Note: Descriptions are shown in the official language in which they were submitted.
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PHENOTYPE AND BIOLOGICAL MARKER IDENTIFICATION SYSTEM
SCOPE OF THE INVENTION
The present invention provides a phenotype and biological marker
identification
system and methods for identifying and using novel patterns of biological
markers related
to disease, disease progression, response to therapy and normal biological
functions. The
discovery and use of novel patterns of biological markers will result in more
cost-effective
drug development, including the improvement of patient selection in clinical
trials and the
identification of therapeutics with greatly improved safety and efficacy.
Phenotype
information and biological markers can also be used in diagnostic
applications.
BACKGROUND OF THE INVENTION
As a result of recent innovations in drug discovery, including genomics,
combinatorial chemistry and high throughput screening, the number of drug
candidates
available for clinical testing exceeds the pharmaceutical industry's
development and
economic capacity. In 1998, the world's top pharmaceutical and biotechnology
companies
spent more than $50 billion on research and development, more than one-third
of which
was spent directly on clinical development. As the result of a number of
factors, including
increased competition and pressure from managed care organizations and other
payors, the
pharmaceutical industry is seeking to increase the quality, including the
safety and efficacy
of new drugs brought to market, and to improve the efficiency of clinical
development.
Recent drug discovery innovations, therefore, have contributed to a clinical
trials
bottleneck. The numbers of therapeutic targets being identified and lead
compounds being
generated far exceed the capacity of pharmaceutical companies to conduct
clinical trials as
they are currently performed. Further, as the industry currently estimates
that the average
cost of developing a new drug is approximately $500 million, it is
prohibitively expensive
to develop all of the potential drug candidates.
The pharmaceutical industry is being forced to seek equivalent technological
improvements in drug development. Clinical trials remain very expensive and
very risky,
and often decision making is based on highly subjective analyses. As a result,
it is often
difficult to determine the patient population for whom a drug is most
effective, the
appropriate dose for a given drug and the potential for side effects
associated with its use.
Not only does this lead to more failures in clinical development, it can also
lead to
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approved products that may be inappropriately dosed, prescribed, or cause
dangerous side
effects. With an increasing number of drugs in their pipelines, pharmaceutical
companies
require technologies to identify objective measurements of a drug candidate's
safety and
efficacy profile earlier in the drug development process.
One approach to deal with the mass of information and technologies is to break
away from the traditional methods of drug identification and development. As a
variety of
different analytical, clinical and information handling technologies
continually advance, it
may be possible to develop a phenotype for an individual or population that
allows for an
unprecedented systematic evaluation of such individual or population. The
phenotype for a
given individual includes, in theory, all measurable characteristics of such
individual at all
points in time. One use of such phenotype information is the identification of
biological
markers.
Biological markers are characteristics that when measured or evaluated have,
inter
alia, a discrete relationship or correlation as an indicator of normal
biologic processes,
pathogenic processes or pharmacologic responses to a therapeutic intervention.
Pharmacologic responses to therapeutic intervention include, but are not
limited to,
response to the intervention generally (e.g., efficacy), dose response to the
intervention,
side effect profiles of the intervention, and pharmacokinetic properties.
Response may be
correlated with either efficacious or adverse (e.g., toxic) changes.
Biological markers
include patterns of cells or molecules that change in association with a
pathological process
and have diagnostic and/or prognostic value. Biological markers may include
levels of cell
populations and their associated molecules, levels of soluble factors, levels
of other
molecules, genotypic information, gene expression levels, genetic mutations,
and clinical
parameters that can be correlated with the presence and/or progression of
disease.
In contrast to such clinical endpoints as disease progression or recurrence or
quality
of life measures (which typically take a long time to assess), biological
markers may
provide a more rapid and quantitative measurement of a drug's clinical
profile. Single
biological markers currently used in both clinical practice and drug
development include
cholesterol, prostate specific antigen ("PSA"), CD4 T cells and viral RNA.
Unlike the well
known correlation between high cholesterol and heart disease, PSA and prostate
cancer,
and decreased CD4 positive T cells and viral RNA in AIDS, the biological
markers
correlated with most other diseases have yet to be identified. As a result,
although both
government agencies and pharmaceutical companies are increasingly seeking
development
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of biological markers for use in clinical trials, the use of biological
markers in drug
development has been limited to date.
Although there are many potential biological markers, there is limited
technology
that is capable of sorting through the vast amounts of information needed to
establish the
correlation of the biological markers with normal biologic processes, disease,
disease
progression and response to therapy. Phenotyping requires the instrumentation
and assays
required to measure hundreds to thousands of parameters, an informatics system
to allow
this data to be easily accessed, software to correlate the patterns of
information with
clinical data and the ability to utilize the resulting information in the drug
development
process. The present invention provides such a technology.
SUMMARY OF THE INVENTION
The present invention relates to phenotyping an organism or a class or
subclass of
organisms. The present invention also includes the identification of
biological markers that
are measured and evaluated as an indicator of normal biologic processes,
pathogenic
processes or pharmacologic responses to a therapeutic intervention. This
invention
includes technology capable of providing quantitative, sensitive reproducible
and rapid
measurements of multiple and diverse biological markers that could accurately
profile an
organism's phenotype or a patient's disease status and response to therapy.
Further,
because blood is the single most information rich tissue and is easily and
readily accessible
for testing, the invention focuses on identifying biological parameters from
small samples
of blood. The invention includes a multidisciplinary format comprising three
principal
elements: instrumentation, assay development and clinical informatics.
BRIEF DESCRIPTION OF THE FIGURES
Figure 1 is a schematic representation of the types of information that are
assimilated to obtain one embodiment of a biological marker identification
system.
Figure 2 depicts a schematic representation of the improved MLSC instrument of
the invention (term "SurroScan" instrumentation).
Figure 3 depicts the integrated information infrastructure for analyzing the
data
obtained in the present invention.
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Figures 4 A - C depict the results obtained in Example 1 showing that CD27+
and
CD27- CD8 T cells vary among samples. Blood samples from three different
donors
(Figures 4A, B, and C) were stained with Cy5 anti-CD27 and Cy5.5 anti-CDB.
Figures SA and B depict robust cellular measurements with 2-color MLSC. Figure
SA demonstrates the consistency of CD8 T cell counts from 6 different
capillaries. Cy5.5
anti-CD8 was combined with a different Cy5 conjugated antibody for each of the
capillaries (anti-CD3, CD25, CD7, CD45RA, CD62L, CD69). Fifty different blood
samples were analyzed. The box-and-whiskers plots show that the distributions
of cell
counts are very similar for each of the capillaries. Pair-wise linear
regression also shows a
high degree of consistency for these assays (data not shown). Figure S shows
the
consistency of two measures of B cells, one with Cy5.5 anti-CD20 and one with
Cy5.5
anti-CD19. The 95% confidence interval (dotted line) of the linear regression
includes a
slope of 1 and the fit has a correlation coefficient of 0.97.
Figure 6 shows a classification matrix comparing CD8 T cells and CD4 T cells
in
RA patient samples and blood bank samples.
Figures 7A and B show results of a three color cellular assay on the SurroScan
instrument.
Figures 8A - C shows the results of staining intracellular molecule as
measured
with MLSC technology.
Figures 9A - C show the results of a 3 detection channel analysis using MLSC
technology.
DETAILED DESCRIPTION OF THE INVENTION
The present invention is directed to the phenotyping of an organism or a class
or
subclass of organisms. In theory, the phenotyping of an organism includes
obtaining all
measurable characteristics of said individual, past and present. While the
complete
phenotyping of any organism is not practical or even possible, the phenotyping
disclosed
and described herein provides an unprecedented quantity of an unprecedented
number of
types of parameters or characteristics so as to provide a resource of
information that will
allow for the analysis of normal biological functions, disease, disease
progression and
changes associated with virtually any perturbation to the organism.
One utility of the phenotyping system taught by the present invention is to
the
identification of biological markers for normal biological processes, diseases
or medical
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
conditions. In order to perform this aspect of the present invention it is
necessary to have i)
biological information from a population of individuals, ii) an adequate
amount of data
from each individual, preferably obtained by multiple sampling over time, and
iii) an
information storage and retrieval system that a) can integratively incorporate
a wide variety
of types of information and b) can perform meaningful correlation analysis of
the disparate
types of data. Figure 1 depicts information that is useful to create a
biological marker
identification system.
Disease and disease progression involves the complex interplay of both genetic
and
environmental factors. The present invention has the potential to identify and
trace
changes in patterns of biological markers reflecting both genetic and
environmental factors
from small samples of blood. Furthermore, the present invention helps decipher
genetic
components of disease susceptibility, disease progression and response to
therapy.
The present invention is capable of monitoring cells, proteins, organic
molecules,
genotype, soluble factors, clinical and environmental factors, all of which
have been used
as biological markers in drug development and as disease markers. Examples of
known
biological marker include the monitoring for decreases in CD4 positive T cells
and viral
RNA levels in AIDS, elevated cholesterol levels as an accepted biological
marker for heart
disease and changing levels of PSA as a protein marker found in the blood of
prostate
cancer patients.
Since the biological characteristics or parameters that might be discovered to
be a
biological marker or part of a marker "grouping" are often not predictable, it
is essential
that the appropriate database contain information regarding as many parameters
as
possible.
The present invention extends to a phenotype of a given organism, methods for
assembling such phenotype and methods for utilizing such phenotype. The
phenotype of
an organism or class or subclass or organisms comprises a large compilation of
data
relating to the organism or class or subclass of organisms. The novel aspect
of the present
invention lies in the disparate nature of the data and the quantity of data
from each of the
various categories of data available on an organism. A phenotype can only
reach its full
usefulness if the data defining the phenotype is extensive. For example, a
phenotype for a
human patient containing a standard blood profile and clinical factors
routinely obtained
from a physical examination can not provide enough information to fully
exploit such a
phenotype. Although the assays involved and data obtained are within the
scientific and
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clinical capabilities of the art, obtaining all of the information from a
single organism is a
novel task. Although the handling and maintenance of a phenotype lends itself
to
computerization, a given phenotype can be kept in traditional formats.
Manipulating
phenotypes to identify a biological marker or to observe the effect of a
perturbation in the
organism is, of course, greatly simplified by the use of computational
analysis via a
computer. As described above, the complete phenotyping of an organism would
include
literally thousands or possibly millions of data points. In the preferred
aspects of this
invention, a phenotype comprises greater than 40 biological parameters, more
preferably
greater than 100 parameters, and most preferably greater than 200 different
parameters, and
in some cases greater than 300 different parameters. The phenotype must
contain
biological parameters that include information from cellular assays, soluble
factor assays
and clinical information. In the preferred embodiment, the results of at least
20 cellular
assays incorporating measurements of at least 20 cell populations and/or cell
associated
molecules and the results of at least 20 soluble factor assays are included in
the phenotype,
1 S along with clinical information. In more preferred embodiments, the
results of at least 40
cellular assays incorporating measurements of at least 40 cell populations
and/or cell
associated molecules and at least 40 soluble factor assays are included,
preferably with an
extensive battery of clinical and environmental parameters. In preferred
embodiments of
the invention there are included more than 20 clinical parameters, preferably
more than 40
and in some cases more than 60 clinical parameters.
A rich and readily accessible source of biological information for a patient
is the
blood. At the present time, there are over 200 identified discrete leukocyte
cell surface
antigens with identified antibodies. In addition, there are literally
thousands of proteins
and other soluble factors and small molecules that can be identified in blood.
The problem,
therefore, is not in finding enough informational content in the blood, but in
efficiently
extracting all of the available information from limited quantities of blood.
Many levels of biological markers may vary widely from individual to
individual.
In many cases, such variations may be random, but this may not always be the
case. For
example, in some situations baseline levels may be individual specific, and
only by taking
multiple readings from an individual would it be possible to identify a
biological marker.
Although it may not be likely that a baseline would be established for a
healthy individual,
there may be valuable information gained from the variations over time in a
given
individual that has a disease or medical condition. For example, a patient
with rheumatoid
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arthritis may show interesting variations when off or on medicine, or when
exhibiting a
severe flare-up of symptoms. If such longitudinal correlations exist, review
of the
longitudinal data of other similarly situated patients could confirm valuable
biological
markers associated with the disease. When longitudinal data over an extended
period of
time exists, the number of individuals necessary for the analysis to be
statistically
significant can be relatively small.
An additional application of the present invention is in monitoring dose
response
studies. In this embodiment, a population of individuals is evaluated before
and after the
administration of drug and after increasing doses of the drug. In this
embodiment, the
selected population may be healthy individuals, and the anticipated biological
dose
response endpoint is toxicity or side effect profiles. In embodiments where
the individuals
have a particular disease or medical condition, markers may be identified for
efficacy along
with the negative effects of the drug. By evaluating the information from
individuals
before and after administration of drugs it will be possible to identify
markers or marker
1 S groupings associated with administration and response to the drug. In some
situations,
such markers could be used as an endpoint for clinical studies. For example,
in contrast to
such clinical endpoints as disease progression or recurrence or quality of
life measures
(which typically take a long time to assess), biological markers may provide a
more rapid
and quantitative measurement of a drug's clinical profile.
In other embodiments of the present invention, longitudinal studies of
individuals
receiving a drug or treatment for the prevention or treatment of a disease or
medical
condition could constitute the population of individuals being evaluated. By
correlating
biological indicators of individuals before they receive treatment with
subsequent clinical
observations, it will be possible to identify biological markers associated
with those
members of a potential patient population that will most benefit from the
treatment therapy.
In such a manner, expensive treatments can be limited to the subpopulation of
patients
most likely to benefit from the treatment.
Another application of the present invention is the use of biological markers
to
identify patients who have very early clinical signs of a disease. This would
be extremely
valuable for a multitude of disease states where a patient may have
"subclinical" signs and
symptoms which are not severe enough to bring them to a doctor's office.
However, if a
patient had a marker which was discovered in their blood and they were advised
to seek
medical attention, their "subclinical" signs could be identified as their
earliest phenotypic
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presentation of a disease. For many diseases, it is extremely advantageous to
diagnose a
disease as early as possible so that therapeutic drugs may be started and
generally lead to
reduced morbidity and mortality of that disease entity for that individual. A
possible
scenario would be if a patient could take a blood test to see if they have a
biological marker
for Rheumatoid Arthritis. If the marker were present, they could then seek
treatment
during the "subclinical" stage where they may only have a sensation of warmth
in their
joints instead of waiting until they have joint pain, swelling and deformity.
That individual
would likely have a much better long-term outcome for Rheumatoid Arthritis in
comparison to someone who waits until they have a much later stage of the
disease before
seeking treatment.
The present invention is directed to the phenotyping of an organism or class
or
subclass of organisms. The phenotype is made up of data from a large number of
data
categories. The principle categories of data included within the scope of this
invention are
i) levels of cell populations including their cell associated molecules in
biological fluid, ii)
levels of soluble factors in the biological fluid, iii) drug dosing and
pharmacokinetics
(measurement of a drug and its metabolites in a body) and iv) clinical
parameters.
Additional categories of data may include, but are not limited to, i) levels
of small
molecule compounds in biological fluid, ii) genotype information regarding the
individual,
including the individual's genetic makeup and gene expression (mRNA or
transcripts)
levels, and iii) data obtained from assays of urine components. In certain
embodiments
data categories may include images such as x-ray, CAT scans of the brain or
body, or
MRIs, or information obtained from biopsies, EKGs, stress tests, endoscopies,
ultrasound
exams, laparascopic procedure, orthroscopic surgeries, PET scans, or any other
measurement of an individual's condition.
In the preferred embodiment, the clinical parameters included in the database
of the
present invention would include, but not be limited to, the individual's age,
gender, weight,
height, body type, medical history (including comorbidities, medication,
etc.),
manifestations and categorization of disease or medical condition (if any) and
other
standard clinical observations made by a physician. Also included among the
clinical
parameters would be environmental and family history factors.
Clinical parameters could be further characterized by the source from which
the
information which is obtained. Patient obtained clinical parameters may
include
information that the patient provides via a questionnaire such as the WOMAC
for
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osteoarthritis, and the Health Assessment Questionnaire for Rheumatoid
Arthritis which
may be filled out in a doctor's office. Similarly, electronic or web-based
questionnaires
addressing all of a patient's current clinical symptoms could be completed by
the patient
prior to a clinic visit. Information obtained by a nurse would include vital
signs,
information from a variety of tests including allergy testing, pulmonary
function testing,
stress-thallium testing, or ECG tests. Clinical parameters collected from a
physician
includes a detailed history of prior illnesses, surgeries, hospitalizations,
medications,
reactions to medications, family history, social history, alcohol/drug/smoking
history, as
well as other behavior which would put a patient at high risk for HIV or
Hepatitis. A
thorough physical exam is also performed by a clinician and is a crucial
component of a
patient's clinical parameters.
In the preferred embodiment, the levels of cell populations and their
associated
molecules are identified by microvolume laser scanning cytometry. Such data
can also be
obtained by flow cytometry, but the volume of blood necessary to perform the
flow
1 S cytometry assays places a serious limit on the number of assays that can
be performed on
blood taken from a given individual at one time. In addition, the sample
preparation
required for performing flow cytometry assays is time consuming, expensive,
and may
interfere with the measurement result.
The levels of soluble factors can be measured by any suitable technique. In
the
preferred embodiment, the levels of soluble factors is measured by standard
immunoassay
techniques, such as ELISA techniques. In an alternative embodiment,
microvolume laser
scanning cytometry is used to obtain levels of soluble factors. Soluble
factors can be
detected by immunoassays such as MLSC, ELISA, etc., mass spectrometry, 2D gel
electrophoresis, combinations of mass spectrometry and immunosorption, and
chemical
assays. In the preferred embodiment, cell populations are detected by MLSC
assays and
soluble factors are detected by immunoassays or mass spectrometry.
The invention includes improved instrumentation for the rapid, reproducible
and
quantitative evaluation of biological parameters from a small quantity of
blood;
miniaturized, high sensitivity assays compatible with improved instrumentation
for the
detection of hundreds to thousands of biological parameters in blood; a broad
clinical
strategy to collect extensive medical information content from patients who
are followed
over time; software, databases and data mining tools to correlate patterns of
parameters
with normal biological functions, specific diseases, disease progression and
response to
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therapy; databases of clinical data and biological markers in collaboration
with academic
centers and clinical research institutes for use in drug development;
development of
diagnostic tests using proprietary patterns of markers and the ability to
improve the
efficiency of drug development by enabling more informed decisions in choosing
lead
compounds and identifying patients more likely to benefit from a given
therapy.
The unique ability to phenotype an organism and to conduct reproducible and
rapid
measurements of large numbers of biological parameters is essential for the
present
invention to identify novel patterns of biological markers from small samples
of blood.
Statistical analyses to date have shown that the assays for the numbers of
different cell
10 subsets or cell populations, are quantitative and highly reproducible. The
present
technology, which uses small volumes of blood and requires limited handling of
patient
samples, has distinct advantages over other commercially available measurement
technologies.
The invention further includes studies of patient populations related to
particular
diseases. These studies are based upon statistical analyses of disease
patterns and require
the collection of large numbers of blood samples from affected individuals. In
addition, the
present invention has utility for phenotyping and identifying biological
markers in plants
and animals and for assisting in preclinical studies.
Definitions
As used herein the term "phenotype" or "phenotyping" refers to a compilation
comprising a substantial subset of all measurable characteristics of an
organism. Such
characteristics or parameters include, but are not limited to, levels of cell
populations and
their associated molecules, levels of soluble factors, levels of other
molecules, genotype
information, gene expression levels, genetic mutations, and clinical
parameters. Such
characteristics or parameters include all historical data and present data.
For example, an
organism's complete phenotype includes all measurable characteristics at the
present time,
as well as all such characteristics at all past points of time. In addition to
technically
measurable characteristics, the phenotype can include an organism's feelings
or emotions
(in the case where the organism is a human, the phenotype includes the
individual's mental
state, e.g., depression, pain, agitation, mental illnesses, chemical
dependencies); diet and
changes in diet, injuries, relational history, sexual practices, socio-
economic status.
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An used herein the term "organism" refers to all plants, animals, viruses and
exoterrestial materials. Included within this definition, but not limited in
any way, are
humans, mice, rats, rabbits, companion animals, natural and genetically
engineered plants,
and natural and genetically engineered animals.
A given phenotype might include a compilation of characteristics of a single
organism or a class or subclass of organisms. For example, the phenotypic data
may be
obtained from a single male individual who has been diagnosed with cancer
before and
after therapeutic intervention, a group of males between the age of 15 and 55,
or a group of
males between the ages of 15 and 55 diagnosed with cancer. In this manner, the
phenotype
may be specific to a given individual, or may represent the average or typical
condition of a
combined group of individuals.
The phenotype of an individual organism or group of organisms may be used for
a
variety of purposes. In the broadest scheme of the invention, the phenotype is
looked at
longitudinally and evaluated after some perturbation to the organism. For
example, the
comparison of the phenotype of an individual before and after exhibiting
symptoms of
asthma could be used to identify biological markers associated with asthma. In
another
example, the phenotype of an individual who has asthma can be compared with
the
phenotype of a population of normal adults. In another example, the phenotype
of a
naturally occurnng plant can be compared with the phenotype of a genetically
altered plant
to determine what measurable characteristics are altered by the introduction
of the genetic
alteration. A further example of the use of phenotyipng information would be
to
periodically monitor well-patient status of an individual and to track
measures of biological
aging processes. The potential uses for comprehensive phenotypic data for an
organism are
almost infinite.
The present invention includes phenotypes for an organism or class or subclass
of
organisms, methods for obtaining such phenotypes and methods for utilizing
such
phenotypes, including for the identification of biological markers.
As used herein the term "biological marker" or "marker" or "biomarker" means a
characteristic or parameter that is measured and evaluated as an indicator of
normal and
abnormal biologic processes, pathogenic processes or pharmacologic responses
to a
therapeutic intervention. Pharmacologic responses to therapeutic intervention
include, but
are not limited to, response to the intervention generally (e.g., efficacy),
dose response to
the intervention, side effect profiles of the intervention, and
pharmacokinetic properties.
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Response may be correlated with either efficacious or adverse (e.g., toxic)
changes.
Biological markers include patterns or ensembles of cells or molecules that
change in
association with a pathological process and have diagnostic and/or prognostic
value.
Biological markers include, but are not limited to, levels of cell populations
and
their associated molecules, levels of soluble factors, levels of other
molecules, gene
expression levels (mRNA or transcripts), genetic mutations, and clinical
parameters that
can be correlated with the presence and progression of disease, normal
biologic processes
and response to therapy. Single biological markers currently used in both
clinical practice
and drug development include cholesterol, PSA, CD4 T cells, and viral RNA.
Unlike the
well known correlations between high cholesterol and heart disease, PSA and
prostate
cancer, and CD4 positive T cells and viral RNA and AIDS, the biological
markers
correlated with most other diseases have yet to be identified. As a result,
although both
government agencies and pharmaceutical companies are increasingly seeking
development
of biological markers for use in clinical trials, the use of biological
markers in drug
1 S development has been limited to date.
As a non-limiting example, biological markers are often thought of as having
discrete relationships with normal biological status, a disease or medical
condition, e.g.,
high cholesterol correlates with an increased risk of heart disease, elevated
PSA levels
correlate with increased risk of prostate cancer, reduced CD4 T cells and
increased viral
RNA correlate with the presence/progression of AIDS. However, it is quite
likely that
useful markers for a variety of diseases or medical conditions may consist of
significantly
more complex patterns. For example, it could be discovered that lowered levels
of one or
more specific cell surface antigens on specific cell types) when found in
conjunction with
elevated levels of one or more soluble factors - - cytokines, perhaps - - is
indicative of a
particular auto-immune disease. Therefore, for the purposes of this invention,
a biological
marker may refer to a pattern of a number of indicators.
As used herein the term "biological marker identification system" means a
system
for obtaining information from a patient population and assimilating the
information in a
manner that enables the correlation of the data and the identification of
biological markers.
A biological marker identification system comprises an integrated database
comprising a
plurality of data categories, data from a plurality of individuals
corresponding to each of
said data categories, and processing means for correlating data within the
data categories,
wherein correlation analysis of data categories can be made to identify the
data category or
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categories where individuals having said disease or medical condition may be
differentiated from those individuals not having said disease or medical
condition, wherein
said identified category or categories are markers for said disease or medical
condition.
Additionally, markers may be identified by comparing data in various data
categories for a
single individual at different points of time, e.g., before and after the
administration of a
drug.
As used herein the term "data category" means any type of measurement that can
be
discerned about an organism. Examples of data categories useful in the present
invention
include, but are not limited to, numbers and types of cell populations and
their associated
molecules in the biological fluid of an organism, numbers and types of soluble
factors in
the biological fluid of an organism, information associated with a clinical
parameter of an
organism, cell volumetric counts per ml of biological fluid of an organism,
numbers and
types of small molecules in the biological fluid of an individual, genomic
information
associated with the DNA of an organism and gene expression levels. For
example, a single
data category would represent the concentration of IL-1 in the blood of an
organism.
Additionally, a data category could be the level of a drug or its metabolites
in blood or
urine. An additional example of a data category would be absolute CD4 T cell
count. The
number or information assigned to an organism or class or subclass of
organisms at any
given point in time in part comprises the phenotype of that organism.
As used herein the term "biological fluid" means any biological substance,
including but not limited to, blood (including whole blood, leukocytes
prepared by lysis of
red blood cells, peripheral blood mononuclear cells, plasma, and serum),
sputum, urine,
semen, cerebrospinal fluid, bronchial aspirate, sweat, feces, synovial fluid
and whole or
manipulated tissue. Biological fluid typically contains cells and their
associated molecules,
soluble factors, small molecules and other substances. Blood is the preferred
biological
fluid in this invention for a number of reasons. First, it is readily
available and can be
drawn at multiple times. Blood replenishes, in part, from progenitors in the
marrow over
time. Blood is responsive to antigenic challenges and has a memory of
antigenic
challenges. Blood is centrally located, recirculates and potentially reports
on changes
throughout the body. Blood contains numerous cell populations, including
surface
molecules, internal molecules, and secreted molecules associated with
individual cells.
Blood also contains soluble factors that are both self, such as cytokines,
antibodies, acute
phase proteins, etc., and foreign, such as chemicals and products of
infectious diseases.
WO X0/65472 CA 02371385 2001-l0-24 PCT/US00/11296
14
As used herein the term "cell population" means a set of cells with common
characteristics. The characteristics may include the presence and level of
one, two, three
or more cell associated molecules, size, etc. One, two or more cell associated
molecules
can define a cell population. In general some additional cell associated
molecules can be
used to further subset a cell population. A cell population is identified at
the population
level and not at the protein level. A cell population can be defined by one,
two or more
molecules. Any cell population is a potential marker.
As used herein the term "cell associated molecule" means any molecule
associated
with a cell. This includes, but is not limited to: 1) intrinsic cell surface
molecules such as
proteins, glycoproteins, lipids, and glycolipids; 2) extrinsic cell surface
molecules such as
cytokines bound to their receptors, immunoglobulin bound to Fc receptors,
foreign antigen
bound to B cell or T cell receptors and auto-antibodies bound to self
antigens; 3) intrinsic
internal molecules such as cytoplasmic proteins, carbohydrates, lipids and
mRNA, and
nuclear protein and DNA (including genomic and somatic nucleic acids); and 4)
extrinsic
internal molecules such as viral proteins and nucleic acid. The preferred cell
associated
molecule is typically a cell surface protein. As an example, there are
hundreds of
leukocyte cell surface proteins or antigens, including leukocyte
differentiation antigens
(including CD antigens, currently through CD166, see, Leucocyte Typing VI,
Kishimoto,
T. et al.. ED, 1997), antigen receptors (such as the B cell receptor and the T
cell receptor),
and major histocompatibility complex. Each of these classes encompass a vast
number of
proteins. A list of exemplary cell surface proteins is provided in Table l,
which is merely
an illustration of the vast number of cell surface proteins and is in no way
intended to be a
comprehensive list.
As used herein the term "soluble factor" means any measurable component of a
biological fluid or tissue that is not a cell population or cell associated
molecule. Soluble
factor includes, but is not limited to, soluble proteins, carbohydrates,
lipids, lipoproteins,
steroids, other small molecules, including metallic, inorganic, ionic and
metallorganic
species and complexes of any of the preceding components, e.g., cytokines and
soluble
receptor; antibodies and antigens; and a drug complexed to anything. Soluble
factors can
be both self, such as cytokines, antibodies, acute phase proteins, etc., and
foreign, such as
chemicals, products of infectious diseases and intestinal flora and fauna.
Soluble factors
may be intrinsic, i.e., produced by the organism, or extrinsic such as a
virus, drug or
environmental toxin. Soluble factors can be small molecule compounds such as
WO 00/65472 CA 02371385 2001-10-24 pCT~S00/11296
prostaglandins, vitamins, metabolites (such as iron, sugars, amino acids,
etc.), drugs and
drug metabolites. A list of exemplary soluble proteins is provided in Table 6,
which is
merely an illustration of the vast number of soluble proteins and is in no way
intended to be
a comprehensive list.
For the purposes of this invention, soluble factors may be either known or
unknown
entities. A variety of techniques are available where a given species may be
identifiable,
but the chemical identity of the species is unknown. In the present invention,
the chemical
identity of the soluble factor need not be currently known or known at the
time the assay is
performed to determine its presence or absence.
10 As used herein the term "small molecule" or "organic molecule" or "small
organic
molecule" means a soluble factor or cell associated factor having a molecular
weight in the
range of 18 to 10,000. Small molecules can include, but are not limited to,
prostaglandins,
vitamins, metabolites (such as iron, sugars, amino acids, etc.), drugs and
drug metabolites.
As used herein the term "disease or medical condition" means an interruption,
15 cessation, disorder or change of body functions, systems or organs.
Examples of disease or
medical conditions include, but are not limited to, immune and inflammatory
conditions,
cancer, cardiovascular disease, infectious diseases, psychiatric conditions,
obesity, and
other such diseases. By way of illustration, immune and inflammatory
conditions include
autoimmune diseases, which further include rheumatoid arthritis (RA), multiple
sclerosis
(MS), diabetes, etc.
As used herein the term "perturbation" means an exterior or interior
measurable
event that can occur to an organism. A simple example would be the
administration of a
therapeutic agent to an individual, or an individual that was healthy and then
developed
asthma. In this application a perturbation may also include differences
between an
individual or groups of organisms that are being compared. For example, a
population of
animals may be considered to be normal, and their phenotype is being compared
to the
phenotype of a similar but genetically altered animal. The individual
genetically altered
animal was perturbed in the sense that its genetic alteration was perturbed
from normal. In
many cases the perturbation is not a single event that occurs at a discrete
point in time. The
perturbation may occur over an extended period of time, and/or may be cyclical
or
intermittent.
As used herein the term "clinical parameter" means information that is
obtained that
may be relevant to a disease or medical condition. Such information may be
supplied by
WO 00/65472 CA 02371385 2001-l0-24 pCT/[JS00/11296
16
the patient or by a medical or scientific observer. Examples of clinical
parameters for
humans include, but are not limited to, age, gender, weight, height, body
type, medical
history, ethnicity, family history, genetic factors, environmental factors,
manifestation and
categorization of disease or medical condition, and any result of a clinical
lab test, such as
blood pressure, MRI, x-ray, etc.
Clinical parameters could be further characterized by the source of
information
which is obtained. Patient obtained clinical parameters may include
information that the
patient provides via a questionnaire such as the WOMAC for osteoarthritis, and
the Health
Assessment Questionnaire for Rheumatoid Arthritis which may be filled out on
paper in a
doctor's office. Similarly, an electronic or web-based questionnaires
addressing all of a
patient's current clinical symptoms could be completed by the patient prior to
a clinic visit.
Information obtained by a nurse would include vital signs, information from a
variety of
tests including allergy testing, pulmonary function testing, stress-thallium
testing, or ECG
tests. Clinical parameters collected from a physician includes a detailed
history of prior
illnesses, surgeries, hospitalizations, medications, reactions to medications,
family history,
social history, alcohol/drug/smoking history, as well as other behavior which
would put a
patient at high risk for HIV or Hepatitis. A thorough physical exam is also
performed by a
clinician and is a crucial component of a patient's clinical parameters.
As used herein the term "genotype information" means any data relating to the
organisms genetic makeup, gene mutations, gene expression, e.g., mRNA or
transcription
levels, and any other measure or parameter associated with the genetic
material of the
organism.
As used herein the term "clinical endpoint" means a characteristic or variable
that
measures how a patient feels, functions, or survives. There are several
mechanism which
are commonly used to measure how a patient feels or functions with a specific
disease and
they often include validated clinical questionnaires. These may be self
administered such
as the Beck's depression questionnaire or the International Prostate
Questionnaire to
determine if changes in urination are due to prostatic hypertrophy v. bladder
outlet
obstruction. These tools may be given by a health care provider who is judging
features
such as facial expression, inability of patient to sit down for more than 10
minutes, level of
agitation etc., while completing the Carrol Questionnaire to determine if a
patient is manic.
And finally, in the case of psychiatric illness, typically patient's who are
admitted for a
hospitalization for an acute exacerbation of their illness will be observed
without realizing
WO 00165472 CA 02371385 2001-10-24 PCT/US00/11296
17
it by a clinician to note their ability to function in a variety of settings
including group
interactions or making lunch. These "clinical endpoints" are highly variable
per disease
entity and subsequently the tools which are used to characterize these
endpoints are also
quite broad.
As used herein the term "Microvolume Laser Scanning Cytometry" or "MLSC"
means a method for detecting the presence of a component in a small volume of
a sample
using a fluorescently labeled detection molecule and subjecting the sample to
optical
scanning where the fluorescence emission is recorded. The MLSC system has
several key
features that distinguish it from other technologies: 1 ) only small amounts
of blood (5-50
~1) are required for many assays; 2) absolute cell counts (cells/ ~l) are
obtained; and, 3) the
assay can be executed either directly on whole blood or on purified white
blood cells.
Implementation of this technology will facilitate measurement of several
hundred different
cell populations from a single harvesting of blood. The MLSC technology is
described in
United States Patent Numbers 5,547,849 and 5,556,764 and in Dietz et al.
(Cytometry
23:177-186 (1996)), and provisional patent application entitled "Laser-Scanner
Confocal
Time-Resolved Fluorescence Spectroscopy System" (United States Provisional
Application
Number 60/144,798, filed July 21, 1999), and the commonly-owned utility
application
filed concurrently with the present application, entitled "System for
Microvolume Laser
Scanning Cytometry", each of which is incorporated herein in its entirety.
Laser scanning
cytometry with microvolume capillaries provides a powerful method for
monitoring
fluorescently labeled cells in whole blood, processed blood, and other fluids.
The present
invention further improves MLSC technology by improving the capacity of the
MLSC
instrument to do simultaneous measurement of multiple biological markers from
a small
quantity of blood. A schematic of the improved SurroScan optical system is
shown in
Figure 2.
As used herein the term "tag" means any entity or species, including but not
limited
to an atom, a molecule, a fragment of molecule or a functional group; a
particle or
combination of particles; a single or sequence of electromagnetic pulses; or
any other form
of matter associated with, attached to (either covalently or non-covalently),
or otherwise
connected to a component of a biological system (a molecule or collection of
molecules
such as cell, a canon, an anion, an atom, or any supramolecular assembly,
including but not
limited to non-covalent complexes between biological molecules) that is used
to, identify,
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
18
quantify, associate, recognize, follow, spot, make out, see, name, track, or
otherwise
distinguish (henceforth I/Q) said component.
Tags are often extrinsic, i.e. not part of the component under investigation.
For
example, a fluorescent dye molecule is often used as a tag, either for
tracking, quantitation,
or both. Likewise, the use of biotin or streptavidin as tag, linked to a
secondary species
such as an enzyme for ELISA, is widespread. Other forms of tags include, but
are not
limited to, isotopic mass tags for protein I/Q by mass spectrometry, Raman-
active tags for
I/Q by Raman scattering, particulate tags for I/Q by light scattering,
fluorescence,
agglutination, energy transfer, and a variety of other detection mechanisms,
including
surface plasmon resonance.
In this regard, there are almost an infinite number of particulate tags, only
a small
number of which have been previously used. As nanoparticle science is in its
infancy (as
was organic chemistry two centuries ago), one can anticipate that the
complexity of
particulate tags will approach molecular complexity. In other words, we expect
that
particulate tags might rival the organic molecules currently used as bead tags
in
combinatorial chemistry, in other words thousands to hundreds of thousands or
even
millions of uniquely identifiable tags. We further anticipate that such tags
will become
small enough to allow all intracellular measurements. For example, there are
now roughly
one-half dozen different luminescent semiconducting quantum dot nanoparticles,
each
fluorescing at a different wavelength. In theory, one could anticipate
production of
thousands or millions of such orthogonal nanoparticulate optical tags,
although the
detection mechanism may or may not involve fluorescence (or even other optical
methods).
The same could be said of supramolecular science, and supramolecular tags. We
anticipate that molecular assemblies held together by non-covalent forces
could ultimately
find use as tags. Furthermore, tags could comprise individual molecules either
covalently
or non-covalently associated with biological components. For example, one
could imagine
using electrochemically-active redox tags to uniquely identify components. If
one had 10
different molecules, each with a different redox potential, and each pre-
functionalized to
react with a particular biological component, then one could carry out
multiplexed tag I/Q,
using the detection of the redox potential as the identifying characteristic.
This is identical
to the strategy currently used with fluorescence, with redox "space" used in
lieu of
"wavelength" space.
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
19
Note also that a tag can be a functional group, as in a carboxylate, an amine,
a
sugar, etc., or even a spin associated with a molecule. For example, we
anticipate the
possibility that two samples could be mixed together, with each sample having
one or more
nuclei imparted with a particular sequence of electromagnetic pulses (of the
sort typically
used in high-field NMR). We further envision that the pulses for two samples
would be
long-lived enough to compare them using a method of detection. In particular,
we envision
that possibility that the signatures for the two samples would cancel for all
species where
the concentrations are identical, leaving behind a signal only for those
species where
concentrations in the two samples are non-identical.
It should be clear to a person skilled in the art that there is no functional
difference
between tags, as defined above, and "reporters" or "reporter molecules", as
typically used
in the chemical and biological literature. Likewise, a "detection molecule" as
defined
below, can itself be a tag (for example when I/Q is based on mass, as in
quartz crystal
microbalances or piezo inertial biosensors).
As used herein the term "detection molecule" means any molecule or molecular
assembly capable of binding to a molecule or other species of interest,
including but not
limited to a cell-associated molecule, a soluble factor, or a small molecule
or organic
molecule. Preferred detection molecules are antibodies. The antibodies can be
monoclonal
or polyclonal. Note, however, that as new types of detection molecules are
discovered and
popularized, they certainly can be used. For example, aptamers are
increasingly being used
for molecular recognition, and organic chemists have now synthesized a large
number of
molecular receptors. Ultimately, these could be used as detection molecules,
either by
themselves or in association with a tag.
As used herein the terms "dye", "fluorophore", "fluorescent dye" are used
interchangeably to mean a molecule capable of fluorescing under excitation by
a laser. The
dye is typically directly linked to a detection molecule in the present
invention, although
indirect linkage is also encompassed herein. Many dyes are well known in the
art and
include, but are not limited to those shown in Table 2. In certain preferred
embodiments,
fluorophores are used which can be excited in the red region (> 600 nm) of the
spectrum.
Two red dyes, Cy5 and Cy5.5, are typically used. They have emission peaks of
665 and
695 nanometers, respectively, and can be readily coupled to antibodies. Both
can be
excited at 633 run with a helium-neon laser. Sets of 3 red dyes that may be
used include,
CyS, Cy5.5 and Cy 7 or CyS, Cy5.5 and Cy 7 APC. See, Mujumdar et al.,
Bioconjugate
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
Chemistry, 7:356 (1996); United States Patent No. 5,268,486; Beavis et al.,
Cytometry,
24:390 (1996); Roderser et al., Cytometry, 24:191 (1996); and United States
Patent No.
5,714,386. Additional novel dyes useful for tagging or detection purposes
within the
present invention are described in commonly-owned United States Provisional
Application
Number 60/142,477, filed July 6, 1999, entitled "Bridged Fluorescent Dyes,
Their
Preparation and Their Use in Assays," incorporated herein in its entirety by
this reference.
As used herein the term "animal model" refers to any experimental animal
system
in which diseases or conditions with similar pathology and progression to
human diseases
or medical conditions can be developed. Suitable animal systems include, but
are not
10 limited to, rats, mice, rabbits, and primates. In some cases, the disease
arises
spontaneously in the animal model. In other cases, the induction of disease in
the animal
model can result from exposure to the same conditions--for example, infection
with a
pathogen, exposure to a toxin, or a particular diet--that causes the disease
in humans.
Alternatively, the disease or condition can be induced in the animal model
with agents that
15 mimic the human disease or medical condition even if the actual initiators)
of the human
disease or medical condition is unknown. The disease or medical condition
might also be
induced through the use of surgical techniques. Genetic manipulation of
experimental
animal model systems provides a further tool for the development of the animal
models,
either standing alone or in combination with the other methods of disease
induction.
Preclinical Applications of Phenotynin~
Currently much effort is being directed towards the identification and
analysis of
biological markers in humans. However, it would be desirable to have a method
for
identifying and analyzing biological markers in experimental animal systems.
For
example, biological markers of the progression of a particular human disease
could be
identified in an experimentally-induced animal model of that disease, e.g.,
the rat adjuvant
model of arthritis (reviewed in Philippe, et al, American Journal of
Physiology 273:81550-
56 (1997)). Using the identified markers, the efficacy of experimental
therapeutics could
be determined in the animal model. Therapeutics that have a highly specific
effect on the
expression of biological markers in animals, which markers are prognostic or
diagnostic of
the same disease in humans, can therefore be identified without conducting
early--and
hence risky--human clinical trials. Alternatively, novel biological markers
can be
identified in experimental animal models of human disease, and then
experiments can be
WO 00/65472 CA 02371385 2001-l0-24 PCT/US00/11296
21
performed to determine whether the same markers, or their human homologues,
are
prognostic or diagnostic of the same disease or medical condition in humans.
In some
cases, biological markers identified in humans can be used to facilitate
preclinical trials
where animal models can be evaluated by the corresponding biological markers.
The
present invention provides methods and instrumentation for performing such
analyses.
In one series of embodiments of the invention, the expression of biological
markers
is studied in an animal model of a human disease. There are currently many
such models
and many more being developed, using a variety of different techniques to
induce the
specific disease. In each case, the biological markers of interest can be
initially identified
in preferred embodiments using MLSC. The identified markers can then be
studied using
MLSC to determine the response of the animal to a candidate therapeutic.
Because MLSC-
based assays typically only require small volumes of biological fluid, MLSC is
uniquely
suited for use in animal model systems (especially in rat and mouse) where
only limited
amounts of fluid can be obtained from an animal without sacrificing it. In
particular, the
use of MLSC will permit multiple time point analysis of an experimental animal
to
determine the pharmacokinetics of a candidate therapeutic.
In some embodiments of the invention, the animal homologues of known or newly
identified human biological markers of a particular disease are studied in an
experimentally-induced animal model of that disease. In many cases, the animal
homologues of human molecules will already be known and characterized. For
example,
through extensive study, a great deal is known about proteins that behave
similarly in
mouse and in humans. The identification of previously unknown animal
homologues of
human biological markers, and the preparation of reagents that can bind to
them, can be
accomplished through the use of standard molecular biology techniques well
known in the
art.
In other embodiments of the invention, novel biological markers--for example,
a
previously unknown pattern of expression of known blood cell-associated
proteins--may be
initially observed in an animal model of a human disease. In this embodiment,
the
relevancy of the identified markers to the progression or development of the
human disease
can be determined by identifying human homologues of the biological markers,
and then
studying their expression in humans suffering from the disease of interest. If
the identified
animal biological markers appear to be relevant to the human disease, then
they can serve:
1 ) as the basis of new diagnostic and prognostic assays for the disease in
humans; and 2)
WO 00/65472 CA 02371385 2001-10-24 PCTNS00/11296
22
as a means for evaluating the specificity and efficacy of candidate
therapeutics in the
animal model of the disease.
In one embodiment of the present invention new and improved animal models may
be developed based on biological markers identified in humans. For example,
utilizing the
biological marker identification system of the present invention it can be
found that for a
given disease or medical condition that the level of given soluble factor in
serum is greatly
increased, while the level of certain cell population is decreased. Based on
this
information, animal models can be tailored -- for example by the use of
genetic knockouts
of homologous factors -- to better simulate the disease in the animal serum.
The phenotyping system of the present invention may also be useful in the
identification of new or improved animal models. For example, by phenotyping a
number
of genetically altered animals, a fuller picture of the manifestations of the
genetic
alternations can be recognized. Utilization of this knowledge can be useful in
identifying
new or improved animal models. For example, it may be possible to create a
number of
genetic knock-out mice that all appear to simulate a chosen human disease
state. However,
by phenotyping each of the various knock-outs, as well as humans that suffer
from the
disease, it will be possible to identify the animal model that most closely
mimics the human
disease.
The present invention can be used in any animal model of a human disease. By
way of illustration only, the present invention can be used to identify and
analyze
biological markers in animal models of many aspects of cardiovascular disease,
including
hypertension, artherosclerosis, cardiac hypertrophy, atherogenesis, and
thrombosis. Many
animal models of congestive heart failure and hypertrophy are currently being
developed,
and a number are reviewed in: Carmeliet, Artherosclerosis, 144:163-93 (1999);
Young et
al., Molecular Basis of Cardiovascular Disease, 37-85 (K.R. Chien, Editor)
(1999);
Hasenfuss, Cardiovasc. Res. 39:60-76 (1998); Krege et al, Fundam. Clin.
Cardiol. 26:271-
92 (1996); Liao et al., Am. J. Therap. 4:149-58 (1997); and Becker et al.,
Hypertension
27:495-501 (1996) The following is a partial list of some animal models of
cardiovascular
disease:
~ The JCR:LA-cp rat model of human vascular disease can be used to identify
and study
biomarkers that correlate with insulin resistance, vasculopathy, and
cardiovascular
disease. O'Brien et al. Can. J. Physiol. Pharmacol. 76: 72-76 (1998).
WO 00/65472 CA 02371385 2001-l0-24 PCT/US00/11296
23
~ Animal models of insulin-dependent diabetes have been used to study the
development
of ischemic heart disease in the diabetic population. Reviewed in: Pierce et
al., Can. J.
Physiol. 75:343-50 (1997).
~ Infection of mouse, rabbits and monkeys with Chlamydia pneumonia has been
used to
investigate that pathogens role in the development of asthma and
cardiovascular disease
in humans, as reviewed in: Saikku et al, Artherosclerosis, 140 (Suppl. 1), S17-
S19
(1998).
~ Spontaneously hypertensive rat (SHR) strains, and SHR strains carrying a
portion of
chromosome 13 (including the renin gene) from normotensive rats (SHR.BN-Ren)
can
be used to investigate the interaction between high blood pressure and
dyslipidemia in
cardiovascular disease. St. Lezin et al, Hypertension, 31:373-377 (1998).
~ Spontaneously occurnng hypertrophic cardiomyopathy in Landrace pigs may be a
useful model of cardiovascular disease in humans. Chiu et al., Cardiovasc.
Pathol.
8:169-75 (1999).
1 S ~ Cardiomyopathic hamster strains can be used to investigate the role of
brain and atrial
natriuretic peptides (BNP and ANP) in human cardiovascular disease. Tamura et
al., J.
Clin. Invest. 94:1059-68 (1994).
~ Hypertensive and atherogenic rat strains have been used as models for the
study of the
effect of dietary salt, protein and lipids on the pathogenesis of human
cardiovascular
disease. Reviewed in: Yamori et al., Nutritional Prevention of Cardiovascular
Disease
(Symposium Proceedings) (1984), Published by: Academic Press, Orlando, Fla.
~ Rat, guinea pig, rabbit, dog, sheep, and baboon models of preeclampsia have
been used
to study the pathophysiology this hypertensive disorder of human pregnancy.
Reviewed in: Hypertension in Pregnancy 12:413-37 (1993).
In other embodiments, the present invention is used to identify and analyze
biological
markers in animal models of inflammatory diseases such as arthritis and
multiple sclerosis.
When used to screen candidate therapeutics, the present invention has a number
of
significant advantages over more traditional screening methodologies. Firstly,
clinical
testing comes at a relatively late stage in the development of the
therapeutic, at which point
the therapeutic is known to have a highly specific effect on the expression of
analogous
animal biological markers; this minimizes the risks to the clinical
participants. Secondly,
using experimental animal models to analyze patterns of biological marker
expression
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
24
means that only relatively small quantities of the potential therapeutic need
be synthesized
initially, thus reducing the cost of therapeutic development.
In other embodiments, the methods and systems of the present invention are
used to
identify markers of disease or medical conditions in animals for veterinary
purposes. The
identified markers can then be used to screen for candidate therapeutics
directed against
that disease or condition. This embodiment can be applied to domesticated
animals,
livestock and plants.
Instrumentation
Any suitable means for obtaining data that meet the requirements of the data
categories is within the scope of the invention. In the preferred embodiments,
Microvolume Laser Scanning Cytometry ("MLSC") is used to obtain the data for
cell
associated molecules and cell type count. In some embodiments, the MLSC
technology is
used with a bead based capture system or with various types of enzyme linked
immunosorbent assays (such as ELISA) to obtain data for soluble proteins.
Another
preferred means for obtaining data for compounds, particularly small
molecules, includes
the use of mass spectrometry. The MLSC technology used in this invention, is a
powerful
method for monitoring fluorescently labeled cells and soluble proteins in
blood. This
technology is currently used in clinical laboratories for the identification
of one or two
cellular markers for diagnostic applications. The present invention uses MLSC
to facilitate
the identification of biological parameters. In one embodiment, the present
invention
improves MLSC technology by improving the capacity of the MLSC instrument to
do
simultaneous measurement of multiple biological characteristics or parameters
from a
small quantity of blood.
Specific enhancements achieved with the instrument of the invention (termed
"SurroScan instrument") include the following: 1) two additional fluorescence
color
channels allow simultaneous detection and measurement of up to four
fluorescent colors;
2) higher laser excitation power improves sensitivity and throughput; 3)
disposable
capillary arrays allow more assays per patient sample using less blood per
assay; 4)
improved software and system integration automates sample measurements and
data
analysis; 5) the capacity of SurroScan instruments is expanded to handle
higher volumes
of patient samples for database creation and biological marker discovery.
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
Microvolume Laser Scanning_Cytometry (MLSC) System Design
The MLSC technology is described in United States Patent Numbers 5,547,849 and
5,556,764 and in Dietz et al. (Cytometry 23:177-186 (1996)), each of which is
incorporated
herein in its entirety. The Imagn 2000 system, commercially available from
Biometric
5 Imaging Inc., is an example of a MLSC system. Laser scanning cytometry with
microvolume capillaries provides a powerful method for monitoring
fluorescently labeled
cells in whole blood, processed blood, and other fluids. The present invention
further
improves MLSC technology by improving the capacity of the MLSC instrument to
do
simultaneous measurement of multiple biological markers from a small quantity
of blood.
10 A schematic of the improved SurroScan optical system is shown in Figure 2.
The preferred
MLSC instrument for use in the present invention is described in commonly
owned United
States Provisional Application No. 60/144,798, filed July 21, 1999, entitled
"System for
Microvolume Laser Scanning Cytometry" and in the commonly-owned utility
application
filed concurrently with the present invention entitled "System for Microvolume
Laser
15 Scanning Cytometry". Both of these applications are incorporated herein by
reference in
their entirety.
One embodiment of the improved optical configuration is shown in Figure 2. A
capillary array 10 contains samples for analysis. In the preferred embodiment,
collimated
excitation light is provided by one or more lasers. In particularly preferred
embodiments,
20 excitation light of 633nm is provided by a He-Ne laser 11. This wavelength
avoids
problems associated with the autofluorescence of biological materials. The
power of the
laser is increased from 3 to 17 mW. Higher laser power has two potential
advantages,
increased sensitivity and increased scanning speed. The collimated laser light
is deflected
by an excitation dichroic filter 12. Upon reflection, the light is incident on
a galvanometer-
25 driven scan mirror 13. The scan mirror can be rapidly oscillated over a
fixed range of
angles by the galvanometer e.g. +/- 2.5 degrees. The scanning mirror reflects
the incident
light into two relay lenses 14 and 15 that image the scan mirror onto the
entrance pupil of
the microscope objective 16. This optical configuration converts a specific
scanned angle
at the mirror to a specific field position at the focus of the microscope
objective. The +/-
degree angular sweep results in a 1 mm scan width at the objective's focus.
The
relationship between the scan angle and the field position is essentially
linear in this
configuration and over this range of angles. Furthermore the microscope
objective focuses
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
26
the incoming collimated beam to a spot at the objective's focus plane. The
spot diameter,
which sets the optical resolution, is determined by the diameter of the
collimated beam and
the focal length of the objective.
Fluorescence samples placed in the path of the swept excitation beam emit
stokes-
shifted light. This light is collected by the objective and collimated. This
collimated light
emerges from the two relay lenses 14 and 15 still collimated and impinges upon
the scan
mirror which reflects and descans it. The stokes-shifted light then passes
through a
dichroic excitation filter (which reflects shorter wavelength light and allows
longer
wavelength light to pass through) and then through first long pass filter 17
that further
serves to filter out any reflected excitation light.
The improved instrument of the instant invention then uses a series of further
dichroic filters to separate the stokes-shifted light into four different
emission bands. A
first fluorescence dichroic 18 divides the two bluest fluorescence colors from
the two
reddest. The two bluest colors are then focussed onto first aperture 19 via a
first focusing
lens 20 in order to significantly reduce any out-of focus fluorescence signal.
After passing
though the aperture, a second fluorescence dichroic 21 further separates the
individual blue
colors from one another. The individual blue colors are then parsed to two
separate
photomultipliers 22 and 23. The two reddest colors are focused onto a second
aperture 24
via a second long pass filter 25, a mirror 26, and a second focusing lens 27
after being
divided from the two bluest colors by first fluorescence dichroic 28. After
passing through
aperture 24, the reddest colors are separated from one another by third
fluorescence
dichroic 28. The individual red colors are then parsed to photomultipliers 29
and 30. In
this way, four separate fluorescence signals can be simultaneously transmitted
from the
sample held in the capillary to individual photomultipliers. This improvement,
for the first
time, allows four separate analytes to be monitored simultaneously. Each
photomultiplier
generates an electronic current in response to the incoming fluorescence
photon flux.
These individual currents are converted to separate voltages by one or more
preamplifiers
in the detection electronics. The voltages are sampled at regular intervals by
an analog to
digital converter in order to determine pixel intensity values for the scanned
image. The
four channels of the instant invention are named channel 0, l, 2, and 3.
The new optical layout has four detection channels to allow simultaneous
measurement of up to 4 fluorescently labeled molecules. In a preferred
embodiment,
multiple-color assays are used. Typically 3 or more fluorescent colors are
used in each
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
27
assay. Under circumstances where appropriate dye combinations are available,
the
instrument is capable of supporting 4-color assays.
An XY translational stage is used to move an array of capillaries relative to
the
optical system. The SurroScan system translation stage holds two arrays, each
of which
has the footprint of a 96-well plate. Capillary arrays have been designed
which have 32
fixed capillaries each and spacing that is compatible with multi-channel
pipettes. The
operator is able to load two plates of 32 capillaries at a time. No operator
intervention is
needed while the plates are scanned and the images are processed. As an
alternative, 16
individual capillaries designed for the Imagn 2000 (VC120) are loaded into
alternative
holders.
Image processing software accommodates images with either 2, 3, or 4 colors of
fluorescent dyes. The software automatically identifies and parameterizes
particles
detected in any of the individual colors. The measured parameters describing
each particle
are saved in a list-mode format, which is made compatible with conventional
cytometry
analysis software, such as FlowJo.
A new disposable cartridge design containing arrays of capillaries has been
developed and is described in Provisional United States Patent applications,
(United States
Provisional Application Number 60/130,876, entitled, "Disposable Optical
Cuvette
Cartridge", filed April 23, 1999; United States Provisional Application Number
60/130,918, entitled "Spectrophotometric Analysis System Employing a
Disposable
Optical Cuvette Cartridge", filed April 23, 1999; and United States
Provisional Application
Number 60/130,875, entitled "Vacuum Chuck for Thin Film Optical Cuvette
Cartridge",
filed April 23, 1999), and the commonly-owned utility application filed April
20, 2000,
entitled "Disposal Optical Cuvette Cartridge", which are incorporated by
reference herein
in their entirety. This capillary cartridge is used in Examples 5, 7 and 8.
The design
currently in use, called Flex-32, contains 32 capillaries. Fill holes in the
FLEX32-plates
have the same 9 mm spacing as 96-well plates and multichannel pipetting
devices. It is
constructed from 2 layers of mylar sandwiched together with a double-sticky
adhesive
layer which is die-cut to define the capillary inner dimensions. The resulting
cartridge can
be manufactured at low cost in high volumes. The cartridge is flexible, which
allows it to
be held onto an optically flat baseplate by vacuum pressure, removing the
requirements for
flatness in the manufacturing process. The capillary spacing was designed to
retain
compatibility with mufti-channel microplate pipetters and robotics.
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
28
Cellular Assays
The invention includes cellular assays, many of which are antibody based, that
are
compatible with instrumentation, preferably MLSC instrumentation and are
capable of
measuring hundreds to thousands of cell populations and their cell associated
molecules
from a single 10 mL tube of blood. In one preferred embodiment, any type of
detection
molecule and assay format compatible with MLSC is encompassed in this
invention,
including, but not limited to cell surface proteins including markers of
activation and
adhesion, intracellular molecules, assays to distinguish changes in activation
states of cells,
assays to concentrate and identify rare white cells, assays for use with whole
blood, and
assays for detection of soluble factors, such as proteins, in blood.
As with flow cytometry, fluorophore-labeled antibodies specific for cell
surface
antigens are used to identify, characterize and enumerate specific
populations. The
reaction can be done in whole blood. In general, there is no need to wash the
reagent away;
quantitative dilution of the blood-antibody mixture is usually sufficient
sample preparation.
The cell-antibody mixture is loaded into an optical-quality capillary of known
volume and
analyzed with a laser-based fluorescence imaging instrument. In order to
operate with
whole blood, fluorophores are used which can be excited in the red region (>
600 nm) of
the spectrum. Purified white blood cells can also be analyzed with the
instrument. In
contrast to flow cytometry, the laser scans over stationary cells rather than
cells flowing
past the laser. A small cylindrical laser spot is scanned across the capillary
in one direction
while the capillary is translated relative to the optical system in a second
direction.
Photomultiplier tubes are used to detect the fluorescent signal. Image-
processing software
is used to analyze the image and identify and enumerate the cells of interest.
This MLSC approach allows one to obtain absolute cell counts on hundreds of
different cell populations from a single tube of blood. For a set of
antibodies to 100
different antigens, there are about 5000 possible 2-color combinations and
about 162,000
possible 3-color combinations. (n combinations r at a time, "Cr = n!/r!(n-r)!)
so careful
thought is needed to develop the most appropriate set of 100 or so assays.
Mufti-color
capability allows more cell populations to be identified with a given amount
of blood than
the original 2-color system. As an example, by multiplexing reagents all
populations
identified in two 2-color assays can be identified in one 3-color assay. For
example it is
possible to assay CD3, CD4 and CD8 in one capillary instead of CD3 and CD4 in
one
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
29
capillary and CD3 and CD8 in another. More importantly, unique cell
populations can be
defined by the simultaneous expression of three or more antigens. For example,
CD8 T
cells can be subsetted into 4 different populations based on the differential
expression of
CD45RA and CD62L.
Immunoassay Procedures
Immunoassays can be run in a variety of formats and any appropriate format is
envisioned in the present invention. Two examples are given below. The MLSC
system
can be used with microsphere-based immunoassays. In this sandwich assay, the
microsphere is used as a solid support for an analyte-specific capture
antibody. Analyte
from a biological fluid is bound to the antibody-coated microsphere and
detected with a
second antibody, which is directly labeled with a fluorescent molecule such as
CyS, and
which binds to a distinct epitope on the analyte. A protocol using amino beads
and a
heterobifunctional crosslinker to covalently attach antibodies via their hinge
region works
well in multiple assays. It is possible to distinguish beads of different
sizes (3 to 20 micron
range) with the MLSC instrument and current software. By coupling different
capture
antibodies to microspheres of different sizes it is possible to multiplex
immunoassays in a
single capillary. Internal indicator dyes can also be used to distinguish
microspheres and
facilitate multiplexing.
The immunoassays for soluble factors discussed in Example 5 are all
chemiluminescent - based sandwich ELISA. Microtiter plates are coated with
capture
antibodies specific for the analyte of interest and blocked. Biological fluid
containing the
analyte is added, incubated and then washed. Biotinylated antibody specific
for a second
epitope on the same analyte is added, incubated and washed followed by an
avidin-
alkaline phosphatase conjugate. The level of analyte is revealed with a
chemiluminescent
alkaline phosphatase substrate. Plates are read in a Wallac Victor2
luminometer or similar
instrument.
Design and implementation for a robust panel of cellular assays to aid the
discovery of
biological markers for diseases or medical conditions
The MLSC system is designed to allow rapid staining of cells using minimal
quantities of blood. Reagents directed against scores of different cell
surface antigens are
WO 00/65472 CA 02371385 2001-l0-24 pCT/US00/11296
developed, which when combined can identify hundreds of different cell
populations. The
strategy for reagent and combination development is discussed below.
A set of monoclonal antibody reagents are employed which are suitable for
developing more than 100 cellular assays. To date, many (about 120) different
monoclonal
S antibodies directed against numerous (about 80) different cell surface
antigens have been
successfully identified and tested with the 2-color MLSC instrument. The small
organic
dyes like Cy5 and Cy5.5 are readily coupled to the amino groups of antibodies
using
single-step NHS chemistry and well established procedures. Preferred dye-to-
antibody
ratios, have been determined for CyS, Cy5.5, and Cy7 reagents, and are
generally in the
10 range of one to four. Protein fluorochromes, like APC, are linked to the
sulfhydryl groups
of moderately reduced antibody in a 3-step procedure using the
heterobifuntional
crosslinking reagent SMCC. Preparation of reagents containing other
fluorophores is also
possible. The preparation of Cy7-APC and (Cy7-APC)-antibody conjugates for
flow
cytometry applications has been previously described. The antibody-fluorophore
coupling
15 chemistry is the same as for APC. All protein-protein conjugates are
purified by traditional
means, such as, by gel filtration on an Akta FPLC. Fluorescent microspheres
can also be
investigated. Antibodies are coupled with 2-step carbodiamide chemistry to
carboxylated
microspheres.
New monoclonal antibodies reagents are titrated on both whole blood and lysed
red
20 blood cells. Reagent specificity, and lack of non-specific binding, is
confirmed with
appropriate counter stains. Analysis is done with any appropriate software
program,
including FlowJo cytometry software (Treestar, Inc available as an Internet
download at
htty//www treestar.com/ flowio/). From the titration the optimal amount of
each reagent
per assay (typically 0.01 to 2 ~g/ml) and preliminary analysis criteria is
determined. In the
25 preferred embodiment, all assays are conducted in homogenous (no wash)
mode. This
generally requires that each antibody reagent have a titer point of <_1 ~g/ml
so that the
fluorescence background is not too high. A potential difficulty may be that a
particular
reagent may not be amenable to conjugation or may have too high of a titer
point. It is
usually possible to substitute a second monoclonal antibody to the same
antigen. There is
30 also a risk that some individual antigens may not be measurable during the
time course of
the study. Multiple antibodies from each antigen category are typically
evaluated.
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
31
Typically, a panel of about 50-100 (or greater) cellular assays is developed
for
monitoring a disease or medical condition. Such assays enable one to enumerate
hundreds
of different cell populations. As an example, it is possible to monitor the
immune and
inflammatory cellular parameters potentially significant for rheumatoid
arthritis (RA). For
RA populations, the cell surface antigens being evaluated for use may be
divided into
different subsets based on the types of cellular antigens recognized. As an
illustration,
antigens found on the major leukocyte subtypes including T cells, B cells,
antigen-
presenting cells, NK cells, and granulocytes, as well as relevant receptors
and structures
found on these cells are included. These may include activation molecules, co-
stimulatory
molecules, adhesion molecules, antigen receptors, cytokine receptors, etc. A
representative, but not exhaustive, list of the antigens that may be evaluated
for RA is
provided in Table 1.
Cellular Assay Formats
The cellular assays described above are designed in either of two formats,
whole
blood or RBC-lysed blood. In the preferred embodiments, the assays are done in
whole-
blood or RBC-lysed blood format. The minimal manipulation ensures that the
most
accurate absolute cell counts (cells/~1 of blood) are obtained. Furthermore
only small
amounts of blood are required per assay so that many assays can be run from a
single tube
of blood. However, for some cell populations an alternative assay format, RBC-
lysed
blood, will be preferable. These include particular antigen-antibody pairs for
which soluble
factors (free Ig, soluble cytokine receptors, etc.) contained in the sera
interfere with cell
labeling and populations of cells that are present in very low frequency. This
procedure is
useful for activated cells expressing CD25 or CD69 which are essentially
undetectable in
whole blood from normal individuals but are increased ten-fold in the lysed
format and
have been shown to be increased in various autoimmune states. Improved
detection of
other minor cell populations such as NK cells has also been demonstrated and
should prove
particularly useful in analyses. As an example, for a panel of 96 assays, it
is estimated that
64 will be done on whole blood and 32 on lysed blood. Alternative sample
processing may
include, preparation of PBMC by Ficoll gradient, ex vivo stimulation with
polyclonal or
antigen specific activators.
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
32
Combining antibody reagents is important for the identification of novel cell
populations that may contribute to the pathogenesis, or be a marker for,
diseases or medical
conditions, such as autoimmune diseases. For example, it is known that
adhesion
molecules can be differentially expressed on T cells thought to be involved in
the
autoimmune process. Furthermore, several studies have indicated that there may
be an
increase in the number of memory CD4 T cells in patients with autoimmune
disease. With
the assays of the present invention it is possible to simultaneously look at
differential levels
of adhesion molecules (e.g., CD1 la+) specifically on a subset of memory (i.e.
CD45R0+) T
cells of the HLA class II-restricted lineage (i.e. CD4+). This should increase
the ability to
identify relevant disease-related cell populations. Multiple-color capability
also allows one
to look for novel populations of cells by choosing combinations of antigens
not typically
found together on a given cell type or markers found on the same cell type at
different
stages of ontogeny.
Determination of A~pronriate Fluorescent D ~~es
As indicated above any appropriate fluorescent dye is within the scope of the
present invention. Two commonly used dyes are cyanine dyes Cy5 (em 667) and
Cy5.5
(em 703). Typically, a single dichroic filter to split the emission signal at
685 nm is used.
More filters will be required when more than two dyes are employed. Dyes are
evaluated
to determine their compatibility in the MLSC system. As an example, a variety
of dyes
were evaluated to determine an appropriate overall 3-color set (see table 2).
Parameters to
consider when evaluating dyes include 1) spectral separation of the 3 dyes, 2)
signal-to-
noise ratio as a function of laser power, 3) suitability of the available
filters, 4) ease of
conjugation, and S) specificity of the resulting antibody-fluorophore
conjugates. Cy5 and
APC are appropriate for the first color and Cy5.5 is appropriate for the
second color.
Several potential dyes are appropriate for the third color. Cy7-APC is
expected to be
suitable for the MLSC system. Preliminary results with the Imagn 2000 system
demonstrate that this dye is detectable in the long wavelength channel (>685
nm) and
distinct from both Cy5 and APC. Emission spectra indicate that overlap with
Cy5.5 should
not be a problem given appropriate filters for the new instrument. Fluorescent
microspheres offer a wide variety of alternative colors and have been used
successfully in
some cytometry applications. Conjugation methods will be used which minimize
the non-
specific binding that occasionally occurs with microsphere reagents.
Typically, each of the
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
33
fluorophores are evaluated in the context of fluorophore-antibody conjugates
using a few
select antibodies e.g. anti-CD3, anti-CD4 and anti-CD20.
Soluble Factor Approaches
There are a large number of bioanalyses carried out by means other than
fluorescence. Prominent among these is mass spectrometry, rapidly becoming the
tool of
choice for detailed identification and analysis of polypeptides and proteins.
There are two
widely-used methods for biomolecular sample introduction in mass spectrometry:
electrospray ionization(ESI) and matrix-assisted laser desorption/ionization
(MALDI).
MALDI-TOF is currently successfully utilized for the analysis of proteins,
polypeptides
and other macromolecules. Even though the introduction of an organic matrix to
transfer
energy to the analyte has advanced tremendously the field of desorption mass
spectrometry, MALDI-TOF still has some limits. For instance, the detection of
small
molecules is not practical because of the presence of background ions from the
matrix. In
such cases ESI or even gas chromatography (GC) mass spectrometry can be used
to detect
or profile.
The complexity of molecular structures and heterogeneous nature of proteins
necessitates the need for multidimensional separation techniques. One of the
major areas
for this is the development of two dimensional gel electrophoresis using
polyacrylamide as
the gel matrix for example. The gel is modified in terms of crosslinking,
addition of
detergents, immobilization of enzymes or antibodies (affinity electrophoresis)
or substrates
(zymography) and pH gradient. This technique is used for the characterization
of proteins
in terms of structural modifications, activities, pI values, and molecular
weights.
Another area is the development of multidimensional chromatographic approaches
also referred to as hyphenated separation techniques. The advantages include
the ability to
more accurately quantify the analyte and better compatibility with online
detection
methods like laser induced fluorescence or mass spectrometry. To date usually
two
separation systems are chosen such that they are orthogonal and lead to a
better peak
capacity (resolution). Major technical hurdles are the integration of the
various separation
techniques with the detection system in terms of maintaining resolution upon
transfer to the
second dimension and the compatibility of the mobile phases with the detection
system, for
examples salts and detergents in the eluant are incompatible with electrospray
mass
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
34
spectrometry. Naylor J. Chromatogr. A 1996, 744 237-78 ; Jorgenson Anal. Chem
1997,
69, 1518-1524.
Chemical derivatization can be selectively employed to activate components m a
mixture that are not ionized enough to yield an ESI mass spectra. For example
sterols
typically devoid of acidic or basic residues that do not ionize under
electrospray conditions
have been coupled with ferrocene carboxylic acids, the electrochemical nature
facilitating
ionization. Anal. Chem. 1994, 66, 209-212. Derivatization can also serve as an
handle to
differentiate between stereoisomers (isobaric species) by using different
fragmentation
patterns in their daughter and granddaughter ions of the parents. The MSn
capability in an
quadrupole ion trap mass spectrometer for example has been used to distinguish
hexosamine monosaccharides, glucosamine, galactosamine and mannosamine
derivatized
with CoCl2(DAP)2Cl where DAP is diaminopropane. Anal. Chem 1999, 71, 4142-
4147.
Affinity based separation followed by mass spectrometric detection is of
clinical
interest as it allows analysis of complex molecules in biological fluids like
blood and urine
with little or no sample preparation. Ciphergen's technology (surface enriched
laser
desorption ionization (SELDI), a variation of MALDI) is based on this
principle.
Ciphergen offers 5-6 different surfaces upon which protein and/or small
molecules are
applied, and then washed with increasing stringency. Since each
surface/stringency
combination leads to a different adsorption profile, the technique provides
means for
analysis of a complex mixture.
Clinical Data and Informatics
The identification and correlation of biological markers with clinical
measurements
requires the integration of vast amounts of biological and medical data and a
search engine
that makes such data accessible and usable. The instrumentation and assays
developed in
the present invention have the ability to identify hundreds to thousands of
independent
markers from a small sample of blood. The present invention includes
developing a broad
clinical strategy to collect extensive medical information from patients that
are followed
over the time of disease progression and response to therapy. In addition, the
present
invention includes software, databases and data mining tools to correlate
patterns of
markers with specific diseases, disease progression and responses to therapy,
including, but
not limited to, databases of assays and clinical information, data conversion
and statistical
analysis tools, and medical questionnaire prototypes. The information system
of the
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
present invention is designed to use common language and common formats for
entry of
disparate types of data and is structured for data-mining purposes.
The universal medial language which will likely become widely used in the next
several years is SnoMed-RT. This language will be readily adaptable with the
current
information system of the present invention. Similarly, the present invention
is adaptable in
that as other languages or technologies become available, they may also become
incorporated into the database. An example would be the eventual development
of tools to
integrate digital x-rays, mammograms, or a virtual colonoscopy which is
obtained via a
C.T. scan of the abdomen.
10 The technical challenges in developing an informatics system capable of
handling
the vast amounts of biological and clinical information necessary to correlate
biological
markers with disease include modeling and integrating a number of diverse,
complex, and
often incompatible information sources, adapting to rapid advances in
scientific and
medical knowledge and methods, and developing a user-friendly interface,
proper format
15 and powerful search tools. The informatics system provided by the present
invention meets
these technical challenges.
Data Analysis
The data output from the cellular analyses includes both numbers of cells per
pl of
20 whole blood for each population identified, the mean intensity of staining
for each cell
associated molecule, which gives an estimate of the antigen density for a
given population,
the mean size of cells, and the expression levels of a particular molecule.
Each number
will be analyzed, because, as explained above, both the actual cell numbers as
well as
expression levels of a particular molecule may vary in a given disease state.
To identify
25 markers (cell counts or staining intensity, levels of soluble factors)
associated with
categorical clinical variables (such as diagnosis of disease) a variety of
discriminant
techniques are used. To identify markers associated with continuous-valued
clinical
variables (such as levels of soluble factors) a variety of regression
techniques are used. For
both discriminant and regression analyses, stepwise variable selection and
cross-validation
30 are used to identify those markers that are most closely associated with
the clinical variable
of interest. Where appropriate, demographic and clinical variables (such as
age, gender,
concomitant drugs, etc.) and genetic parameters are included as covariates in
the models.
WO 00/65472 CA 02371385 2001-10-24 pCT~S00/11296
36
These techniques are applied in the analyses of data, typically, using the SAS
and Statistica
statistical analysis software packages.
The architecture of the integrated informatics infrastructure (see Figure 3)
of the
present invention, comprises a mufti-tiered structure. The lowest level
consists of a set of
data sources. The first source comprises the scientific data which includes,
but is not
limited to, cellular assay data and soluble factor assay data. The second
source may be
semi structured data which is in a combined form of textual and tabular data
describing
protocols for assay development and protocols for the execution of clinical
studies. The
structure may be encoded as a data type definition (DTD), defining tags that
serve both for
information indexing and querying as well as selective information display on
web
browsers. The DTD tags also define an information exchange model enabling the
high-
level electronic sharing of the information with other parties. The third data
source is the
clinical data gathered and restructured to meet the clinical study
requirements. Clinical
questionnaires that are optimized to maximize, under time constraints, the
collection of
useful and quantifiable information from patients, are used to gather
information and to
provide the necessary quality control. If necessary, the questionnaires will
be mufti lingual
and adapted to physical challenges (e.g., the inability to use a computer
keyboard) that the
respondents may have to face. The technology of choice for this data source
may be XML.
In addition, the clinical information gathering system also comprises of non-
textual means
of input. A respondent may interact via visual and graphical displays to
provide health
related information by pointing at images of the human anatomy so as to
indicate a
problem without having to articulate it. Other means, e.g. a simple measure of
vital
capacity and FeV 1 sec. in asthmatic or emphysematous patients or monitoring
devices to
detect and/or correct cardiac arrhythmias, etc. could also be used for input.
The fourth
source of data is the instrumentation data containing all of the relevant
parameter settings
required for the execution of the scientific assays on a combination of
different
instruments, such as Imagn, SurroScan and the ELISA plate reader.
Additionally, data can be collected and recorded in lists. In list form,
measurement
values for each individual cell are recorded. This facilitates identification
and analysis of
individual cell populations that express a complex set of different molecules.
Alternative
analysis schemes are readily explored, facilitating optimal data analysis.
Likewise, the
complete set of patient data (cell populations, soluble factors, medical
history, clinical
parameters, etc.) can be stored in lists for each patient sample.
WO 00/65472 CA 02371385 2001-10-24 pCT/US00/11296
37
As indicated in Figure 3, these data sources are integrated and warehoused
using a
common schema. This schema coordinates the interpretation of the information
from the
constituent data sources. The interpretation is in a manner that is
independent of the logical
or physical storage detail of each of the constituent data sources. The common
schema
provides that data sources can be added or modified over time (management of
change)
without significantly affecting the tool set or user interface that ultimately
use the compiled
data. The common schema provides a buffer between the ever changing data
sources and
the application programs which use the compiled data and derive knowledge from
the data.
Similarly, if in the future additional instrumentation, e.g., NMR, is included
for the
generation of additional data, it can be added without upsetting the
organization of the
already existing warehouse.
The schema is augmented with an ontology of common concepts and their
relationships in immunology and related clinical areas. The ontology will be
used by the
data mining tools and by the user interface to assist in the interpretation of
user specified
requests for information from the underlying data sources and for the
specification of data
mining tasks. The ontology will also be utilized in the verification of the
collected clinical
data.
The toolkit of programs includes programs for statistical analysis, for data
mining
and for the visualization of the results. A result of the analysis by the
toolkit programs
provides a set of rules relating a set of conditions to a set of consequences.
These rules are
applied over a statistically significant portion of the underlying data and
are of the form:
if condl and cond2 and ... and condN then consequencel, consequence2, ...
For example, when applied to the cellular data source and the clinical data
source,
the toolkit can derive relationships between cellular assay and soluble factor
measurements
that were previously unknown. The results of the analysis by the toolkit are
recycled to the
users and to the database reuse in the future. The architecture is intended to
improve the
knowledge discovery process by storing the accumulated discovery experience
and by
integrating this experience for continued improvement.
Other tools for data mining include methods for clustering in highly
dimensional
data. These tools are intended to augment and replace the existing method of
manual gating
as presently used. Unlike current cytometry software, which considers one
assay from one
patient at a time, the cytometry tools of the present invention may examine
list mode data
across an assay from multiple patient samples in order to determine the
optimal set of
WO 00/65472 CA 02371385 2001-10-24 pCT/US00/11296
38
circumscribed population (gates). The system is coupled with a multi
dimensional
visualization system that will simultaneously project the computed clusters on
selected
subsets of two-dimensional and three-dimensional views.
The final tier is a user interface. This part of the system serves the user
interaction
and is used to plan and execute tasks related to clinical studies. Tasks
supported at the user
interface level include, knowledge discovery from study data, clinical study
planning,
protocol planning and evaluation and assay development.
The user interface will accept requests for information in a uniform way. It
may
combine a graphical interface and may allow for "drilling down" of information
from the
abstract concept level to the stored detail. It may allow for information
requests that
include both data and text (e.g., documentation pertaining to assay protocol
planning) and
may allow for interaction over a network.
EXAMPLES
Example One
Use of the present invention to identi~ bioloøical markers for Rheumatoid
Arthritis
The present invention can be used to identify biological markers for
rheumatoid
arthritis (RA). Microvolume laser scanning cytometry (MLSC) is used to help
create data
for identifying biological markers for RA. Marker discovery efforts are
focused on readily
accessible biological fluids, most notably blood. A two-color instrument and
antibody-
based assays have demonstrated the potential of this technique for identifying
and
enumerating scores of different cell populations with only a small amount of
whole blood.
Multiparameter cell analysis, in combination with multiple assays for soluble
factors, small
molecules and an extensive clinical database, is a powerful tool for future
biological
marker discovery. Such markers have the potential to lead to new and more
effective ways
to predict and monitor disease activity and responses to therapy.
Rheumatoid Arthritis is a chronic inflammatory disorder of the small joints,
which
also has pronounced systemic consequences. Although the etiology of the
disease is
unknown, its pathology evolves with common characteristics over time. Early
events
appear to include an inflammatory response initiated by unknown mediators.
Activated
CD4+ T cells appear to amplify and perpetuate the inflammation. The presence
of
activated T cells can induce polyclonal B-cell activation and production of
Rheumatoid
Factor (RF). Tissue damage accrues, releasing autoantigens, and the extent of
the T cell
WO 00/65472 CA 02371385 2001-l0-24 PCT/US00/11296
39
response broadens. Eventually, the constant inflammatory environment may lead
to
transformation of the synovial fibroblasts, yielding destructive potential
that is independent
of T cells and macrophages. The pro-inflammatory cytokines, produced mainly by
macrophages in the joint and the cytokines they induce such as IL-6, are
systemically
active, present in the serum and augment hepatic synthesis of acute-phase
proteins.
Throughout the various stages of the disease, there are changes in the
molecules and cells
in the synovium and blood that have potential to be markers of disease. Blood,
because of
its ready accessibility and circulation throughout the body, provides an
attractive window
for monitoring disease activity and is thus the major target of this
invention.
The present invention is useful to identify biological markers of diagnostic
and
prognostic value for Rheumatoid Arthritis. Such markers are required for
classifying
different forms of the disease, for example identifying the subset of patients
in whom joint
erosion occurs more rapidly than in others. Furthermore, the markers are
critical for
evaluating the efficacy of intervention and developing early, non-toxic and
successful
therapies. Many investigations have been made of cells and soluble factors in
blood,
synovium and urine that are candidate markers for the disease. In general, one
to several
markers at a time have been investigated. While some factors, such as
rheumatoid factor
and C-reactive protein have been associated with RA, there is no consensus
panel of RA-
specific markers. There is a strong need to simultaneously evaluate multiple
candidate
markers. This is achieved with multiple assays and by increasing the number of
parameters
(colors) that can be measured in a single assay.
The present invention is capable of developing a platform for identifying
markers of
disease and applying it to RA. In general, each assay combination consists of
one or more
reagents to identify the major cellular subsets (left column of Table 1). Some
of these
antigens, e.g. CD4, are targeted in multiple assays. The major markers are
combined with
different subletting antibodies (right column of Table 1) in order to maximize
information
about the sample. Properties of the fluorochomes and the target antigens are
considered in
developing each assay combination. For example, brighter fluorochromes are
used with
less abundant antigens. For other assays it is important to use reagents with
the best
spectral differences for certain targets. In general for each antibody triplet
FlowJo software
is used to analyze 1 to 3 different 3-color combinations (e.g., Cy5 CD3, Cy5.5
CD4,
Cy7APC-CD45RA vs. Cy5 CD45RA, Cy5.5 CD4, Cy7APC-CD3) to determine the best
combination for distinguishing the different cell populations.
WO 00/65472 CA 02371385 2001-l0-24 PCT/US00/11296
Designing a successful panel of assays requires some empirical knowledge. The
process is typically an iterative one, with each experiment building upon the
previous one.
As an example, an overview of candidate combinations with potential value for
RA is
given below.
S T cells. The major antigens being evaluated in a T cell panel include CD2,
CD3,
CD4, CDS, CD7, and CDB. Many kinds of molecules on these T cell subpopulations
can
be investigated. These include surface antigens which help to distinguish
naive (CD45RA)
vs. memory cells (CD45R0, CD26), and antigens that play a role in activation
(CD25,
CD69, CD71, HLA class II) or co-stimulation (CD27, CD28). In addition, markers
that
10 may play a role in adhesion to inflammatory sites are assayed (CD62L,
CDlla/CD18,
CD44, CD54, and CD58). Subpopulations of T cells based on expression of
a~iTCR,
yBTCR, and a panel of V(3 TCR genes are evaluated.
B cells. The major antigens being evaluated in a B cell panel include CD19,
CD20,
CD21, CD22, CD23, and CD72. In addition, various markers on these B cell
subsets
15 including markers that may indicate a more activated phenotype (CD40, CD80,
CD86,
HLA class II, CDS) and those that have been implicated in lymphocyte homing
and
adhesion (CD62L, CD44, CD1 la/CD18) are analyzed. IgM, IgG, and IgA receptors
for
specific antigens are also evaluated.
Antigen~resentin~ cells. Antigen-presenting cells are evaluated using markers
to
20 the major antigens CD13, CD14, CD15, and CD33. In addition, a variety of
adhesion
molecules (CDlla, CD18, CD29, CD44, CD54, CD58, CD62L) and co-stimulatory
molecules (CD80, CD86) on these cells are analyzed. Other relevant receptors
including
CD 16 (FcyRIII), CD32 (FcyRII), and CD64 (FcyRI) are assayed.
Other cell tykes and antigens. Only a few studies have investigated the
expression
25 of NK markers and granulocyte markers in RA, and in general these have
given
inconsistent results. NK subpopulations using the markers CD16, CD56, CD57,
and NKB1
are analyzed. Granulocytes, including neutrophils and eosinophils, may be
phenotyped
using CD 13, CD 1 S, and CD 16. A panel of adhesion molecules and receptors
similar to
that described above is used to further subset these populations.
30 There are many antigens whose expression has been associated with a more
activated or memory phenotype, implicated in adhesion or co-stimulation, or
shown to be
the receptor for an important ligand. Examples are outlined in Table 1.
Several of these
markers have been examined in several autoimmune conditions and the expression
has
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
41
been found to be variable. For example, T cells from RA patients show higher
levels of the
adhesion receptor LFA-1 (CD1 la/CD18) but no change in the expression of the
IL2
receptor (CD25), which is normally increased on activated cells, or a marker
for activation
and co-stimulation (CD80).
Some examples illustrating the kinds of indicators and cell populations that
may be
examined are discussed below.
T cells. There are several lines of evidence that implicate T cells in RA
(Fox, D.A.
(1997) Arthritis Rheum 40, 598-609). Such evidence includes the association of
RA with
MHC class II alleles that share a common sequence in the third hypervariable
region
(Weyand, C.M. and Goronzy, J.J. (1997) Ann N Y Acad Sci 815, 353-6 and Weyand,
C.M.
and Goronzy, J.J. (1997) Med Clin North Am 81, 29-55). Since CD4+ T cells
recognize
antigen bound to MHC class II antigens, the association of RA with expression
of specific
class II molecules implies a role for CD4 T cells in RA. In addition, studies
in animal
models of RA, such as collagen induced arthritis or adjuvant arthritis, have
shown that T
cells transferred from affected animals can induce synovitis in susceptible
hosts.
Furthermore, studies in RA patients have shown that strategies aimed at
eliminating T cells
or interfering with T cell function can ameliorate rheumatoid inflammation.
Perhaps more relevant to the present invention, examination of the phenotype
of T
cells, either in the synovial fluid, synovial tissue and/or peripheral blood
of RA patients,
have led to some interesting findings (Cush, J.J. and Lipsky, P.E. (1991) Clin
Orthop , 9-
22). Increased numbers of activated T cells are detectable in the peripheral
blood and
synovial fluid of RA patients. These T cells express CD3 and CD4 cell surface
markers at
a lower antigen density compared to controls, similar to the levels seen in
mitogen
activated T cells in vitro (Luyten, F., Suykens, S., V eys, E.M., Van
Lerbeirghe, J.,
Ackerman, C., Mielants, H. and Verbruggen, G. (1986) J Rheumatol 13, 864-9).
There is
also a slightly decreased number of CD8+ cells in most active RA causing an
increase in
the CD4/CD8 ratio. In addition T cells from patients with RA express increased
amounts
of the early activation marker CD69 (Pitzalis, C., Kingsley, G., Lanchbury,
J.S., Murphy, J.
and Panayi, G.S. (1987) J Rheumatol 14, 662-6), increased numbers of CD4+CD29+
and
CD4+CD45R0+ memory cells, and increased expression of MHC class II products
(Pitzalis, C., Kingsley, G., Murphy, J. and Panayi, G. ( 1987) Clin Immunol
Immunopathol
45, 252-8). Expression of CD44-dependent primary adhesion strongly correlates
with
concurrent symptomatic disease in juvenile RA and systemic lupus erythematosus
(Estess,
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
42
P., DeGrendele, H.C., Pascual, V. and Siegelman, M.H. (1998) J Clin Invest
102, 1173-82)
and may be important in adult RA. Some studies have shown increased numbers of
y~
TCR T cells and increased HLA expression on these cells (Reme, T., Portier,
M.,
Frayssinoux, F., Combe, B., Miossec, P., Favier, F. and Sany, J. (1990)
Arthritis Rheum
33, 485-92). An increase in CD8+57+ cells in RA, sometimes associated with
restricted
TCR, has also been reported (Money, J.K., Batliwalla, F.M., Hingorani, R. and
Gregersen,
P.K. (1995) J Immunol 154, 6182-90 and Serrano, D., Monteiro, J., Allen, S.L.,
Kolitz, J.,
Schulman, P., Lichtman, S.M., Buchbinder, A., V inciguerra, V.P., Chiorazzi,
N. and
Gregersen, P.K. (1997) J Immunol 158, 1482-9). Three-color assays can monitor
restricted
V(3 expression on this specific T cell subset.
B cells. Phenotypic analysis of B cells has also been performed in RA
patients. A B
cell subpopulation expressing the pan T cell marker CD5 has been shown to be
elevated
(Sowden, J.A., Roberts-Thomson, P.J. and Zola, H. (1987) Rheumatol Int 7, 255-
9, Hardy,
R.R., Hayakawa, K., Shimizu, M., Yamasaki, K. and Kishimoto, T. (1987) Science
236,
81-3 and Casali, P., Burastero, S.E., Nakamura, M., Inghirami, G. and Notkins,
A.L. (1987)
Science 236, 77-81). This subset is also elevated in autoimmune mice where IgM
autoantibodies have been shown to be constitutively expressed (Hayakawa, K.
and Hardy,
R.R. (1988) Annu Rev Immunol 6, 197-218). In humans however CD5+ B cells do
not
preferentially produce autoantibodies (Suzuki, N., Sakane, T. and Engleman,
E.G. (1990) J
Clin Invest 85, 238-47) and the role of CD5+ B cells in the pathogenesis of
autoimmunity
in humans is still unclear, perhaps reflecting the presence of activated B
cells (Werner-
Favre, C., Vischer, T.L., Wohlwend, D. and Zubler, R.H. (1989) Eur J Immunol
19, 1209-
13). Circulating B cells from RA patients also demonstrate increased
expression of HLA
DR molecules, again indicative of an activated B cell phenotype (Eliaou, J.F.,
Andary, M.,
Favier, F., Carayon, P., Poncelet, P., Sany, J., Brochier, J. and Clot, J.
(1988)
Autoimmunity l, 217-22). Three-color assay are able to monitor increased HLA
class II
expression specifically on CDST CD19+ B cells.
Anti~en~resentin~ cells. Several cell types can serve as antigen-presenting
cells,
including monocytes, macrophage, dendritic cells, B cells and other cells
induced to
express class II antigens. In general these cells show an activated phenotype
demonstrated
by increased expression levels of HLA class II antigens in patients with
autoimmune
disease (Lipsky, P.E., Davis, L.S., Cush, J.J. and Oppenheimer-Marks, N.
(1989) Springer
Semin Immunopathol 1 l, 123-62). Antigen-presenting cells are abundant in the
synovial
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
43
compartment (Viner, N.J. (1995) Br Med Bull 51, 359-67) and blood-derived
macrophages
have been associated with human cartilage glycoprotein 39 expression in some
studies
(Kirkpatrick, R.B., Emery, J.G., Connor, J.R., Dodds, R., Lysko, P.G. and
Rosenberg, M.
(1997) Exp Cell Res 237, 46-54).
Soluble factor assays. Soluble factor assays provide an additional battery of
potential biological markers. There are many important soluble factors that
have been
identified in RA patients. These include levels of circulating cytokines such
as TNFa and
IL-6, cytokine receptors, chemokines, rheumatoid factors of different
isotypes,
immunoglobulin with different forms of glycosylation, hormones, acute-phase
proteins
such as C-reactive protein and serum amyloid A, and soluble adhesion
molecules, as well
as matrix metalloproteinases and their inhibitors. Many of these soluble
factors are known
to be present at varying levels in RA patients at different stages of disease
(Choy, E.H. and
Scott, D.L. (1995) Drugs 50, 15-25, Feldmann, M., Brennan, F.M. and Maini,
R.N. (1996)
Annu Rev Immunol 14, 397-440, and Wollheim, F.A. (1996) Apmis 104, 81-93).
Therefore, assays can be conducted to measure these soluble factors and look
for statistical
correlations with the cell populations identified.
Medical histories. In addition to soluble factors, information in the medical
history
of patients are included in the database. The clinical parameters will include
information
on age, gender, stage of disease, outside laboratory tests such as ESR,
previous therapy and
any concomitant drugs or therapies. This information is relevant to the
evaluation. For
example, it is known that immunosuppressive drugs, such as those often taken
by RA
patients, can have a profound effect on the expression of cell surface
antigens. Patients
treated with methotrexate show a decrease in CD19' and CDS+19+ B cells.
Patients treated
with cyclophosphamide show a decrease in activated T cells expressing CD25 or
HLA DR.
Patients treated with prednisone express several changes in cell surface
phenotype.
including a decrease in activated CD3+25+ T cells, a decrease in CDS+19+ B
cells, and a
decrease in CD16+ and CD56+ NK cells (Lacki, J.K. and Mackiewicz, S.H. (1997)
Pol
Arch Med Wewn 97, 134-43). Other clinical variables such as disease duration
may also be
useful.
Distin uiu Shlrig_patient populations. A review of the cellular assay
literature as it
relates to autoimmune disease reveals that there are apparently conflicting
reports. For
example, some reports indicate an increase in levels of CDS B cells
(Markeljevic, J.,
Batinic, D., Uzarevic, B., Bozikov, J., Cikes, N., Babic-Naglic, D., Horvat,
Z. and Marusic,
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
44
M. (1994) J Rheumatol 21, 2225-30) in RA patients, while other studies do not
(Liu, S.T.,
Wang, C.R., Liu, M.F., Li, J.S., Lei, H.Y. and Chuang, C.Y. (1996) Clin
Rheumatol 15,
250-3). These publications suggest that there may be other confounding factors
that have
important implications for cellular phenotypes, and perhaps cellular function,
in RA
patients. Segregating the patient populations selected for study, based on
levels of soluble
factors circulating in the serum, stage of disease, and therapy could in part
explain the
apparent discrepancy with respect to the CD5+19+ B cell levels in RA patients
discussed
above. For example, it is known that there is a significant correlation
between the levels of
IgM rheumatoid factor and the percentage of CD5 B cells (Youinou, P.,
Mackenzie, L.,
Katsikis, P., Merdrignac, G., Isenberg, D.A., Tuaillon, N., Lamour, A., Le
Goff, P.,
Jouquan, J., Drogou, A. and et al. (1990) Arthritis Rheum 33, 339-48).
Furthermore the
level of IgA rheumatoid factor is associated with the level of CD5 B cells as
well as
CD4+CD45R0+ T cells (Arinbjarnarson, S., Jonsson, T., Steinsson, K.,
Sigfusson, A.,
Jonsson, H., Geirsson, A., Thorsteinsson, J. and Valdimarsson, H. (1997) J
Rheumatol 24,
269-74). Simultaneous measurement of multiple parameters increases the
probability of
identifying key variables for segregating patient groups.
This generic example illustrates that this invention is uniquely suited for
identifying
ensembles of biological markers to characterize diseases. The MLSC technology,
which
requires only a very small sample volume, provides that numerous assays can be
completed
on a single blood sample and ensures that the maximum amount of biological
information
can be acquired. The biological marker identification system can accommodate a
mixture
of assay types, including whole blood and RBC-lysed blood, among others. The
assays
conducted are considered relevant for the clinical indication and allow a
broad survey.
Relevant biological markers can be identified using the technology of the
present
invention.
Example Two
Use of the present invention to identif~bioloøical markers for Multiple
Sclerosis
The present invention can be used to identify biological markers for Multiple
Sclerosis (MS). The biological marker identification system is employed to
identify
markers for Multiple Sclerosis. MS is an autoimmune inflammatory disease of
the central
nervous system. MS is characterized clinically by relapsing and remitting
episodes of
neurologic dysfunction. The etiology of the disease remains unknown, however
the
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
presence of inflammatory cells in the brain, spinal cord, and cerebrospinal
fluid implies
that an immune attack against CNS myelin is central to the pathogenesis of MS.
The
hallmark of the MS lesion is an area of demyelination called a plaque that may
be found
throughout the brain and spinal cord. Inflammatory cells are seen at the edges
of the
5 plaque and scattered throughout the white matter. The main inflammatory
cells include
activated lymphocytes and monocyte derived macrophages. CD4 T cells accumulate
at the
edges of the plaque; CD8 T cells are not found as frequently in active
disease, but are
present in longstanding lesions. Autoreactive T cells recognizing myelin basic
protein and
other non-myelin self antigens circulate in the blood and upon activation can
pass through
10 the blood-brain barrier. Up-regulation of adhesion molecules,
histocompatability antigens,
and other markers of lymphocyte and monocyte activation (IL2R, FcR) are all
connected
with the activation and homing process. Furthermore, there is in increase m
proinflammatory cytokines that serves to amplify the immune response. The
autoimmune
response also includes pronounced B cell stimulation. The autoantibodies
produced can
15 activate the complement system and promote demyelination. Throughout the
various
stages of disease, there are changes in the molecules and cells in the CNS and
the blood
that have potential to be markers of disease.
The present invention can identify disease markers of diagnostic and
prognostic
value for Multiple Sclerosis. Such markers are valuable for classifying
different forms of
20 the disease, for example identifying the subset of patients with relapsing-
remitting disease
who are most likely to develop those secondary progressive disease.
Furthermore, the
markers are valuable for evaluating the efficacy of intervention and
developing early, non-
toxic and successful therapies. Many investigations have been made of cells
and soluble
factors in blood, cerebrospinal fluid (CSF) and urine that are candidate
markers for the
25 disease. In general, one to several markers at a time have been
investigated. While some
factors, such as oligoclonal immunoglobulin in the CSF, have been associated
with MS,
there is no consensus panel of MS-specific markers. There is a strong need to
simultaneously evaluate multiple candidate markers.
T cells. There are several lines of evidence that implicate T cells in MS.
Such
30 evidence includes the association of MS with MHC class II (particularly HLA
DR) alleles
(Hauser, S.L., Fleischnick, E., Weiner, H.L., Marcus, D., Awdeh, Z., Yunis,
E.J. and Alper,
C.A. (1989) Neurology 39, 275-7). Since CD4+ T cells recognize antigen bound
to MHC
class II antigens, the association of MS with expression of specific class II
molecules
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
46
implies a role for CD4 T cells in MS. In addition, studies in animal models of
MS such as
mouse or rat experimental allergic encephalomyelitis have shown that myelin
antigen
specific CD4 T cells can induce disease when adoptively transferred to naive
animals
(Cross, A.H. and Raine, C.S. (1990) J Neuroimmunol 28, 27-37 and Cross, A.H.,
Cannella,
B., Brosnan, C.F. and Raine, C.S. (1990) Lab Invest 63, 162-70). Furthermore,
studies in
MS patients have shown that strategies aimed at eliminating T cells or
interfering with T
cell function can slow progression of MS.
Perhaps more relevant to the present invention, studies examining the
phenotype of T
cells, either in the cerebrospinal fluid and/or peripheral blood of MS
patients have led to
some interesting findings. There is a reduction in CD8+ T cells in the blood
of MS patients.
The subset showing the most marked decrease was the CD8+CD1 lb+ subset
(Ilonen, J.,
Surcel, H.M., Jagerroos, H., Nurmi, T. and Reunanen, M. (1990) Acta Neurol
Scand 81,
128-30 and Oksaranta, O., Tarvonen, S., Ilonen, J., Poikonen, K., Reunanen,
M., Panelius,
M. and Salonen, R. (1996) Neurology 47, 1542-5). There is also an increase in
activated T
cells bearing the CD71 and CD25 markers particularly in active MS (Genc, K.,
Dona, D.L.
and Reder, A.T. (1997) J Clin Invest 99, 2664-71 and Strauss, K., Hulstaert,
F., Deneys, V.,
Mazzon, A.M., Hannet, L, De Bruyere, M., Reichert, T. and Sindic, C.J. (1995)
J
Neuroimmunol 63, 133-42). Furthermore, the majority of T cells in the
cerebrospinal fluid
and peripheral blood show a memory phenotype with high levels of CD45R0 and
CD29 on
both the CD4 and CD8 T cell populations (Vrethem, M., Dahle, C., Ekerfelt, C.,
Forsberg,
P., Danielsson, O. and Ernerudh, J. (1998) Acta Neurol Scand 97, 215-20). This
leads to a
reduction in CD4+CD45RA+ (Strauss, K., Hulstaert, F., Deneys, V., Mazzon,
A.M.,
Hannet, L, De Bruyere, M., Reichert, T. and Sindic, C.J. (1995) J Neuroimmunol
63, 133-
42) and CD8+CD27-CD45RA+ (Hintzen, R.Q., Fiszer, U., Fredrikson, S., Rep, M.,
Polman,
C.H., van Lier, R.A. and Link, H. (1995) J Neuroimmunol 56, 99-105) naive T
cells in the
peripheral circulation. A recent study has concluded that CD4+, CD4+ SLAM+,
and
CD4+CD7+ cells (preferentially T helper 1 cytokine producing cells) are
increased in MS
patients relative to controls (Ferrante, P., Fusi, M.L., Saresella, M.,
Caputo, D., Biasin, M.,
Trabattoni, D., Salvaggio, A., Clerici, E., de Vries, J.E., Aversa, G.,
Cazzullo, C.L. and
Clerici, M. (1998) J Immunol 160, 1514-21). Furthermore some studies have
shown
skewed TCR variable beta usage in the peripheral blood of MS patients
indicative of a
restricted TCR repertoire (Gram B., Gestri, D., Sottini, A., Quiros Roldan,
E., Bettinardi,
A., Signorini, S., Primi, D., Ballerini, C., Taiuti, R., Amaducci, L. and
Massacesi, L. (1998)
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
47
J Neuroimmunol 85, 22-32). A restricted pattern of gene rearrangement has also
been
described in the yb T cell subset (Michalowska-blender, G., Nowak, J. and
blender, M.
(1998) Folia Neuropathol 36, 1-5).
B cells. Phenotypic analysis of B cells has also been performed in MS
patients. A
B cell subpopulation expressing the pan T cell marker CDS has been shown to be
elevated
(Mix, E., Olsson, T., Correale, J., Baig, S., Kostulas, V., Olsson, O. and
Link, H. (1990)
Clin Exp Immunol 79, 21-7). This subset is also elevated in autoimmune mice
where they
have been shown to constitutively express IgM autoantibodies (Hardy, R.R.,
Hayakawa,
K., Shimizu, M., Yamasaki, K. and Kishimoto, T. (1987) Science 236, 81-3). In
humans,
however, CDS+ B cells do not preferentially produce autobodies (Suzuki, N.,
Sakane, T.
and Engleman, E.G. (1990) J Clin Invest 85, 238-47) and the role of CDS+ B
cells in the
pathogenesis of autoimmunity in humans is still unclear, perhaps reflecting
the presence of
activated B cells (Werner-Favre, C., Vischer, T.L., Wohlwend, D. and Zubler,
R.H. (1989)
Eur J Immunol 19, 1209-13). Consistent with this conclusion, high levels of
the memory
marker CD45R0 were found on circulating CD20+ B cells from patients with MS
(Yacyshyn, B., Meddings, J., Sadowski, D. and Bowen-Yacyshyn, M.B. (1996) Dig
Dis
Sci 41, 2493-8). The number of circulating CD80+ B cells is also increased
significantly in
MS patients with active disease, but is normal in stable MS (Genc, K., Dona,
D.L. and
Reder, A.T. (1997) J Clin Invest 99, 2664-71).
Antigen-presenting cells. Several cell types can serve as antigen-presenting
cells,
including monocytes, macrophage, dendritic cells, B cells and other cells
induced to
express class II antigens. In general these cells show an activated phenotype
demonstrated
by increased expression levels of HLA class II antigens in patients with
active MS (Genc,
K., Dona, D.L. and Reder, A.T. (1997) J Clin Invest 99, 2664-71). A recent
study has also
shown that CD86 and CD95 (fas) expressing monocytes are increased in MS as
compared
with healthy controls (Genc, K., Dona, D.L. and Reder, A.T. (1997) J Clin
Invest 99, 2664-
71).
Other cell types. Only a few studies have looked at the expression of NK
markers
and granulocyte markers in MS. One study shows a decrease in CD16+ NK cells in
chronic, progressive MS (Kastrukoff, L.F., Morgan, N.G., Aziz, T.M., Zecchini,
D.,
Berkowitz, J. and Paty, D.W. (1988) J Neuroimmunol 20, 15-23).
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
48
Soluble factor assays. Soluble factor assays provide an additional battery of
potential biological markers. There are many important soluble factors that
have been
identified in MS patients. For example, levels of soluble Apo A-1/Fas
(Ferrante, P., Fusi,
M.L., Saresella, M., Caputo, D., Biasin, M., Trabattoni, D., Salvaggio, A.,
Clerici, E., de
Vries, J.E., Aversa, G., Cazzullo, C.L. and Clerici, M. (1998) J Immunol 160,
1514-21) is
augmented in acute MS compared with the levels seen in patients with stable
disease or
healthy controls. In addition, levels of soluble adhesion molecules such as
soluble
intracellular adhesion molecule 1 (ICAM-1) (Giovannoni, G., Lai, M., Thorpe,
J., Kidd, D.,
Chamoun, V., Thompson, A.J., Miller, D.H., Feldmann, M. and Thompson, E.J.
(1997)
Neurology 48, 1557-65) and soluble E -selectin (Giovannoni, G., Thorpe, J.W.,
Kidd, D.,
Kendall, B.E., Moseley, LF., Thompson, A.J., Keir, G., Miller, D.H., Feldmann,
M. and
Thompson, E.J. (1996) J Neurol Neurosurg Psychiatry 60, 20-6) have been shown
to be
increased in MS patients at different stages of disease. Proinflamatory
cytokines like
TNFa and IFNy, are known to be present at varying levels in MS patients at
different
stages of disease (Navikas, V. and Link, H. (1996) J Neurosci Res 45, 322-33).
Other
relevant proteins, such as cytokines and cytokine receptors, chemokines,
matrix
metalloproteinases and their inhibitors, neopterin, and myelin basic protein,
have also been
shown to be present at varying levels in MS patients at different stages of
disease and
healthy controls. Therefore, assays can be conducted to measure these soluble
factors and
look for statistical correlations with the cell populations identified.
Medical histories and distin~uishin~ patient populations. In addition to
soluble
factors information in the medical history of patients will be included in the
database. The
clinical history will include information on age, gender, stage of disease,
outside laboratory
evidence (magnetic resonance imaging, cerebrospinal fluid analysis for
oligoclonal
immunoglobulin and evoked potential recordings), previous therapy and any
concomitant
drugs or therapies. This information is relevant for segregating patient
populations.
It is evident that treatment effects play a role in the phenotype of the
cells. While
untreated MS patients display a greater population of CD3+CD4+CD8+ circulating
T cells
compared with healthy donors, this population of cells is reduced following
corticosteroid
treatment [30]. In addition, the number of CD71+ and HLA DR+ lymphocytes and
monocytes is increased in active MS. However therapy with IFN(3-lb reduces the
number
of activated HLADR+, CD71+ and CD25+ cells. Furthermore, although the number
of
circulating CD80+ B cells is decreased, the number of CD86+ monocytes is
increased
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
49
following therapy [14]. Other clinical variables such as disease duration may
also be
useful. For example it has been shown that in MS patients with restricted TCR
V(3
repertoires, the median disease duration is shorter than in patients who do
not have a
restricted repertoire (Gram B., Gestri, D., Sottini, A., Quiros Roldan, E.,
Bettinardi, A.,
Signorini, S., Primi, D., Ballerini, C., Taiuti, R., Amaducci, L. and
Massacesi, L. (1998) J
Neuroimmunol 85, 22-32).
Example Three
Study Comparing Rheumatoid Arthritis Patient to Control Patient Blood
In a pilot study, a panel of 40 two-color cellular assays was prepared for the
Imagn
2000 and evaluated blood samples from about 50 donors. Half of the samples
came from
the Stanford Blood Bank and half came from the Rheumatology Clinic at Stanford
University. The study was designed to evaluate and develop key components of
the
biomarker search engine of the invention: instruments, assays and analytical
tools. It was
not necessarily designed to elucidate biomarkers. All assays were done on
whole blood,
without washing to remove unbound reagents. Thirty-eight of the assays
comprised 27
different antibody reagents to 23 different cell surface antigens. Eighteen
were conjugated
to Cy5 and nine were conjugated to Cy5.5. Each of these cellular assay
comprises one
antigen conjugated to each dye to make up a two-color combination. Two assays
monitored
cell viability with a DNA intercalating dye. The cellular assays allowed us to
identify
approximately 100 different cell populations including sets of T cells, B
cells, NK cells,
monocytes, and granulocytes.
Methods
Cellular assays
The panel of assays is shown in Table 3. Each reagent is tested and titrated
before
preparing the reagent combinations in order to optimize assay performance.
Sample preparation
For this study all cellular assays were applied to whole blood, in homogeneous
mode (no post stain washing). Aliquots (20 uL) of fluorescently labeled
antibody reagent
combinations of DNA dye were distributed with a mufti-channel pipette from
prepared
racks into discrete wells of a microtiter plate. Whole blood or diluted whole
blood (30 uL)
was added with a mufti-channel pipette and the sample mixed. Cells were
incubated for 20
minutes followed by the addition of 100 uL of diluent and mixing. A portion of
each
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
stained sample (50 uL, corresponding to 10 uL of blood) was added to
volumetric
capillaries (VC120) and loaded into the modified Imagn 2000 instrument. Scans
were
initiated and executed without operator intervention. Data files are
transferred over the
computer network and converted to the Flow Cytometry Standard format. FlowJo
cytometry software was used to identify cell populations and obtain numerical
values for
cell counts (cells per uL), relative cell size, and relative fluorescence
intensity, which is an
estimate of the antigen density for each gated (boxed) cell population.
Soluble factors
Serum levels of C-reactive protein were measured on the Imagn 2000 with a bead-
10 based immunoassay. Beads coated with anti-CRP antibody were used to capture
the
analyte. Cy5 conjugated anti-CRP antibody was used reveal the captured
analyte.
Patient medical information
An abbreviated medical history, including age, gender, parameters of disease
severity, co-morbidities and medications was obtained from each patient. Data
for the
15 blood bank samples was limited to age and gender.
Database and statistics
Data output from the cellular assays, soluble factor assay and medical
histories
were combined into a single database. To identify potential biological markers
(cell counts
or staining intensity, serum concentration) associated with categorical
clinical variables
20 (such as diagnosis of disease) a variety of discriminant techniques was
used including
Fisher linear and quadratic discriminant analysis, logistic regression, and
classification
trees. To identify markers associated with continuous-valued clinical
variables (such as
erythrocyte sedimentation rate) we use a variety of regression techniques
including
multivariate linear regression and regression trees. For both discriminant and
regression
25 analyses, stepwise variable selection and cross-validation was used to
identify those
markers that are most closely associated with the clinical variable of
interest. Where
appropriate, demographic and clinical variables (such as age, gender, and
concomitant
drugs) were included as covariates in the models. These techniques were
implemented and
applied using the SAS and Statistica statistical analysis software packages.
30 Results
_Common analysis strategies can be used for most patient samples
Although one of the drawbacks of cell population studies is often the
variability
among donor samples, identical analysis windows (gates) were used across all
donors for
WO 00/65472 CA 02371385 2001-10-24 pCT/US00/11296
51
95% of the cell populations analyzed. This demonstrates the robustness and
consistency of
these assays and cell analysis systems. The remaining 5% of the gates were
adjusted to
account for new populations appearing in certain donors or a reagent that
appeared
unreliable for a few donors. In the latter case the problem reagent can be
replaced with an
improved version in future studies.
An example of a 2-color combination with variation among donors is shown in
Figure 4. The cells were stained with CD27 conjugated to Cy5 in combination
with CD8
conjugated to Cy5.5. This combination allowed CD8+ T cells (MHC class I
restricted) to
be monitored, which are CD27+ (activated) and CD27-. CD8~, CD27+ cells (which
are
actually activated CD4, MHC class II restricted, T cells) are also detected.
Although there
is variation among the donors, a single gating strategy can be implemented.
Three cell
populations are identified which differ among the donors. In Figure 4A the
majority of
CD8+ T cells are CD27 negative. In Figure 4B the majority of CD8+ cells are
CD27
positive. Finally, in Figure 4C, the CD8 population is split between those
that are CD27
positive and those that are CD27 negative. FlowJo, our cytometry software,
calculates the
cell count for each of the gated populations. In addition, the mean
fluorescence intensity
for each antigen was obtained. This is indicative of the antigen density on
the cell surface.
The relative cell size for each cell population was also obtained. The numbers
were
compared and compute statistics across all donors. The differences shown here
with
respect to CD27 expression on CD8+ cells are typical of the kinds of changes
that are
observed when comparing patient and control populations in our clinical study.
Excellent correlation amon~related measurements
Another goal of this initial study was to assess the robustness of the 2-color
Imagn
system and develop statistical tools. The study was designed so that several
capillaries
contained the same antibody reagent conjugated to either the same or the
alternative dye.
This allowed the same measurement to be obtained anywhere from 2-6 times from
the
same donor for CD3, CD4, CDS, CD7, CD8, CD19, CD20 and CD27 antigens. The same
cell populations were also measured using antibodies to different antigens
found on them.
For example, total T cells were enumerated using CD3 as well as CDS. B cells
were
enumerated using CD19 as well as CD20, etc. In this preliminary study,
excellent
consistency was seen both between capillaries containing the identical reagent
and
capillaries containing different antibodies staining similar cell populations.
Correlation
coefficients for the same antigen across different capillaries averaged 0.94.
The correlation
WO 00/65472 CA 02371385 2001-10-24 pCT~S00/11296
52
coefficient was 0.97 for both CD3 vs. CDS and CD19 vs. CD20. Examples of these
correlations are given in Figure 5 and Figure 6.
Differences are observed among RA and blood bank samples
Several measured parameters were used to segregate general blood bank samples
and RA patient samples as shown in Table 4. The best single markers accurately
segregate
80 to 86% of the sample (7 to 10 incorrect assignments). Some, two cell
population pairs
segregate 90% of the samples, suggesting that sets of cell populations may be
more useful
than single cell populations for segregating patient populations.
Example Four
Expanded RA Study
This Example expands the measurement capabilities in an RA study. Cell
populations and soluble factors from rheumatoid arthritis (RA) patients were
monitored.
The RA patients were part of a clinic study, receiving methotrexate and either
AR.AVA or a
placebo. Patients were monitored longitudinally over about 2 months. At each
time point,
cell population data, soluble factor data, and clinical information was
collected.
Cellular assays
Most of the cellular assays are done in whole-blood format as described in
Example
three. The minimal manipulation ensures that the most accurate absolute cell
counts
(cells/~1 of blood) are obtained. Furthermore only small amounts of blood are
required per
assay (40 ul) so that many assays can be run from a single tube of blood.
However, for
some cell populations an alternative assay format, RBC-lysed blood, is
preferable. These
include particular antigen-antibody pairs for which soluble factors (free Ig,
soluble
cytokine receptors, etc.) contained in the sera interfere with cell labeling
and populations of
cells that are present in very low frequency. For example the RBC-lysed sample
preparation is useful for activated cells expressing CD25 or CD69 which are
essentially
undetectable in whole blood from normal individuals but are increased ten-fold
in the lysed
format and are likely to be increased in various autoimmune states. Improved
detection of
other minor cell populations such as NK cells has also been demonstrated.
For this protocol, the cellular assays included a panel of 60 2-color
combinations
comprising 46 whole blood assays and 14 RBC-lysed whole blood. A total of 39
different
antibody reagents (30 conjugated to Cy5 and 9 conjugated to Cy5.5), targeting
35 distinct
cell surface antigens, were used. All assays are done in homogeneous mode (no
wash after
WO 00/65472 CA 02371385 2001-l0-24 PCT/US00/11296
53
staining). This assay panel enables the identification of more than 150
different cell
populations. The reagent combinations and the cell populations that can be
identified are
provided in Table S.
Soluble Factor Assays
Sera are aliquoted and frozen for each blood sample for subsequent measurement
of
multiple soluble factors. These include levels of circulating cytokines such
as TNFa and
IL-6, cytokine receptors, chemokines, rheumatoid factors (RF) of different
isotypes,
immunoglobulin, acute-phase proteins such as C-reactive protein and serum
amyloid A,
and soluble adhesion molecules, as well as matrix metalloproteinases and their
inhibitors.
The initial panel of 22 soluble factors assayed is shown in Table 6.
Additional targets are
also provided in Table 6. All assays are done in a sandwich ELISA format using
matched
antibody pairs to ensure the required sensitivity and specificity.
Patient medical information
A medical history with more detailed disease-specific information is included
with
each sample in the study.
Example Five
Cellular Assays on a 4-channel MLSC Instument
More assays, with greater information content per assay, can be run on the 4-
channel SurroScan instrument. Assays are developed using 3 color reagent
combinations.
Effective dye combinations include CyS, Cy5.5 and Cy7 and CyS, Cy5.5 and Cy7-
APC
allow simultaneous and independent measurment of three target antigens. Three
color
combinations facilitate the acquisition of more information per capillary than
2 color
combinations by 1) eliminating redundancy (e.g. measuring CD3, CD4 and CD8 in
one
capillary instead of measuring CD3 + CD4 and CD3 + CD8 in two capillaries) and
2)
identifing new populaitons that are defined by the simultaneous expression of
3 antigens
(e.g. naive CD4+ T cells that express both CD45RA and CD62L). Given
appropriate
fluorescent dyes with distinct emission spectra, it is possible to
simultaneously monitor
additional target antigens either in the fourth channel, or in some cases, in
the existing
channels. Figure 7 provides the results of a 3-color assay on the SurroScan
instrument.
Assays on the SurroScan instrument can be executed with capillary arrays which
use about 1/3 less sample than the VC120 capillaries. For whole blood assays
it is
possible to process 10 uL or less per 3 color assay, giving the potential for
up to 1000
WO 00/65472 CA 02371385 2001-l0-24 PCT/US00/11296
54
assays per 10 mL tube of blood. For RBC-lysed blood samples with a 10-fold
increase in
leukocyte concentration, about 100 assays could be done per tube of blood. A
panel of 64
3-color assays with 50 or more target antigens is under development using both
whole
blood and lysed formats. It should allow identification of more than 200 cell
populations.
Example Six
Intracellular staining
Intracellular molecules can be measured with MLSC technology. PBMC were
cultured for 5 hours in the presence of PHA and ionomycin. Cells were stained
with Cy5.5
anti-CD8 to identify cytoxic T cells, fixed, permiablized, and stained with
Cy5 anti-
inerferon-gamma (IFN-y) to detect the intracellular cytokine. Data in Figure 8
shows that
IFN-y is detected only in stimulated cells. A control reagent (MOPC) does not
label the
cells. Among the CD8 T cells, 20 % express intracellular IFN-y
Example Seven
Identi~~~ Biological Markers for the Treatment of Aller~y and Asthsma
The present invention can be used to identify biological markers for allergic
asthma. Asthma is common chronic lung disease of uncertain etiology. It is
characterized
by inflammation of the airways leading to symptoms of coughing, wheezing,
chest
tightness, and shortness of breath. These clinical symptoms are thought to be
due to hyper-
responsiveness of the airways and a long-term inflammatory process causing
obstruction of
airflow. The disease causes extreme discomfort and can at times be fatal in
the absence of
appropriate treatment. The clinical manifestations of asthma are thought to
result from the
superimposition of a variety of environmental factors on genetic
predispositions that
increase the likelihood of developing asthma. Atopy, the hypersensitivity to
environmental
allergens, is common in asthma, but not all atopic individuals develop asthma.
The relative
importance of allergic mechanisms is not completely understood.
Corticosteroids (inhaled
and systemic) are efficacious in asthma but have associated with perceived and
real side
effects that limit their usefulness. A more complete understanding of response
to
corticosteroids might allow for the development of drugs with only local
effects within the
lungs or drugs that have beneficial effects without side effects.
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
A study has been designed to identify biological markers of atopy, asthma and
the
response to corticosteroid therapy. Subjects are screened for four study
groups of 20: 1)
mild asthmatics who have tested positive to skin test allergens, 2) mild
asthmatics who
have tested negative to skin test allergens, 3) non-asthmatics who have tested
positive to
5 skin test allergens, and 4) non-asthmatics who have tested negative to skin
test allergens
(healthy subjects). All eligible subjects are entered onto a single-blinded,
placebo
controlled, randomised parallel study to investigate the effect of the drug
prednisone on
biological markers after 3 days bid treatment. Blood samples are taken prior
to treatment
on the morning of day 1 and 12 hours after the last dosing on the morning of
day 4.
10 Subjects undergo rigorous screening including detailed medical history and
clinical
tests for lung function and allergy. Mild asthmatics have a 1) FEVI >_ 80%
predicted, 2)
documented diagnosis of asthma or history of any of the following: cough,
worse
particularly at night, recurrent wheeze, recurrent difficult breathing,
recurrent chest
tightness and 3) a positive methacholine challenge test (Cockcroft DW, et al
Clin Allergy
15 1977; 7:235 and Juniper EF, et al Thorax 1984; 39:556). Non asthmatics have
a 1) FEV1
80% predicted 2) no history of asthma and 3) a negative methacholine challenge
test.
Allergic subjects have a positive skin test to at least one of a panel of
allergens.
Examples of clinical data include Haematology: white blood cell count (WBC),
red
blood cell count (RBC), hemoglobin (Hb), hematocrit (HCT), mean cell volume
(MCV),
20 mean cell haemoglobin (MCH), mean cell haemoglobin concentration (MCHC)
platelet
count, neutrophil count lymphocyte count, monocyte count, eosinophil count,
basophil
count and ESR - erythrocyte sedimentation rate; blood biochemistry: alkaline
phosphatase,
alanine transaminase, aspartate transaminase, gamma-glutamyl transpeptidase,
albumin,
total protein, total bilirubin, urea, creatinine, sodium, potassium, glucose;
urinalysis:
25 protein, glucose, ketones, bilirubin, blood, leucocytes; Hepatitis and HIV
testing: HIV I
and II, Hepatitis B surface antigen, Hepatitis C antibody. All clinical
history and test
parameters will be included in the master database for statistical analysis,
evaluation as
covariates and data mining.
Atopic asthma is an immunologic disease mediated by IgE antibodies. Exposure
to
30 allergen causes B cells to synthesize IgE, which binds to the high affinity
receptor mast
cells residing in the mucosa of the airways. On re-exposure to the allergen,
antigen-
antibody interactions on the surface of the mast cells triggers release of
mediators of
anaphylaxis stored in mast cell granules, including: histamine, tryptase,
PGD2, leukotriene
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
56
C4 and D4, and platelet activating factor (PAF). These soluble factors induce
contraction of
air smooth muscle and cause an immediate fall in the FEV 1. Re-exposure to
allergens also
leads to the synthesis and release of a variety of cytokines: IL-4, IL-5, GM-
CSF, TNF-a,,
TGF-(3, from T cells and mast cells. These cytokines attract and activate B
cells, which
leads to the production of more IgE, and eosinophils and neutrophils, which
produce
eosinophil cationic protein (ECP), major basic protein (MBP) and PAF. These
factors
cause edema, mucus hypersecretion, smooth muscle contraction and increase the
bronchial
reactivity that is typically associated with the late asthmatic response,
indicated by a fall in
FEV 1 about 4-6 hours after exposure.
A broad panel of cellular and soluble factor measurements are applied to the
subject
blood samples with the goal of discovery biomarkers. The study design supplies
information of inter-individual variability within groups, and inter-group
differences in
marker expression. It is believed that the inter-group differences (e.g.
allergic non-
asthmatic versus non-allergic non-asthmatic) will be greater than inter-
individual
variability within groups. It is further believed that prednisone therapy will
result in
significant intra-individual changes in marker expression.
Cellular assays
A panel of 64 three color cellular assay, focusing on immune and inflammatory
parameters in the blood, has been prepared and tested for initial atopic
asthma. The panel
is given in Table 7.
Soluble Factors
The study will also look at a broad panel of soluble factors. Immunoassays, in
the
sandwich-based chemiluminescent ELISA format, are used for the following
targets:
Cytokines, chemokines and their soluble receptors: IL-1 alpha, IL-1 beta, IL-1
RA,
IL-1 sRI, IL-1 sRII, IL-2, IL-2sR, IL-3IL-4, IL-5, IL-6, IL-6 sR, IL-8, IL-10,
IL-12 p40,
IL-12 p70, IL-13, IL-16, IL-17, MIF, MIP-1 alpha, MIP-1 beta, RANTES,
sTNFalpha RI
*, sTNFalpha RII *, TGF beta, TNF alpha, alpha, TGF beta2, TGF beta3,
Oncostatin M,
M-CSF, GM-CSF, IGF-l, PDGF-BB, FGF-4, FGF-6, FGF-7, Fas, VEGF, MCP-1, PF-4,
EOTAXIN, IFN gamma, Immunoglobulin: IgAI Kappa, IgAI Lambda, IgAl, 2 Kappa,
IgAI, 2 Lambda, IgA2 Kappa, IgA2 Lambda, IgE total, IgGI Kappa, IgGl Lambda,
IgGl
total, IgG2 Kappa, IgG2 Lambda, IgG2 total, IgG3 Kappa, IgG3 Lambda, IgG3
total, IgG4
Kappa, IgG4 Lambda, IgG4 total, IgG total , IgG total Kappa, IgG total Lambda,
IgM
Kappa, IgM Lambda, IgM total, RFIgA, RFIgG, RFIgM, RF total, Acute phase
proteins:
WO 00/65472 CA 02371385 2001-l0-24 PCT/US00/11296
57
CRP, SAA; Matrix metalloproteinases and their inhibitors: MMP-3, MMP-9, TIMP-
1,
TIMP-2; Soluble adhesion molecules: sCD54 (ICAM-1), sCD62E, sCD62P.
Additional soluble factors which are measured by immunoassays or mass
spectroscopy assay include, but are not limited to, Cytokines, chemokines and
their soluble
receptors: IL-9, IL-11, IL-14, IL-15, IL-18, sCD23, eosinophil proteins: ECP,
MBP,
Immunoglobulin: Allergen specific IgE, carbohydrate modified Ig; a variety of
prostaglandins; a variety of leukotrienes, histamine.
Data output from the cellular assays, soluble factor assays, medical histories
and
screening labels are combined into a single database. To identify potential
biological
markers (cell counts, antigen intensity on particular cell types, soluble
factor
concentrations, etc) associated with categorical clinical variables (disease
status,
prednisone or placebo, before or after therapy) a variety of ANOVA and
discriminant
techniques can be used. Where appropriate, demographic and clinical variables
(such as
age, gender, specific history results) can be included as covariates in the
models.
Techniques can be implemented with SAS, Statistica, Statview or similar
statistical
analysis software packages.
Example Eight
Use of the present invention to identify,bioloaical markers following the
administration of
aspirin
The present invention can be used to identify biological markers for
evaluating the
effects of drug administration on cellular and soluble factors to be performed
on small
samples of peripheral blood. It is expected that these assays will make
possible analysis of
the effects of different doses of drugs on cellular and soluble markers in
human peripheral
blood. In this example the widely used over-the-counter drug, aspirin
(acetylsalicylic acid),
is administered to human volunteers. Different doses of the drug will be
orally
administered; blood is drawn before and at various time points after
administration, and
panels of cellular and soluble factor assays are undertaken. The aspirin is
expected to
cause changes in the cellular and soluble components of blood.
Aspirin is routinely used for two main indications: 1) to reduce the risk of
coronary
and cerebral thrombosis and 2) as an analgesic/anti-inflammatory agent. The
mechanism
underlying the first indication is believed to be irreversible inhibition of
the enzyme PGH-
synthatase in platelets. A prostaglandin product of this enzyme in platelets
is converted to
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
58
thromboxane A2, which facilitates platelet aggregation and thrombosis. A side
effect of
prostaglandin synthesis is the generation of oxygen free radicals, which in
the presence of
redox-oxidative metals convert unsaturated fatty acids into aldehydes. A
relatively stable
product of lipid oxidation is malondialdehyde (MDA). This compound is
routinely assayed
colorimeterically or fluorometrically following interaction with
thiobarbituric acid (TBA.
Aspirin, by inhibiting prostaglandin synthesis, is expected to decrease MDA
levels in
peripheral blood platelets. This is one parameter that is expected to change
following
aspirin administration. Changes in other markers of platelet activation such
as changes in
the expression of CD62P and CD63 may also occur.
E-type prostaglandins suppress lymphocyte activation and the production of
tumor
necrosis factor-a (TNF-a) by the cells of the monocyte-macrophage lineage. If
there is
some level of lymphocyte activation and TNF-a production in normal healthy
persons, this
may be increased after aspirin treatment and detectable in the peripheral
blood. These are
examples of expected changes following aspirin administration; if many markers
are
assayed, unexpected changes may also be found, and may prove to be more
interesting than
those expected.
The study is designed to identify the effects of aspirin on blood parameters.
Eligible
subjects are randomly assigned to orally administer aspirin according to one
of three dosing
schemes. Group I, 1 dose (325 mg tablet) after breakfast, Group II. 2 doses
(650 mg) after
breakfast and Group III, 2 doses after breakfast and 2 doses after diner (1300
mg total).
There are 10-12 subjects per cohort. Blood samples are taken before, during
and after
aspirin administration. The schedule is given in Table 8. Subjects are healthy
individuals
age 18-65 who are not taking other aspirin other non-steroidal anti-
inflammatory drugs nor
currently under care, which requires the use of anti-inflammatory (steroidal
or non-
steroidal) drugs.
_Cellular assays
A panel of 42 three-color cellular assays are used for the initial study see
Table 9.
The panel includes immune and inflammatory parameters and contains some of the
assays
listed in Example 7. It also includes a series of assays for platelet function
(1-17). These
assays include direct measurements in diluted whole blood (WB, 1-9) as well as
thrombin
stimulation assays (TRT, 10-13) and stimulation controls (NTRT, 14-17).
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59
Soluble Factors
A broad panel of soluble factors as described in Example 7 will be part of the
study.
Additional measurements include : Von Willebrand factor, b-Thromboglobulin,
Thromboxane B2, 6-keto PGF and malondialdehyde. Soluble factors will be
measured
from plasma. In addition, some soluble factors (e.g MDA, prostaglandins
leukotrienes)
will be evaluated for the stimulated samples and controls.
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
TABLE 1
Ma'or Markers Subsettin anti ens
T Cell: Memory/Activation/Co-stimulation:
CD6,
CD2, CD3, CD4, CDS, CD25, CD26, CD27, CD28, CD38, CD43,
CD7,
CD8 CD45RA/RO, CD49a-f, CD69, CD70,CD71,
CD80, CD86, CD152(CTLA4),
CD 154(CD40L)
B Cell: Adhesion: CDlla/b/c(integrins),
CD18, CD29,
CD 19, CD20, CD21, CD31,
CD22,
CD23, CD72 CD44, CD54(ICAM-1), CD58(LFA3),
CD62E/L/P(selectins), CD102(ICAM-2),
CD 104, CD 13 8
Antigen-presenting Antigen receptors:
Cell:
CD13, CD14, CD15, CD33TCR: a(3TcR, yBTcR, specific TcR
V~3 panel
SIg: IgM, IgG, IgA
NK cell: FcR: CD16(FcyRIII), CD32(FcyRII),
CD16, CD56, CD57, NKBlCD64(FcyRI)
Granulocyte: Cytokine receptors: CD25/CD122(IL2R),
CD13, CD15, CD16, CD33CD95(Fas), CD116 (GMCSFR),
CDw119(IFNyR), CD120(TNFR),
CD121a/b(IL1R), CD123(IL3R),
CD124(IL4R), CDw125(ILSR), CD126(IL6R),
CD127(IL7R), CDwl28(ILBR)
Nonlineage: CD9, CD35, CD40, CD45,
HLA
class II DR, DP, DQ, PAN, CDw150(SLAM)
*Some cell surface antigens are in more than one category.
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61
TABLE 2
Dyes (Excitation/Emission
maximum)
Dye Type 1St Color 2'' Color 3rd Color
Cyanine Dyes Cy5 (650/667) Cy5.5 (678/703) Cy7
(743/770)
Bodipy' BODIPY 630/650-XBODIPY 650/66-X
PhycobiliproteinsAPC (652/660)
Tandem Dyes' Cy7-APC
(652/780)
PEG Stabilized'~ La Jolla Blue
(680/705)
Microspheres' Scarlet (645/680)Dark Red (660/680)Far Red
~
(690/720)
Infrared
(715/755)
Transfluor
(633/720)
' Amersham, lMolecular Probes, ' Multiple vendors, ''PharMingen REF ~' Diatron
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
62
TABLE 3
Assay Panel
Combo #
Cy5 Cv5.5
Potential
o ulations
detected
Comments
Ma'or T s (secondarily based on CD7
cell subset monocytes
based
on CD4,
NK
1 CD3/SRO CD4/SRO~ Total CD4 In Pro-5001,
54 1 Total CD3 (5 new pops)
CD3+4+ (CD4 T)
CD3+4- (CD8 T) i
CD3-4+ (mono)
2 CD3/SRO CD8/SR12 Total CD3 In Pro-5001,
54 3 Total CD8 (4 new pops)
CD3+8+ (CD8 T)
CD3+8- (CD4 T)
CD3-8+
3 CD27/SR CD4/SRO~ Total CD27 New CD27
162 1 Total CD4 (4 new pops)
CD27+4+ (CD4 T)
CD27+4- (CD8 T)
CD27-4+ (mono)
4 CD27/SR CD8/SR12 Total CD27 New CD27
162 3 Total CD8 (3 new pops)
CD27+8+ (CD8 T)
CD27+8- (CD4 T)
CD27-8+
CD7/SR1 CD4/SROS Total CD7 New CD7
29 1 Total CD4 (4 new pops)
CD7+4+ (CD4 T)
CD7+4- (NK + 8)
CD7-4+ (mono)
6 CD7/SR1 CD8/SR12 Total CD7 New CD7
29 3 Total CD8 (3 new pops)
CD7+8+ (CD8 T)
CD7+8- (NK + 4)
CD7-8+
7 CDS/SRO CD7/SR13 Total CD7 New CD7
52 6 Total CDS (4 new pops)
CD7+5+ (T)
CD7+5- (NK)
CD7-5+
8 a(3TCR/SCD7/SR13 Total CD7 New a~3TCR
8089 6 Total a(3TCR (4 new pops)
CD7+a(3TCR+ (T)
CD7+a(3TCR- (NK)
CD7-+a(3TCR+
Minor T
cell subsets
(included
are CD45RA,
CD62L,
CD69,
CD25)
9 CD45RAi CD4/SRO~ CD4+45RA- Irz Pro-5001
SR181 1 CD4+45RA+ (2 new o s) I
CD45RA~'CD8/SR12 CD8+45RA- In Pro-5001 I
SR181 3 CD8+45RA+ (2 new pops)
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63
TAR><,F 3 lC'ONTINUEDI
11 CD62L/S CD4/SR04 CD4+62L- Irr Pro-5001
8098 1 CD4+62L+, (2 new pops)
12 CD62L/S CD8/SR12 CD8+62L- In Pro-5001
8098 3 CD8+62L+ (2 new pops)
13 CD69/SR CD4/SR05 CD4+69- In Pro-5001
099 1 CD4+69+ (2 new pops)
14 CD69/SR CD8/SR12 CD8+69- In Pro-5001
099 3 CD8+69+ (2 new o s)
15 CD25/SR CD3/SR05 CD3+25- New CD2~
095 ~ CD3+25- (2 new o s)
B cells
and B
cell subsets.
Combo # Cy5 Cy5.5 Potential populationsComments I
detected by this
combination
of stains
16 CDS/SRO CD19/SRO Total CD5+ In Pro-5001
52 50 Total CD19+ (2 new pops)
CD5+19+ (CD5+ B cells)
17 CD25/SR CD19/SRO CD19+25- New CD25
095 50 CD19+25+ (2 new pops)
18 CD69/SR CD19/SRO CD19+69- In Pro-5001
099 50 CD19+69+ (2 new o s)
19 CD80/SR CD19/SRO CD19+80- New CD80
101 50 CD19+80+ (2 new o s)
20 CD86/SR CD19/SRO CD19+86- New CD86
143 50 CD19+86+ (2 new o s)
21 CD62L/S CD20/SR1 Total CD20+ Replace CD19
8098 60 CD20+62L- (3 new pops)
CD20+62L+
22 CD45RA/ CD20/SR1 CD20+45RA- Replace CD19
SR181 60 CD20+45RA+ (2 new pops)
Monocvte
subsets
also B,
CD4 T)
Cy5 Cy5.5 Potential populationsComments
detected by this
combination
of stains
23 HLA CD20/SRl Total PAN II+ New PAN II
PAN/SR1 60 Total CD20+ (3 new pops)
51 PAN II+20+ (B)
PAN II+20- (mono)
24 HLA CD20/SR1 Total DR+ New DR
2DR/SR1 60 Total CD20+ (3 new pops)
47 DR+20+ (B)
DR+20- (mono)
25 HLA CD4/SR05 Total PAN II+ New P AN II
PAN/SRl 1 Total CD4+ (3 new pops)
51 PAN II+4+ (mono)
PAN II+4- (B)
PAN II-4+(T)
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TABLE 3 (CONTINUED)
26 HLA 2 CD4/SR05Total DR+ New DR
DR/SR14 1 Total CD4+ (3 new pops)
7 DR+4+ (mono)
DR+4- (B)
DR-4+ (T)
27 CD33/SR CD4/SR05Total CD4+ New way to detect
094 1 Total CD33+ mono
CD33+4+ (mono) (3 new pops)
CD33-4+ (CD4 T)
28 CD 14/SR CD4/SR05Total CD4+ New CD 14
179 1 Total CD 14+ (2 new pops)
CD4+14+ (mono)
29 CD14/SR CD3/SR05Total CD14+ Confirmation
of CD3
179 5 Total CD3+ and CD14 subsets
(no
new)
30 CD80/SR CD33/SR1Total CD33+ (2 new pops)
101 06 CD33+80-
CD33+80+
31 CD86/SR CD33/SR1Total CD33+ (2 new pops)
143 06 CD33+86-
CD33+86+
32 CD45RA/ CD33/SR1Total CD33+ (2 new pops)
SR181 06 CD33+45RA-
CD33+45RA+
33 CD62L/S CD33/SR1Total CD33+ (2 new pops)
8098 06 CD33+62L-
CD33+62L+
Granulocyte
subsets
Combo Cy5 Cy5.5 Potential populationsComments
detected by this combination
of stains
34 CD16/SR CD45/SR1Total CD45+ (total In Pro-5001
wbc)
065 39 Total CD16+ (6 new pops)
Large, small CD45+16+
Large, small CD45+16-
35 CD16/SR CD1 lb/SRTotal CDllb+ In Pro-5001
065 070 Total CD 16+ (4 new pops)
Large, small CD16+11b+
Large, small CD 16-11
b+
36 CD62L/S CD45/SR1Total CD45 New gran combo
8098 39 Large, small CD45+62L+(4 new pops)
Large, small CD45+62L-
37 CDllb/S CD45/SR1Total CD45 New gran combo
8102 39 Large, small CD45+1 (4 new pops)
lb+
Large, small CD45+1
lb-
38 CD45RB/ CD4/SR05Total CD4+ I New CD45RB
SR144 1 Total CD45RB+ (3 new pops?)
CD4+45RB+
CD4-45RB+
WO 00/65472 CA 02371385 2001-l0-24 PCT/US00/11296
TABLE 3 (CONTINUED
Others
39 none none TCC 1 new pop
-
total cells
40 none none DCC 1 new pop
-
dead cells
* This
is
an
example
of
possible
cell
populations
to
monitor.
Alternative
and/or
additional
populations could be monitored.
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
66
TABLE 4
Pilot study- linear discriminant analysis
Best parameters for distinguishing RA and Blood Bank samples in data set
Samples, n=51, Blood bank = 26, RA = 25
Incorrect Single Marker
sample
assi nment
Sin le Markers
7 TCR-a,(3 T cells as a % of leukocytes
9 CD7 cells as a % of leukocytes
CD3 cells as a % of leukocytes
CDS cells as a % of leukocytes
CD4 T cells as a % of leukocytes
11 CD8 T cells as a % of leukocytes
12 CD27 T cells as a % of leukocytes
13 CD16 cells as a % of leukocytes
CD45 cells (all leukocytes)
CD8 T cells as a % of leukocytes
14 CD20 intensity on B cells
Marker Pairs
5 CD4 T cells as a % of leukocytes
CD8 T cells as a % of leukocytes
CD20 intensity on B cells
CD7 cells as a % of leukocytes
6 CD4~ cells (all leukocytes)
CD8 T cells as a % of leukocytes
CD20 T cells as a % of leukocytes
CD7 T cells as a % of leukocytes
Most measurements are averages from 2 to 6 assays
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TABLE 5
Information on reagent combinations in panel for Pro-5003
No. Cy5 Cy5.5 Po ulations Comments
001 CD2/SR306 CD4/SR051 3 populations
CD2+4+
CD2-4+ I
CD2+4-
002 CD2/SR306 CD8/SR212 4 populations
CD2+8+bright
CD2+8+dull
CD2+8+total
CD2+8- t
003 CD3/SR054 CD4/SR051 3 populations Pro-5002
CD3+4+
CD3+4-
CD3-4+
004 CD3/SR054 CD8/SR212 3 populations Pro-5002
CD3+8+
CD3+8-
CD3-8+
005 CD7/SR208 CD4/SR051 3 populations Pro-5002
CD7+4+
CD 7+4-
CD 7-4+
006 CD7/SR208 CD8/SR212 3 populations Pro-5002
CD7+8+
CD7+8-
CD7-8+
007 a(3TCR/SRO CD7/SR211 5 populations Pro-5002 I
89 a(3+7+
a(3-7+bright
a(3-7+dull
a(3-7+total
a(3+7-
008 CD27/SR162 CD4/SR051 3 populations Pro-5002 j
CD 2 7+4+ I
CD27+4-
CD27-4+
009 CD27/SR162 CD8/SR212 7 populations Pro-5002
CD27+8+bright
CD27+8+dull
CD27+8+total
i
CD27+8-
I
CD27-8+bright
CD 2 7-8+dull
CD27-8+total
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TAR1,F 5 (CONTINUEDI
010 CD6/SR364 CD4/SRO51 3~populations
I CD6+4-
CD6+4+
CD6-4+
011 CD6/SR364 CD8/SR212 7 populations
CD6+8+bright
CD6+8+dull
CD6+8+total
CD6+8-
CD6-8+bright
CD6-8+dull
CD6-8+total
012 CD26/SR363 CD4/SRO51 3 populations
i
i I CD26+4-
' li CD26+4+
CD26-4+
013 CD26/SR363 CD8/SR212 5 populations
CD26+8-
CD26+8+
CD26-8+bright
CD26-8+dull
CD26-8+total
014 CD57/SR197 CD4/SROS 2 populations
1
CD57+4+
CD 5 7-4+
O15 CD57/SR197 CD8/SR212 6 populations
CD57+8+bright
CD57+8+dull
CD57+8+total
CD57-8+bright
I i CD 5 7-8+dull
CD57+8+total
016 NKBl/SR37 CD6/SR362 2 populations I
NKB 1 +6+
NKB 1-6+
017 CD45RA/SR CD4/SRO51 3 populations Pro-5002
346 CD45RA-4+
CD45RA+4+
CD45RA+4-
018 CD45RA/SR ~ CD8/SR212 3 populations Pro-5002
346 j CD45RA-8+
CD45RA+8+
CD45RA+8-
019 ~ CD62L/SR2CD4/SRO~ 3 populations Pro-5002
1
27 CD62L+4+bright
CD62L-4+bright
CD 62 L+4+dul
l
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69
TART.E 5 ICONT1NUEDl
020 CD62L/SR2 CD8/SR212 6 populations Pro-5002
~
27 CD62L+8+bright
CD62L+8+dull
i
CD62L+8+total
CD62L-8+bright
CD62L-8+dull
CD62L-8+total
I
021 CD69/SR099 CD4/SRO51 2 populations Use lysed (Sx)
~I
CD69+4+
CD69-4+
022 CD69/SR099 CD8/SR212 2 populations Use lysed (10x)
CD69+8+ j
CD69-8+
023 CD25/SR231 CD4/SROS 2 populations Use lysed (Sx)
1 I
CD25+4+ I
CD25-4+
024 CD2~/SR231 CD8/SR212 2 populations II Use lysed
(10x)
CD25+8+
CD25-8+
025 TCR- CD8/SR212 3 populations Use lysed (10x)
VB3/SR215 TCRVB3+8+
TCRVB3-8+
TCRVB3+8-
026 TCR- CD8/SR212 3 populations I Use lysed (lOx)
VBS/SR216 TCRVBS+8+
TCRVBS-8+
TCRVBS+8-
027 TCR- CD8/SR212 3 populations
Use lysed (10x)
VB8/SR217 TCRVB8+8+
TCRVBB-8+
TCRVB8+8-
028 NKBl/SR37 CD4/SRO51 3 populations
Use lysed (Sx)
NKB 1 +4+
NKB 1 +4-
NKB 1-4+
029 NKB1/SR37 CD8/SR212 3 populations
~, Use lysed
(lOx)
5 NKB 1 +8+
NKB 1 +8-
NKB 1-8+
030 CDS/SR297 CD19/SRO50 3 populations
I Pro-5002
CDS-19+
CDS+19+
CD5+19-
031 CD6/SR364 CD19/SR050 3 populations
CD6+19-
CD6+19+
CD6-19+
WO 00/65472 CA 02371385 2001-l0-24 PCT/US00/11296
T A RT .F. 5 l('.(~NT1NUEDl
032 CD27/SR162 CD19/SR050 3 populations
CD27-19+
CD27+19+
CD27+19-
033 CD2/SR306 CD19/SR050 3 populations Stanford study
CD2+19-
CD2-19+
CD2+19+
034 CD80/SR228 CD19/SR050 2 populations Use lysed (10x)
CD80+19+
CD80-CD 19+
035 CD86/SR236 CD19/SR050 2 populations Use lysed (10x)
CD86+19+
CD86-19+
036 CD25/SR231 CD19/SR050 2 populations Use lysed (lOx)
CD25+19+
CD25-19+
037 CD69/SR099 CD19/SR050 2 populations Use lysed (10x)
CD69+19+
CD69-19+
038 CD62L/SR2 CD20/SR224 1 population Pro-5002
27 CD62L+20+
039 CD45RA/SR CD20/SR224 2 populations Pro-5002
346 CD45RA+20+
CD45RA+20-
040 HLA CD20/SR224 2 populations Pro-5002 I
PAN/SR229 HLAPAN II+20+
HLAPAN II+20-
041 HLA CD20/SR224 2 populations Pro-5002 i
2DR/SR230 HLADR+20+
HLADR+20-
042 HLA CD20/SR224 2 populations
DP/SR370 HLADP+20+
HLADP+20-
043 HLA CD4/SR051 3 populations Pro-5002
PAN/SR229 HLAPAN+4+
HLAPAN+4-
HLAPAN-4+
044 HLA CD4/SR051 3 populations Pro-5002
2DR/SR230 HLADR+4+
HLADR+4- ,
HLADR-4+
045 HLA CD4/SR051 3 populations
i
DP/SR370 HLADP+4+
HLADP+4-
HLADP-4+
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
71
TABLE 5 (CONTINUED)
046 CD33/SR232 CD14/SR366 4 populations Good crosscheck
on
CD33+14+ CD33
CD33+14+total use doped down
CD33du1114+ CD14
CD33+14-
047 CD33/SR232 CD4/SR051 3 populations Pro-5002
CD33+4+ Second check
on
CD33-4+ CD33
CD33+4-
048 CD16/SR065 CD14/SR366 5 populations
~
CD 16+14+bright
CD 16+ 14+dull
CD 16+14+total
CD16-14+
CD16+14-
049 CD64/SR182 CD4/SR051 2 populations
'
CD64+4+
CD64-4+
050 CD64/SR182 CD16/SR072 3 populations Stanford study
CD 64+ 16+
CD64+16-
CD64-16+
051 CD45RA/SR CD14/SR366 2 populations Doped down CD14
346 CD45RA+14+
CD45RA+14-
052 CD62L/SR2 CD14/SR366 1 population Doped down CD14
27 CD62L+14+
053 CD86/SR236 CD14/SR366 1 population Doped down CD14
CD86+14+
054 CD45/SR132 CD14/SR366 4 populations Nice breakdown
of
CD45+14-total lymphs, grans,
mono
CD45brightl4- Stanford study
CD45du1114- Lysed, 1:4 diluted
CD45+14+ (0.5x)
055 CD45/SR132 CD16/SR072 3 populations Pro-5002
+
total CD45+ 1:4 diluted blood
i
CD45+16+hi sl
CD45+16+lo sl
CD45+16-
I
056 CD15/SR195 CD16/SR072 2 populations 1:4 diluted blood
CD15+16+
CD15-16+
057 CD18/SR374 CD15/SR372 2 populations 1:4 diluted blood
CD 1 b+15+
i
CD 18+15-
WO 00/65472 CA 02371385 2001-10-24 pCT/US00/11296
72
TABLE 5 (CONTINUED)
058 CD45/SR132CD14/SR366 4 populations Nice breakdown
~ of
CD45+14-total lymphs, grans,
mono
CD45brightl4- Stanford study
CD45du1114- 1:4 diluted blood
CD45+14+
059 CDl lb/SR06CD15/SR372 2 populations 1:4 diluted blood
3 CDl lb+15+
CDl lb+15-
060 CD32/SR180CD15/SR372 2 populations 1:4 diluted blood
CD32+15+
CD32+15-
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
73
TABLE 6
Soluble Factor Immunoassays
No ASSAY No ASSAY
1 IL-1 alpha 23 IL-2
2 IL-1 beta 24 IL-3
3 IL-lra 25 IL-4
4 IL-IsRI 26 IL-5
IL-1 sRII 27 MMP-1
6 IL-6 28 MMP-2
7 IL-8 29 MMP 13
8 IL-10 3 0 TIMP-2
9 RF (all isotypes) 31 TIMP-3
RF IgM 32 sCD44
11 RF IgG 33 ScD54 ICAM-1
12 RF IgA 34 sCD62L
13 CRP 35 RANTES
14 SAA 36-51 Immunoglobulin H
and L
isotypes (16 assays)
MMP-3
16 MMP-9
17 TIMP-1
18 TNF alpha
19 INF gamma
TGF beta
21 sCD62E
22 sCD62P
S = soluble
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
74
TART.F.7
Assay
Numbersve ntiaenSR###ve nti SR###ve nti SR###Format
en en
General
~y7- B
SY3-149Cy5CD45 SR712Cv5.5CD14 SR503PC CD16 SR4330.25x
Cy7- Lysed
SY3-132Cv5CD45 SR712Cv5.5CD14 SR503PC CD16 SR4330.25x
T Cellsor
all both
4
and
8
~y7_
SY3-001Cy5CD4 SR349Cy5.5CD8 SR212APC CD3 SR435B
~y7_
SY3-150Cy5CD2 SR306Cy5.5CD4 SR506C CD8 SR529B
Cy7
SY3-066Cy5CRa,(3SR660Cy5.5CRvd SR663APC CD3 SR435B
Cy7-
SY3-151Cy5CRa[3SR660Cy5.5CD4 SR506PC CD8 SR529B '
CD8
Cells
I I CY7_
SY3-055C CD62LSR227C CD45RA SR453PC CD8 SR529B
5 5.5
Cy7-
SY3-008C CD57 SR342C CD6 SR362PC CD8 SR529B
5 5.5
Cy7-
SY3-152C CD27 SR225C CD45RA SR453C CD8 SR529B
5 5.5
Cy7-
SY3-178C CD28 SR675C CD62L SR454PC CD8 SR529B
5 5.5
Cy
7-
SY3-179C CD28 SR675C CD45RA SR453C CD8 SR529B
5 5.5
Cy7-
SY3-079C CD69 SR235C CD25 SR616IAPCCD8 SR529L sed
5 5.5 5x
Cy7-
SY3-080C CD71 SR654C CD57 SR619PC CD8 SR529L sed
5 5.5 5x
Cy7-
SY3-089C CD38 SR671C CD72 SR592APC CD8 SR529B ,
~5 'S.5
Cy7-
SY3-090C CD28 SR675Cv5.5CD26 SR343PC CD8 SR529B
5
~Cy7- I
SY3-091C CCRS SR502C CD8 SR212~APCCD3 SR435B
5 5.5
Cy7-
SY3-142Cv5CD4 SR349Cv5.5CD7 SR211C CD8 SR529B
Cy7- B.
SY3-145Cy5CD44 SR558Cv5.5CD7 ~SR211APC CD8 SR5290.25x
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
T n uT F ~f lrlINTTNTTF,T)1
ssav
umbers ve nti SR###ve nti SR###ve nti SR###Format
en en en
CD4 ls
T Cel
Cy7-
SZ'3-056Cy5CD62L SR227Cy5.5CD45RA SR453PC CD4 SR530B
I
Cy7-
Sl'3-004Cy5CD4 SR349Cy5.5CD27 SR161C CD3 SR435B
CY7_
S~'3-153Cy5CD26 SR363Cy5.5CD4 SR506C CD3 SR435B
Cy7-
SY3-154Cy5CD57 SR342Cy5.5CD4 SR506C CD3 SR435B
Cy7_
S1'3-155Cy5CD62L SR227Cy5.5CD4 SR506C CD3 SR435B
Cy7-
SY3-188Cy5CD27 SR225Cy5.5CD45RA SR453PC CD4 SR530B
I I Cy7-
SI'3-180Cy5CD28 SR675Cy5.5CD45RA SR453C CD4 SR530B
- cy7_
SI'3-063Cv5CD7 SR208Cy5.5CD6 SR362C CD4 SR530B
CY7- B-
S1'3-156Cy5CD44 SR558Cy5.5CD4 SR506PC CD3 SR4350.25x
I
Cy7_ B-
SY3-157Cy5CD89 SR447Cy5.5CD4 SR506C CD3 SR4350.25x
CD4 Mono
T and
Cy7-
S1'3-137Cy5ICD69 SR235Cy5.5CD14 SR503PC CD4 SR530Lysed-2x
Cy7-
SI'3-138Cy5CD25 SR231Cy5.5CD14 SR503C CD4 SR530Lysed-2x
Cy7-
S1'3-158Cy5CCRS SR502Cy5.5CD4 SR506C CD14 SR719B
cy7_
SY3-159Cy5CD38 SR671Cy5.5CD14 SR503PC CD4 SR530YB
Cy7- I
I
SY 3-160Cy5CD86 SR236Cy5.5CD14 SR503C CD4 SR530B
CY7_ I
SI'3 Cy5CD71 SR654Cy5 CD14 SR503IAPCCD4 SR530Lysed-2x
139 5
WO 00/65472 CA 02371385 2001-10-24 PCT/[JS00/11296
76
TAR1.F 7 ICONTINUEDI
Assay
Numbersve nti SR###ve nti SR###a nti SR### Format
en en en
B Cells
ASY3- Cy7-
1143 Cy5CD5 SR297Cy5.5CD19 SR050APC CD20 SR718 WB
SY3- Cy7-
161 Cy5CD72 SR100Cy5.5CD19 SR050APC CD20 SR718 WB
SY3- Cy7-
162 Cy5CD80 SR228Cy5.5CD86 SR706APC CD20 SR718 WB
SY3- Cy7-
163 Cy5CD69 SR235Cy5.5CD71 SR655APC CD20 SR729 Lysed-5x
B Cell
and
Mono
SY3- Cy7-
164 Cy5HLADP SR370Cy5.5CD14 SR503APC CD20 SR718 WB
SY3- Cy7-
165 Cy5HLADQ SR500Cv5.5CD14 SR503APC CD20 SR718 WB
SY3- Cy7-
166 C HLADR SR230Cy5.5CD14 SR503APC CD20 SR718 WB
5
SY3- LAPA Cy7-
167 Cy5N SR229Cy5.5CD SR503APC CD20 SR718 WB
14
SY3- Cy7-
168 Cy5CD14 SR179Cy5.5CD45RASR453APC CD20 SR729 WB
SY3- CY7-
169 Cy5CD40 SR634Cy5.5CD14 SR503APC CD20 SR718 WB
SY3- Cy7-
170 Cy5CD62L SR227Cy5.5CD SR503APC CD20 SR729 WB
14
onocVte
SY3- Cy7-
171 C CD33 SR232C CD14 SR503APC CD4 SR530 WB
5 5.5
SY3- Cy7- WB-
172 C CD4 SR349C CDllb SR371APC CD14 SR719 0.25x
5 5.5
SY3- i ' Cy7- WB-
045 C CDl6b SR359C CD66b SR536APC CD16 SR433 0.25x
5 5.5
SY3- Cy7- WB-
173 C CD64 SR365C CD14 SR503APC CD16 SR433 0.25x
5 5.5
SY3- ( Cy7- WB-
049 C CD32 SR379C CD15 SR372APC CD16 SR433 0.25x
5 5.5
SY3- ~ WB-
Cy7-
047 C CD18 SR374C CDllb SR371APC CD16 SR433 0.25x
5 5.5
SY3- Cy7- ' WB-
I
!
174 C CD44 SR558C CD15 SR372APC CD14 SR719 0.25x
5 5.5 ~
SY3- ; Cy7- I WB-
175 C CD89 SR658C CD15 ~ APC CD14 SR719 0.25x
'S 5.5 SR372
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
77
TAR1.F 7lC'(1NTTNIIFDI
Assay
plumbersve nti SR###ve nti SR###ve nti SR### Format
en en en
SY3- Cy7- Lysed
i
128 C CD9 SR310C CDl~ SR372APC CD16 SR433 0.25x
5 5.5
SY3- Cy7- WB
148 C CD123 SR289C CD32 SR704APC CD16 SR433 0.25x
5 5.5
SY3- I Cy7- WB
147 C CD123 SR289C CD1~ SR372APC CD16 SR433 0.2~x
5 5.5
K
SY3- Cy7-
038 Cy5NKBl SR375Cy5.5CDR SR298APC CD7 SR490 WB
SY3- Cy7_
071 Cy5CD57 SR342Cy5.5CD5 SR298APC CD7 SR490 WB
SY3- Cy7-
085 Cy5CD56 SR676Cy5.5CD2 SR352APC CD3 SR435 WB
SY3- I I
Cy7-
086 Cy5CD56 SR676Cy5.5CDR SR298APC CD7 SR490 WB
Controls
SY3- Cy7-
050 Cy5MOPC SR344C MOPC SR350APC MOPC SR624 WB
5.5
SY3- Cy7_
176 Cy5CDS SR297Cy5.5CD14 SR503APC CD20 SR729 WB
SY3- Cy7-
177 Cy5CDS SR297Cy5.5CD14 SR503APC CD20 SR729 Lysed
-lx
SY3- Cy7- I Lysed
082 Cy5CD4 SR349Cy5.5CD8 SR212APC CD3 ~ SR435-
lx
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
78
TABLE 8
BLOOD SAMPLING/DOSING SCHEDULE
DAY DAY DAY DAY DAI' DAY DAY
Fri Mon , Tue Wed Thurs Fri Thurs
!
-3 0 1 2 3 4 10
Screening
- Aspirin AspirinAs irin As irin - -
Blood Blood Blood Blood Blood Blood
Blood draw between 8 and 9 am each day
TABLE 9
Ch0 Chl Ch2
Assay Dye AntigenSR## Dye AntigeSR### Dye AntigenSR###Form
# I
# n at
I
1 ASY3- Cy5 CD36 SR67 Cy5.5CD9 SR678 Cy7 CD61 SR641WB
102 9 APC
2 ASY3- Cy5 CD42a SR68 Cy5.5CD4laSR683 Cy7 CD61 SR641WB
103 4 APC
3 ASY3- Cy5 CD42a SR68 Cy5.5CD62pSR590 Cy7 CD61 SR641WB
104 4 APC
4 ASY3- Cy5 CD42b SR68 Cy5.5CD4laSR683 Cy7 CD61 SR641WB
105 5 APC
ASY3- Cy~ CD42b SR68 Cy5.5CD62pSR590 Cy7 CD61 SR641WB
106 5 APC
6 ASY3- Cy5 CD62p SR68 Cy5.5CD61 SR681 Cy7 CD4la SR640WB
107 6 APC
7 ASY3- Cy5 CD63 SR68 Cy5.5CD61 SR681 Cy7 CD4la SR640WB
108 7 APC
8 ASY3- Cy5 PAC-1 SR67 Cy5.5CD9 SR678 Cy7 CD61 SR641WB
I
109 3 APC
9 ASY3- Cy5 CD29 SR15 Cy5.5CD9 SR678 Cy7 CD4la SR640WB
'
,
110 0 APC
ASY3- Cy5 CD62p SR68 Cy5.5CD61 SR681 Cy7 CD4la ISR640TRT
I~
111 6 APC I
11 ASY3- Cy5 CD63 SR68 Cy5.5CD61 SR681 Cy7 CD4la SR640TRT
j
112 7 APC I
12 AS1'3-Cy5 PAC-1 SR67 Cy5.5CD9 SR678 Cy7 CD61 SR641TRT
I,
113 3 ~ ~ APC
13 ASY3- Cy5 CD42b SR68 Cy5.5CD62pSR590 Cy7 CD61 SR641TRT
114 5 APC
14 ASY3- Cy~ CD62p SR68 Cy5.5CD61 SR681 Cy7 CD4la ISR640NTRT
115 6 ~ APC
WO 00/65472 CA 02371385 2001-10-24 PCT/US00/11296
79
TABLE 9 (CONTINUEDI
15 ASY3- Cy5 CD63 SR68 Cy5.5CD61 SR681 Cy7 CD4la SR640NTRT
~
~ 116 7 APC
16 ASY3- Cy5 PAC-1SR67 Cy5.5CD9 SR678 Cy7 CD61 SR641NTRT
117 3 APC
17 ASY3- Cy5 CD42bSR68 Cy5.5CD62pSR590 Cy7 CD61 SR641NTRT
I ~ I
118 5 APC
18 ASY3- SR66 Cy7A
066 Cv5 TCRab0 Cy5.5TCRgdSR663 PC CD3 SR435WB
19 ASY3- SR66 Cy7A
151 Cv5 TCRab0 Cy5.5CD4 SR506 PC CD8 SR529WB
20 ASY3- SR22 CD45 Cy7A
055 Cv5 CD62L7 Cy5.5RA SR453 PC CD8 SR529WB
21 ASY3- SR67 Cy7A
~ 178 Cy5 CD28 5 Cy5.5CD62LSR454 PC CD8 SR529WB
22 ASY3- SR50 Cy7A
I l
091 Cv5 CCRS 2 Cy5.5CD8 SR212 PC CD3 SR435WB
23 ASY3- SR34 Cy7A I
'
142 Cv5 CD4 9 Cy5.5CD72 SR211 PC CD8 SR529WB
24 ASY3- SR22 CD45 Cy7A
056 Cy5 CD62L7 Cy5.5RA SR453 PC CD4 SR530WB
25 ASY3- SR67 CD45 Cy7A
180 Cy5 CD28 5 Cy5.5RA SR453 PC CD4 SR530WB
26 ASY3- SR50 Cy7A
158 Cy5 CCRS 2 Cy5.5CD4 SR506 PC CD14 SR719WB
27 ASY3- SR23 Cy7A
160 Cy5 CD86 6 Cy5.5CD14 SR503 PC CD4 SR530WB
28 ASY3- SR29 Cy7A
186 Cy5 CD5 7 Cy5.5CD19 SR050 PC CD20 SR729WB
29 ASY3- SR22 Cy7- ~SR729
181 Cy5 CD80 8 Cy5.5CD86 SR706 APC CD20 VVB
30 ASY3- ~ SR37 ~ Cy7A SR729
182 Cv5 HLADP0 Cy5.5CD1-lSR503 PC CD20 WB
31 ~ASY3- HLAD SR50 Cy7A SR729
183 Cy5 Q 0 Cy5.5CD1-1SR503 PC CD20 WB
32 ASY3- HLAD SR23 Cy7A SR729
184 Cv5 R 0 Cy5.5CD1-1SR503 PC CD20 WB
33 ASY3- SR63 Cy7A SR729
185 Cy5 CD40 4 Cy5.5CD SR503 PC CD20 WB
1-l
34 ASY3- SR23 Cy7Ai
171 C CD33 2 Cy5.5CD1-1SR503 PC CD4 SR530WB
5
35 ASY3- SR37 I Cy7A
038 C NKB1 5 Cv5.5CDR SR298 PC CD7 SR490WB
~5
WO 00165472 CA 02371385 2001-10-24 PCT/US00/11296
TAR1.F 9 lr'WNTTNUEDI
36ASY3- SR67 Cy7A
085 Cy5 CD56 6 Cy5.5CD2 SR352PC CD3 SR43~WB
37ASY3- SR34 Cy7A
050 Cy~ MOPC 4 Cy5.5MOPC SR350PC MOPC SR624WB
38ASY3- SR55 Cy7A WB-
156 Cy5 CD44 8 Cy5.5CD4 SR506PC CD3 SR43~0.25x
39ASY3- SR35 I Cy7A WB-
045 Cy5 CDl6b 9 Cy5.5CD66bSR536PC CD16 SR4330.25x
40ASY3- SR36 Cy7A WB-
173 Cy5 CD64 ~ Cy5.5CD14 SR503PC CD16 SR4330.25x
41ASY3- SR28 Cy7- WB
148 Cy~ CD123 9 Cy5.5CD32 SR704APC CD16 SR4330.25x
42ASY3- SR28 Cy7- WB
147 Cy5 CD123 9 Cy5.5CDIS SR372APC CD16 SR4330.25x
