 6,130,231; 6,153,628; 6,322,770; and PCT publication
WO 01/97848, incorporated herein by reference. "Non-peptide"
molecules, in general, are molecules other peptide, i.e., simply
polymers of amino acids, either gene encoded or non-gene encoded.
Thus, "non-peptide ligands" are moieties, which are commonly
referred to as "small molecules"; in some embodiments non-peptide
ligands are lacking in polymeric character and characterized by the
requirement for a core structure other than a polymer of amino
acids. The non-peptide ligands may be coupled to peptides or may
include peptides coupled to portions of the ligand which are
responsible for affinity to its respective binding molecule, e.g.,
a sCD antigen or soluble MHC Class I antigen, but it is the
non-peptide regions of this ligand which account for its binding
ability. A ligand can also be a polypeptide that specifically binds
an epitope on an antigen, and the ligand can be, for example, an
antibody.
[0116] The phrase "selectively binds" is used interchangeably with
the phrase "specifically binds"; the two phrases having identical
definitions. A protein epitope is a recognition site that comprises
a minimum of three amino acids, and can include many more amino
acids. An epitope can also recognize non-polypeptide moieties, or
moieties that are a mixture of polypeptides and non-polypeptide
determinants.
[0117] In one embodiment, a ligand comprises a non-immunoglobulin
scaffold, e.g., CTLA4, fibronectin, lipocalin, e.g., lipocalins
Rbp, Bbp or ApoD, a natural bacterial receptor such as
staphyloccocus A protein (SpA) or GroEL, transferrin, e.g.,
Biorexus's Trans-body.TM., tetranectin e.g., human C-lectin, an
Avimer.TM. and an Affibody.TM. scaffold, and further comprises one
or more sites that specifically binds an epitope on an antigen,
e.g. sCD antigen or soluble MHC Class I antigen, where the one or
more sites that specifically bind an antigen are preferably on the
surface of the non-immunoglobulin scaffold. Thus a ligand for a sCD
antigen or a soluble MHC Class I antigen can comprise a
non-immunoglobulin scaffold and one or more epitope interaction
sites which are preferably on the surface of the non-immunoglobulin
scaffold, where the epitope interaction site specifically binds a
sCD antigen or a soluble MHC Class I antigen, respectively. The
non-immunoglobulin scaffold can be a human, non-human, synthetic,
or semi-synthetic scaffold that is a scaffold other than an
antibody scaffold. Yet further, alternative protein scaffolds that
are loosely based around the basic fold of antibody molecules have
been suggested and may be used in the preparation of inventive
interaction partners (e.g., see Ku and Schultz Proc. Natl. Acad.
Sci. USA. 92:6552, 1995). Antibody mimics comprising a scaffold of
a small molecule such as 3-aminomethylbenzoic acid and a
substituent consisting of a single peptide loop have also been
constructed. The peptide loop performs the binding function in
these mimics (e.g., see Smythe et al., J. Am. Chem. Soc. 116:2725,
1994). A synthetic antibody mimic comprising multiple peptide loops
built around a calixarene unit has also been described (e.g., see
U.S. Pat. No. 5,770,380 to Hamilton et al.).
[0118] In a preferred embodiment, the epitope interaction site
specifically binds a sCD antigen. In another preferred embodiment,
the epitope interaction site specifically binds a soluble MHC Class
I antigen. In a preferred embodiment, the epitope interaction site
comprises one or more CDR regions, e.g., one or two or three of
CDR1, CDR2 and CDR3 from an immunoglobulin variable domain. In a
further preferred embodiment, the epitope interaction site is
composed of one or more CDRs grafted on to a non immunoglobulin
scaffold, including, but preferably not limited to, CTLA4,
fibronectin, lipocallin, e.g., lipocalins plasma retinol binding
protein (Rbp), bilin binding protein (Bbp) or Apolipoprotein
(ApoD), a natural bacterial receptor such as staphyloccocus A
protein (SpA) or GroEL, an Avimer.TM. and an Affibody.TM. scaffold.
In a further preferred embodiment, the epitope interaction site
comprises one or two or three of CDR1, CDR2 and CDR3 from an
immunoglobulin variable domain, preferably from a single variable
domain. These CDR regions can be provided on a heavy or a light
immunoglobulin chain framework region, as well as a
non-immunoglobulin scaffold. Alternatively, one or more antibody V
regions are provided on a non-immunoglobulin scaffold.
Immunoglobulin frameworks include but are not limited to one or
more VII frameworks, such as VH3 and VMH frameworks described
supra, as well as VL frameworks, including Vkappa and Vlambda
frameworks. In some embodiments, the variable domain comprises at
least one human framework region having an amino acid sequence
encoded by a human germ line antibody gene segment, or an amino
acid sequence comprising up to five amino acid differences relative
to the amino acid sequence encoded by a human germ line antibody
gene segment. In other embodiments, the variable domain comprises
four human framework regions, FW1, FW2, FW2 and FW4, having amino
acid sequences encoded by a human germ line antibody gene segment,
or the amino acid sequences of FW1, FW2, FW3 and FW4 collectively
containing up to ten amino acid differences relative to the amino
acid sequences encoded by the human germ line antibody gene
segment. Suitable scaffolds and techniques for such CDR grafting or
Variable region grafting will be clear to the skilled person and
are well known in the art, see for example U.S. application Ser.
No. 07/180,370, WO 01/27160, EP 0 605 522, EP 0 460 167, U.S.
application Ser. No. 07/054,297, Nicaise et al., Protein Science
(2004), 13:1882-1891; Ewert et al., Methods, 2004 October;
34(2):184-199; Kettleborough et al., Protein Eng. 1991 October;
4(7): 773-783; O'Brien and Jones, Methods Mol. Biol. 2003: 207:
81-100; and Skerra, J. Mol. Recognit. 2000: 13: 167-187, and
Saerens et al., J. Mol. Biol. 2005 Sep. 23; 352(3):597-607, and the
further references cited therein.
[0119] One or more of the ligands specific for a sCD antigen can
further contain one or more entities including, but preferably is
preferably not limited to, a label, a tag and a drug. Such ligand
can be present in a kit, a composition, including a pharmaceutical
composition, containing one or more of the ligands, preferably a
plurality of the ligands and a carrier thereof.
[0120] As used herein the term a `sCD sub-category` describes a
sub-group of sCDs, which show a defined fingerprint/profile
(sub-fingerprint) of sCD levels within a larger fingerprint of one
or more disease states wherein each sub-group of sCDs exhibits
common characteristics distinguishing it from any other sub-group
within those one or more disease states.
[0121] In a further aspect still, the present invention provides a
sCD reference database comprising pathological and/or healthy sCD
fingerprint patterns and/or sCD fingerprints from individuals
without the disease or condition in question.
[0122] In a further aspect still, the present invention provides a
sCD/soluble MHC class I/cytokine/chemokine reference database
comprising pathological and/or healthy sCD soluble MHC class
I/cytokine/chemokine fingerprint patterns and/or sCD soluble MHC
class I/cytokine/chemokine fingerprints from individuals without
the disease or condition in question.
[0123] In a further aspect still, the present invention provides a
sCD reference database comprising pathological and/or healthy sCD
fingerprint patterns and/or sCD fingerprints from individuals
without the disease or condition in question or a sCD/soluble MHC
class I/cytokine/chemokine patterns and/or sCD soluble MHC class
I/cytokine/chemokine fingerprints from individuals without the
disease or condition in question in combination with corresponding
gene expression signatures. The term "RT-PCR" has been variously
used in the art to mean reverse-transcription PCR (which refers to
the use of PCR to amplify mRNA by first converting mRNA to double
stranded cDNA) or real-time PCR (which refers to ongoing monitoring
in `real-time` of the amount of PCR product in a reaction in order
to quantify the amount of PCR target sequence initially present. As
used herein, the term "RT-PCR" means reverse transcription PCR. The
term "quantitative RT-PCR" (qRT-PCR) means real-time PCR applied to
determine the amount of MRNA initially present in a sample.
[0124] The term "response" means any measure of patient response to
treatment with a drug including those measures ordinarily used in
the art, such as complete pathologic response, partial response,
stable disease, time to disease progression, etc.
[0125] The term "microarray" refers to an ordered arrangement of
hybridizable array elements, preferably polynucleotide probes, on a
substrate. Microarrays include, without limitation, an ordered
arrangement of polynucleotide probes on a microchip and a
collection of polynucleotide coated beads on an arrangement of
microfibers.
[0126] The term "polynucleotide," when used in singular or plural,
generally refers to any polyribonucleotide or
polydeoxribonucleotide, which may be unmodified RNA or DNA or
modified RNA or DNA. Thus, for instance, polynucleotides as defined
herein include, without limitation, single- and double-stranded
DNA, DNA including single- and double-stranded regions, single- and
double-stranded RNA, and RNA including single- and double-stranded
regions, hybrid molecules comprising DNA and RNA that may be
single-stranded or, more typically, double-stranded or include
single- and double-stranded regions. In addition, the term
"polynucleotide" as used herein refers to triple-stranded regions
comprising RNA or DNA or both RNA and DNA. The strands in such
regions may be from the same molecule or from different molecules.
The regions may include all of one or more of the molecules, but
more typically involve only a region of some of the molecules. One
of the molecules of a triple-helical region often is an
oligonucleotide. The term "polynucleotide" specifically includes
cDNAs. The term includes DNAs (including cDNAs) and RNAs that
contain one or more unusual bases, such as inosine or one or more
modified bases such as tritiated bases. Moreover the term includes
DNAs (including cDNAs) and RNAs that contain one or more modified
sugars, such as in locked nucleic acids. DNAs or RNAs with modified
backbones, such as for example, phosphorothioates and peptide
nucleic acids, and DNAs or RNAs with modified 5' or 3' phosphate
moieties such as for example when conjugated with minor groove
binders, are "polynucleotides" as that term is intended herein. In
general, the term "polynucleotide" embraces all chemically,
enzymatically and/or metabolically modified forms of unmodified
polynucleotides, as well as the chemical forms of DNA and RNA
characteristic of viruses and cells, including simple and complex
cells.
[0127] The term "oligonucleotide" refers to a relatively short
polynucleotide, including, without limitation, single-stranded
deoxyribonucleotides, single- or double-stranded ribonucleotides,
RNA:DNA hybrids and double-stranded DNAs. Oligonucleotides, such as
single-stranded DNA probe oligonucleotides, are often synthesized
by chemical methods, for example using automated oligonucleotide
synthesizers that are commercially available. Modified bases can be
readily incorporated into chemically synthesized oligonucleotides
made using automated synthesizers.
[0128] Oligonucleotides can also be made by a variety of other
methods, including in vitro recombinant DNA-mediated techniques and
by expression of DNAs in cells and organisms.
[0129] The term "gene expression" describes the conversion of DNA
gene sequence information into transcribed RNA (either the initial
unspliced RNA transcript or the mature MRNA) or the encoded protein
product. Gene expression can be monitored by measuring the levels
of either RNA or protein products of the gene or subsequences.
[0130] The phrase "gene amplification" refers to a process by which
multiple copies of a gene or gene fragment are formed in a
particular cell or cell line. The duplicated region (a stretch of
amplified DNA) is often referred to as "amplicon." Often, the
amount of the messenger RNA (mRNA) produced, i.e., the level of
gene expression, also increases in proportion to the number of
copies made of the particular gene expressed.
[0131] "Antibody-capture agent" refers to a molecule or a
multi-molecular complex that can bind an antibody to itself. The
protein-capture agent may comprise a biomolecule such as a protein
or a polynucleotide. Examples of antibody-capture agents include
immunoglobulins, antigens, receptors, or other proteins, or
portions or fragments thereof.
[0132] The terms "signature," "gene expression signature,"
"molecular signature," and "genetic fingerprint," all used
interchangeably herein, refer to a group of genes or gene products
which represent a particular physiological state including
diseased, and non diseased. They can be characterized by an
increased or decreased expression in individuals with disease
relative to those without disease; and may show a high degree of
correlation of signals with each other; and may display a similar
time course of expression.
[0133] "Transcript" refers to an RNA product transcribed from DNA.
The category of "transcripts" includes, but is not limited to,
pre-mRNA nascent transcripts, transcript processing intermediates,
mature mRNAs and degradation products thereof.
[0134] Throughout this specification, the word "comprise," or
variations thereof, will be understood to imply the inclusion of a
stated element, integer or step, or group of elements, integers or
steps, but not the exclusion of any other element, integer or step,
or group of elements, integers or steps.
[0135] The pattern of expression exhibited by the sCD antigens
and/or soluble MHC antigens and/or cytokines and/or chemokines from
a body fluid may be captured by any method known to the art.
Arrays
[0136] In some embodiments, solid surfaces are chemically patterned
for attachment of biological macromolecules (e.g., nucleic acids or
proteins). In some embodiments, the present invention further
provides solid supports comprising arrays of biological
macromolecules. In preferred embodiments, arrays comprise at least
50, preferably at least 100, even more preferably at least 1000,
still more preferably, at least 10,000, and yet more preferably, at
least 100,000 distinct biological macromolecules. In preferred
embodiments, each distinct biological macromolecule is addressed to
a specific location on the array. This allows simultaneous
screening of all the arrayed molecules, and allows for the
immediate identification of any molecule that interacts with a cell
product. In preferred embodiments, each addressable location is
larger than 25, and preferably, larger than 50 microns.
[0137] The present invention is not limited to a particular method
of fabrication or a specific type of array. Any number of suitable
chemistries known to one skilled in the art may be utilized. In
preferred embodiments, the target molecules are attached to the
substrate by a cleavable disulfide bond. In some embodiments,
target molecules are attached to surfaces configured for label-free
(e.g., SPR) detection. Target molecules are contemplated to
comprise proteins, enzymes, or other ligands of soluble CD antigens
and/or soluble MHC Class I antigens. In some preferred embodiments,
arrays of molecules are attached to the solid surfaces. In some
embodiments, multiple copies of the same molecule targets are
attached to different places on the array. In other embodiments,
different target molecules are attached to each place on the
array.
[0138] An exemplary method is through the use of microarrays, for
example, using protein microarrays, peptide microarrays, or
combinations thereof. Microarrays refer to surface microarrays,
membrane microarrays, bead microarrays, solution microarrays, and
the like comprised of discrete proteins, antibodies, protein
fragments, antibody fragments, antibody-mimetics, peptides,
peptide-mimetics, organic molecules and/or other molecules capable
of selectively and specifically binding the sCD antigens and/or
soluble MHC antigens, thus permitting their detection and
measurement for the purpose of capturing a pattern of
expression.
[0139] The detection of sCD antigens and/or soluble MHC antigens
and/or chemokines, cytokines, and other antigens from a body fluid
may include multiple mass spectrophotometric analyses performed in
parallel or any other method of detecting hundreds to thousands of
proteins or peptide fragments derived there from at one time from a
single body fluid sample from a single individual. The antigens and
ligands specific to these antigens are detected using mass
spectrophotometric, fluorescent, radioactive or other techniques
and the expression levels of each soluble CD antigen or soluble MHC
antigen is assessed.
[0140] In yet another embodiment of the invention, the
determination of a pattern of expression further comprises ranking
the captured pattern of expression of sCD antigens and/or soluble
MHC antigens from a body fluid. The expression levels of the
antigens, captured on the antibody or other type of microarray, are
ranked from the lowest expressed protein being assigned a rank of 1
to the most highly expressed protein. For example, if 100,000
proteins were assessed from a single blood sample, the lowest
expressed protein would be assigned a value of 1 and the most
highly expressed protein a value of 100,000 with every other
protein having a value in between. The ranks of the proteins with
individuals with a specific disease or disorder or undergoing a
specific treatment are compared to other individuals with other
conditions, the same condition, or to normal healthy controls.
[0141] Any expression method known in the art may be used to define
the pattern of expression captured. A preferred method is the
Significance Analysis of Microarrays (SAM) and class prediction, as
taught by Tusher, Proceedings National Academy of Sciences, 98:
5116 (2001); Golub et al., Science, 286: 531-537 (1999). Other
expression methods are available, including neural network
modelling, clustering, computer programs, and entropy methods, and
could be used as alternatives. The significance analysis of
microarray (SAM) and class prediction may be used to define the
pattern of expression captured. The significance analysis of
microarrays uses permutations of repeated measurements to estimate
the percentage of sCD and soluble MHC Class I antigens or cytokines
or chemokines identified by chance. Once the molecules are
identified that are regulated in a specific disease or condition,
this set of molecules is said to define the pattern expression for
that disease or condition. To determine whether a test sample is
consistent with the normal pattern of expression or is consistent
with the pattern for a specific disease or disorder, the following
general procedure is followed. The expression value for each
soluble CD antigen and/or soluble MHC Class I antigen or cytokines
or chemokines in the test sample is compared to the expression
value in the normal sample. A class prediction method is then used
to determine whether the test sample fits the normal or diseased
pattern.
[0142] To do this, the expression value for soluble antigen is
determined to be closer to the control or the diseased state, and a
weighted vote is made for each molecule for the injury pattern. The
diagnosis or detection of the disease is made if PS>0.3 when PS
is the prediction strength, defined as PS=(Vw-VL)/(Vw+VL). If there
is no difference between the samples, then PS will equal zero and
the sample would fall in the class of the control or healthy body
fluid sample. If PS>0.3, then the sample would be classified as
the diseased state. In one embodiment of the invention, the most
regulated proteins for a given condition that had the lowest
variance may be identified using SAM analysis for various medical,
neurological, genetic and other conditions. These regulated genes
or proteins may be used to define a pattern for each condition, a
class prediction, or classification that would be used to analyze
unknown samples to determine whether they would fit the pattern for
a specific disease or condition or not with a 90, 95 or 99%
confidence level.
[0143] Once the pattern of expression is captured and defined, the
pattern of expression exhibited by the test body fluid is compared
to a database such as described above, to assess the detection
and/or diagnosis of a specific disease. This database may comprise
a pattern of expression or multiple patterns of expression based on
a specific body fluid, a specific disorder or disease, or
combinations thereof. Further, the database may be a commercially
available database or a database created from the pattern of
expression of the soluble antigens captured and defined by the
obtained body fluids for a host of different patients or healthy
individuals. As herein described the term `a reference database`
refers to a collection of sCD fingerprints from healthy
`non-diseased` and/or diseased individuals. Advantageously, the
database is computer generated and/or stored. Advantageously the
data from more than 5 individuals is present in the database.
[0144] More advantageously the data from more than 25, 10, 100, or
1000 individuals comprises the database. Advantageously the
database, in addition to sCD data and data from gene fingerprinting
analyses, will also comprise clinical information relating to
various patients and/or disease conditions.
[0145] As used herein the term "assessing (or assessed)" is
intended to include quantitative and qualitative determination of
the identity and/or quantity of a moiety, e.g., a protein or
nucleic acid, present in the sample or on the microdevices or in
whatever form or state. Assessment would involve obtaining an
index, ratio, and percentage, visual or other value indicative of
the identity of a moiety in the sample and may further involve
obtaining a number, an index, or other value indicative of the
amount or quantity or the concentration of a moiety present in the
sample or on the microdevice or in whatever form or state.
Assessment may be direct or indirect.
Immunoassay
[0146] As described above, in one embodiment, a sCD specific ligand
and/or a soluble MHC specific ligand or a cytokine specific ligand
or a chemokine specific ligand is an antibody. A variety of
immunoassay formats built around chemiluminescent, ELISA,
fluorescence or radio-immunoassay technologies, can be used in the
methods described herein comprising detecting and/or quantitating
the level of soluble CD antigens and/or soluble MHC Class I
antigens and/or chemokines and or cytokines in the body fluid from
an individual. For example solid-phase ELISA immunoassays are
routinely used to bind ligands, especially monoclonal antibodies,
specifically immunoreactive with an analyte, and can be readily
adapted to binding soluble CD antigens, and/or cytokines, and/or
chemokines and/or soluble MHC Class I antigens. See Harlow and
Lane, ANTIBODIES: A LABORATORY MANUAL, Cold Springs Harbor
Publications, New York, (1988) for a description of immunoassay
formats and conditions that can be used to determine specific
immunoreactivity. Typically a specific or selective reaction will
be at least twice background signal to noise, and more typically
more than 10 to 100 times greater than background.
Luminex
[0147] As described in 20070178607, the use of microparticles
allows performance of the assays to detect sCD antigens, and/or
cytokines, and/or chemokines and/or soluble MHC Class I antigens in
a small, well-mixed volume with favorable binding kinetics. An
example of fluorescence-based particle identification is Luminex
Corporation's FlowMetrix.TM. system and Laboratory Multi-Analyte
Profiling (LabMAP.TM.) technology. This system allows up to about
100 to 1000 analytes to be measured sequentially by flow cytometry.
This technology incorporates microspheres that are internally
labeled with two or more distinct fluorescent dyes. The
microspheres are further coded with varying combinations of
intensities of the fluorophores. The process also includes a third
different fluorophore integrated to a reporter molecule for
quantification of reactions on the surface of the encoded
microspheres. The fabrication of the encoded microspheres and the
system is described in, for example, Chandler, V. S., et al.,
"Multiplexed analysis of clinical specimens apparatus and methods,
U.S. Pat. No. 5,981,180 (1999). Due to the relatively wide emission
spectra of many fluorophores, a moderate number of patterns can be
uniquely distinguished with this class of labels, typically less
than 1000.
Chips
[0148] In some embodiments, the solid support is a "chip." As used
herein, "chip" refers to a solid substrate with a plurality of
one-, two- or three-dimensional micro-structures or micro-scale
structures on which certain processes, such as physical, chemical,
biological, biophysical or biochemical processes, etc., can be
carried out. The micro-structures or micro-scale structures such
as: channels and wells, electrode elements, electromagnetic
elements, are incorporated into, fabricated on or otherwise
attached to the substrate for facilitating physical, biophysical,
biological, biochemical, chemical reactions or processes on the
chip. The chip may be thin in one dimension and may have various
shapes in other dimensions, for example, a rectangle, a circle, an
ellipse, or other irregular shapes. The size of the major surface
of chips used in the present invention can vary considerably, e.g.,
from about 1 mm.sup.2 to about 0.25 m.sup.2. Preferably, the size
of the chips is from about 4 mm.sup.2 to about 25 cm.sup.2 with a
characteristic dimension from about 1 mm to about 7.5 cm. The chip
surfaces may be flat, or not flat. The chips with non-flat surfaces
may include channels or wells fabricated on the surfaces. Chips may
be made of any suitable material including, but not limited to,
metal, plastic, polymer, and glass. Several commercial sources for
chips, with and without already arrayed biological molecules,
exist.
[0149] Commercial sources include, but are not limited to,
Motorola, Schaumburg, Ill.; ACLARA BioSciences, Inc., Hayward,
Calif.; Agilent Technologies Inc., Palo Alto, Calif.; Aviva
Biosciences Corp., Dan Diego, Calif.; Caliper Technologies Corp.,
Palo Alto, Calif.; Clontech, Palo Alto, Calif.; Corning, Acton,
Mass.; Gene Logic Inc., Columbia, Md.; Hyseq Inc., Sunnyvale,
Calif.; Incyte Genomics, Palo Alto, Calif.; Micronics Inc.,
Redmond, Wash.; Mosaic Technologies, Waltham, Mass.; OriGene
Technologies, Rockville, Md.; Packard Instrument Corp., Meriden,
Conn.; Rosetta Inpharmatics, Kirkland, Wash.; Sequenom, San Diego,
Calif., and GenTel Biosciences.
SPR Surfaces
[0150] In other embodiments, the solid support is an SPR surface,
as described in US Patent publication 20040147045. Surface Plasmon
Resonance (SPR) techniques involve a surface coated with a thin
film of a conductive metal, such as gold, silver, chrome or
aluminum, in which electromagnetic waves, called Surface Plasmons,
can be induced by a beam of light incident on the metal glass
interface at a specific angle called the Surface Plasmon Resonance
angle. Modulation of the refractive index of the interfacial region
between the solution and the metal surface following binding of the
captured macromolecules causes a change in the SPR angle which can
either be measured directly or which causes the amount of light
reflected from the underside of the metal surface to change. Such
changes can be directly related to the mass and other optical
properties of the molecules binding to the SPR device surface.
Several biosensor systems based on such principles have been
disclosed (see e.g., WO 90/05305). In some embodiments, the metal
(e.g., gold) layer is chemically patterned for attachment of
molecular probes (e.g., biomolecules). In other embodiments,
antibodies are utilized for enhancing the SPR signal generated by
cellular item target molecule complexes. The cellular item directly
binds to the arrayed target molecule. In some embodiments, the SPR
signal is then enhanced by the binding of an antibody to the target
molecule. In some embodiments, the antibody is labelled (e.g., with
fluorescent labels such as fluorescein), enzymatic detection labels
(such as horse radish peroxidase), and metal labels (such as gold).
This method has the further advantage of immunologically confirming
the identity of the protein binding to the target molecule.
[0151] In some embodiments, kits are provided for performing the
process described herein. The kits of the present invention may
comprise individual ligands specific for individual soluble CD
and/or soluble MHC Class I antigens and/or cytokines and/or
chemokines described herein, plus buffers, and so on.
[0152] The methods described herein are not restricted to the
analysis of whole blood, serum and plasma; indeed sCD molecules,
soluble MHC Class I antigens, cytokines and chemokines are known to
be present in many other body fluids, as described above.
Furthermore, the methods described herein are not restricted to use
in humans, and indeed such a method may prove to be of immense use
in veterinary applications, having immense use in non humans,
including, but not limited to felines, canines, equine, avian,
murine, rats, rodents, hamsters, rabbits, tigers, elephants, bears,
nonhuman primates.
[0153] By "confusion table" it is meant a table that associates
common errors in the noisy process with probabilities that those
errors occurred.
[0154] One embodiment described herein is a sCD fingerprint
comprising the levels of plurality of sCDs where the sCD
fingerprint represents one or more disease states. Also described
herein is a method of generating a sCD fingerprint of one or more
disease state/s comprising the step of measuring the levels in
parallel of a plurality of shed or secreted sCDs from one or more
individuals and collating the data. Patterns may then be discerned
from this collated data using mathematical algorithms such as
neural networks. The sCD fingerprint can be associated with a
disease state including but not limited to an: infectious,
neoplastic, cardiovascular, immunological, autoimmune, metabolic,
degenerative, diet-related, psychological, psychiatric, iatrogenic,
inflammatory, drug or toxin related, traumatic and endocrine
disease. As such, the disease state can be any one or more selected
from the group consisting of the following: infection, multiple
myeloma (Bence Jones proteinuria), chronic myeloid leukemia, acute
myeloid leukemia (AML), other acute leukemias and myelodysplastic
syndromes, colorectal cancer, chronic renal failure, crohn's
disease, diabetic nephropathy, cardiovascular pathology, infection,
Liver damage, Lymphoma, Macrocytic anaemia, Prostate cancer,
oligoclonal banding and pulmonary embolism, deep vein thrombosis
and appendicitis. An exemplary sCD fingerprint can include any one,
two, three, four, or more of the following sCD antigens: sCD14,
sCD25, sCD30, sCD31, sCD44, sCD50, sCD54, sCD62E, sCD62L, sCD86,
sCD95, sCD106, sCD116, sCD117, sCD124, sCD130, sCD138, sCD141,
sCD40L, sCD8, sCD23, sCD30, sCD40 and their homologues present in
other mammalian or non-mammalian species and can in addition
include other soluble CD antigens and other soluble antigens
including soluble MHC Class I antigens, cytokines or chemokines.
The sCD levels can be measured using any one or more of the methods
selected from the group consisting of the following: multiplexed
particle flow cytometry, chip-based monoclonal antibody technology,
chips comprising engineered antibodies, and/or non-protein agents
which bind to one or more sCDs. This list of technologies is not
though exhaustive and the levels of sCD molecules, cytokines,
chemokines and soluble MHC class I molecules can, in principle, be
measured by any technology capable of documenting the levels of
these molecules in body fluids to a sufficiently quantitative
extent. The patterns fo these molecules as such are in this sense
technology independent, with the technology simply being the
process by which the patterns may in principle be defined.
Representative antibodies with specificity to soluble isoforms of
CD antigens are listed in Table 46, and representative molecules
capable of detecting the representative antibodies are listed in
Table 47.
[0155] Described herein are methods for predicting the presence of
one or more disease states in an individual comprising the step of
comparing a sCD fingerprint/s, comprising the levels of a plurality
of sCDs generated from that individual with one or more reference
sCD fingerprint/s. Disclosed herein is a method for detecting the
presence of one or more disease states in an individual comprising
the step of comparing a sCD fingerprint/s comprising the levels of
a plurality of sCDs generated from that individual with one or more
reference sCD fingerprint/s. Disclosed herein is a method for
detecting the extent of one or more disease states in an individual
comprising the step of comparing sCD fingerprint/s, comprising the
levels of a plurality of sCDs, generated from that individual with
one or more reference sCD fingerprint/s. Disclosed herein is a
method for assessing the progression of a disease state in an
individual comprising the step of comparing the sCD fingerprint of
an individual, comprising the levels of a plurality of sCDs, at two
or more periods during the course of the disease. Disclosed herein
is a method for assessing the effect of one or more agent/s on one
or more disease states in an individual comprising the step of
comparing a sCD fingerprint of an individual, comprising the levels
of a plurality of sCDs, at two or more different time periods.
Disclosed herein is a method for sub-categorising a sCD fingerprint
profile, comprising the levels of a plurality of sCDs, comprising
the steps of identifying within one disease category one or more
group/s of sCDs wherein each group of sCDs exhibits common
characteristics distinguishing it from any other group within that
disease category. Disclosed herein is a method of creating a sCD
database comprising pathological and/or normal sCD fingerprint
patterns, in which a sCD fingerprint comprises the levels of
plurality of sCDs, comprising the step of measuring the levels in
parallel a plurality of sCDs from one or more individuals and
collating the data.
WORKING EXAMPLES
[0156] Described herein are working examples exemplifying the
products and methods described herein for the diagnosis, diagnostic
sub-classification, prognostic stratification and monitoring of
diseases and disorders, as exemplified by the human disease of
leukemia. Though leukemia is the disease exemplified by these
working examples, these methods of these working examples can be
applied to other diseases, disorders and conditions. These working
examples encompass measuring the expression levels of sCD molecules
produced by shedding, secretion or other molecular mechanims in
human body fluid samples, both diseased and healthy normal
controls, preferably using a chip- or bead-based technology, but in
no way being restricted to these technologies. Surprisingly, we
have successfully demonstrated that the detected antigen expression
levels can be used to predict leukemia with a considerably high
accuracy of 79%-89%. We carefully examined the issue of alternative
normalization strategies, which lead to a comparable and meaningful
data basis for classification.
[0157] By varying the analysis method, considering only acute
myeloid leukaemia (AML) samples, normal samples (normMix), and all
other samples as labels, we demonstrated the high predictive value
of sCD antigen expression profiles/fingerprints for one subset of
the leukemia families. The discrimination of AML (acute myeloid
leukemia) versus other leukemias, e.g., CML (chronic myeloid
leukemia), NHL (non-Hodgkin's lymphoma), and CLL (chronic
lymphocytic leukemia), and healthy control samples yielded
especially promising results using the five sCD antigen probes
employed in this investigation.
[0158] In order to evaluate the predictive power of using multiple
sCD antigen probes as opposed to a single sCD antigen probe, we
tested the predictive power using only one soluble CD antigen,
sCD117, versus using all 5 sCD antigens. We demonstrated that
although this single sCD antigen has a significant predictive
performance on its own, the addition of the other sCD antigens
increases the discriminative power in a statistically significant
manner, despite the low number of samples. This suggests that the
predictive performance could be increased even further by adding in
additional sCD antigens. Indeed the utilisation of multiple sCD
antigens in excess of the 5 employed here is predicted by these
experiments to increase the sensitivity and specificity of this
method and thus the ability of such a test to discriminate between
different leukaemia subclasses and different subtypes of same
subclass and indeed between different disease states, very
significantly. As such the use of multiple sCD antigens may be used
for monitoring the response to therapeutic interventions in those
individuals with leukaemia, for diagnosis and classification of
leukaemia subtypes, as well as for the prognosistic stratification
of specific cases of leukaemia and for the determination of minimal
residual disease (MRD).
[0159] The Iterative Weighted Least. Squares (BATS) mean extended
data method in its current form is expected to be of considerable
use in diagnosing and prognosing human leukemias, and in particular
acute myeloid leukaemia (AML) for which there is a significant
unmet medical need for diagnostic and prognostic biomarkers, as
well as in identifying biomarkers that can be used to monitor
response to therapy, to act a surrogate end-points in clinical
trials, to detect early remission, to detect attainment of full
remission, and to detect early relapse and to predict drug
sensitivity. The utilisation of sCD and/or soluble MHC Class I
antigen profiling/fingerprinting in AML and other human leukemias
is expected to be of considerable clinical utility both in primary
and tertiary settings and it is expected that the use of sCD
profiling/fingerprinting in therapeutic contexts will help
facilitate the detection of minimal residual disease following
therapy and also the monitoring of individual response to
therapeutic interventions and the reoccurrence of disease. The
identification of poor prognostic groups through sCD antigen
pattern based prognostic stratification using this technology
should enable pre-selection of those individuals requiring more
aggressive therapeutic interventions and those who need more
aggressive and frequent monitoring of therapeutic response. The
method may also help predict those individuals that are likely to
be intolerant to a particular therapeutic intervention and those
individuals that are likely or be responders, non-responders, or
rapid responders to a particular therapeutic intervention. It is
predicted that the individual sCD antigens compromising the
pattern/profile/fingerprint may themselves also be potential
targets for therapeutic intervention, either alone or in
combination through multiple simultaneous targeting and as such
this method also provides a means of identifying sCD antigens and
their cell surface counterparts that might in principle be targeted
by therapeutic interventions.
[0160] The levels of five sCD antigens were assayed in plasma taken
from healthy (normal) controls and from patients with the following
leukemias: AML (acute myeloid leukemia), CML (chronic myeloid
leukemia), NHL (non-Hodgkin's lymphoma), and CLL (chronic
lymphocytic leukemia). For this purpose five soluble CD antigens:
sCD14, sCD30, sCD54, sCD117, sCD130 were measured using a
single-blinded protocol in plasma taken from both patients and
healthy controls using matched antibody pairs which comprised
either two monoclonals or one monoclonal and a polyclonal, that
were either attached to Luminex microbeads, or arrayed onto a chip
using the chip-based methodology and the chip-based proteomic
protein microarray technology of GenTel Biosciences Inc.
[0161] The purpose of the three different experiments described
below was to determine if patterns of 5 or more sCD antigens
measured in plasma (or in whole blood or in principle in serum or
in any other body fluids outlined already in detail above such as:
pleural fluid, urine, ascitic fluid, saliva, uveal fluid and so on)
can be used to generate sCD protein expression signatures that are
characteristic of cancer as opposed to normals, or that are
characteristic of a particular disease state, namely in this
instance of one particular leukemia type vs. other leukemia types
and healthy normal controls. Our finding was that patterns of 5 or
more sCD antigens are significant indicators of a specific disease
state whether it is cancer vs. healthy controls or cancer sub-type
vs. other cancer sub-types than individual sCD antigens on their
own. The utility of a test of this sort is determined by
documenting its sensitivity and specificity for determining the
target disease.
Working Example 1
Experimental Setup: GenTel Biosciences Inc Chip Platform
Composition of the Samples:
[0162] The dataset for each of the three experiments was acquired
via antibody array chip experiments using the following 47
samples:
TABLE-US-00001 TABLE 1 Composition of the sample classes Sample
Type Number of Samples AML 9 CML 12 CLL 12 NHL 6 NormMix (normal
controls) 8 TOTAL 47
Chip Layout
[0163] Soluble CD antigens in the 47 plasma samples were measured
using the chip-based technology of GenTel Biosciences Inc. Assays
for this investigation were performed on a standard GenTel
Biosciences PATH slide. The PATH slide consisted of a standard
sized glass substrate containing an adhesion layer onto which a
thin coat of nitrocellulose was applied.
[0164] The layout of the chip with a total size of
25.times.75.times.1 mm is illustrated in FIG. 1. Each well is
9.times.9 mm in dimension with a total number of 16 wells per chip.
Each sample was replicated onto two wells (left/right) and within
each well, each probe was replicated 6 times leading to a total of
12 replicates per sample and antigen probe.
[0165] FIG. 2 illustrates five sCD antigen probes; a positive
control and an additional negative normal probe were assessed,
leading to a total number of 84 measured probes per sample
distributed over two wells. Arraying of the capture antibodies was
performed using a Gesim NanoPlotter 2.0/E printer. This is a piezzo
non-contact instrument with the printing parameters as follows (see
Table 2).
TABLE-US-00002 TABLE 2 Parameters for the printing process of the
slides. Antibody Printing Concentration 0.5 mg/ml Printing Buffer
1xGenTel Print Buffer Printing Temperature Ambient Printing
Humidity 60% Batch Size ~30 slides Spot Diameter ~220 .mu.m Spot
Pitch ~350 .mu.m
[0166] The fluorescent signals on the slides were scanned using a
confocal laser scanner (Tecan LS 200 Reloaded). Scanning was
performed applying a single-scanning protocol with a scanning
resolution of 10 .mu.m/pixel. The resulting images were analysed
with ArrayVision 8.0, performing spot finding, as well as
measurement and background estimation. Background relative
fluorescence units (RFUs) were determined from four measured
background spots around each single probe. Most of these
implementation details are standard, but it is important to note
that probes were allocated to their address on the chip using a
regular grid rather than a randomized procedure.
Experimental Procedure Outline:
[0167] Experiments were conducted over two days. On both days one
set of standard curves was constructed. The experimental protocol
included the following steps: block slides, assemble the separator
apparatus, antigen addition, incubate, wash, detector antibody
addition, incubate, wash, apply detection reagent, incubate, wash,
rinse, dry and scan.
Data Analysis:
[0168] The final goal of the data analysis was to create a
predictor engine that is able to predict the sample class of a new,
unseen sample, namely disease type or healthy control based upon
the training data of labelled samples. Intuitively this is achieved
by learning patterns of expression levels for all measured
antigens, which potentially could look very different depending on
the sample class.
[0169] The raw measured expression levels from the chip experiments
are first consolidated into one consistent data table.
Normalization thereafter ensures that the measured values are
comparable across different experiments. Normalized data with
labels is then used to train a predictor for the disease classes
whose performance is evaluated by means of cross validation.
Data Preparation:
[0170] The raw measured expression level data provided by GenTel
Biosciences were first consolidated into a verified consistent data
table (preparation), partially by editing the data manually and
partially using automated custom methods for this project. The full
annotated raw data consisted of the expression levels for each
probe (RFU values), individual background RFUs for each spot and
coordinate information of the corresponding spots on the chip
(fullrecords.dat).
Normalization, Summarization & Standard Curves:
[0171] Normalization is a crucial pre-processing step to make the
data originating from multiple experiments comparable. In
non-automated experimental designs, as implemented in this pilot
study, it is essential to remove systematic effects, such as
operator effects, day or time drifts or spatial correlations in the
dataset. Because of the significance of such effects, several
alternative normalization strategies were evaluated by means of
explorative data analysis. Effects of normalization were studied,
as were their ability to remove spurious correlations and their
net-influence on the classification performance. The different
normalization methods employed were briefly: [0172] 1. Use of
Background RFU measurements as a means of removing spatial
variation of measurement background intensity on the chip.
Background subtraction on a linear scale or on log scale. [0173] 2.
Use of Negative Probe measurements as a means of removing spatial
variation of measurement background intensity on the chip.
Background subtraction on linear scale or on log scale. [0174] 3.
Use of standard curves for intensity range normalization.
Background RFU Versus Negative Probe Measurement:
[0175] We investigated normalization characteristics using either
the background RFUs, measured for each individual probe, or the
negative probes measurements per well to subtract a background
signal. Analyzing the correlation of the background RFU values and
the Negative Probe expressions revealed the very low correlation of
27%. A visualisation of the signal from the negative normal probes
and of the background RFUs from all probes on the chip illustrates
their systematic differences.
[0176] This indicates that the negative probes not only capture
spatial effects but also additional effects such as well location,
and operator or day-specific influences. Normalizing the Negative
Probes themselves by subtracting their background RFUs amplified
the differences in Negative Probe measurements on the two days
(0.51 versus 0.27) and increased interquartile range (IQR) (0.32
versus 0.11) as well as the residual error in the ANOA model fitted
to the background.
[0177] The observation that for a fraction of 2% of the
measurements the background RFU was actually higher than the
Negative Probe expression value supports the conclusion that the
background RFUs are less meaningful then Negative Probes. This
suggests that Negative Probes are a better choice for the removal
of measurement bias than the background RFUs.
[0178] The reported correlations were analysed via an ANOVA model,
which revealed significant day and left/right (well location)
effects, while spatial effects (x/y location of the spot) were of
minor significance. Using an iterative weighted least squares
(IWLS) mean (a robust mean estimator) of the Negative Probes, per
well, as a background-model, removes the strong well bias and the
day influence in the ANOVA model. Furthermore, all measurements
showed intensities consistently higher than the background
estimated via the Negative Probe well mean. Hence due to its
efficiency in removing well and day effects, IWLS mean extended
data is the preferred normalization method
(fullrecords-minusNegByWell.dat). Positive probes were all
saturated and therefore had to be discarded.
Standard Curves:
[0179] For both days, standard curves were recorded (FIG. 6).
Negative probe corrected curves, as described above, are
considerably improved (FIG. 7). From these curves, intensity
readings can directly be converted to concentrations, although only
in the intensity range covered by the standard curve (FIG. 8). For
some measurements, as for a larger number of the sCD30 measurements
where the intensity value was lower than a zero concentration, the
measured value was outside the range of the standard curve and
hence had to be truncated leading to a loss of information. The
effect of this can be observed by mean of a reduced correlation
between identical samples run on two days (98.5% versus 91.1%) and
a significantly lower prediction accuracy compared to using the raw
normalized intensity values (confusion tables for classifiers,
Tables 9 and 10). This suggests a considerable background drift of
the setup during the experiments on any one day. If concentration
levels and hence standard curves were required they would need to
be measured more frequently in order to reflect the actual
measurement conditions more accurately. Most likely this is less of
an issue for a more automated experimental setup, which is less
vulnerable to drifts.
Summarization:
[0180] In order to provide a strong and consistent signal for the
classifier it is essential to reduce the dimensionality of the
normalized data and thereby gain signal. Each of the five antigen
probes is 12 fold replicated on the chip and consequently a
standard option would be to take the arithmetic mean for all
replicates of one probe. Since it is to be expected that the noise
distribution is rather heavy tailed, a more robust mean estimator
such as IWLS may be more appropriate. Especially in case of large
number of replicates, for instance compared to a typical microarray
experiments, robust estimators become feasible.
[0181] The following summarization methods were evaluated for each
of the classification experiments: [0182] 1. rawByWell.mean.dat:
untouched raw data without background subtraction, standard mean
was used to summarise the replicates of any one probe. [0183] 2.
rawByWell.robust.dat: untouched raw data without background
subtraction, IWLS average by well to summarise the replicates of
any one probe. [0184] 3. minusNegByWell.robust.dat: IWLS mean
normalized data (background from Negative probes), IWLS average by
well to summarise the replicates of any one probe. [0185] 4.
minusNegByWellNormalizedWithinCurveRange.robust.dat: as above but
projected through day-specific experimental standard-calibration
curves. Note: values that were outside the calibration range were
truncated.
[0186] For each of these methods the probes from each well were
background corrected by subtracting the well-local background value
on the original intensity scale (linear).
[0187] The data was then put on a log10 scale and summarized by
means of averaging on this log scale. The replicates of the same
sample in the two wells (left/right) were treated as separate
samples in the analysis. This seems to double the amount of samples
used for the training but of course we expect very similar
expression levels in both wells. Cross normalization over wells is
prone to distortions and not necessary since the copy number in any
single well is sufficiently high.
[0188] Concluding the discussion about the different normalization
options we note that minusNegByWell.robust is the strongest
candidate for summarization. This method combines the successful
removal of day and well effects (background subtraction based on
Negative Probes) with a robust mean estimator (IWLS) of the probe
replicates.
Classification
[0189] The normalized and summarized data forms the basis for the
training of a classifier. The patterns that potentially may be
picked up by the classifiers can be visualised by plotting this
five dimensional input data.
[0190] FIG. 9 illustrates the summarized intensity values for the
antigens for all 47 samples using minusNegByWell.robust
summarization. It is visually apparent that probe number 4 (sCD117)
separates many of the different classes quite well. A more formal
evaluation comparing classification performance using single probes
or multiple probes will be given later. FIG. 10 is identical but
only visualizes disease vs. NormMix samples, illustrating that
these two groups show very strong patterns.
[0191] We used this data as the input for classification, comparing
two alternative classifiers--standard k-nearest-neighbours
classifiers (kNN) and the Multi Layer Perceptron (MLP), trained
with the evidence framework as introduced by David MacKay et al.
(1995).
Classification of all Sample Types:
[0192] The classification performance was evaluated for the four
normalization/summarization alternatives stated above and for both
classifiers.
[0193] For all classification experiments we give confusion tables
summarizing the result of leave-one-out cross validation. It is
stated how often a specific sample type (rows) was predicted as any
one of the five classes (columns). An ideal predictor would hence
yield a matrix with non-zero entries only on the diagonal.
Confusion tables can easily be converted to specificity and
sensitivity characterisation of the classification performance for
each class. We state those results for the best of the
normalization/classifier result respectively.
[0194] For the first experiment using all probes predicting all
five classes, the various summarization methods and classifiers
performed very similarly, with the notable exception of experiments
in which the classifier
minusNegByWellNormalizedWithinCurveRange.robust was used for
summarization. As mentioned earlier, this was expected due to the
truncation of out of range intensity values. The remaining
experiments yielded consistently good predicted performance of
57-64%, which is 2-3 fold as good as random guessing. These
differences of 6% are not significant for the sample number used in
this experiment.
[0195] The confusion tables and estimates for the generalization
performance for the classifiers (kNN and MLP), separately for all 4
summarization methods, are listed Tables 3-10.
[0196] A summarising table stating specificity and sensitivity for
each of the tests using the MLP classifier with rawByWell.robust
summarization is given in Table 11.
[0197] Notably the specificity is generally high for all 5 classes.
Sensitivity of the classification is especially encouraging for AML
samples and normMix/disease, i.e. general classification of disease
versus healthy (normal) samples.
Tables 3 and 4: Confusion Tables and Generalization Performance for
rawByWell.mean Summarization for kNN and MLP Classifier
TABLE-US-00003 [0198] TABLE 3 Data: rawByWell.mean, Confusion
Table, MLP, Evidence Framework, Generalization Accuracy: 0.64
Predicted AML CML CLL NHL normMix TRUE AML 16.00 0.00 2.00 0.00
0.00 CML 0.00 14.00 6.00 2.00 2.00 CLL 0.00 8.00 9.00 2.00 5.00 NHL
0.00 3.00 3.00 6.00 0.00 normMix 1.00 0.00 0.00 0.00 15.00
TABLE-US-00004 TABLE 4 Data: mean, rawByWell.mean, Confusion Table,
k Nearest Neighbor, Generalization Accuracy: 0.60 Predicted AML CML
CLL NHL normMix TRUE AML 12.00 3.00 1.00 0.00 2.00 CML 0.00 17.00
5.00 2.00 0.00 CLL 0.00 10.00 11.00 1.00 2.00 NHL 0.00 4.00 2.00
6.00 0.00 normMix 0.00 2.00 4.00 0.00 10.00
Tables 5 and 6: Confusion Tables and Generalization Performance for
rawByWell.robust Summarization for kNN and MLP Classifier
TABLE-US-00005 [0199] TABLE 5 Data: rawByWell.robust, Confusion
Table, MLP, Evidence Framework, Generalization Accuracy: 0.61
Predicted AML CML CLL NHL normMix TRUE AML 15.00 0.00 2.00 0.00
1.00 CML 1.00 13.00 7.00 2.00 1.00 CLL 0.00 8.00 9.00 3.00 4.00 NHL
0.00 4.00 2.00 6.00 0.00 normMix 0.00 2.00 0.00 0.00 14.00
TABLE-US-00006 TABLE 6 Data: rawByWell.robust, Confusion Table, k
Nearest Neighbor, Generalization Accuracy: 0.57 Predicted AML CML
CLL NHL normMix TRUE AML 12.00 4.00 1.00 0.00 1.00 CML 0.00 16.00
6.00 2.00 0.00 CLL 0.00 10.00 10.00 2.00 2.00 NHL 0.00 3.00 3.00
6.00 0.00 normMix 0.00 3.00 3.00 0.00 10.00
Tables 7 and 8: Confusion Tables and Generalization Performance for
minusNegByWell.robust Summarization for kNN and MLP Classifier
TABLE-US-00007 [0200] TABLE 7 Data: minusNegByWell.Robust,
Confusion Table, MLP, Evidence Framework, Generalization Accuracy:
0.61 Predicted AML CML CLL NHL normMix TRUE AML 15.00 0.00 2.00
0.00 1.00 CML 1.00 12.00 8.00 2.00 1.00 CLL 0.00 9.00 10.00 1.00
4.00 NHL 0.00 4.00 2.00 6.00 0.00 normMix 2.00 0.00 0.00 0.00
14.00
TABLE-US-00008 TABLE 8 Data: minusNegByWell.Robust, Confusion
Table, k Nearest Neighbor, Generalization Accuracy: 0.64 Predicted
AML CML CLL NHL normMix TRUE AML 14.00 3.00 1.00 0.00 0.00 CML 2.00
14.00 7.00 1.00 0.00 CLL 0.00 4.00 16.00 2.00 2.00 NHL 0.00 4.00
2.00 6.00 0.00 normMix 0.00 1.00 5.00 0.00 10.00
Tables 9 and 10: Confusion Tables and Generalization Performance
for minusNegByWellNormalizedWithinCurveRange.robust Summarization
for kNN and MLP Classifier
TABLE-US-00009 [0201] TABLE 9 Data:
minusNegByWellNormalizedWithinCurveRange.robust, Confusion Table,
MLP, Evidence Framework, Generalization Accuracy: 0.45 Predicted
AML CML CLL NHL normMix TRUE AML 11.00 2.00 2.00 0.00 3.00 CML 0.00
8.00 11.00 3.00 2.00 CLL 0.00 13.00 6.00 2.00 3.00 NHL 0.00 5.00
2.00 5.00 0.00 normMix 3.00 1.00 0.00 0.00 12.00
TABLE-US-00010 TABLE 10 Data:
minusNegByWellNormalizedWithinCurveRange.robust, Confusion Table, k
Nearest Neighbor, Generalization Accuracy: 0.50 Predicted AML CML
CLL NHL normMix TRUE AML 11.00 4.00 0.00 0.00 3.00 CML 0.00 19.00
3.00 1.00 1.00 CLL 0.00 17.00 4.00 0.00 3.00 NHL 0.00 6.00 4.00
2.00 0.00 normMix 0.00 1.00 4.00 0.00 11.00
TABLE-US-00011 TABLE 11 Sensitivities/Specificities for
minusNegByWell.robust summarization with MLP classifier: overall
specifity for healthy versus disease at the end. Sample type
Specificity Sensitivity AML 96% 83% CML 81% 50% CLL 83% 42% NHL 96%
50% Disease 88% 92%
Working Example 2
Classifier Performance AML/other/normMix:
[0202] In a second experiment we evaluated the predictive
performance distinguishing only three sample classes: AML, normMix
and all of the remainder. Again all four summarization methods for
two classifiers were compared with a very similar outcome as
before. Differences in predictive accuracy of about 6% have no
significance. The classification performance varied between
79%-89%, (which is about 140% as good as you would get from
guessing (64%)).
[0203] Confusion tables and estimates for the generalization
performance for all individual classifier/normalization pairings
are listed in Tables 12-19. A conversion to sensitivity/specificity
of the MLP classifier with minusNegByWell summarization can be
found in Table 20.
[0204] Training the classifier only on three classes yields a very
similar specificity/sensitivity than for the five classes
experiment. Differences compared to the first experiment are not
significant.
[0205] Sensitivity and Specificity of AML versus healthy normals is
very similar to the 5-class experiment.
[0206] We can conclude that AML can be well discriminated from the
other classes based on the five sCD antigens with a considerably
high precision.
Tables 12 and 13: Confusion Tables and Generalization Performance
for rawByWell.mean Summarization for kNN and MLP Classifier
Discriminating Between 3 Classes
TABLE-US-00012 [0207] TABLE 12 Data: rawByWell.mean, Confusion
Table, k Nearest Neighbor, Generalization Accuracy: 0.82 Predicted
AML CML normMix TRUE AML 10.00 7.00 1.00 other 0.00 60.00 0.00
normMix 0.00 9.00 10.00
TABLE-US-00013 TABLE 13 Data: rawByWell.mean, Confusion Table, MLP,
Evidence Framework, Generalization Accuracy: 0.87 Predicted AML CML
normMix TRUE AML 13.00 4.00 1.00 other 0.00 58.00 2.00 normMix 3.00
2.00 11.00
Tables 14 and 15 Confusion Tables and Generalization Performance
for rawByWell.robust Summarization for kNN and MLP Classifier
Discriminating Between 3 Classes
TABLE-US-00014 [0208] TABLE 14 rawByWell.robust, Confusion Table, k
Nearest Neighbor, Generalization Accuracy: 0.81 Predicted AML CML
normMix TRUE AML 10.00 7.00 1.00 other 0.00 60.00 0.00 normMix 0.00
10.00 6.00
TABLE-US-00015 TABLE 15 rawByWell.robust, Confusion Table, MLP,
Evidence Framework, Generalization Accuracy: 0.86 Predicted AML CML
normMix TRUE AML 14.00 3.00 1.00 other 1.00 57.00 2.00 normMix 4.00
2.00 10.00
Tables 16 and 17: Confusion Tables and Generalization Performance
for minusNegByWell.robust Summarization for kNN and MLP Classifier
Discriminating Between 3 Classes
TABLE-US-00016 [0209] TABLE 16
minusNegByWellNormalizedWithinCurveRange.robust, Confusion Table, k
Nearest Neighbor, Generalization Accuracy: 0.85 Predicted AML CML
normMix TRUE AML 13.00 5.00 0.00 other 0.00 60.00 0.00 normMix 0.00
9.00 7.00
TABLE-US-00017 TABLE 17
minusNegByWellNormalizedWithinCurveRange.robust, Confusion Table,
MLP, Evidence Framework, Generalization Accuracy: 0.89 Predicted
AML CML normMix TRUE AML 16.00 2.00 0.00 other 0.00 56.00 4.00
normMix 2.00 2.00 12.00
Tables 18 and 19: Confusion Tables and Generalization Performance
for minusNegByWellNormalizedWithinCurveRange.robust Summary of
Results for kNN and MLP Classifier Discriminating Between 3
Classes
TABLE-US-00018 [0210] TABLE 18
minusNegByWellNormalizedWithinCurveRange.robust, Confusion Table, k
Nearest Neighbor, Generalization Accuracy: 0.79 Predicted AML CML
normMix TRUE AML 7.00 7.00 4.00 other 0.00 58.00 2.00 normMix 0.00
7.00 9.00
TABLE-US-00019 TABLE 19
minusNegByWellNormalizedWithinCurveRange.robust, Confusion Table,
MLP, Evidence Framework, Generalization Accuracy: 0.45 Predicted
AML CML normMix TRUE AML 13.00 4.00 1.00 other 0.00 57.00 3.00
normMix 4.00 2.00 10.00
TABLE-US-00020 TABLE 20 Sensitivities/Specificities for
minusNegByWell.robust summarization with MLP classifier, overall
specificity for healthy versus disease at the end. Sample type
Specificity Sensitivity AML 97% 89% Other 88% 93% Disease 75%
95%
Working Example 3
[0211] Classification Performance Only Using sCD117 Versus all Five
sCD Antigens:
[0212] The raw classifier input (FIG. 6) suggests that sCD117 gives
rise to a very strong signal and may already provide a considerable
classification performance on its own. The analysis described was
repeated before restricting to only one regressor (sCD117) and
compared to the prediction results using all five sCD antigen
probes, as in the first experiment. Experiments were performed to
test whether using all regressors yielded a significantly improved
generalization performance compared to predictions based on sCD117
only.
[0213] Except for one particular normalization method and
classifier, where there was a tie, all tests consistently revealed
significantly higher classification accuracies when using all five
sCD antigen regressors, with an improvement of between 8% and 10%
points. Significance levels were typically around p=5% and are
expected to reduce to even higher significance for larger datasets.
Details of significance levels and the confusion tables, again
separately for all four summarization methods and both classifiers,
can be found in Tables 21-33.
[0214] Specificity and sensitivity values as stated in Tables 34-35
ease interpretation of the results.
[0215] We observe that sCD117 alone is already a good marker to
discriminate AML samples from healthy (norm-mix) samples. But the
single sCD117 marker does not separate normMix samples from the
other cancer types very well, leading to the very low sensitivity
of the combined disease class.
[0216] The change of expression values was studied using the
minusNegByWell.robust normalization. A significant change of
expression of a single antigen between the three classes normal,
cancer and AML can only be observed for sCD117. For normal
control-samples we find a mean expression value of 3.534+/-0.285.
Cancer-samples excluding AML range at 3.4569+/-0.1358. AML-cancer
samples at 3.86+/-0.37. (All error bars are +/-1 standard
deviation). We can conclude that on average cancer samples cause a
slight decrease in expression of sCD117 compared to normal samples
while AML samples cause the expression level of sCD117 increase.
The observed decrease is well described for cancer samples is
within 1 standard deviation error bars of either class and hence is
not significant. In contrast the increase of expression for AML
samples exceeds a 1 Standard deviation cut-off and hence meets this
significance-criterion. This simplistic analysis suggests that
sCD117 is not a promising marker for distinguishing between non-AML
cancer and healthy normals, but instead is a statistically
significantly marker for distinguishing AML samples from non-AML
samples.
[0217] The result of this low-level analysis is in agreement with
the confusion tables for the predictive power of sCD117 indicating
a particular strong predictive power for the AML-sample class. The
remaining markers give rise to a weaker signal which only allows
sensible interpretation when used jointly as input for a classifier
as discussed elsewhere.
[0218] Adding the additional four sCD antigens helps overcome this
problem. Although sCD117 is a good single marker, the use of
additional sCD probes improves the predictive accuracy (in terms of
specificity) significantly. Consequently, a further improvement in
specificity is reasonably expected by adding even more sCD antigen
probes, beyond the five sCD antigens used in this experiment. An
improvement in sensitivity is also expected by adding further
individual sCD markers that individually have a defined sensitivity
for a particular disease, in this case AML. The properties of
sCD117 as a single marker also suggest that some of the sCD
antigens, like sCD117, are very sensitive for a specific cancer
family, in this case AML. The other markers studied in this
investigation in contrast contribute to the classification jointly
and only pattern learning on the five dimensional patterns allows
us to extract useful information.
Tables 21, 22, 23, and 24 Below: rawByWell.mean
Summarization--Confusion Tables for Classification Using sCD117 as
Single Regressor Versus Using all Five Antigen Probes. Table 25
Below: Statistical Significance
TABLE-US-00021 [0219] TABLE 21 Data: RawByWell.mean, All Variables,
Confusion Table, MLP, Evidence Framework, Generalization Accuracy:
0.89 Predicted AML CML normMix TRUE AML 13.00 4.00 1.00 other 0.00
55.00 5.00 normMix 3.00 2.00 11.00
TABLE-US-00022 TABLE 22 Data: RawByWell.mean, sCD117 only,
Confusion Table, MLP, Evidence Framework, Generalization Accuracy:
0.76 Predicted AML CML normMix TRUE AML 14.00 4.00 0.00 other 3.00
57.00 0.00 normMix 4.00 12.00 0.00
TABLE-US-00023 TABLE 23 Data: RawByWell.mean, All Variables,
Confusion Table, k-Nearest Neighbor, Generalization Accuracy: 0.84
Predicted AML CML normMix TRUE AML 12.00 6.00 1.00 other 0.00 60.00
0.00 normMix 0.00 9.00 7.00
TABLE-US-00024 TABLE 24 Data: RawByWell.mean, sCD117 only,
Confusion Table, k-Nearest Neighbor, Generalization Accuracy: 0.84
Predicted AML CML normMix TRUE AML 13.00 5.00 0.00 other 0.00 54.00
6.00 normMix 0.00 16.00 0.00
TABLE-US-00025 TABLE 25 Summary information accuracy and
significance that using all variables provides better
classification than using sCD117 only Sig. Classification Method
Gen, Acc.All. GenAcc CD117 only Level Evid, App.Framework 84.04
75.53 0.07 K. Nearest Neighbors 84.04 71.28 0.00
Tables 26, 27, 28 and 29 Below: rawByWell.robust
Summarization--Confusion Tables for Classification Using sCD117 as
Single Regressor Versus Using all Five Antigen Probes. Table 30
Below: Statistical Significance
TABLE-US-00026 [0220] TABLE 26 Data: rawByWell.robust, All
Variables, Confusion Table, MLP, Evidence Framework, Generalization
Accuracy: 0.87 Predicted AML CML normMix TRUE AML 14.00 3.00 1.00
other 0.00 57.00 3.00 normMix 3.00 2.00 11.00
TABLE-US-00027 TABLE 27 Data: rawByWell.robust, sCD117 only,
Confusion Table, MLP, Evidence Framework, Generalization Accuracy:
0.78 Predicted AML CML normMix TRUE AML 15.00 3.00 0.00 other 1.00
58.00 1.00 normMix 3.00 13.00 0.00
TABLE-US-00028 TABLE 28 Data: rawByWell.robust, All Variables,
Confusion Table, k-Nearest Neighbor, Generalization Accuracy: 0.82
Predicted AML CML normMix TRUE AML 10.00 7.00 1.00 other 0.00 60.00
0.00 normMix 0.00 9.00 7.00
TABLE-US-00029 TABLE 29 Data: rawByWell.robust, sCD117 only,
Confusion Table, k-Nearest Neighbor, Generalization Accuracy: 0.74
Predicted AML CML normMix TRUE AML 13.00 3.00 2.00 other 0.00 55.00
5.00 normMix 0.00 14.00 2.00
TABLE-US-00030 TABLE 30 Summary information accuracy and
significance that using all variables provides better
classification than using CD117 only Sig. Classification Method
Gen, Acc.All. GenAcc sCD117only Level Evid, App.Framework 87.23
77.66 0.02 K. Nearest Neighbors 81.91 74.47 0.06
Tables 31, 32, 33 and 34 Below: minusNegByWell.robust
Summarization--Confusion Tables for Classification Using sCD117 as
Single Regressor Versus Using all Five Antigen Probes. Table 35
Below: Statistical Significance
TABLE-US-00031 [0221] TABLE 31 Data: minusNegByWell.Robust, All
Variables, Confusion Table, MLP, Evidence Framework, Generalization
Accuracy: 0.88 Predicted AML CML normMix TRUE AML 14.00 4.00 0.00
other 0.00 56.00 4.00 normMix 1.00 2.00 13.00
TABLE-US-00032 TABLE 32 Data: minusNegByWell.Robust, sCD117 only,
Confusion Table, MLP, Evidence Framework, Generalization Accuracy:
0.80 Predicted AML CML normMix TRUE AML 15.00 3.00 0.00 other 0.00
60.00 0.00 normMix 4.00 12.00 0.00
TABLE-US-00033 TABLE 33 Data: minusNegByWell.Robust, All Variables,
Confusion Table, k-Nearest Neighbor, Generalization Accuracy: 0.85
Predicted AML CML normMix TRUE AML 13.00 5.00 0.00 other 0.00 60.00
0.00 normMix 0.00 9.00 7.00
TABLE-US-00034 TABLE 34 Data: minusNegByWell.Robust, sCD117 only,
Confusion Table, k-Nearest Neighbor, Generalization Accuracy: 0.79
Predicted AML CML normMix TRUE AML 13.00 2.00 3.00 other 0.00 57.00
3.00 normMix 1.00 11.00 4.00
TABLE-US-00035 TABLE 35 Summary information accuracy and
significance that using all variables provides better
classification than using sCD117 only Gen, GenAcc Sig.
Classification Method Acc.All. sCD117 only Level Evid,
App.Framework 88.30 79.79 0.06 K. Nearest Neighbors 85.11 78.72
0.05
Tables 36, 37, 38 and 39:
minusNegByWellNormalizedWithinCurveRange.robust
Summarization--Confusion Tables for Classification Using sCD117 as
Single Regressor Versus Using all Five Antigen Probes. Table 40
Below: Statistical Significance
TABLE-US-00036 [0222] TABLE 36 Data:
minusNegByWellNormalizedWithinCurveRange.robust, All Variables,
Confusion Table, MLP, Evidence Framework, Generalization Accuracy:
0.85 Predicted AML CML normMix TRUE AML 13.00 4.00 1.00 other 1.00
57.00 2.00 normMix 3.00 3.00 10.00
TABLE-US-00037 TABLE 37 Data:
minusNegByWellNormalizedWithinCurveRange.robust, sCD117 only,
Confusion Table, MLP, Evidence Framework, Generalization Accuracy:
0.79 Predicted AML CML normMix TRUE AML 14.00 4.00 0.00 other 0.00
60.00 0.00 normMix 6.00 10.00 0.00
TABLE-US-00038 TABLE 38 Data:
minusNegByWellNormalizedWithinCurveRange.robust, All Variables,
Confusion Table, k-Nearest Neighbor, Generalization Accuracy: 0.81
Predicted AML CML normMix TRUE AML 8.00 7.00 3.00 other 0.00 58.00
2.00 normMix 0.00 6.00 10.00
TABLE-US-00039 TABLE 39 Data:
minusNegByWellNormalizedWithinCurveRange.robust, sCD117 only,
Confusion Table, k-Nearest Neighbor, Generalization Accuracy: 0.81
Predicted AML CML normMix TRUE AML 10.00 2.00 6.00 other 0.00 59.00
1.00 normMix 3.00 6.00 7.00
TABLE-US-00040 TABLE 40 Summary information accuracy and
significance that using all variables provides better
classification than using sCD117 only Gen, GenAcc Sig.
Classification Method Acc.All. sCD117 only Level Evid,
App.Framework 85.11 78.72 0.09 K. Nearest Neighbors 80.85 80.85
0.64
TABLE-US-00041 TABLE 41 Sensitivities/Specificities for
minusNegByWell.robust summarization with MLP classifier. First: Top
part of the table: sCD117 as the only regressor, below: using all
five antigen probes. Sample type Specificity Sensitivity Using
sCD117 only AML 95% 83% other 56% 100% Disease 0% 100% Using all
five antigens AML 99% 78% other 82% 93% Disease 81% 95%
TABLE-US-00042 TABLE 42 Sensitivities/Specificities for
minusNegByWell.robust summarization with kNN classifier. First: Top
part of the table: sCD117 as the only regressor, below: using all
five antigen probes. Sample type Specificity Sensitivity Using
sCD117 only AML 98% 81% other 68% 95% Disease 25% 92% Using all
five sCD antigens AML 100% 81% other 68% 100% Disease 44% 100%
[0223] We can conclude that although sCD117 is a good single
marker, the use of additional sCD probes improves the predictive
accuracy significantly. We can consequently reasonably expect a
further improvement by adding even more sCD antigen probes, beyond
the five sCD antigens used in this experiment. The properties of
sCD117 as a single marker also suggest that some of the antigens,
like sCD117, are very sensitive for a specific cancer family, in
this case AML. The other markers studied in this investigation in
contrast contribute to the classification jointly and only pattern
learning on the five dimensional patterns allows us to extract
useful information.
[0224] In data not shown here, we have found that on average cancer
samples cause a slight decrease in expression of sCD117 compared to
normal samples, while AML samples show an increase in expression
level of sCD117. The change of expression values was studied using
the minusNegByWell.robust normalization. A significant change of
expression of a single antigen between the three classes of normal,
cancer and AML was only be observed for sCD117.
[0225] For normal control-samples we find a mean expression value
of 3.534+/-0.285. Cancer-samples excluding AML range at
3.4569+/-0.1358. AML-cancer samples at 3.86+/-0.37. (All error bars
are +/-1 standard deviation). We can conclude that on average
cancer samples cause a slight decrease in expression of sCD117
compared to normal samples while AML samples cause the expression
level increase. The observed decrease is well described for cancer
samples is within 1 standard deviation error bars of either class
and hence is not significant.
[0226] In contrast the increase of expression for AML-samples
exceeds a 1 standard deviation cut-off and hence meets this
significance-criterion. This simplistic analysis suggests that
sCD117 is not a promising marker for distinguishing between non-AML
cancer and healthy normal controls, but instead is a statistically
significantly marker for distinguishing AML samples from non-AML
samples. The result of this low-level analysis is in agreement with
the confusion tables for the predictive power of sCD117 indicating
particular strong predictive power for the AML-sample class. The
remaining markers give rise to a weaker signal which only allows
sensible interpretation when used jointly as input for a classifier
as discussed elsewhere.
[0227] As described herein, the applicability of sCD antigens in
the diagnosis, classification and monitoring of human leukemias was
studied. We studied the predictive performance using a plurality of
sCD antigens, e.g., only 5 sCD antigens, whose expression levels in
human samples, both diseased and in healthy control samples, were
measured using a chip-based antibody array technology.
[0228] It could be successfully demonstrated that the detected sCD
antigen expression levels can be used to predict leukemia with a
considerably high accuracy of 79%-89%. We carefully examined the
issue of alternative normalization strategies, which lead to a
comparable and meaningful data basis for classification.
[0229] By varying the analysis method, considering only AML,
normMix and all other samples as labels, we demonstrated the high
predictive value of sCD antigen expression profiles/fingerprints on
this subset of the leukemia families. The discrimination of AML
versus other leukemias and healthy control (normal) samples yielded
especially promising results using the five sCD antigen probes
employed in this investigation.
[0230] In order to evaluate the predictive power of utilising
multiple sCD antigen probes as opposed to a single sCD antigen
probe, we tested the predictive power using only one soluble CD
antigen, sCD117, versus using all 5 sCD antigens. We demonstrated
that although this single sCD antigen has a significant predictive
performance on its own, the addition of the other sCD antigens
increases the discriminative power in a statistically significant
manner, despite the low number of samples. This suggests that the
predictive performance as measured by the specificity could be
increased even further by adding more sCD antigens. Indeed the
utilisation of multiple sCD antigens in excess of the 5 employed
here is predicted by these experiments to increase the sensitivity
and specificity of this method and thus the ability to discriminate
between different leukaemia subclasses and indeed between different
disease states, very significantly. As such the use of multiple sCD
antigens may in principle be used for monitoring the response to
therapeutic interventions in those individuals with leukaemia, for
diagnosis and classification of the leukaemia subtype and most
likely consequently also for prognostic insights. There are likely
also to be other uses such as the detection of minimal residual
disease, detection of early relapse, prognostic stratification,
early diagnosis, early detection of relapse, and individual
sensitivity to a particular therapeutic compound or biologic.
[0231] The methods described herein are not restricted to the
analysis of whole blood, serum or plasma and indeed sCD molecules
are known to be present in many other body fluids. Furthermore the
methods described herein are not restricted to use in humans, and
indeed such a method may prove to be of immense use in veterinary
applications. Neither is the technology used to measure levels of
the sCD molecules in body fluid necessarily restricted to those
technologies such as bead-based and chip-based outlined above.
[0232] The current method in its present form is expected to be of
considerable use in human leukemias and in particular in acute
myeloid leukaemia (AML) for which there is a significant unmet
medical need for diagnostic, prognostic and `theranostic`
(diagnostic tests that diagnose the disease, help choose the
correct treatment regime and assist monitoring of the patient
response) biomarkers. The utilisation of sCD
profiling/fingerprinting in AML and other human leukemias is
expected to be of considerable clinical utility both in primary and
tertiary settings and it is expected that the use of sCD
profiling/fingerprinting in therapeutic contexts will help
facilitate the detection of minimal residual disease following
therapy and also the monitoring of individual response to
therapeutic interventions. The identification of poor prognostic
groups using this technology can enable pre-selection of those
individuals requiring more aggressive therapeutic interventions and
in addition those that require more frequent intensive monitoring.
The method can help predict those individuals that are likely to be
intolerant to a particular therapeutic intervention and those
individuals that are likely or be responders, non-responders, or
rapid responders to a particular therapeutic intervention. It is
predicted that the individual sCD antigens compromising the
pattern/profile/fingerprint may themselves also be potential
targets for therapy and as such this method also provides a means
of identifying sCD antigens and their cell surface counterparts
that might, in principle, be targeted by therapeutic
interventions.
[0233] Though the methods encompassing the detection of soluble
antigens in body fluids is not limited to any particular method of
technology, an exemplary protocol using GenTel antibody array
technology platform is described below.
Multiplex Assay Methods Using the GenTel Antibody Array Technology
Platform
[0234] 1. Procurement of matched antibody pairs with priority to
match pairs including multiple clones of capture and detector
antibody specificities. 2. Formulation of antibodies and antigens:
(a) Aliquots of all reagents are prepared upon suspension and
stored at -20.degree. C. (b) Capture antibodies intended for
arraying are suspended in sterile 1.times. GenTel Rinse Buffer,
unless incompatible with vendor specifications at a final
concentration of 1.0 mg/mL. (c) Aliquots of recombinant proteins
are suspended in sterile 1.times. GenTel Rinse Buffer at a final
concentration of 100 ug/mL. (d) Aliquots of detector antibodies are
suspended in sterile 1.times. GenTel Rinse Buffer at a final
concentration of 200 .mu.g/mL.
[0235] All slides are arrayed using a Gesim Nanoplotter 2.0/E
piezoelectric instrument using the following printing parameters:
(i) Well layout: 2.times.8 arrays per 1.times.3 slide, (ii)
Replicate spots: 3, (iii) Spot OD: .about.220 um, (iv) Spot pitch:
.about.350 um, (v) Positive control: Cy3 labeled IgG at 50 ug/mL in
1.times. GenTel Print Buffer, (vi) Positive control: BSA/Biotin at
100 ug/mL in 1.times. GenTel Print Buffer, (vii) Capture antibody:
printed at 500 ug/mL in 1.times. GenTel Print Buffer, (viii)
Negative control: 1.times. GenTel Print Buffer, (ix) Relative
Humidity: 60%, (x) Temperature: ambient room temperature.
[0236] Slides are cured before use by incubating for 3 days under
vacuum with copious desiccant. Printed slides are quality
controlled by sampling every 5 slides and scanned to inspect for
the following structural and functional characteristics: (i) Spot
morphology, (ii) Potential missed spots, and (iii) Correct
alignment.
3. Reagent specificity screening is performed to identify which
reagent sets are functionally specific by screening of materials as
follows: (a) Every capture antibody specificity is printed as
microspots in every array, (b) Each array is probed with a
different individual antigen (20 ng/mL) and the corresponding
single biotinylated detector antibody specificity, (c) Each array
is probed with a single detector antibody specificity in the
absence of antigen to measure capture antibody/detector antibody
cross reactivity. 4. The high and low endpoints of standard curves
are determined using matched pairs that demonstrate minimal
non-specific binding (less than 3% of intended signal). The matched
pairs are selected to prepare eight (8) point single plex standard
curves including one (1) blank (no antigen) in 1.times. GenTel Wash
Buffer. (a) Standard curves are prepared using single
antigen/single detector antibody pairs on slides listed using
serially diluted purified antigens (initial concentration is 200
ng/mL), (b) Commercially available normal human serum/plasma is
serially diluted pooled using dilutions ranging from 1:2 through
1:300 in 1.times. GenTel Wash Buffer, (c) The wells containing the
highest signal in the linear portion of the standard curve but
below saturation are selected to measure any capture
antibody/antigen specificity. (d) The concentration of the antigen
cocktail is selected based on the highest signal before saturation
on the single plex standard curves. 5. Dynamic range and Levels of
Detection (LOD) are measured with menus of analytes, which are
selected using specificity evaluation data and sample dilution
experiments. (a) Cocktail of antigen is serially diluted to prepare
two eight (8) point standard curves including with 1 blank (no
antigen) on each slide. (b) Multiplex standard curves are graphed
and dynamic range and LOD are measured and reported. 6.
Optimization of multiplex immunoassays is adjusted using the
following parameters: (a) Standard curve concentrations, (b)
Detector antibody concentrations, (c) Printed capture antibody
concentrations and (d) Possible application of diluents with
carrier (BSA, FBS). 7. Panel validation: Standard curve slope
consistency and precision are evaluated by preparing two (2) eight
(8)-point standard curves including blanks per curve (n=2 standard
curves) on three (3) three slides per day (n=6 standard curves) for
three (3) consecutive days (n=18 standard curves). Precision
measurements are reported as the following: (i) Mean % CV within
slide, (ii) Mean % CV slide to slide (iii) Mean % CV day to day.
Slopes and precision are measured and recorded. The percentage
accuracy (dilutional recovery) is measured using the two-slide
validation format as discussed above using a scattered well format.
(i) Five (5) dilution points within the dynamic range of standard
curves are prepared in triplicate. (ii) Data reported as the
percent accuracy of expected values.
[0237] Additional validation procedures may also be include: (i)
Replicate standard curves with replicate blanks and spiked samples
near the LOD to measure LOQ, (ii) Replicate pooled normal human
serum/plasma samples for sample replicate consistency, (iii) Spiked
serum/plasma sample evaluation for accuracy and consistency, and/or
(iv) Spiked matrix sample evaluation for accuracy and
consistency.
Exemplary General Assay Procedure:
[0238] 1. Reagents and Materials include 1.times. GenTel Wash
Buffer, 1.times. GenTel Rinse Buffer, 1.times. GenTel Protein Free
Blocking Buffer, 5.times. GenTel Print Buffer, GenTel PATHplus Thin
Film Nitrocellulose Slides, GenTel SiMplex 16/64 Well Separator
Device, Monoclonal Capture Antibodies, Recombinant Antigen
standards, Detector Antibodies, and Streptavidin/Dy549. 2. Slide
Printing: Printing is performed using a Gesim Nanoplotter 2.0/E
with the following parameters for geometric layouts and sample
constituents: Well layout--2.times.8, Replicate spots--3, Spot
OD--.about.220 um, Spot pitch--.about.350 um, Positive control--Cy3
labelled IgG at 50 ug/mL in 1.times. GenTel Print Buffer, Positive
control--BSA/Biotin at 100 ug/mL in 1.times. GenTel Print Buffer,
Capture antibody--printed at 500 ug/mL in 1.times. GenTel Print
Buffer, Negative control--1.times. GenTel Print Buffer, RH--60% and
Temperature--ambient. Post printing, slides are cured before use by
incubating for at least 3 days under vacuum with copious
desiccant.
3. Assay Procedure:
[0239] Blocking: Slides to be used in the assay are plunged into a
50 mL conical tube containing approximately 45 mL of 1.times.
GenTel Protein Free Blocking Buffer, and gently inverted five times
to agitate, and incubated at room temperature for one hour.
[0240] Assembling slide/well apparatus: The slides are removed from
the blocking buffer and immediately placed into the lower structure
of the SIMplex well separator device. The upper structure of the
SIMplex well separator device is attached to the lower structure.
Excess liquid is removed by rigorously flicking into liquid waste
receptacle
[0241] Assay Procedure: Add 70 .mu.L of standards or body fluid
samples to the wells. Place plate-sealing tape over the well plate.
Incubate at room temperature for 1 hour with gentle agitation on
rotator shaker. Remove plate-sealing tape. Wash well plate using
1.times. GenTel Wash Buffer either by hand or using automated plate
washer with the following conditions: 6 replicate washes at 150
.mu.L per wash effected by flicking excess liquid out of wells. Add
70 .mu.L of Detector Antibody to all wells, place plate sealing
tape over the well plate. Incubate at room temperature for 1 hour
with gentle agitation on rotator shaker. Remove plate-sealing tape.
Wash well plate as above. Add 70 .mu.L of Detection Reagent to all
wells. Place plate-sealing tape over the well plate. Incubate at
room temperature for 1 hour with gentle agitation or rotator
shaker. Remove plate-sealing tape. Wash well plate as above.
Carefully remove upper structure from SIMplex unit. Briefly and
gently rinse slides with 1.times. GenTel Rinse Buffer using a
squirt bottle. Dry slides under a gentle stream of compressed
nitrogen.
[0242] Slide Scanning: Scanner--Tecan Reloaded LS300 (or LS400),
Gain--130, Resolution--10 .mu.m.
[0243] A standard curve was generated using the above described
techniques on dilutions of sCD178 in normal sera and the following
reagents: 15 point standard curve with one blank (no antigen), 16
Normal Pooled Human plasma samples at different dilution levels,
CD178, Capture antibody Anti Human Fas Ligand/TNFSF6 Monoclonal
Antibody (Clone 100419), R&D Systems, MAB126;
Analyte--Recombinant Human Fas Ligand/TNFSF6, R&D Systems,
126-FL-010; and Detector Antibody--Anti Human Fas Ligand/TNFSF6
Biotinylated Affinity Purified Polyclonal Antibody, R&D
Systems, BAF126. See FIG. 11.
[0244] A Standard curve was generated using the above described
techniques on dilutions of CD127 in normal sera and the following
reagents: Capture Antibody--Anti Human IL-7 R alpha MAb (Clone
40131) Antibody, R&D Systems, MAB306; Analyte--Recombinant
Human IL-7 R alpha/Fc Chimera, CF, R&D Systems, 306-IR-050; and
Detector Antibody--Anti Human IL-7 R alpha Biotinylated Affinity
Purified Polyclonal Antibody, R&D Systems, BAF306. See FIG.
12.
Combination of a Specific Set of sCD Antigens:
[0245] The levels of a five sCD antigens were assayed in sera from
healthy controls (normals) and from patients with the following
leukemias: AML (acute myeloid leukemia), CML (chronic myeloid
leukemia), NHL (non-Hodgkin's lymphoma), and CLL (chronic
lymphocytic leukemia). For this purpose five soluble CD antigens:
sCD14, sCD30, sCD54, sCD117, sCD130 were measured using a
single-blinded protocol in plasma taken from both patients and
healthy controls using matched monoclonal antibody pairs that were
either attached to Luminex microbeads, or arrayed onto a chip using
the chip-based methodology and technology of GenTel Biosciences
Inc. Patterns of 5 or more sCD antigens measured in serum (or in
principle in plasma or in any other body fluids such as: pleural
fluid, urine, ascitic fluid, saliva, uveal fluid and so on) can be
used to generate sCD protein expression signatures that are
characteristic of cancer as opposed to normals, or that are
characteristic of a particular disease state, namely in this
instance of one particular leukemia type vs. other leukemia types
and healthy normal controls. The hypothesis was that patterns of 5
or more sCD antigens would be more significant indicators of a
specific disease state--whether it be cancer vs. healthy controls
or cancer sub-type vs. other cancer sub-types--than individual sCD
antigens on their own.
Working Example 4
[0246] The interchangeability of various sCD assay techniques was
confirmed by comparison of the Luminex platform to the GenTel
biochip. The results summarized below were obtained by twenty-fold
cross-validation and resampling fifty times. This ensures that the
random variation due to single-mode approximations in the evidence
approximation MLP and the instability of k-NN are minimized. For
every sample for which both GenTel as well as Luminex measurements
were available data have been paired. In general we do not observe
any significant differences between the generalization accuracies
of matching assays. The main conclusion is therefore that we cannot
conclude one of the approaches to be superior of the other.
However, there is a borderline significant difference (p=0.06) in
the four-class problem of separating AML, CLL, CML, and NML
samples, in which Luminex measurements provided more information
for the separation of cancer types. The specificity/sensitivity
differences reflect this.
[0247] For AML/Other separation we were unable to detect a
significant difference between both approaches,
sensitivity/specificity differences are within range of expected
fluctuations.
Classification Performance AML/Other
TABLE-US-00043 [0248] TABLE 48 Data: Luminex Confusion Table, MLP,
Evidence Framework, Generalization Accuracy: 0.89 Predicted AML
normMix TRUE AML 9.96 6.04 other 2.44 59.56
TABLE-US-00044 TABLE 49 Data: Luminex Confusion Table, k Nearest
Neighbor, Generalization Accuracy: 0.83 Predicted AML normMix TRUE
AML 3.82 12.18 other 0.90 61.10
TABLE-US-00045 TABLE 50 Data: GenTel Confusion Table, MLP, Evidence
Framework, MLP, Evidence Framework, Generalization Accuracy: 0.94
Predicted AML normMix TRUE AML 12.00 4.00 other 0.72 61.28
TABLE-US-00046 TABLE 51 Data: GenTel Confusion Table, k Nearest
Neighbor Generalization Accuracy: 0.91 Predicted AML normMix TRUE
AML 8.98 7.02 other 0.00 62.00
TABLE-US-00047 TABLE 52 Generalization Accuracy and Significance
for GenTel and Luminex based dichotomous classification. Note that
the observed differences are not significant. Gen, GenAcc Sig.
Classification Method Acc.Luminex. GenTel Level Evid, App.Framework
89.13 93.95 0.98 K. Nearest Neighbors 83.23 91.00 1.00
[0249] The main conclusion for this experiment is that both
platforms perform very comparable.
[0250] For easy interpretation we list specificity/sensitivity
tables for both platforms side by side.
TABLE-US-00048 TABLE 53 Sensitivity/specificity comparison
GentelBio vs. Luminex for MLP classifier. Sample type Specificity
Sensitivity Gentel AML 98% 75% Other 98% 99% Luminex AML 96% 62%
Other 85% 96%
Classification Performance all Four Classes
TABLE-US-00049 [0251] TABLE 54 Data: Luminex Confusion Table, MLP
Evidence Framework, Generalization Accuracy: 0.78 Predicted AML CLL
CML NHL TRUE AML 12.34 2.06 1.02 0.58 CLL 0.06 19.66 1.68 2.60 CML
0.64 1.98 20.96 0.42 NHL 1.18 4.16 0.02 6.64
TABLE-US-00050 TABLE 55 Data: Luminex Confusion Table, K, Nearest
Neighbor, Generalization Accuracy: 0.62 Predicted AML CLL CML NHL
TRUE AML 4.26 5.94 2.00 3.80 CLL 0.00 21.06 2.08 0.86 CML 0.08 5.94
17.90 0.08 NHL 1.96 6.14 0.00 3.90
TABLE-US-00051 TABLE 56 Data: Gentel Confusion Table, MLP Evidence
Framework, Generalization Accuracy: 0.66 Predicted AML CLL CML NHL
TRUE AML 13.62 2.02 0.34 0.02 CLL 1.40 15.12 4.84 2.64 CML 1.02
4.26 16.34 2.38 NHL 0.00 3.50 3.642 4.86
TABLE-US-00052 TABLE 57 GenTel Confusion Table, K, Nearest
Neighbor, Generalization Accuracy: 0.58 Predicted AML CLL CML NHL
TRUE AML 11.76 1.24 3.00 0.00 CLL 0.74 12.60 9.46 1.20 CML 2.70
5.96 14.18 1.16 NHL 0.00 2.14 4.10 5.76
TABLE-US-00053 TABLE 58 Generalization Accuracy and Significance
for GenTel and Luminex based polychotomous classification. Note
that the observed difference in Row 1 is borderline significant,
the difference in Row 2 is not significant. Sig. Classification
Method Gen, Acc.Luminex. GenAcc GenTel Level Evid, App. Framework
78.42 65.71 0.06 K. Nearest Neighbors 62.00 58.29 0.37 Sample type
Specificity Sensitivity Gentel AML 95% 85% CLL 80% 63% CML 82% 68%
NHL 81% 40% Luminex AML 96% 72% CLL 84% 82% CML 95% 87% NHL 94%
55%
Working Example 5
Patterns for 5-Plex Experiment
[0252] The classification performance from the classifiers
described above proves the usefulness of multiple antigens for the
purpose of disease classification. The purpose of this additional
document is to depict the information that is inherent to patterns
of expression level of multiple antigens. For this illustration we
will restrict the focus on three different disease classes--AML,
all other cancers and healthy controls. The plots in this document
are generated on basis of the "minusNegByWell.robust" normalization
method as described above. In other words, prior to the generation
of scatter plots the data has been normalized using the IWLS robust
mean estimator on a per well basis.
[0253] For each of 5 sCD specificities we plotted 2d-scatter plots
for all possible pairings. A specificity plotted against itself
resembles univariate analysis as commonly undertaken. The
additional scatter plots yield 2 dimensional projections of the
5-dimensional space that illustrate additional patterns and
structure that can only be recovered by examining multiple
dimensions (here 2) simultaneously. The algorithms as discussed
above perform classification on all 5 dimensions which yields an
additional improvement, but the learned parameters of those
algorithms, specifying a pattern structure, is implementation
specific. Hence scatter plots are illustrated here as a means of
capturing the nature of the patterns we identify.
[0254] First CD 117 is studied and illustrated. This is the only
marker that can yields good classification performance on its own.
The discriminative performance can clearly be read of from the
first scatter plot CD117 against itself. The other 4 scatter plots
illustrate that additional specificities help tease out structure
that cannot be captured by a single antigen. For instance CD11 vs
CD14 illustrates the benefit from introducing the additional
dimension in the pattern.
[0255] Similar scatter plots are provided for the remaining
pairings of the full set of the 5 soluble CD antigens described in
the above working examples.
[0256] One skilled in the art will readily appreciate that the
present invention is well adapted to carry out the objects and
obtain the ends and advantages mentioned as well as those inherent
therein. The immunological methods and devices for detecting
analytes in biological samples as described herein are presently
representative of preferred embodiments, are exemplary and not
intended as limitations on the scope of the invention. Changes
therein and other uses will occur to those skilled in the art which
are encompassed within the spirit of the invention or defined by
this scope with the claims.
[0257] It will be readily apparent to one skilled in the art that
varying substitutions and modifications may be made to the
invention disclosed herein without departing from the scope and
spirit of the invention. All references and citations disclosed
herein are incorporated by reference in their entirety.
REFERENCES
[0258] 1. Probable networks and plausible predictions--a review of
practical Bayesian methods for supervised neural networks; Mackay
D. J. C. 1995; Network: Computation in Neural Systems [0259] 2.
Nearest neighbour (NN) norms: NN pattern classification techniques;
Dasarathy, B. V.; Los Alamitos: IEEE Computer Society Press, 1990
[0260] 3. Robust regression using iteratively reweighted
least-squares; Holland, P. W. and Welsch, R. E.; Communications in
Statistics-Theory and Methods [0261] 4. Pattern Classification;
Duda, R. O. and Hart, P. E. and Stork, D. G.; Wiley-Interscience
2nd edition, Wiley, 2001
TABLE-US-00054 [0261] TABLE 43 Human CD Antigen Differentiation
Molecules (as of November 2007). List taken from: the url:
hcdm.org/CD1toCD350.htm downloaded on Nov. 6, 2007 MOLECULE Gene
Name GeneID CD1a T6/leu-6, R4, HTA1 CD1A 909 CD1b R1 CD1B 910 CD1c
M241, R7 CD1C 911 CD1d R3 CD1D 912 CD1e R2 CD1E 913 CD2 T11; Tp50;
sheep red blood CD2 914 cell (SRBC) receptor; LFA-2; CD3d CD3
complex, T3, Leu4 CD3D 915 CD3e CD3E 916 CD3g CD3G 917 CD4 OKT4,
Leu 3a, T4 CD4 920 CD5 Tp67; T1, Ly1, Leu-1 CD5 921 CD6 T12 CD6 923
CD7 Leu 9, 3A1, gp40, T cell CD7 924 leukemia antigen CD8.alpha.
OKT8, LeuT, LyT2, T8 CD8A 925 CD8.beta. CD8B1 926 CD9 Drap-27,
MRP-1, p24, CD9 928 leucocyte antigen MIC3 CD10 CALLA, membrane
metallo- MME 4311 endopeptidase CD11a alphaL; LFA-1, gp180/95 ITGAL
3683 CD11b alphaM; alpha-chain of C3bi ITGAM 3684 receptor,
gp155/95, Mac-1, Mo1 CD11c alphaX; a-chain of: ITGAX 3687
complement receptor type 4 (CR4); gp150/95 CDw12 P90-120 23444 CD13
Aminopeptidase N, APN, ANPEP 290 gp150, EC 3.4.11.2 CD14 LPS
receptor CD14 929 CD15 Lewis X, CD 15u: sulphated carbohydrate
Lewis X. CD 15s: sialyl antigen Lewis X CD16a Fc gamma R IIIa,
FCGR3A 2214 CD16b Fc gamma R IIIb FCGR3B 2215 CD17 LacCer,
lactosylceramide carbohydrate antigen CD18 .beta.2-Integrin chain,
ITGB2 3869 macrophage antigen 1 (mac- 1) CD19 Bgp95, B4 CD19 930
CD20 B1; membrane-spanning 4- MS4A1 931 domains, subfamily A,
member 1 CD21 C3d receptor, CR2, gp140; CR2 1380 EBV receptor CD22
Bgp135; BL-CAM, Siglec2 CD22 933 CD23 Low affinity IgE receptor;
FCER2 2208 FceRII; gp50-45; Blast-2 CD24 heat stable antigen CD24
934 homologue (HSA), BA-1 CD25 Interleukin (IL)-2 receptor a- IL2RA
3559 chain; Tac-antigen CD26 Dipeptidylpeptidase IV; DPP4 1803
gp120; Ta1 CD27 T14, S152 TNFRSF7 939 CD28 Tp44 CD28 940 CD29
Integrin .beta.1 chain; platelet ITGB1 3688 GPIIa; VLA (CD49) beta-
chain CD30 Ki-1 antigen, Ber-H2 antigen TNFRSF8 943 CD31 PECAM-1;
platelet GPIIa'; PECAM1 5175 endocam CD32 Fcgamma receptor type II
FCGR2A 2212 (FcgRII), gp40 CD33 My9, gp67, p67 CD33 945 CD34 My10,
gp105-120 CD34 947 CD35 C3b/C4b receptor; CR1 1378 complement
receptor type 1 (CR1) CD36 platelet GPIV, GPIIIb, CD36 948 OKM-5
antigen CD37 gp40-52 CD37 951 CD38 T10; gp45, ADP-ribosyl CD38 952
cyclase CD39 gp80, ectonucleoside ENTPD1 953 triphosphate
diphosphohydrolase 1 CD40 Bp50, TNF Receptor 5 TNFRSF5 958 CD41
platelet glycoprotein GPIIb ITGA2B 3674 CD42a platelet glycoprotein
GPIX GP9 2815 CD42b platelet glycoprotein GPIb-a GP1BA 2811 CD42c
platelet glycoprotein GPIb-.beta. GP1BB 2812 CD42d platelet
glycoprotein GPV GP5 2814 CD43 Leukosialin; gp95; SPN 6693
sialophorin; leukocyte sialoglycoprotein CD44 Pgp-1; gp80-95,
Hermes CD44 960 antigen, ECMR-III and HUTCH-I. CD44R CD44 variant;
CD44v1-10 960 CD45 LCA, B220, protein tyrosine PTPRC 5788
phosphatase, receptor type, C CD45RA Restricted T200; gp220; see
CD45 isoform of leukocyte common antigen CD45RO Restricted T200;
gp180; see CD45 CD45RB Restricted T200; isoform of see CD45
leukocyte common antigen CD45RC Restricted T200; isoform of see
CD45 leukocyte common antigen CD46 Membrane cofactor potein MCP
4179 (MCP) CD47 Integrin-associated protein CD47 961 (IAP), Ovarian
carcinoma antigen OA3 CD48 BLAST-1, Hulym3, OX45, CD48 962 BCM1
CD49a Integrin a1 chain, very late ITGA1 3672 antigen, VLA 1a CD49b
Integrin a2 chain, VLA-2- ITGA2 3673 alpha chain, platelet gpIa
CD49c Integrin a3 chain, VLA-3 ITGA3 3675 alpha chain CD49d
Integrin a4 chain,, VLA-4- ITGA4 3676 alpha chain CD49e Integrin a5
chain,, VLA-5 ITGA5 3678 alpha chain CD49f Integrin a6 chain,,
VLA-6 ITGA6 3655 alpha chain, platelet gpIc CD50 ICAM-3,
intercellular ICAM3 3385 adhesion molecule 3 CD51 Integrin alpha
chain, ITGAV 3685 vitronectin receptor a chain CD52 Campath-1, HE5
CDW52 1043 CD53 MRC OX-44 CD53 963 CD54 ICAM-1, intercellular ICAM1
3383 adhesion molecule 1 CD55 DAF, Decay Accelerating DAF 1604
Factor CD56 NKHI, Neural cell adhesion NCAM1 4684 molecule (NCAM)
CD57 HNK1 CD57 964 CD58 LFA-3, lymphocyte function CD58 965
associated antigen-3 CD59 MACIF, MIRL, P-18, CD59 966 protectin
CD60 GD3 (CD60a), 9-0-acetyl carbohydrate GD3 (CD60b), antigen
7-0-acetyl GD3 (CD60c) CD61 Glycoprotein IIIa, beta3 ITGB3 3690
integrin CD62E E-selectin, LECAM-2, SELE 6401 ELAM-1 CD62L
L-selectin, LAM-1, Mel-14 SELL 6402 CD62P P-selectin, granule
membrane SELP 6403 protein-140 (GMP-140) CD63 LIMP, gp55, LAMP-3
CD63 967 neuroglandular antigen, granulophysin CD64 FcgR1,
FcgammaR1 FCGR1A 2209 CD65 Ceramide dodecasaccharide carbohydrate
4c, VIM2 antigen CD65s Sialylated-CD65, VIM2 carbohydrate Antigen
antigen CD66a BGP, carcinoembryonic CEACAM1 634 antigen-related
cell adhesion molecule 1 CD66b CGM6, NCA-95 CEACAM8 1088 CD66c
nonspecific crossreaction CEACAM6 4680 antigen, NCA-50/90 CD66d
CGM1 CEACAM3 1084 CD66e CEA CEACAM5 1048 CD66f PSG, Sp-1, pregnancy
specific PSG1 5669 (b1) glycoprotein CD68 gp110, macrosialin CD68
968 CD69 AIM, activation inducer CD69 969 molecule, MLR3, EA1, VEA
CD70 CD27 ligand, KI-24 antigen TNFSF7 970 CD71 Transferrin
receptor TFRC 7037 CD72 Lyb-2, Ly-19.2, Ly32.2 CD72 971 CD73
Ecto-5'-nucleotidase NT5E 4907 CD74 MHC Class II associated CD74
972 invariant chain (Ii) CD75 Lactosamines carbohydrate antigen
CD75s Alpha-2,6-sialylated carbohydrate lactosamines (formerly
antigen CDw75 and CDw76) CDw76 Since HLDA7, CDw76 has carbohydrate
been renamed CD75s antigen CD77 Pk blood group antigen;
carbohydrate Burkitt's lymphoma antigen associated antigen CD79a
MB-1; Iga CD79A 973 CD79b B29; Ig.beta. CD79B 974 CD80 B7-1; BB1
CD80 941 CD81 Target of an antiproliferative CD81 975 antibody
(TAPA-1); M38 CD82 R2; 4F9; C33; IA4, kangai 1 KAI1 3732 CD83 HB15
CD83 9308 CD84 p75, GR6 CD84 8832 CD85a ILT5; LIR3; HL9 LILRB3
11025 CD85d ILT4; LIR2; MIR10 LILRB2 10288 CD85k ILT3; LIR5; HM18
LILRB4 11006 CD85j LIR-1, ILT2 LILRB1 10859 (immunoglobulin- like
transcript 2); MIR7 CD86 B7-2; B70 CD86 942 CD87 Urokinase
plasminogen PLAUR 5329 activator-receptor (uPA-R) CD88 C5a-receptor
C5R1 728 CD89 Fca-receptor, IgA-receptor FCAR 2204 CD90 Thy-1 THY1
7070 CD91 a2-macroglobulin receptor LRP1 4035 (ALPHA2M) CD92 p70
CDW92 23446 CD93 GR11 23447 CD94 kP43, killer cell lectin-like
KLRD1 3824 receptor subfamily D, member 1 CD95 APO-1, Fas, TNFRSF6
TNFRSF6 355 CD96 TACTILE (T cell activation CD96 10225 increased
late expression) CD97 BL-KDD/F12 CD97 976 CD98 4F2, FRP-1 SLC3A2
6520 CD99 MIC2, E2 CD99 4267 CD100 SEMA4D SEMA4D 10507 CD101 V7,
P126 IGSF2 9398 CD102 ICAM-2 ICAM2 3384 CD103 Integrin alpha E
subunit, ITGAE 3682 HML-1 CD104 Integrin beta 4 subunit, TSP- ITGB4
3691 1180 CD105 Endoglin ENG 2022 CD106 VCAM-1 (vascular cell VCAM1
7412 adhesion molecule-1), INCAM-110 CD107a Lysosomal associated
LAMP1 3916 membrane protein (LAMP)-1 CD107b Lysosomal associated
LAMP2 3920 membrane protein (LAMP)-2 CD108 GPI-gp80; John-Milton-
SEMA7A 8482 Hagen (JMH) human blood group antigen CD109 Platelet
activation factor; N/A 8A3, E123 CD110 Thrombopoietin receptor; c-
MPL 4352 mpl CD111 PRR1, Nectin 1, Hve C1, PVRL1 5818
poliovirus receptor related 1 protein CD112 PRR2, Nectin 2, Hve B,
PVRL2 5819 poliovirus receptor related 2 protein CD113 PVRL3,
Nectin3 PVRL3 25945 CD114 G-CSFR, HG-CSFR, CSFR3 CSF3R 1441 CD115
M-CSFR, CSF-1, C-fms CSF1R 1436 CD116 GMCSF R alpha subunit, CSF2RA
1438 CD117 SCFR, c-kit, stem cell factor KIT 3815 receptor CD118
LIFR LIFR 3977 CD119 IFN gamma receptor alpha IFNGR1 3459 chain
CD120a TNFRI; TNFRp55 TNFRSF1A 7132 CD120b TNFRII; TNFRp75 TNFRSF1B
7133 CD121a Type I IL-1 receptor IL1R1 3554 CD121b Type II IL-1
receptor IL1R2 7850 CD122 IL-2 receptor betachain, p75 IL2RB 3560
CD123 Interleukin-3 receptor alpha IL3RA 3563 chain (IL-3Ra) CD124
IL-4 R alpha chain IL4R 3566 CD125 Interleukin-5 receptor a chain
IL5RA 3568 CD126 IL-6 receptor alpha chain IL6R 3570 CD127 IL-7
receptor alpha chain, IL7R 3575 p90 (CD129) IL-9 receptor alpha
chain IL9R 3581 CD130 gp130 IL6ST 3572 CD131 Common .beta. chain,
low-affinity CSF2RB 1439 (granulocyte-macrophage) CD132 Common
gamma chain, IL2RG 3561 interleukin 2 receptor, gamma CD133 AC133,
PROML1, prominin 1 PROM1 8842 CD134 OX 40, TNFRSF4 TNFRSF4 7293
CD135 FLT3, STK-1, flk-2 FLT3 2322 CD136 Macrophage stimulating
MST1R 4486 protein receptor, MSP-R, RON CD137 4-1BB, Induced by
TNFRSF9 3604 lymphocyte activation (ILA) CD138 Syndecan-1, B-B4
SDC1 6382 CD139 23448 CD140a a-platelet derived growth PDGFRA 5156
factor (PDGF) receptor CD140b b-platelet derived growth PDGFRB 5159
factor (PDGF) receptor CD141 Thrombomodulin (TM), THBD 7056
fetomodulin CD142 Tissue factor, thromboplastin, F3 2152
coagulation factor III CD143 Angiotensin-converting ACE 1636 enzyme
(ACE), peptidyl dipeptidase A CD144 VE-cadherin, cadherin-5 CDH5
1003 CDw145 None N/A CD146 Muc 18, MCAM, Mel-CAM, MCAM 4162 s-endo
CD147 Basigin, M6, extracellular BSG 682 metalloproteinase inducer
(EMMPRIN) CD148 DEP-1, HPTP-n, protein PTPRJ 5795 tyrosine
phosphatase, receptor type, J CD150 SLAM, signalling lymphocyte
SLAMF1 6504 activation molecule, IPO-3 CD151 Platelet-endothelial
tetra-span CD151 977 antigen (PETA)-3 CD152 Cytotoxic T lymphocyte
CTLA4 1493 antigen (CTLA)-4 CD153 CD30 Ligand TNFSF8 944 CD154 CD40
Ligand; TRAP (TNF- TNFSF5 959 related activation protein)-1; T-BAM
CD155 Polio virus receptor (PVR) PVR 5817 CD156a ADAM-8, a
disintegrin and ADAM8 101 metalloproteinase domain 8 CD156b TACE,
ADAM 17 snake ADAM17 6868 venom like protease CSVP CD156C ADAM10
ADAM10 102 CD157 BST-1 BP-3/IF7 Mo5 BST1 683 CD158e1/2 killer cell
Ig-like receptor, KIR3DL1 3811 three domains, long cytoplasmic
tail, 1 CD158i killer cell Ig-like receptor, two KIR2DS4 3809
domains, short cytoplasmic tail, 4 CD158k killer cell Ig-like
receptor, two KIR2DL2 3803 domains, long cytoplasmic tail, 2 CD159a
killer cell lectin-like receptor KLRC1 3821 subfamily C, member 1
CD159c NKG2C KLRC2 3822 CD160 BY55, NK1, NK28 CD160 11126 CD161
NKR-P1A, killer cell lectin- KLRB1 3820 like receptor subfamily B,
member 1 CD162 P selectin glycoprotein ligand SELPLG 6404 1, PSGL-1
CD162R PEN5 see CD162 CD163 GHI/61, D11, RM3/1, M130 CD163 9332
CD164 MUC-24, MGC 24, multi- CD164 8763 glycosylated core protein
24 CD165 AD2, gp 37 23449 CD166 ALCAM, KG-CAM, activated ALCAM 214
leukocyte cell adhesion molecule CD167 Discoidin receptor DDR1 (CD
DDR1 780 167a) and DDR2 (CD 167b) CD168 RHAMM (receptor for HMMR
3161 hyaluronan involved in migration & motility) CD169
Sialodhesin, Siglec-1 SN 6614 CD170 Siglec 5 (sialic acid binding
SIGLEC5 8778 Ig-like lectin 5) CD171 Neuronal adhesion molecule,
L1CAM 3897 LI CD172a SIRPa, signal inhibitory PTPNS1 140885
regulatory protein family member CD172b SIRPbeta SIRPB1 10326
CD172g SIRPgamma SIRPB2 55423 CD173 Blood Group H2 carbohydrate
antigen CD174 Lewis Y blood group, LeY, FUT3 2525
fucosyltransferase 3 CD175 Tn Antigen (T-antigen carbohydrate
novelle) antigen CD175s Sialyl-Tn carbohydrate antigen CD176
Thomsen-Friedenreich carbohydrate antigen (TF) antigen CD177 NB 1
None assigned CD178 FAS ligand, CD95 ligand TNFSF6 356 CD179a V pre
beta VPREB1 7441 CD179b Lambda 5 IGLL1 3543 CD180 RP105, Bgp95 LY64
4064 CD181 CXCR1, (was CDw128A) IL8RA 3577 CD182 CXCR2, (was
CDw128B) IL8RB 3579 CD183 CXCR3 chemokine receptor, CXCR3 2833 G
protein-coupled receptor 9 CD184 CXCR4 chemokine receptor, CXCR4
7852 Fusin CD185 CXCR5 BLR1 643 CD186 CXCR6 CXCR6 10663 CD191 CCR1
CCR1 1230 CD192 CCR2 CCR2 1231 CD193 CCR3 CCR3 1232 CD194 CCR4 CCR4
1233 CD195 CCR5 chemokine receptor CCR5 1234 CD196 CCR6 CCR6 1235
CD197 CCR7 CCR7 1236 CDw198 CCR8 CCR8 1237 CDw199 CCR9 CCR9 10803
CD200 MRC OX 2 CD200 4345 CD201 Endothelial protein C receptor
PROCR 10544 (EPCR) CD202b TIE2, TEK TEK 7010 CD203c E-NPP3, PDNP3,
PD-1beta ENPP3 5169 CD204 MSR, SRA, Macrophage MSR1 4481 scavenger
receptor CD205 DEC-205 LY75 4065 CD206 Macrophage mannose MRC1 4360
receptor (MMR) CD207 Langerin CD207 50489 CD208 DC-LAMP LAMP3 27074
CD209 DC-SIGN CD209 30835 CDw210 IL-10 receptor IL10RA 3587 IL10RB
3588 CD212 IL-12 receptor beta chain IL12RB1 3594 CD213a1 IL-13
receptor alpha 1 IL13RA1 3597 CD213a2 IL-13 R alpha 2 IL13RA2 3598
CD217 IL-17 receptor IL17R 23765 CD218a IL18Ralpha IL18R1 8809
CD218b IL18Rbeta IL18RAP 8807 CD220 Insulin Receptor INSR 3643
CD221 IGF I Receptor, type I IGF IGF1R 3480 receptor CD222
Mannose-6-phosphate IGF2R 3482 receptor, insulin like growth factor
II R CD223 LAG-3 (Lymphocyte LAG3 3902 activation gene 3) CD224
Gamma-glutamyl transferase, GGT1 2678 GGT CD225 Leu-13,
interferon-induced 8519 transmembrane protein 1 CD226 DNAM-1, DTA-1
CD226 10666 CD227 MUC 1 MUC1 4582 CD228 Melanotransferrin, p97 MFI2
4241 CD229 Ly9 LY9 4063 CD230 Prion protein, PrPI, PrP(sc) PRNP
5621 abnormal form CD231 TALLA-1, TM4SF2 TM4SF2 7102 CD232 VESPR
PLXNC1 10154 CD233 Band 3, AE1, anionexchanger SLC4A1 6521 1, Diego
blood group antigen CD234 DARC, Fy-glycoprotein, FY 2532 Duffy
blood group antigen CD235a Glycophorin A GYPA 2993 CD235b
Glycophorin B GYPB 2994 CD236 Glycophorin C/D GYPC 2995 CD236R
Glycophorin C GYPC 2995 CD238 Kell blood group antigen KEL 3792
CD239 B-CAM, utheran glycoprotein LU 4059 CD240CE Rh blood group
system, RHCE 6006 Rh30CE CD240D Rh blood group system, RHD 6007
Rh30D CD240DCE Rh30D/CE crossreactive mabs CD240CE, CD240D CD241
RhAg, Rh50, Rh associated RHAG 6005 antigen CD242 LW blood group,
Landsteiner- ICAM4 3386 Wiener blood group antigens CD243 MDR-1,
P-glycoprotein, pgp ABCB1 5243 170, multidrug resistance protein I
CD244 2B4 CD244 51744 CD245 p220/240, DY12, DY35 N/A CD246
Anaplastic lymphoma kinase ALK 238 (ALK) CD247 T cell receptor zeta
chain, CD3Z 919 CD3 zeta CD248 TEM1, Endosialin CD164L1 57124 CD249
Aminopeptidase A ENPEP 2028 CD252 OX40L TNFSF4 7292 CD253 TRAIL
TNFSF10 8743 CD254 TRANCE TNFSF11 8600 CD256 APRIL TNFSF13 8741
CD257 BLYS TNFSF13B 10673 CD258 LIGHT TNFSF14 8740 CD261 TRAIL-R1
TNFRSF10A 8797 CD262 TRAIL-R2 TNFRSF10B 8795 CD263 TRAIL-R3
TNFRSF10C 8794 CD264 TRAIL-R4 TNFRSF10D 8793 CD265 TRANCE-R
TNFRSF11A 8792 CD266 TWEAK-R TNFRSF12A 51330 CD267 TACI TNFRSF13B
23495 CD268 BAFFR TNFRSF13C 115650 CD269 BCMA TNFRSF17 608 CD271
NGFR (p75) NGFR 4804 CD272 BTLA BTLA 151888 CD273 B7DC, PDL2
PDCD1LG2 80380 CD274 B7H1, PDL1 PDCD1LG1 29126 CD275 B7H2, ICOSL
ICOSL 23308 CD276 B7H3 N/A 80381 CD277 BT3.1 BTN3A1 11119
CD278 ICOS ICOS 29851 CD279 PD1 PDCD1 5133 CD280 ENDO180 MRC2 9902
CD281 TLR1 TLR1 7096 CD282 TLR2 TLR2 7097 CD283 TLR3 TLR3 7098
CD284 TLR4 TLR4 7099 CD286 TLR6 TLR6 10333 CD288 TLR8 TLR8 51311
CD289 TLR9 TLR9 54106 CD290 TLR10 TLR10 81793 CD292 BMPR1A BMPR1A
657 CDw293 BMPR1B BMPR1B 658 CD294 CRTH2 GPR44 11251 CD295 LeptinR
LEPR 3953 CD296 ART1 ART1 417 CD297 ART4 DO 420 CD298 Na+/K+-ATPase
.beta.3 ATP1B3 483 CD299 DCSIGN-related CD209L 10332 CD300a CMRF35H
11314 CD300c CMRF35A 10871 CD300e CMRF35L1 CD301 MGL, CLECSF14
CLECSF14 10462 CD302 DCL1 N/A 9936 CD303 BDCA2 CLECSF7 170482 CD304
BDCA4, Neuropilin 1 NRP1 8829 CD305 LAIR1 LAIR1 3903 CD306 LAIR2
LAIR2 3904 CD307 IRTA2 N/A 83416 CD309 VEGFR2, KDR KDR 3791 CD312
EMR2 EMR2 30817 CD314 NKG2D KLRK1 22914 CD315 CD9P1 PTGFRN 5738
CD316 EWI2 IGSF8 93185 CD317 BST2 BST2 684 CD318 CDCP1 N/A 64866
CD319 CRACC SLAMF7 57823 CD320 8D6A N/A 51293 CD321 JAM1 F11R 50848
CD322 JAM2 JAM2 58494 CD324 E-Cadherin CDH1 999 CD325 N-Cadherin
CDH2 1000 CD326 Ep-CAM TACSTD1 4072 CD327 siglec6 SIGLEC6 946 CD328
siglec7 SIGLEC7 27036 CD329 siglec9 SIGLEC9 27180 CD331 FGFR1 FGFR1
2260 CD332 FGFR2 FGFR2 2263 CD333 FGFR3 FGFR3 2261 CD334 FGFR4
FGFR4 2264 CD335 NKp46 NCR1 9437 CD336 NKp44 NCR2 9436 CD337 NKp30
NCR3 259197 CD338 ABCG2, BCRP ABCG2 9429 CD339 Jagged-1 JAG1 182
CD340 Her-2 ERBB2 2064 CD344 Frizzled-4 FZD4 8322 CD349 Frizzled-9
FZD9 8326 CD350 Frizzled-10 FZD10 11211
TABLE-US-00055 TABLE 44 sCD ANTIGEN SPECIFICITIES sCD ANTIGEN NAME
(1) sCD Specificities Gentel Name CD14 LPS-R (LPS Receptor) CD23
FceRII (low affinity IgE receptor) CD25 IL2-R-alpha chain, Tac
antigen CD26 gp120, Ta1 CD27 T14 (Integrin beta 2), S152 CD30 Ki-1
antigen, B4, Ber-H2 antigen CD32b/c FcGRII, B1, gp40 CD40 TNF
Receptor-5, Bp50 CD54 ICAM-1 (intercellular adhesion molecule 1)
CD62E E-selectin (ELAM-1) CD62L L-selectin (LAM-1) CD80 B7-1, BB1
CD86 B7-2, B70 CD87 Urokinase plasminogen activator R (uPA-R) CD95
Fas, (APO-1), TNFRSF6 CD102 ICAM-2 CD105 Endoglin CD106 VCAM-1
CD114 G-CSFR CD115 M-CSFR, C-fms CD117 c-kit, stem cell factor
receptor CD120a TNFR-I CD120b TNFR-II CD121b IL-1 R 2 (type II IL-1
receptor) CD124 IL-4 R alpha chain CD126 IL-6 R alpha chain CD127
IL-7 R alpha chain CD130 gp130 CD132 Il-2 R gamma CD152 CD166 ALCAM
(activated leukocyte cell adhesion molecule) CD170 Siglec 5 (sialic
acid binding Ig-like lectin 5) CD178 Fas ligand CD213a1 IL-13 R
alpha 1 CD213a2 IL-13 R alpha 2 CD221 IGF1R (IgF1 receptor) CD239
B-CAM (utheran glycoprotein) CD258 LIGHT CD263 TRAIL-R3 CD309
VEGFR2, KDR CD324 E-Cadherin CDw329 Siglec9
TABLE-US-00056 TABLE 45 sCD Marker ID Identity CD11b Integrin
.alpha. M CD11c Integrin .alpha. X CD13 Aminopeptidase N CD15 LEWIS
x CD33 Siglec-3 CD36 SR-B3 CD64 Fc gamma RI CD49d Integrin .alpha.
4 CD29 Integrin .beta. 1 CD38 CD38 CD71 Transferrin CD4 T4 CD34
gp105 CD9 p24 CD41 Integrin .alpha. 2B CD43 sialophorin CD45 LCA
CD4 L3T4 CD200 OX2 CD31 sPECAM1 CD55 DAF CD56 NCAM-1 CD66a CEACAM-1
CD64 Fc gamma RI CD83 HB15 CD85d ILT4 CD85j ILT2 CD97 CD97 CD147
EMMPRIN CD202b Tie-2 CD212b1 IL-12 R.beta.1 CD212b2 IL-12 R.beta.2
CDw217 IL-17R CD217 IL-17 CD217F IL-17F CD217E IL-17E CD217D IL-17D
CD217C IL-17C CD217B/r IL-17B R CD217B IL-17B CD217rD IL-17 RD
CD222 ILF2 R CD226 DNAM-1 CD244 2B4/SLAMF4 CD235a Glycophorin A
CD44 H-Cam CD90 Thy-1 CD116 GM-CSFR CD123 IL-3Ralpha CD46 MCP CD16
FcgammaRIIIA CD35 CR1 CD8 (alpha) T8 CD1c R7 CD20 MS4A1 CD19 B4 CD7
gp40 CD1a R4 CD1d R3 CD2 T11 CD10 Neprilysin CD40L CD40L CD62P
Selectin - P CD110 Thrombopoietin CD129 CD129 CD137 4-1BB CD143 ACE
CD148 DEP-1 CD156b TACE CD171 L1CAM CD195 CCR5 CD220 Insulin Rec.
CD264 TRAIL R4
TABLE-US-00057 TABLE 46 Detector Antibodies ID Vendor Description
Catalogue # CD116 Apollo Human GM-CSF R alpha 1102H hcx .TM., Fc
Chimera CD11b Spring Bioscience Human CD11b, aa 936-1154 P7868
CD11c Abnova Human ITGAX Partial, H00003687-Q01 GST
Conjugated/Tagged CD123 R&D Systems Recombinant Human IL-3
301-R3-025/CF sR alpha, CF CD13 R&D Systems Recombinant Human
3815-ZN-010 Aminopeptidase N/ANPEP, CF CD137L MBL Mouse Anti-Human
K0030-3 CD137L/41BBL Monoclonal Antibody, Unconjugated, Clone 5F4
CD147 R&D Systems Recombinant Human 972-EMN-050 EMMPRIN/Fc
Chimera (NS0-expressed), CF 9 CD16 R&D Systems Recombinant
Human Fc 1597-FC-050/CF gamma RIIIB/CD16b, CF CD19 Novus Human CD19
Partial, H00000930-Q01 GST Conjugated/Tagged CD195 ProSpec CCR5
Protein 1112P CD1a Raybiotech Recombinant Human IP-03-467 CD1a CD1c
Novus Human CD1C Partial, H00000911-Q01 GST Conjugated/Tagged CD1d
Novus CA1d Full Length H00000912-P01 Recombinant - GST/Tagged CD2
Raybiotech Recombinant Human IP-03-468 CD2 CD20 Novus Human MS4A1
Full H00000931-P01 length, GST Conjugated/Tagged CD200 R&D
Systems Recombinant Human 627-CD-100 CD200/Fc Chimera, CF CD202b
R&D Systems Recombinant Mouse Tie- 313-TI-100 2/Fc Chimera, CF
CD212b1 R&D Systems Recombinant Human IL- 839-B1-100 12 R beta
1/Fc Chimera, CF CD212b2 R&D Systems Recombinant Human IL-
1959-B2-050 12 R beta 1/Fc Chimera, CF CD217 R&D Systems
Recombinant Human IL- 317-IL-050 17, CF CD217B R&D Systems
Recombinant Human IL- 1248-IB-025 17B, CF CD217B/r R&D Systems
Recombinant Human IL- 1207-BR-050 17B R/Fc Chimera, CF CD217C
R&D Systems Recombinant Human IL- 1234-IL-025 17C, CF CD217D
R&D Systems Recombinant Human IL- 1504-IL-025 17D, CF CD217E
R&D Systems Recombinant Human IL- 1258-IL-025 17E, CF CD217F
R&D Systems Recombinant Human IL-1 1335-IL-025 7F, CF CD217rD
R&D Systems Recombinant Human IL- 2275-IL-050 17 RD/SEF CD222
R&D Systems Recombinant Human 2447-GR-050 IGF-II R, CF CD226
R&D Systems Recombinant Human 666-DN-050 DNAM-1/Fc Chimera, CF
CD235a Sigma Glycophorin G5017 Predominantly glycophorin A from
blood type MN CD235a Sigma Glycophorin G7903 Predominantly
glycophorin A from blood type MM CD244 R&D Systems Recombinant
Human 1039-2B-050 2B4/CD244/SLAMF4/Fc Chimera, CF CD29 Spring
Bioscience Human CD29, aa 579-799 P7892 from Spring Bioscience CD31
Raybiotech Recombinant Human IP-03-471 CD31 CD33 R&D Systems
Recombinant Human 1137-SL-050 Siglec-3/CD33/Fc Chimera, CF CD34
Spring Bioscience Human CD34 Full-Length P7122 CD35 Anaspec
Cripto-1, CR-1 60630 CD36 R&D Systems Recombinant Human
1955-CD-050 CD36/SR-B3/Fc Chimera, CF CD38 R&D Systems
Recombinant Human 2404-AC-010 CD38, CF CD4 R&D Systems
Recombinant Human 514-CD-050/CF sCD4, CF CD41 BACHEM Human CD41
H-3032.0005 CD43 Spring Bioscience Human CD43, aa 271-401 P7896
CD44H R&D Systems Recombinant Human 3660-CD-050 CD44/Fc
Chimera, CF CD45 Calbiochem Human Protein Tyrosine 217614-20ug
Phosphatase CD45 CD46 Santa Cruz CD46 sc4530 CD49d Novus (Abnova)
Human ITGA4 Partial, H00003676-Q01 GST Conjugated/Tagged CD55
R&D Systems Recombinant Human 2009-CD-050 CD55/DAF, CF CD56
R&D Systems Recombinant Human 2408-NC-050 NCAM-1/CD56, CF CD64
R&D Systems Recombinant Human Fc 1257-FC-050 gamma RIA/CD64, CF
CD66a R&D Systems Recombinant Human 2244-CM-050 CEACAM-1/CD66a,
CF CD7 Spring Human CD7 Full-Length P7841 CD71 Raybiotech Human
CD71, aa 461-760 DS-01-0048 CD8 Santa Cruz Human CD8alpha sc-4265
(alpha) CD83 R&D Systems Recombinant Human 2044-CD-050 CD83/Fc
Chimera, CF CD85d R&D Systems Recombinant Human 2078-T4-050
ILT4/CD85d/Fc Chimera, CF CD85j R&D Systems Recombinant Human
2017-T2-050 ILT2/CD85j/Fc Chimera, CF CD9 Spring Bioscience Human
CD9 Full-Length P7878 CD90 Novus Human THY1 Full length,
H00007070-P01 GST Conjugated/Tagged CD97 R&D Systems
Recombinant Human 2529-CD-050 CD97, CF CDw217 R&D Systems
Recombinant Human IL- 177-IR-100 17 R/Fc Chimera, CF CD138 Cell
Sciences Human SYNDECAN-1/ 850.640.096 CD138 ELISA Kit, DIACLONE
CD141 R&D Systems Recombinant Human 3947-PA-010
Thrombomodulin/CD141, CF CD50 R&D Systems Recombinant Human
715-IC-050 (Matched Set) ICAM-3/CD50/Fc Chimera, CD52 Raybiotech
Human CD52 IP-03-487P CD70 R&D Systems Mouse CD27 Ligand/
783-CL-050 TNFSF7, Unconjugated CD171 Novus L1CAM-L1 Human
H00003897-Q01 Recombinant Protein HLA A Abnova Human HLA-A Protein
H00003105-P01 Full-Length, GST Conjugated/Tagged CD170 R&D
Systems Recombinant Human 1072-SL-050 Protein Siglec 5 CD10 R&D
Systems Recombinant Human 1182-ZN-010 Neprilysin, CF CD102 R&D
Systems Recombinant Human 803-I2-050 ICAM-2/CD102/Fc Chimera, CF
CD105 R&D Systems Recombinant Human 1097-EN-025 Endoglin/CD105
CD106 R&D Systems Recombinant Human 809-VR-050 VCAM-1/CD106, CF
CD110 R&D Systems Recombinant Human 1016-TR-050 Thrombopoietin
R/Fc Chimera, CF CD114 R&D Systems Recombinant Human G-
381-GR-050/CF CSF sR/CD114 CD115 R&D Systems Recombinant Human
M- 329-MR-100 CSF R/Fc Chimera CD117 R&D Systems Recombinant
Human 332-SR-050 SCF sR/c-kit CD120a R&D Systems Recombinant
Human 636-R1-025 sTNF RI/TNFRSF1A CD120b R&D Systems
Recombinant Human 1089-R2-025 TNF RII/TNFRSF1B (aa 24-206) CD121a
R&D Systems Recombinant Human IL-1 269-1R-100 sRI CD121b
R&D Systems Recombinant Human IL-1 263-2R-050 sRII CD124
R&D Systems Recombinant Human IL-4 230-4R-025/CF sR CD125
R&D Systems Recombinant Human IL-5 253-5R-025 sR alpha CD126
R&D Systems Recombinant Human IL-6 227-SR-025 sR CD127 R&D
Systems Recombinant Human IL-7 306-IR-050 R alpha/Fc Chimera, CF
CD129 R&D Systems Recombinant Human IL-9 290-RNS-025 sR
(NS0-expressed) CD130 R&D Systems Recombinant Human 228-GP-010
sgp130 CD132 R&D Systems Recombinant Human 384-RG-050 Common
gamma Chain CD137 R&D Systems Recombinant Human 4- 838-4B-100
1BB/TNFRSF9/Fc Chimera, CF CD14 R&D Systems Recombinant Human
383-CD-050 CD14 CD143 R&D Systems Recombinant Human 929-ZN-0101
ACE, CF CD148 R&D Systems Recombinant Human 1934-DP-010
DEP-1/CD148 (aa 997-1337), CF CD152 R&D Systems Recombinant
Human 325-CT-200 CTLA-4/Fc Chimera CD156b R&D Systems
Recombinant Human 930-ACB-010 TACE/ADAM17, CF CD166 R&D Systems
Recombinant Human 656-AL-100 ALCAM/Fc Chimera, CF CD171 R&D
Systems Recombinant Human 777-NC-100 NCAM-L1/Fc Chimera, CF CD178
R&D Systems Recombinant Human Fas 126-FL-010 Ligand/TNFSF6
CD195 Assay Designs Human CCR5, N- 908-132 terminus CD1d BD Bio
Human CD1d:Ig 557764 CD2 Spring Bio Human CD2, aa25-209 P3044
CD213a1 R&D Systems Recombinant Human IL- 146-IR-100 13 R alpha
1/Fc Chimera, CF CD213a2 R&D Systems Recombinant Human IL-
614-INS-100 13 R alpha 2/Fc Chimera (NS0), CF CD220 R&D Systems
Recombinant Human 1544-IR-050 Insulin R/CD220 (aa 28-956) CD221
R&D Systems Recombinant Human 391-GR-050 IGF-I sR, CF CD23
R&D Systems Recombinant Human Fc 123-FE-050 epsilon RII/CD23,
CF CD239 R&D Systems Recombinant Human 148-BC-100 BCAM/Fc
Chimera, CF CD25 R&D Systems Recombinant Human IL-2 223-2A-005
sR alpha CD258 R&D Systems Recombinant Human 664-LI-025
LIGHT/TNFSF14 CD26 R&D Systems Recombinant Human 1180-SE-010
DPPIV/CD26, CF CD263 R&D Systems Recombinant Human 630-TR-100
TRAIL R3/TNFRSF10C/Fc Chimera CD264 R&D Systems Recombinant
Human 633-TR-100 TRAIL R4/TNFRSF10D/Fc Chimera, CF CD27 R&D
Systems Recombinant Human 382-CD-100 CD27/TNFRSF7/Fc Chimera, CF
CD28 R&D Systems Recombinant Human 342-CD-200
CD28/Fc Chimera, CF CD295 R&D Systems Recombinant Human
389-LR-100 Leptin R/Fc Chimera CD30 R&D Systems Human
CD30/TNFRSF8 813-CD-100 Recombinant Protein (Fc Chimera) (Carrier
Free) CD309 R&D Systems Recombinant Human 357-KD-050 VEGF
R2/KDR/Fc Chimera CD324 R&D Systems Recombinant Human E-
648-EC-100 Cadherin/Fc Chimera, CF CD32b/c R&D Systems
Recombinant Human Fc 1875-CD-050 gamma RIIB/C (CD32b/c), CF CD33L2
R&D Systems Recombinant Siglec-5/Fc 1072-SL Chimera CD33L2
R&D Systems Recombinant Siglec-5/Fc 1072-SL Chimera CD40
R&D Systems Recombinant Human 1493-CD-050 CD40/TNFRSF5/Fc
Chimera, CF CD40L R&D Systems Recombinant Human 617-CL-050 CD40
Ligand/TNFSF5 (aa 108-261) CD50 R&D Systems Recombinant Human
715-IC-050 ICAM-3/CD50/Fc Chimera, CF CD54 R&D Systems
Recombinant Human ADP4-050 ICAM-1/CD54, CF CD58 R&D Systems
Recombinant Human 1689-CD-050 CD58/LFA-3 CD6 R&D Systems
Recombinant Human 627-CD-100 CD6/Fc Chimera, CF CD62E R&D
Systems Recombinant Human E- ADP1-050 Selectin/CD62E, CF CD62L
R&D Systems Recombinant Human L- ADP2-050 Selectin/CD62L, CF
CD62P R&D Systems Recombinant Human P- ADP3-050 Selectin/CD62P,
CF CD80 R&D Systems Recombinant Human B7- 140-B1-100 1/CD80/Fc
Chimera, CF CD84 R&D Systems Recombinant Human 1855-CD-050
CD84/SLAMF5 CD86 R&D Systems Recombinant Human B7- 141-B2-100
2/CD86/Fc Chimera, CF CD87 R&D Systems Recombinant Human
807-UK-100 uPAR CD95 R&D Systems Recombinant Human 326-FS-050
Fas/TNFRSF6/Fc Chimera CDw329 R&D Systems Recombinant Human
1139-SL-050 Siglec-9/Fc Chimera, CF
TABLE-US-00058 TABLE 47 Capture Antibodies CD Marker ID Vendor Full
Identity Catalogue # CD10 R&D Systems Human Neprilysin DuoSet
DY1182 CD110 Upstate Rabbit Anti-TPO R/c-Mpl Polyclonal 06-944
Antibody, Unconjugated CD116 R&D Systems Human GM-CSF R alpha
MAb (Clone MAB706 31916) CD116 Beckman Purified anti-human CD116
305901 CD11b R&D Systems Human Integrin alpha M/CD11b MAb
(Clone MAB16992 238439) CD11b R&D Systems Human Integrin alpha
M/CD11b MAb (Clone MAB16991 238446) CD11b R&D Systems Human
Integrin alpha M/CD11b MAb (Clone MAB1699 ICRF44) CD11c R&D
Systems Human Integrin alpha X/CD11c MAb (Clone MAB1777 ICRF 3.9)
CD11c BD Mouse Anti-CD11c Monoclonal Antibody, 555391 Unconjugated,
Clone B-ly6 CD123 R&D Systems Human IL-3 R alpha MAb (Clone
32703) MAB301 CD123 Abcam Mouse Anti-IL3RA Monoclonal Antibody,
ab21562 Unconjugated, Clone 6H6 CD13 Abcam Mouse Anti-CD13
Monoclonal Antibody, ab20136 Unconjugated, Clone 22A5 CD13 BD Mouse
Anti-CD13 Monoclonal Antibody, 555393 Unconjugated, Clone WM15
CD137 R&D Systems Human 4-1BB/TNFRSF9 DuoSet DY838 CD143
R&D Systems Human ACE DuoSet DY929 CD147 R&D Systems Human
EMMPRIN MAb (Clone 109403) MAB972 CD147 BD Mouse Anti-CD147
Monoclonal Antibody, 555961 Unconjugated, Clone HIM6 CD156b R&D
Systems Human TACE/ADAM17 DuoSet DY930 CD16 R&D Systems Human
Fc gamma RIIIA/B (CD16a/b) MAb MAB2546 (Clone 245536) CD16 BD Mouse
Anti-CD16 Monoclonal Antibody, 556617 Unconjugated, Clone 3G8 CD171
Abcam Mouse Anti-L1CAM Monoclonal Antibody, ab20148 Unconjugated,
Clone UJ127.11 CD171 Abcam Mouse Anti-L1CAM Monoclonal Antibody,
ab20149 Unconjugated, Clone UJ181.4 CD171 BD Mouse Anti-CD171
Monoclonal Antibody, 554273 Unconjugated, Clone 5G3 CD19 Abcam
Mouse Anti-CD19 Monoclonal Antibody, ab212 Unconjugated, Clone LT19
CD19 Abcam Mouse Anti-CD19 Monoclonal Antibody, ab25177
Unconjugated, Clone MB19 CD1A Abcam Mouse Anti-CD1 Monoclonal
Antibody, ab24055 Unconjugated, Clone NA1/34 CD1A Abcam Mouse
Anti-CD1 Monoclonal Antibody, ab23607 Unconjugated, Clone RIV12
CD1c Abcam Mouse Anti-CD1 Monoclonal Antibody, ab24055
Unconjugated, Clone NA1/34 CD1c Abcam Mouse Anti-CD1c Monoclonal
Antibody, ab18216 Unconjugated, Clone M241 CD1d Abcam Mouse
Anti-CD1d Monoclonal Antibody, ab11076 Unconjugated, Clone NOR3.2
(NOR3.2/ 13.17) CD1d BD Rat Anti-CD1d Monoclonal Antibody, 559438
Unconjugated, Clone 1B1 CD20 R&D Systems Human MS4A1/CD20 MAb
(Clone 396444) MAB4225 CD20 Abcam Mouse Anti-CD20 Azide free
Monoclonal ab46701 Antibody, Unconjugated, Clone MEM-97 CD200
R&D Systems Human CD200 MAb (Clone 325520) MAB627 CD200 R&D
Systems Human CD200 MAb (Clone 325516) MAB27241 CD202b R&D
Systems Human Tie-2 MAb (Clone 83711) MAB313 CD202b R&D Systems
Human Tie-2 MAb (Clone 83715) MAB3131 CD212b1 R&D Systems Human
IL-12 R beta 1 MAb (Clone 69310) MAB839 CD212b2 R&D Systems
Human IL-12 R beta 1 MAb (Clone 69310) MAB1959 CD217 R&D
Systems Human IL-17 DuoSet DY317 CD217B R&D Systems Human
IL-17B MAb (Clone 174113) MAB1248 CD217B R&D Systems Goat
Anti-Human IL-17B Polyclonal AF1248 Antibody, Unconjugated CD217B/r
R&D Systems Human IL-17B R DuoSet DY1207 CD217C R&D Systems
Human IL-17C MAb (Clone 177114) MAB1234 CD217D R&D Systems
Human IL-17D MAb (Clone 246002) MAB1504 CD217D R&D Systems
Human IL-17D MAb (Clone 246018) MAB15041 CD217E R&D Systems
Human IL-17E MAb (Clone 182203) MAB1258 CD217E Cell Sciences Rabbit
Anti-Human IL-17E Antibody, PA0694 Unconjugated CD217F R&D
Systems Human IL-17F MAb (Clone 197315) MAB1335 CD217F Abcam Rabbit
Anti-Human IL-17F Polyclonal ab46000 Antibody, Unconjugated CD217rD
R&D Systems Human IL-17 RD/SEF MAb (Clone 309539) MAB2275 CD220
R&D Systems Human Total Insulin R DuoSet IC, 2 Plate DYC1544-2
CD222 R&D Systems Human IGF-II R Affinity Purified Polyclonal
AF2447 Ab CD222 Abcam Mouse Anti-IGF2 Receptor Monoclonal ab8093
Antibody, Unconjugated, Clone MEM-238 CD226 R&D Systems Human
DNAM-1 MAb (Clone 102511) MAB666 CD226 Abcam Mouse Anti-CD226
Monoclonal Antibody, ab24041 Unconjugated, Clone DX11 CD235a
R&D Systems Human Glycophorin A MAb (Clone R10) MAB1228 CD235a
Abcam Mouse Anti-Human Glycophorin A ab35760 Monoclonal Antibody,
Unconjugated, Clone BRIC 256 CD235a Abcam Mouse Anti-Glycophorin A
Monoclonal ab14486 Antibody, Unconjugated, Clone 0.N.312 CD244
R&D Systems Human 2B4/CD244/SLAMF4 MAb (Clone MAB1039 146510)
CD244 BD Mouse Anti-CD244 Monoclonal Antibody, 550814 Unconjugated,
Clone 2-69 CD264 R&D Systems Human TRAIL sR4/TNFRSF10D DuoSet
DY633 CD29 R&D Systems Human Integrin beta 1/CD29 MAb (Clone
MAB1778 4B7R) CD29 R&D Systems Human Integrin beta 1/CD29 MAb
(Clone MAB17782 P4G11) CD31 R&D Systems Human CD31/PECAM-1 MAb
(Clone 9G11) BBA7 CD31 R&D Systems Human CD31/PECAM-1 Affinity
Purified AF806 Polyclonal Ab CD33 R&D Systems Human
Siglec-3/CD33 MAb (Clone 6C5/2) MAB1137 CD34 Abcam Mouse Anti-CD34
Monoclonal Antibody, ab6330 Unconjugated, Clone BI-3C5 CD34 BD
Mouse Anti-CD34 Monoclonal Antibody, 555820 Unconjugated, Clone 581
CD35 Abcam Mouse Anti-CD35 Monoclonal Antibody, ab25 Unconjugated,
Clone E11 CD35 Exalpha Mouse Anti-CD35 351 CD35 Abcam Mouse
Anti-CD35 Monoclonal Antibody, ab25 Unconjugated, Clone E11 CD36
R&D Systems Human CD36/SR-B3 MAb (Clone 255606) MAB19551 CD36
R&D Systems Human CD36/SR-B3 MAb (Clone 255619) MAB1955 CD38
R&D Systems Human CD38 MAb (Clone 240726) MAB24041 CD38 R&D
Systems Human CD38 MAb (Clone 240742) MAB2404 CD4 R&D Systems
Human CD4 MAb (Clone 34930) MAB379 CD4 R&D Systems Human CD4
MAb (Clone 34915) MAB3791 CD40L R&D Systems Human CD40
Ligand/TNFSF5 DuoSet DY617 CD41 Abcam Mouse Anti-Human Integrin
alpha 2b/beta 3 ab38431 Monoclonal Antibody, Unconjugated, Clone
CRC64 CD41 Abcam Mouse Anti-Integrin alpha 2 beta, Integrin ab662
beta 3 Monoclonal Antibody, Unconjugated, Clone 237 CD41 Abcam
Mouse Anti-Integrin alpha 2b/beta 3 ab19775 Monoclonal Antibody,
Unconjugated, Clone F11 CD43 R&D Systems Human CD43 MAb (Clone
290111) MAB2038 CD43 Abcam Mouse Anti-CD43 Monoclonal Antibody,
ab9088 Unconjugated, Clone MEM-59 CD44H R&D Systems Human CD44H
MAb (Clone 2C5) BBA10 CD44H Abcam Mouse Anti-CD44 Monoclonal
Antibody, ab6337 Unconjugated, Clone A3D8 CD44H Abcam Mouse
Anti-CD44 Monoclonal Antibody, ab19657 Unconjugated, Clone J-173
CD45 R&D Systems Human CD45 MAb (Clone 2D1) MAB1430 CD45 Abcam
Mouse Anti-Human CD45 Azide free ab34316 Monoclonal Antibody,
Unconjugated, Clone B-A11 CD46 R&D Systems Human CD46 MAb
(Clone 344519) MAB2005 CD46 Abcam Mouse Anti-CD46 Monoclonal
Antibody, ab19739 Unconjugated, Clone J4.48 CD49d R&D Systems
Human Integrin alpha 4/CD49d MAb (Clone MAB1354 7.2R) CD49d R&D
Systems Human Integrin alpha 4/VLA-4/CD49d MAb BBA37 (Clone 2B4)
CD55 R&D Systems Human CD55/DAF MAb (Clone 278803) MAB2009 CD55
R&D Systems Human CD55/DAF MAb (Clone 278810) MAB20091 CD56
R&D Systems Human NCAM-1/CD56 MAb (Clone 301040) MAB2408 CD56
R&D Systems Human NCAM-1/CD56 MAb (Clone MAB24081 301021) CD62P
R&D Systems Human P-Selectin/CD62P DuoSet DY137 CD64 R&D
Systems Human Fc gamma RI/CD64 MAb (Clone MAB1257 10.1) CD64
R&D Systems Human Fc gamma RI/CD64 MAb (Clone MAB12571 276426)
CD66a R&D Systems Human CEACAM-1 MAb (Clone 283340) MAB2244
CD66a R&D Systems Human CEACAM-1 MAb (Clone 283324) MAB22441
CD7 Abcam Mouse Anti-CD7 Monoclonal Antibody, ab8236 Unconjugated,
Clone MEM-186 CD7 BD Mouse Anti-CD7 Monoclonal Antibody, 555359
Unconjugated, Clone M-T701 CD71 R&D Systems Human TfR MAb
(Clone 29806) MAB2474 CD71 Abcam Chicken Anti-Human Transferrin
Receptor ab37632 Polyclonal Antibody, Unconjugated CD71 Abcam Mouse
Anti-Human Transferrin Receptor ab47094 Azide free Monoclonal
Antibody, Unconjugated, Clone B-G24 CD8 (alpha) R&D Systems
Human CD8 alpha MAb (Clone 37006) MAB1509 CD8 (alpha) Abcam Mouse
Anti-CD8 Monoclonal Antibody, ab20133 Unconjugated, Clone 14 CD83
R&D Systems Human CD83 MAb (Clone HB15e) MAB1774 CD83 BD Mouse
Anti-CD83 Monoclonal Antibody, 556854 Unconjugated, Clone HB15e
CD85d R&D Systems Human ILT4/CD85d MAb (Clone 287219) MAB2078
CD85d R&D Systems Human ILT4/CD85d Affinity Purified AF2078
Polyclonal Ab CD85j R&D Systems Human ILT2/CD85j MAb (Clone
292303) MAB2017 CD85j R&D Systems Human ILT2/CD85j MAb (Clone
292305) MAB20171 CD9 R&D Systems Human CD9 MAb (Clone 209306)
MAB1880 CD9 BioLegend Mouse Anti-Human CD9 Monoclonal 312102
Antibody, Unconjugated, Clone HI9a CD9 BD Mouse Anti-CD9 Monoclonal
Antibody, 555370 Unconjugated, Clone M-L13 CD90 R&D Systems
Human CD90/Thy1 MAb (Clone Thy-1A1) MAB2067 CD90 Abcam Mouse
Anti-CD90/Thy1 Monoclonal ab23894 Antibody, Unconjugated, Clone
AF-9 CD90 Abcam Mouse Anti-CD90/Thy1 Monoclonal ab20147 Antibody,
Unconjugated, Clone aTHy-1A1 CD97 R&D Systems Human CD97 MAb
(Clone 380903) AF2529 CD97 BD Mouse Anti-CD97 Monoclonal Antibody,
555772 Unconjugated, Clone VIM3b CDw217 R&D Systems Human IL-17
R DuoSet DY177 CD138 Abcam Mouse Anti-Human Syndecan Monoclonal
ab34164 Antibody, Unconjugated, Clone B-A38 CD138 BD Mouse
Anti-CD138 Monoclonal Antibody, 550804 Unconjugated, Clone DL-101
CD138 R&D Systems Human Syndecan-1 MAb (Clone 359103) MAB2780
CD141 Abcam Mouse Anti-Human Thrombomodulin ab27393 Monoclonal
Antibody, Unconjugated, Clone B-A35 CD141 BD Mouse Anti-CD141
Monoclonal Antibody, 559780 Unconjugated, Clone 1A4 CD50 R&D
Systems Human ICAM-3/CD50 MAb (Clone ICAM- BBA15 (Matched Set) 3.3)
CD52 Abcam Mouse Anti-CD52 Monoclonal Antibody, ab2576
Unconjugated, Clone HI186 CD52 BD Mouse Anti-CD52 Monoclonal
Antibody, 558211 Unconjugated, Clone H24-930 CD70 BD Mouse
Anti-CD70 Monoclonal Antibody, 555833 Unconjugated, Clone Ki-24
CD70 R&D Systems Human CD27 Ligand/TNFSF7 MAb (Clone MAB2738
301731) CD10 R&D Systems Duoset 842131 CD116 R&D Systems
Human GM-CSF R alpha Biotinylated Affinity BAF706 Purified PAb
CD11b R&D Systems Human Integrin alpha M/CD11b Biotinylated
BAM1699 MAb (Clone ICRF44) CD11c Biolegend Mouse Anti-Human CD11c
Monoclonal 301612 Antibody, Biotin Conjugated, Clone 3.9 CD123
R&D Systems Human IL-3 R alpha Biotinylated Affinity BAF841
Purified PAb CD129 Biolegend Biotin anti-human IL-9 Receptor 310409
CD13 Abcam Mouse Anti-CD13 Monoclonal Antibody, ab25723 Biotin
Conjugated, Clone 22A5 CD137 R&D Systems Duoset 840975 CD143
R&D Systems Duoset 841366 CD147 R&D Systems Human EMMPRIN
Biotinylated Affinity BAF972 Purified PAb CD15 Abcam Mouse
Anti-CD15 Monoclonal Antibody, ab25725 Biotin Conjugated, Clone TG1
CD152 BD CD152/Biotin 555852 CD156b R&D Systems Duoset 847976
CD16 Abcam Mouse Anti-Human CD16 Monoclonal ab28091 Antibody,
Biotin Conjugated, Clone MEM- 154 CD16 Abcam Mouse Anti-CD16
Monoclonal Antibody, ab6998 Biotin Conjugated, Clone LNK16 CD19
Abcam Mouse Anti-CD19 Monoclonal Antibody, ab19665 Biotin
Conjugated, Clone SJ25-
CD19 Abcam Rat Anti-CD19 Monoclonal Antibody, Biotin ab22477
Conjugated, Clone 6D5 CD1a Biolegend Biotin anti-human CD1a 300112
CD20 Abcam Mouse Anti-CD20 Monoclonal Antibody, ab27729 Biotin
Conjugated, Clone 2H7 CD200 R&D Systems Human CD200
Biotinylated Affinity Purified BAF627 Pab CD202b R&D Systems
Human/Mouse Tie-2 Biotinylated Affinity BAF313 Purified Pab CD202b
Abcam Mouse Anti-TIE2 Monoclonal Antibody, ab27852 Biotin
Conjugated, Clone 16 CD212b1 R&D Systems Human IL-12 R beta1
Biotinylated Affinity BAF839 Purified Pab CD212b2 R&D Systems
Human IL-12 R beta1 Biotinylated Affinity BAF1959 Purified Pab
CD217 R&D Systems Duoset 840714 CD217B R&D Systems Mouse
Anti-Human IL-17B Monoclonal BAM12481 Antibodies, Biotin
Conjugated, 174106 CD217B R&D Systems Goat Anti-Human IL-17B
Polyclonal BAF1248 Antibody, Biotin Conjugated CD217B R R&D
Systems Duoset 842060 CD217C R&D Systems Human IL-17C
Biotinylated Affinity Purified BAF1234 Pab CD217D R&D Systems
Human IL-17D Biotinylated Affinity Purified BAF1504 Pab CD217E
R&D Systems Human IL-17E Biotinylated Affinity Purified BAF1258
PAb CD217F R&D Systems Human IL-17F Biotinylated Affinity
Purified BAF1335 PAb CD217R R&D Systems Duoset 842045 CD217R
R&D Systems Duoset 842045 CD217rD R&D Systems Human IL-17
RD/SEF Biotinylated Affinity BAF2275 Purified Pab CD220 R&D
Systems Duoset 841873 CD222 R&D Systems Human IGF-II R
Biotinylated Affinity Purified BAF2447 Pab CD226 R&D Systems
Human DNAM-1 Biotinylated Affinity Purified BAF666 Pab CD235a
R&D Systems Human Glycophorin A Biotinylated MAb BAM12281
(Clone R10) CD244 R&D Systems Human 2B4/CD244/SLAMF4
Biotinylated BAF1039 Affinity Purified Pab CD264 R&D Systems
Duoset 840944 CD29 R&D Systems Human Integrin beta 1/CD29
Biotin Affinity BAF1778 Purified Pab CD31 Abcam Mouse Anti-CD31
Monoclonal Antibody, ab7385 Biotin Conjugated, Clone WM59 CD33
Abcam Mouse Anti-Human CD33 Monoclonal ab30373 Antibody, Biotin
Conjugated, Clone WM53 Company Abcam CD33 Abcam Mouse Anti-CD33
Monoclonal Antibody, ab21892 Biotin Conjugated, Clone HIM3-4 CD34
Abcam Mouse Anti-CD34 Monoclonal Antibody, ab21893 Biotin
Conjugated, Clone 4H11[APG] CD35 Exalpha Anti-CR1/Biotin 353 CD36
R&D Systems Human CD36/SR-B3 Affinity Purified AF1955
Polyclonal Ab CD36 Abcam Rabbit Anti-Human CD36 Polyclonal ab36978
Antibody, Biotin Conjugated CD38 Abcam Mouse Anti-Human CD38
Monoclonal ab30418 Antibody, Biotin Conjugated, Clone AT13/5 CD4
R&D Systems Human CD4 Biotinylated Affinity Purified BAF379 Pab
CD4 Abcam Mouse Anti-CD41/Integrin alpha 2b ab30434 Monoclonal
Antibody, Biotin Conjugated, Clone PM6/248 CD40L R&D Systems
Duoset 841131 CD41 Abcam Mouse Anti-CD41/Integrin alpha 2b ab19699
Monoclonal Antibody, Biotin Conjugated, Clone M148 CD43 R&D
Systems Human CD43 Biotinylated Affinity Purified BAF2038 Pab CD44H
Abcam Mouse Anti-CD44 Monoclonal Antibody, ab30404 Biotin
Conjugated, Clone F10-44-2 CD44H Abcam Mouse Anti-Human CD44
Monoclonal ab28105 Antibody, Biotin Conjugated, Clone MEM-85 CD45
Abcam Mouse Anti-Human CD45 Monoclonal ab30468 Antibody, Biotin
Conjugated, Clone F10-89-4 CD46 R&D Systems Human CD46
Biotinylated Affinity Purified BAF2005 PAb CD49d R&D Systems
Human Integrin alpha 4/CD49d Biotinylated BAM1354 MAb (Clone 7.2R)
CD55 R&D Systems Human CD55/DAF Biotinylated Affinity BAF2009
Purified Pab CD56 R&D Systems Human NCAM-1/CD56 Biotinylated
Affinity BAF2408 Purified Pab CD6 Abcam CD6/Biotin CD62P R&D
Systems Duoset 841155 CD64 R&D Systems Human Fc gamma RI/CD64
Biotinylated BAF1257 Affinity Purified PAb CD66a R&D Systems
Human CEACAM-1 Biotinylated Affinity BAF2244 Purified Pab CD7 Abcam
Mouse Anti-Human CD7 Monoclonal ab34293 Antibody, Biotin
Conjugated, Clone LT7 CD71 R&D Systems Human TfR Biotinylated
Affinity Purified Pab BAF2474 CD71 Abcam Mouse Anti-Human
Transferrin Receptor ab28116 Monoclonal Antibody, Biotin
Conjugated, Clone MEM-75 CD8 (alpha) Abcam Mouse Anti-Human CD8
Monoclonal ab28090 Antibody, Biotin Conjugated, Clone MEM-31 CD8
(alpha) Abcam Mouse Anti-CD8 Monoclonal Antibody, ab34282 Biotin
Conjugated, Clone LT8 CD83 R&D Systems Human CD83 Biotinylated
Affinity Purified BAF2044 Pab CD85d R&D Systems Human
ILT4/CD85d Biotinylated Affinity BAF2078 Purified Pab CD85j R&D
Systems Human ILT2/CD85j Biotinylated Affinity BAF2017 Purified Pab
CD9 Abcam Mouse Anti-Human CD9 Monoclonal ab28094 Antibody, Biotin
Conjugated, Clone MEM-61 CD90 BD Mouse Anti-CD90 Monoclonal
Antibody, 555594 Biotin Conjugated, Clone 5E10 CD90 Abcam Mouse
Anti-CD90/Thy 1 Monoclonal ab11154 Antibody, Biotin Conjugated,
Clone F15-42-1 CD97 R&D Systems Human CD97 Biotinylated
Affinity Purified BAF2529 PAb CD138 R&D Systems Human
Syndecan-1 Biotinylated Affinity BAF2780 Purified PAb CD141 R&D
Systems Anti-Mouse Thrombomodulin/CD141 AF3894 Affinity Purified
Polyclonal Antibody, Unconjugated CD50 R&D Systems Human
ICAM-3/CD50 Biotinylated Affinity BAF813 (Matched Set) Purified PAb
CD52 Santa Cruz Rabbit Anti-Human CD52 (FL-61) Polyclonal sc-25838
Antibody, Unconjugated CD70 Genetex Goat Anti-CD27 Polyclonal
Antibody, GTX10952 Unconjugated HLA A Abcam Mouse Anti-HLA ABC
Monoclonal Antibody, ab21148 Biotin Conjugated, Clone W6/32 CD10
R&D Systems Human Neprilysin Ectodomain MAb (Clone MAB1182
212504) CD102 R&D Systems Human ICAM-2/CD102 MAb (Clone 86911)
MAB244 CD105 R&D Systems Human Endoglin/CD105 MAb (Clone
MAB10972 166713) CD106 R&D Systems Human VCAM-1/CD106 MAb
(Clone HAE- MAB809 2Z) CD110 R&D Systems Human Thrombopoietin R
MAb (Clone MAB1016 167639) CD110 R&D Systems Human
Thrombopoietin R MAb (Clone MAB10161 167620) CD114 R&D Systems
Human G-CSF R/CD114 MAb (Clone MAB381 38660) CD115 R&D Systems
Human M-CSF R MAb (Clone 61715) MAB3292 CD117 R&D Systems Human
SCF R/c-kit MAb (Clone 47233) MAB332 CD120a R&D Systems Human
TNF RI/TNFRSF1A MAb (Clone MAB625 16805) CD120b R&D Systems
Human TNF RII/TNFRSF1B MAb (Clone MAB726 22210) CD121a R&D
Systems Human IL-1 RI MAb (Clone 35730) MAB269 CD121b R&D
Systems Human IL-1 RII MAb (Clone 34141) MAB663 CD124 R&D
Systems Human IL-4 R MAb (Clone 25463) MAB230 CD125 R&D Systems
Human IL-5 R alpha MAb (Clone 26815) MAB253 CD126 R&D Systems
Human IL-6 R MAb (Clone 17506) MAB227 CD127 R&D Systems Human
IL-7 R alpha MAb (Clone 40131) MAB306 CD129 R&D Systems Human
IL-9 R MAb (Clone 33423) MAB290 CD129 R&D Systems Human IL-9 R
MAb (Clone 33401) MAB2902 CD129 R&D Systems Human IL-9 R MAb
(Clone 33449) MAB2901 CD130 R&D Systems Human gp130 MAb (Clone
28105) MAB628 CD132 R&D Systems Human Common gamma Chain MAb
(Clone MAB2841 31134) CD132 R&D Systems Human Common gamma
Chain MAb (Clone MAB284 38024) CD137 R&D Systems Human
4-1BB/TNFRSF9 Affinity Purified AF838 Polyclonal Ab CD14 R&D
Systems Human CD14 MAb (Clone 50040) MAB3833 CD143 R&D Systems
Human ACE MAb (Clone 171417) MAB929 CD143 R&D Systems Huan ACE
Mab (Clone 171409) MAB9291 CD148 R&D Systems Human DEP-1/CD148
MAb (Clone 143-41) MAB1934 CD148 R&D Systems Human DEP-1/CD148
MAb (Clone 261922) MAB19341 CD152 R&D Systems Human CTLA-4 MAb
(Clone 48815) MAB325 CD156b R&D Systems Human TACE/ADAM17
Cytosolic MAb MAB21291 (Clone 136133) CD156b R&D Systems Human
TACE/ADAM17 Ectodomain MAb MAB9302 (Clone 111623) CD156b R&D
Systems Human TACE/ADAM17 Cytosolic MAb MAB2129 (Clone 136121)
CD156b R&D Systems Human TACE/ADAM17 Ectodomain MAb MAB9301
(Clone 111633) CD156b R&D Systems Human TACE/ADAM17 Ectodomain
MAb MAB930 (Clone 111636) CD166 R&D Systems Human ALCAM MAb
(Clone 105902) MAB6561 CD170 R&D Systems Human Siglec-5 MAb
(Clone 194128) MAB10721 CD171 R&D Systems Human NCAM-L1 MAb
(Clone 84321) MAB777 CD178 R&D Systems Human Fas Ligand/TNFSF6
MAb (Clone MAB126 100419) CD195 R&D Systems Human CCR5 MAb
(Clone 45502) MAB180 CD195 R&D Systems Human CCR5 MAb (Clone
CTC8) MAB1801 CD195 R&D Systems Human CCR5 MAb (Clone 45549)
MAB183 CD195 R&D Systems Human CCR5 MAb (Clone 45529) MAB184
CD195 R&D Systems Human CCR5 MAb (Clone 45523) MAB181 CD195
R&D Systems Human CCR5 MAb (Clone 45531) MAB182 CD195 R&D
Systems Human CCR5 MAb (Clone CTC5) MAB1802 CD1d BD Bio Mouse
Anti-CD1d Monoclonal Antibody, 550254 Unconjugated, Clone CD1d42
CD2 R&D Systems Mouse Anti-Human CD2 Monoclonal MAB1856
Antibody, Unconjugated, Clone 299813 CD2 R&D Systems Human CD2
MAb (Clone 299813) MAB18561 CD213a1 R&D Systems Human IL-13 R
alpha 1 MAb (Clone 116730) MAB146 CD213a2 R&D Systems Human
IL-13 R alpha 2 MAb (Clone 83807) MAB6141 CD220 R&D Systems
Human Insulin R/CD220 MAb (Clone MAB1544 243524) CD220 R&D
Systems Human Insulin R/CD220 MAb (Clone MAB15441 243523) CD221
R&D Systems Human IGF-I R MAb (Clone 33255) MAB391 CD23 R&D
Systems Human Fc epsilon RII/CD23 MAb (Clone MAB123 138628) CD239
R&D Systems Human BCAM MAb (Clone 87207) MAB1481 CD25 R&D
Systems Human IL-2 R alpha MAb (Clone 22722) MAB223 CD25 R&D
Systems Human IL-2 R alpha MAb (Clone 24204) MAB623 CD258 R&D
Systems Human LIGHT/TNFSF14 MAb (Clone MAB664 115520) CD26 R&D
Systems Human DPPIV/CD26 MAb (Clone 222113) MAB1180 CD263 R&D
Systems Human TRAIL R3/TNFRSF10C MAb (Clone MAB6301 90905) CD264
R&D Systems Human TRAIL R4/TNFRSF10D MAb (Clone MAB633 104918)
CD27 R&D Systems Human CD27/TNFRSF7 MAb (Clone 57703) MAB382
CD28 R&D Systems Human CD28 MAb (Clone 37407) MAB342 CD295
R&D Systems Human Leptin R MAb (Clone 52208) MAB389 CD295
R&D Systems Human Leptin R MAb (Clone 52263) MAB867 CD30
R&D Systems Human CD30/TNFRSF8 MAb (Clone 81337) MAB229 CD30
R&D Systems Mouse Anti-Human CD30/TNFRSF8 MAB2291 Monoclonal
Antibody, Unconjugated, Clone 81316 CD309 R&D Systems Human
VEGF R/KDR2 MAb (Clone 89109) MAB3573 CD324 R&D Systems Human
E-Cadherin MAb (Clone 77308) MAB18382 CD32b/c R&D Systems Human
Fc gamma RIIB/C MAb (Clone MAB18751 190710) CD33L2 R&D Systems
Human Siglec-5 MAb (Clone 194117) MAB1072 CD40 R&D Systems
Human CD40/TNFRSF5 MAb (Clone 82102) MAB6322 CD40 R&D Systems
Human CD40/TNFRSF5 MAb (Clone 82105) MAB632 CD40 R&D Systems
Human CD40/TNFRSF5 MAb (Clone 82111) MAB6321 CD40L R&D Systems
Human CD40 Ligand/TNFSF5 MAb (Clone MAB617 40804) CD50 R&D
Systems Human ICAM-3/CD50 MAb (Clone Cal 3.10) BBA29 CD50 R&D
Systems Human ICAM-3/CD50 MAb (Clone Cal 3.34) BBA28 CD50 R&D
Systems Human ICAM-3/CD50 MAb (Clone ICAM- BBA15 3.3) CD54 R&D
Systems Human ICAM-1/CD54 MAb (Clone BBIG-I1) BBA3 CD54 R&D
Systems Human ICAM-1/CD54 MAb (Clone 14C11) MAB720 CD58 R&D
Systems Human CD58/LFA-3 MAb (Clone 248310) MAB1689 CD6 R&D
Systems Human CD6 MAb (Clone 123119) MAB627 CD62E R&D Systems
Human E-Selectin/CD62E MAb (Clone BBA16 BBIG-E4) CD62L R&D
Systems Human L-Selectin/CD62L MAb (CL 4G8) BBA24 CD62P R&D
Systems Human P-Selectin/CD62P MAb (CI 9E1) BBA30 CD80 R&D
Systems Human B7-1/CD80 MAb (Clone 37711) MAB140 CD84 R&D
Systems Human CD84/SLAMF5 MAb (Clone 273508) MAB1855 CD86 R&D
Systems Human B7-2/CD86 MAb (Clone 37301) MAB141 CD87 R&D
Systems Human uPAR MAb (Clone 62022) MAB807 CD95 R&D Systems
Human Fas/TNFRSF6 MAb (Clone 50830) MAB144
CDw329 R&D Systems Human Siglec-9 MAb (Clone 191240) MAB1139
CD10 R&D Systems Human Neprilysin Biotinylated Affinity BAF1182
Purified PAb CD102 R&D Systems Human ICAM-2/CD102 Biotinylated
Affinity BAF244 Purified PAb CD105 R&D Systems Human
Endoglin/CD105 Biotinylated Affinity BAF1097 Purified PAb CD106
R&D Systems Human VCAM-1/CD106 Biotinylated Affinity BAF809
Purified PAb CD110 R&D Systems Human Thrombopoietin R
Biotinylated BAF1016 Affinity Purified PAb CD114 R&D Systems
Human G-CSF R/CD114 Affinity Purified AF-381-PB Polyclonal Ab CD115
R&D Systems Human M-CSF R Biotinylated Affinity BAF329 Purified
PAb CD117 R&D Systems Human SCF R/c-kit Biotinylated Affinity
BAF332 Purified PAb CD120a R&D Systems Human TNF RI/TNFRSF1A
Biotinylated BAF225 Affinity Purified PAb CD120b R&D Systems
Human TNF RII/TNFRSF1B Biotinylated BAF726 Affinity Purified PAb
CD121a R&D Systems Human IL-1 RI Biotinylated Affinity Purified
BAF269 PAb CD121b R&D Systems Human IL-1 RII Biotinylated
Affinity Purified BAF263 PAb CD124 R&D Systems Human IL-4 R
Biotinylated Affinity Purified BAF230 PAb CD125 R&D Systems
Human IL-5 R alpha Biotinylated Affinity BAF253 Purified PAb CD126
R&D Systems Human IL-6 R Biotinylated Affinity Purified BAF227
PAb CD127 R&D Systems Human IL-7 R alpha Biotinylated Affinity
BAF306 Purified PAb CD129 R&D Systems Human IL-9 R Biotinylated
Affinity Purified BAF290 Ab CD130 R&D Systems Human gp130
Biotinylated Affinity Purified BAF228 PAb CD132 R&D Systems
Human Common gamma Chain Biotinylated BAF284 Affinity Purified PAb
CD137 R&D Systems Human 4-1BB/TNFRSF9 Biotinylated Affinity
BAF838 Purified PAb CD137 R&D Systems Human 4-1BB/TNFRSF9
Affinity Purified AF838 Polyclonal Ab CD14 R&D Systems Human
CD14 Biotinylated Affinity Purified BAF383 PAb CD143 R&D
Systems Human ACE Bitotinylated Mab (171417) BAM929 CD148 R&D
Systems Human/Mouse/Rat DEP-1/CD148 Affinity AF1934 Purified
Polyclonal Ab CD152 R&D Systems Human CTLA-4 Biotinylated
Affinity Purified BAF386 PAb CD156b R&D Systems Human
TACE/ADAM17 Ecto Biotinylated BAF930 Affinity Purified PAb CD166
R&D Systems Human ALCAM Biotinylated Affinity Purified BAF656
PAb CD170 R&D Systems Human Siglec-5 Biotinylated MAb (Clone
BAM10722 194111) CD171 R&D Systems Human NCAM-L1 Biotinylated
Affinity BAF277 Purified PAb CD178 R&D Systems Human Fas
Ligand/TNFSF6 Biotinylated BAF126 Affinity Purified PAb CD195
R&D Systems Human CCR5 Biotinylated MAb (Clone FAB182B 45531)
CD195 R&D Systems Human CCR5 Biotinylated MAb (Clone FAB181B
45523) CD195 R&D Systems Human CCR5 Biotinylated MAb (Clone
FAB183B 455049) CD195 R&D Systems Human CCR5 Biotinylated MAb
(Clone FAB180B 45502) CD1d BD Bio Rat Anti-CD1d Monoclonal
Antibody, Biotin 553844 Conjugated, Clone 1B1 CD2 R&D Systems
Goat Anti-Human CD2 Polyclonal Antibody, BAF1856 Biotin Conjugated
CD213a1 R&D Systems Human IL-13 R alpha 1 Biotinylated Affinity
BAF152 Purified PAb CD213a2 R&D Systems Human IL-13 R alpha 2
Biotinylated Affinity BAF614 Purified PAb CD220 R&D Systems
Human Insulin R/CD220 Biotinylated MAb BAM1544 (Clone 243524) CD221
R&D Systems Human IGF-I R Biotinylated Affinity Purified BAF391
PAb CD23 R&D Systems Human Fc epsilon RII/CD23 Biotinylated
BAF123 Affinity Purified PAb CD239 R&D Systems Human BCAM
Biotinylated Affinity Purified BAF148 PAb CD25 R&D Systems
Human IL-2 R alpha Biotinylated Affinity BAF223 Purified PAb CD258
R&D Systems Human LIGHT/TNFSF14 Biotinylated Affinity BAF664
Purified PAb CD26 R&D Systems Human DPPIV/CD26 Biotinylated
Affinity BAF1180 Purified PAb CD263 R&D Systems Human TRAIL
R3/TNFRSF10C Biotinylated BAF630 Affinity Purified PAb CD264
R&D Systems Human TRAIL R4/TNFRSF10D Biotinylated BAF633
Affinity Purified PAb CD27 R&D Systems Human CD27/TNFRSF7
Biotinylated Affinity BAF382 Purified PAb CD28 R&D Systems
Human CD28 Biotinylated Affinity Purified BAF342 PAb CD295 R&D
Systems Human Leptin R Biotinylated Affinity Purified BAF389 PAb
CD30 R&D Systems Goat Anti-Human CD30/TNFRSF8 BAF229 Polyclonal
Antibody, Biotin Conjugated CD309 R&D Systems Human VEGF R2/KDR
Biotinylated Affinity BAF357 Purified PAb CD324 R&D Systems
Human E-Cadherin Biotinylated Affinity BAF648 Purified PAb CD32b/c
R&D Systems Human Fc gamma RIIB/CD32b Biotinylated BAF1330
Affinity Purified Pab CD32b/c R&D Systems Human Fc gamma RIIB/C
Biotinylated MAb BAM1875 (Clone 190703) CD40 R&D Systems Human
CD40/TNFRSF5 Biotinylated Affinity BAF632 Purified PAb CD40L
R&D Systems Human CD40 Ligand/TNFSF5 Biotinylated BAF617
Affinity Purified PAb CD50 R&D Systems Human ICAM-3/CD50
Biotinylated Affinity BAF813 Purified PAb CD50 R&D Systems
Human ICAM-3/CD50 Biotinylated Affinity BAF715 Purified PAb CD54
R&D Systems Human ICAM-1/CD54 Biotinylated Affinity BAF720
Purified PAb CD58 R&D Systems Human CD58/LFA-3 Biotinylated
Affinity BAF1689 Purified PAb CD6 R&D Systems Human CD6
Biotinylated Affinity Purified BAF627 PAb CD62E R&D Systems
Human E-Selectin/CD62E Biotinylated MAb BBA8 (Clone BBIG-E5) CD62L
R&D Systems Human L-Selectin/CD62L Biotinylated BAF728 Affinity
Purified PAb CD62P R&D Systems Human P-Selectin/CD62P
Biotinylated BAF137 Affinity Purified PAb CD80 R&D Systems
Human B7-1/CD80 Biotinylated MAb (Clone BAM1402 37721) CD84 R&D
Systems Human CD84/SLAMF5 Biotinylated Affinity BAF1855 Purified
PAb CD86 R&D Systems Human B7-2/CD86 Biotinylated Affinity
BAF141 Purified PAb CD87 R&D Systems Human uPAR Biotinylated
Affinity Purified BAF607 PAb CD95 R&D Systems Human Fas/TNFRSF6
Biotinylated Affinity BAF326 Purified PAb CDw329 R&D Systems
Human Siglec-9 Biotinylated Affinity Purified BAF1139 Pab
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