AU773832B2 - Phenotype and biological marker identification system - Google Patents

Description

WO 00/65472 PCT/US00/11296 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 WO 00/65472 PCT/US00/1 1296 2 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 efficacy), dose response to the intervention, side effect profiles of the intervention, and pharmacokinetic properties. Response may be correlated with either efficacious or adverse 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 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 WO 00/65472 PCT/US00/11296 3 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.

WO 00/65472 PCT/US00/1 1296 4 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-CD8.

Figures 5A and B depict robust cellular measurements with 2-color MLSC. Figure 5A demonstrates the consistency of CD8 T cell counts from 6 different capillaries. 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 5 shows the consistency of two measures of B cells, one with Cy5.5 anti-CD20 and one with anti-CD 19. 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 PCT/US00/1 1296 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 WO 00/65472 PCT/US00/I 1296 6 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, along with clinical information. In more preferred embodiments, the results of at least 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 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 WO 00/65472 PCT/US00/11296 7 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 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 WO 00/65472 PCT/US00/11296 8 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 WO 00/65472 PCT/US00/11296 9 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 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 WO 00/65472 PCTUS00/1 1296 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 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, depression, pain, agitation, mental illnesses, chemical dependencies); diet and changes in diet, injuries, relational history, sexual practices, socio-economic status.

WO 00/65472 PCT/US00/1 1296 11 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 occurring 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 efficacy), dose response to the intervention, side effect profiles of the intervention, and pharmacokinetic properties.

WO 00/65472 PCT/US00/I 1296 12 Response may be correlated with either efficacious or adverse 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 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 type(s) 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 WO 00/65472 PCT/US00/1 1296 13 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, 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-I 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 00/65472 PCT/US00/I 1296 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 1, 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, 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, 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 PCT/US00/1 1296 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.

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, 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 multiple sclerosis 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 PCT/US00/11296 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, 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 00/65472 PCT/US00/1 1296 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 Igl) are required for many assays; 2) absolute cell counts (cells/ tl) 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 cation, 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 PCT/US00/I 1296 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 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 PCT/US00/I 1296 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 nm with a helium-neon laser. Sets of 3 red dyes that may be used include, Cy5.5 and Cy 7 or Cy5, Cy5.5 and Cy 7 APC. See, Mujumdar et al., Bioconjugate WO 00/65472 PCT/US00/1 1296 Chemistry, 2:356 (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 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 mimic the human disease or medical condition even if the actual initiator(s) 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 Phenotvting 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, the rat adjuvant model of arthritis (reviewed in Philippe, et al, American Journal of Physiology 273:R1550- 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 PCT/US00/11296 WO 00/65472 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 MLSCbased 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 PCT/US00/11296 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 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 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. 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 occurring hypertrophic cardiomyopathy in Landrace pigs may be a useful model of cardiovascular disease in humans. Chiu et al., Cardiovasc. Pathol.

8:169-75 (1999).

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 PCTIUSOO/11 2 96 WO 00/65472 means that only relatively small quantities of the potential therapeutic eed be synthesized initially, thus reducing the cost of therapeutic development.

In other embodiments, the methods and systems of the present invention ae sed 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 i d en t ified ma r k er s can lied to domesticated animals, 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, ctegries is within the scopeohein obtain the data for cell Microvolune 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 spectromet. 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 facilitate cellular markers for diagnostic applications. he present invention uses LSC 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.ent of the invention (termed Specific enhancements achieved with the instrument of the invention (termed "SurroScan instrument") include the following: two additiona fluorescen color channels allow simultaneous detection and measurement f 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 softwarrays and system integration automates sample measurements and data analysis; 5) the capacity of SurrScan instruments is expanded to handle higher volumes of patient samples for database creation and biological marker discovery.

WO 00/65472 PCT/US00/1 1296 Microvolume Laser Scanning Cvtometry (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 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.

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 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, 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 galvanometerdriven 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 PCT/US00/11296 WO 00/65472 26 The spot diameter, 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 c the focal length of the objective. m emit stokes- Fluorescence samples placed in the path of the swept excitation beam emit stoke shifted light. This light is collected by the objective and collimated. This collimated light emeres fm light. T 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 mirror which reflects an avelength light and allows longer dichroic excitation filter (which reflects shorter w elnger 17 that furthler wavelength light to pass through) and then through first long pass filt serves to filter out any reflected excitation light. ses The improved instrument of the instant invto o ien t ss s s o 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 first fluorescence dichroic 18 divides the two d nto first aperture 19 via a first focusing reddest. The two bluest colors are then focussed oo frs r signal After pas sing lens 20 in order to significantly reduce any out-offocus fluorescence individual blue though the aperture, a second fluorescence dichric 21 furthe par t s eparate colors from one another. The individual blue colors are then parsed to two sertre 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 via a second long passfler2at fluorescence dichroic 28. After passing through divided from the two bluest colors by first fuor ne c another by third florescence aperture 24, the reddest colors are separated from one other by third fluorescence dichroic 28. The individual red colors are then parsed multeou liers 29 and 3from this way, four separate fluorescence signals can besultaneusly transmitted from the sample held in the capillary to individual photonultipliers. This imprvementa for the first time, allows four separate analytes to be monitored simultaneously. Each photomultiplier time, allows four separate analytes to be otithe incoming fluorescence photon flux generates an electronic current in response to the ncoming fluorescence photon flux.

These individual currents are converted to separate voltages by one or more preamplifiers These individual currents are nvled at regular intervals by an analog to in the detection electronics. The voltages are sampled at r ine a a aage The digital converter in order to determine pixel intensit values for the scanned image.

four channels of the instant invention are named channel 0, 1, 2, and 3.

The new optical layout has four detection channels to allow simultaneous measurement of up to 4 fluorescently labeled molecules. In a p red embodiment, multiple-color assays are used. Typically 3 or more fluorescent colors are used in each pCT/US00111 2 9 6 WO 00/65472 27 ailable3 the assay. Under circumstances where appropriate dye combinations are available, the instrument is capable of supporting 4-color assays. r An XY translational stage is used to move an array of wcap e ea hich optica system. The SurroScan system translation stage holds i e w hich ave Shas the footprint of a 96-wellplate. Capillary arrays have been designed which have 32 has the footprint of a 96-well plate. ulti-channel pipettes. The fixed capillaries each and spacing that is compatible with multicha pe intervention is operator is able to load two plates of 32 capillaries at a time. No operatorternative, 16 neededwhile the plates are scanned and the images are processed. As a needed w2000 (VC120) are loaded into alternative individual capillaries designed for the Imagn 200

(V

holdersocessing software accommodates images with either 2, 3, or 4 colors of Image processing software a o d a e s particles 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 appicat (Unite 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 Analysi 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 230,875, and the commony-owned utility application filed April 20, 2000, filed April 23, 1999), and the co o eed by reference here entitled "Disposal Optical Cuvette Cartridge", which are incororatd by eference de ein in their entirety. This capillary cartridge is used in Examples 5, 7 and 8. The design intheirentire. This capillary3 2 contains 32 capillares Fill holes in the FLEX32-plates currently in use, called Flex-32, contain s ipeting devices. It is have the same 9 mm spacing as 96-well plates and multichannel pipetting devices. It is have the same 9 mm spacing asdouble-sticky adhesive constructed from 2 layers of mylar sandwiched together with a duble-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 multi-channel microplate pipetters and robotics.

pCTfUS00I1 2 9 6 WO 00/65472 28 C The venL udes cellular assays, many of which are antibody based, that are The invention includes cellular assaysnd are capable of 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 10mL tube of blood. In one preferred embodiment; any type of detection from a single 10 mL tube of blood SC is encompassed in this invention, molecule and assay format compatible with MSC is ncompased in this invention, including, but not limited to cell surface proteins including makers activation adhesion, intracellular molecules, assays to distinguish changes in activation tates of cels, assays to concentrate and identify rare white cells, assays for use with whole blood, and assays for detection of soluble factors, such as protein in bloodcell surface As with flow cytometry, fluorophore-labeled antibodies specific for cell surface antigens are used to identify, characterize and enumerate specific opulations. The reaction can be done in whole blood. In general, there is no need to wash the reagent away; reaction can be done in wholeblood. sufficient sample peparation.

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 cell-antbodymxureiso ian insumentIn order to operate with 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 n) 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 cels flowing past the laser. A small cylindrical laser spot is scanned across the capilla 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 geprocessn 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 t 100 different antigens, there are about 5000 possible 2-color combinations and about 162,000 different antigens, there are abonsrat a time, C so careful possible 3-color combinations. (n combinatieons r o a a t r so) s o ca Multi-color thought is needed to develop the most appropriate et of 1 or o asa thani 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 inal 2-color assays can be identified in one 3-color assay. For example it is possible to assay CD3, CD4 and CD in one capllary nstead of CD3 and CD4 in one pCT/US00/11296 WO 00/65472 29 ell ulations can be 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 defined by the simultaneous expression oof cells can be subsetted into 4 different populations based on the differen and CD62L.

Immunoassav Procedures m munoassays an be run in a variet of formats and any appropriate format is Immunoassays can be run in a variety.en below. The MLSC system envisioned in the present invention. Two examples are give e he syste can be used with microsphere-based immunoassays. In this sandwich assay, the canbeup ort for arantibodyte microsphere is used as a solid support for an analyte-specific capture antibody. nalte from a biological fluid is bound to the antibody-coated mic olecule such as Cy5, and second antibody, which is directly labeled with a fluorescent moleule s as and which binds to a distinct epitope on the analyte. A protocol u ing o bead 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 antibodies to microspheres odiffeentsz be used to distinguish microspheres and single capillary. Internal indicator dyes can also be use facilitate multiplexing. d ssed i Example 5 are all The immunoassays for soluble factors discusse in Example 5 are all chemiluminescent based sandwich ELISA. Microtiter plates are contai i apture 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 avidinalkaline phosphatase conjugate. The level of analyte is revealed with a chemiluminescent 2alkaline phosphatase conjugbstrate. lates are read in a Wallac Victor 2 luminometer or similar alkaline phosphatase substrate. Plates aerea in instrument.

Desi and implementation fo a robustpael of cellular ass s to aid the discove of ers for diseases or medicalells using minimal 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 pCTUSOo/112 9 6 WO 00/65472 developed, which whencombined can identify hundreds of different cell populations. The developed, which when combined can idenify h 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 monOconal antibodies directed against numerous (about 80) different cell surface antigens have been ant esf idetified anwith the 2-color MLSC instrument. The small organic successfully identified and tested groups of antibodies using dyes like Cy5 and Cy5.

5 are readily coupled to the amino gPreferred dyeato-antibody single-step NHS chemistry and well established procedures. Preferred generaly in the ratios, have been determined for Cy5, Cy5.5, and Cy7 reagents, and are generally in the range of onneto four. Protein fluorochromes, like APC, are linked to the sulfhydryl groups rangeofonetofour. Protein fluorochromes, of moderately reduced antibody in a 3-step procedure using the heterobifuntional crosslinking reagent SMCC. Preparation of reagents containing other fuorophores possible. The preparation of Cy7-APC and (Cy7AP)antibody conjugates for fupling cytometry applications has been previously described. The antibody-furied by trcoupling chemistry is the same as for APC. All protein-protein conjugates are purified by traditional chemi as, by gel filtration on an Aka FPLC. Fluorescent microspheres can also be means, such as, by gel filtration on i2 carbodiamide chemistry to carboxylated investigated. Antibodies are coupled with 2-step carbo New monoclonal antibodies reagents are titrated on both whole blood and lysed red New monoclonal antib srea e c binding, is confirmed with bloodcels. eagent specifici and lack of onspeciconfirmed with appropriate counter stains. Analysis is done with any appropriate software program, including FlowJO cytometry software (Treestar, Inc available as an Internet download at includit wl o 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 per assay (typically o (no wash) mode. T h is preferred embodiment, all assays are conducted in homogenous (no wash) mode. This generally requires that each antibody reagent have a titer point of 1 ig/ml so that the uorescence background is not too high. A potential difficuly may be that a paticular 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 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- PCTIUSOOI 1 2 9 6 WO 00/65472 31 developed for Typially, a panel of about 50-100 (or greater) cellular assays isdeeloed onitorilg a disease or medical condition. Such assays enable one to enumerate hundreds of different cell populations A an example, it is possible to monitor the immune and inflammatory cellular parameters potentiall significant for rhema divtoid arthritis or 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, different subsets based on the types ldi T cells, B cells, antigenantigens found on the major leukocyte subtypes including T cell s, an stigenpresenting cells, NK cells, and granulocytes, as well as relevant receptors and structures found on these cells are included. These may include activation molecules, costiulat 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 sdescribed above are designed in either of two formats, whole The cellular assays nts, the assays are done in wholeblood or RBC-lysed blood. In the preferred embodiments, the asay are e o blood or RBC-lysed blood format. The minimal manipulation ensures that the most accurate absolute cell counts (cells/Pl of blood) are obtained. Furthermore only small amounts of blood are required per assay so that man 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 whole blood from normal individuasbuare mune states. Improved detection of have been shown to be increased in various autoimmune states. mprovted detection of other minor cell populations such as NK cells has also been demonstrate an sould p particularly useful in analyses. As an example, for a panel of 96 assays, it is estimated that particularly useful in analysessample processing may 64 will be done on whole blood and 32 on lysed blood. Alternative sample processing may include, preparation of PBMC by Ficoll gradient, ex vivostimulation with polyclonal or antigen specific activators.

PCTIUSOO/11 2 9 6 WO 00/65472 32 dentification of novel cell Combining antibody reagents is important for the e diseases or medical populations that may contribute to the pathogenesis, or be a marker for, diseasnown that adhesdical conditions, such as autoimmune diseases. For exale t to be invold in the molecules can be differentially expressed onT cells thought to be involved in the autoimmuneprocess. Furthermore, severalstudieshave Wi o4 T cells in patients with autoimmune seaseincrease in the number of memory CD4 T cells in patients loo at i feental levels the assays of the present invention it is possible to sultaneously look at differen ee of adhesion molecules CD la specifically on a subset of memory CD45RO

T

cells of the HLA class II-restricted lineage CD4 This should increase the ability to identify relevant disease-related cell populations. Multiple-color capability also allows one identify relevant disease- edce of antigens not typically 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 Appropriate Fluorescent Des As indicated above an appropriate fluorescent dye is within the scope of the present invention. Two commonly used dyes are cyanine dyes Cy5 (em 667) and used.

(em 703). Typically, a single dichroic filter to split the emission signal at 685 n is used More filters will be required when more than two dyes are employed. Dyes are evaluated More filters will be requied As an ex am p l e a variety of dyes to determine their compatibility in the MLSC system. As an example, a varety of dyes were evaluated to determine an appropriate overall 3-color set (se te e2). arameters to consider when evaluating dyes include 1) spectral separation of the 3 dyes, signal-tonoise ratio as a function of laser power, 3) suitability of the available filters, 4) ease of conjugation, and 5) specificity of the resulting antibd urophore 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 nonspecific binding that occasionally occurs with microsphere reagents. Typically, each of the pCTIUSO011I 2 9 6 WO 00/65472 33ibodY conjugates using a few fluorophores are evaluated in the context of fluorophore-antibod conjugates using a few select antibodies e.g. anti-CD 3 anti-CD 4 and anti-CD 2 0 Soluble Fcto Ap he es carried out by means other than There are a large number of bioanalysebe in th of fluorescence.Prominent among these is mass spectroetry, rapidly ecoming the tool of choice for detailed identification and analysis of polypeptides and proteins. There are two widely-used methods for biomolecular sample introductionin mas s tromtr electrospray ionization(ESI) and matrix-assisted laser desorptiolionization

(MALDI).

0 MALDITOF is currently successfully utilized for the analysis of proteins, polypeptides and other macromolecules Even though the introduction of an organic matrix to transfer and other macromolecuesdously the field of desorption mass energy to the analyte has advanced tremend s For instance, the detection f small spectrometry, MALDI-TOF still has some hlimts For b ions from the matrix. In molecules is not practical because of the presence of bac rond n used to detect such cases ESI or even gas chromatography (GC) mass spectmet can or profile. tures and heterogeneous nature of proteins The complexity of molecular structres One of the major areas necessitates the need for multidimensional separation techniques. One of the mar aras for this is the development of two dimensional gel electrophoresis using polyacrlaide a the gel matrix for example. The gel is modified in terms of crsslinking, addition of detergents, immobilization of enzymes or antibodies (affinity electrophoresis) or substrates detergents, immobilization of enzymes or d haracterization of proteins (zymography) and pH gradient. This technique is used for the charace n tes in terms of structural modifications, activities, pI values, and molecular weights.

Another area is the development of multidimensional chroatographic approaches also referred to as hyphenated separation techniques. The advantag indethe tyt more accurately quantify the analyte and better mpatibility wt t To date usuall to methods like laser induced fluorescence or mass spectrmet. o ae a separation systems are chosen such that they are orthogonal and lead to a better peak capacity (resolution). Major technical hurdles are the integration o t rou sfer to the techniques with the detection system in terms of maintaining resolution upon transfer to the techniques with the detection syst ityo hemobile phases with the detection system for second dimension and the compatibilit of the mobile with e eect sysm examples salts and detergents in the eluant are incompatible with electrospray mass pCTIUS0O'11 2 9 6 WO 00/65472 34 Anal. Chem 1997, Nylor J. Chromatogr. A1996, 744 237-78 Jorgenson spectrometry. Naylor j. Chromaogr. A 1996 69, 1518-1524. activate components in a Chemical derivatization can be selectively employed to mp 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 ochemical naectrospray conditions have been coupled with ferrocene carboxyic acids, the electrochemical nature litating ionization. Anal. Chem. 1994, 66, 209-212. Derivatization can alo se fragmentation differentiate between stereoisomers (isobaric species) by using ieent a enta patterns in their daughter and granddaughter ions of the parents. The MSn capabili in an quadrupole ion trap mass spectrometer for example has been used to distinguish hexosamine monosaccharides, glucosamine, galactosamine and mannosamine derivatized with CoC12(DAP)2Cl where DAP is diaminopropane. Anal. Chem 1999, 71, 4142-4147.

Affinity based separation followed by mass spectrometic detection is of clinicaln interest as it allows analysis of complex molecules in olog ical fluids like blood and urine with little or no sample preparation. Ciphergen's technlogy (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 Ciphergen offers 5-6 different surfaces upon Since each surface/stringency 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.

ClinicalData and Informnatcs 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 developendent the present invention have the ability to identify hundreds to thou ds oinad markers from a small sample of blood. The present invention includes developing a broad b dcalirnoation from patients that are followed clinical strategy to collect extensive medical noratn r pation the pfowe 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 markers with specific diseases, dseaenversion ad statistical not limited to, databases of assays and clinical information, data conversion and statistical analysis tools, and medical questionnaire prototypes The information system of the pCT-/US00111 2 9 6 WO 00/65472 present invention desgnedtouse and common formats for entry of disparate types of data and is structured for data-mining purposes.

disparate types ofwhich will likely become widely used in the next 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 several years is SnoMed-RT This language ilarl the present invention is adaptable i information system of the present invention. Similarl, the present eo be that as oer-languages or technologies become available, they may also become that as other languages or technologies etual development of tools to incorporated into the database. An example ould be the eental deelo ent o tools to integrate digital x-rays, mammograms, or virtual onoscy which is obtained via a The technical challenges in developing an inforatics system capable of handling the vast amounts of biological and clinical information necessary to correlate iolgic 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 and powerful search tools. The informatics system provided by the present invention meets these technical challenges.

Datases includes both numbersAnalysis of cells per l of The data output from the cellular analyses includes both numbers f cels per whole blood for each population identified, the mean intensity of taining for each cell associated molecule, which gives an estimate of the antigen density for a given population, socaedolecue,hichgivesanels of a particular molecule. Each number the mean size of cells, and the expression levels of a particular molecule. Each number will be analyzed, because, as explained ov, both the actual cell numbers as well as expression levels of a particular molecule may vary in a given disease state. To identify markers (cell counts or staining intensity, levels of soluble factors) associated with categorical clinical variables (such as diagnosis of disease) -valued clinical techniques are used. To identify markers associated with continuous ues are us.

variables (such as levels of soluble factors) a variety of regression techique are used. For both discriminant and regression analyses, stepwise variable selection and cross-validation are used to identify those markers that are most closely associated with the clinical variable of interest. Where apprriate demgraphic and clinical varables (such asde concomitant drugs, etc.) and genetic paramneters are included as covariates in the models.

PCT/US00/11 2 9 6 WO 00/65472 36ally, using the SAS and Statistica These techniques are applied in the analyses of data, typicall us statistical analysis software packages.cture (see Figure 3) of the The architecture of the integrated inforrnatics infrastructure (see Figure 3) of the present invention, compses a mlti-tiered structure. The lowest level consists of a set of present invention, comprises a mult ntific data which includes, but is not data sources. The first source comprises the scientific data w n u limited to, cellular assay data and soluble factor assay data. The second source may be semi structured data which is in a combined for of textual and tabular data describing semi structured data which is in a combined on ot t o 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 browsersTheDTD tags also define an information exchange model enabling the highbrowsers. The DTD tags also dee an nfor a rtie s. The third data source is the level electronic sharing of the information with other p arties eir t s is t 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 questionnares will be multi lingual and adapted to physical challenges 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 infonnation gathering system also comprises of nontextual means of input. A respondent may interact via visual and graphical displays to provide health related information by pointing at images of he human anatomy so as to indicate a problem without having to articulate it. Other means, e.g. a simple measure of vital capacity and FeVlsec. in asthmatic or emphysematous patients or moniting 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.

pCT/US00/11296 WO 00/65472 3 7 rated and warehoused using a As indicated in Figure 3, these data sources are integrated and arehoused using a common schema. This schema coordinates the interpretation ofthe information from the constituent data sources. The interpretation is in a manner that isindependent of the logical or physical storage detail of each of the constituent data sources. The common schema provides ha 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, NMR, is included for the genSieratiyon of additional data, it can be added without upsetting the organization of the generation of additional data, already existing warehouse. their The schema is augmented with an ontology of common cocepts and their relationships in immunology and related clinical areas. The ontology will be used by the elatmionshipg ts an i bm y te uaer interface to assist in the interpretation of user specified data mining tools and by the user terfaceoces and for the specification of data requests for information from the underlying data sources cationd for the collected clinical mining tasks. The ontology will also be utilized in the verification o data. toolkit f programs includes programs for statistical analysis, for data mining and for the visualizatin of the resul. 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: applied over a statistically Sig encel, consequence2,. if condl and cond2 and and condN then consequ dhe cini a source, For example, when applied to the cellular data source and the clinical dat surements the toolkit can derive relationships between cellular assay and ol e ccled to the that were previously unknown. The results of the analysis by the toolkit are recycled to the sers and to three viouslyuin the future. The architecture is intended to improve the nsersand tohe daaaser reuse by in g the accumulated discovery experience and by knowledge discovery process by storing integrating this experience for continued improvement. in hihly diensona Other tools for data mining include methods for clustering in 3 data. These tools foare intended to augment and replace the existing method of manual gating as presently used. Unlike current cytometry software, which considers one assay from one as presently used. Unlike cu rr en t c terson may examine list mode data patient at a time, the cytometry tools of the present invention may examine list m across an assay from multiple patient samples in order to determine the optimal set of pCTUS00/112 9 6 WO 00/65472 38 with a multi dimensional circumscribed population (gates). The system is coupled wth ult ensonael visualization system that will simultaneously project the computed clusters on elected 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 and is used to plan and execute study data, clinical study planning, interface level include, knowledge discovery from study data, c 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 combine a graphical interface and ny a abstract concept level to the stored detail. It may allow for information requests that include both data and text documentation pertaining to assay protocol planning) and include both data and text may allow for interaction over a network.

EXAMPLES

Emptle Otned thriti Useof the resent inventionl nf ical rs for Reumaoid umatoid The present invention can be used to identi biological makers he at for identifying biological markers for RA. Marker discover efforts are focused aniody accessible biological fluids, most notably blood A two-color instrument and based assays have demonstrated the potential of this ecnique for entifying and erating scores of different cell populations with only a small amount of whole blood.

Multiparaneter cell analysis, in cell pombination with multiple assays for soluble factors, small lular aetercell analysis,incowerful 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 therapyints, which Rheumatoid Arthritis is a chronic inflammatory disorder of the disease is also has pronounced systemic consequences. Athough the ertisooy oe tme Eary events unknown, its pathology evolves with common aateid ty unknown me. Earl events appear to include an inflammatory response ated b unknon meiators cie CD4R T cells appear to amplify and perpetuate the inflammation. of heumatoid activatedT cells can induce polyclonal B-cell at n and t tnt of the T cell Factor Tissue damage accrues, releasin g of pCTUS001112 96 WO 00/65472 response broadens. Eventually, the constant inflammatory oenvir hatise my pende t 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 S acrpesent in the serum and augment hepatic synthesis of acute-phase proteins.

activepresent he es the disease, there are changes in the molecules and cells Throughout the various stagesoo the potential to be markers of disease. Blood, because of in the synovium and clati e p t ghot the body, provides an attractive window its ready accessibility and circulaty and isor thus the major target of this invention.

for monitoring disease actvyarkers of diagnostic and The present invention is useful to identify biological markers o iaosicfyng prognostic value for Rheumatoid Arthritis. Such markers are requird for classifyin different forms of the disease, for example identifying the subset of patients in whom joint different forms of orFurthermore, the markers are critical for erosion occurs more rapidly than in others Frerore, te marer and e crical f evaluating the efficacy ofintervention and developing early, non-toxic and successful the efficacy estigations inthave been made of cells and soluble factors in blood, S erapis. m any investigationsdae mker for the disease. In general, one to several synovium and urine that are canddaile some factors, such as rheumatoid facor markers at a time have been investigated. hile some is no factors, such a s rheumatoid factor and C-reactive protein have been associated with RA, there is no evaluate multiple candidate specific markers. There is a strong need to simltaneousy evaluate num ber of parameters markers. This is achieved with multiple assays (colors) that can be measured in a single assaydentifying makers of The present invention is capable of developing a platform for identifying markers of The present invention is apa combination consists of one or more disease and applying it to RA. In general, each assay com fTabe Some of these reagents to identify the major cellular subsets (left column of Table ombined oith antigens, e.g. CD4, are targeted in multiple assays Te i ord maer a ize information different subsetting antibodies (right column of Table 1) in order to axreconsidered in about the sample. Properties of the fluorochoes and the target antigens are used i developing each assay combination. For example, brighter fluorochromes are used with developing each assay comb natsonh it is important to use reagents with the best less abundant antigens. For other assa -are spectral differences for certain targets. In general for each antibody triplet FlowJ sofCD4, is used to analyze 1 to 3 different 3-color combinations Cy5 CD3, Cy5.5 CD4, Cy7APCCD45RA vs. Cy5 CD45RA, Cy5.5 CD4, Cy7APC-CD 3 to determine the best combination for tinguishing the different cell populations.

PCT/US00/ 1296 WO 00/65472 Designing a successful panel of assays re e empical wledge 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 Th major angens being evaluated in a T cell panel include CD2, CD3, T es. The major antigens being evlese T cell subpopulatons can CD4, CD5, CD7, and CD8. Many kinds of molecules on these T cell subpopulation can be investigated. These include surface antigens which help to distinguh ainve vs. memory cells (CD45RO, CD26), and antigens that play a r i aition m s ha CD69, CD71, HLA class II) or co-stimulation (CD27, CD28) In addition, markers that may play a role in adhesion to inflammatory sites are assayed (CD62L, CDlI CD8, CD44, CD54, and CD58). Subpopulations of T cells based on expression of oxTCR, and a panel of VP TCR genes are evaluated.19, B es. The major antigens being evaluated n aese B cell subsets CD21, CD22, CD23, and CD72. In addition, various markers on these B cell subsets including markers that may indicate a more activated phenotype (CD40, CD80, CD86, HLA class II, CD5) 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.

sp fntiensp resentn celalso cells are evaluated using markers to ACand e tign addition, a variety of adhesion the major antigens CD13, CD14, CD15, and CD33 In addition, aai ta i molecules (CDI 1 a, CD1 8, CD29, CD44, CD54, CD58, CD62L) and costimulatoy molecules (CD80, CD86) on these cells are analyzed. Other relevant receptors including CD16 (FcYRIII) CD32 (FcYRII) and CD64 (FcyRI) are assayed.

Other c ell es CD 2Only a few studies have investigated the expression

O

t he l l e l s s an i O l y a igeneral these have given ofNK markers and granulocyte markers in RA, ar k g ers D16 C 6 7n N inconsistent results. NK subpopulations using themarkers CD16, CD56, CD57, n are analyzed. Granuloctes, including neutrophils and eosinophils, may be phenotyped using CD13, CD15, and CD16. A panel of adhesion molecules and receptors similar to that described above is used to further subset these populations.

There are any antigens whose expression has been associated with a more There are many antigensor co-stimulation, or shown to be activated or memory phenotype, implicated in adhesion orla on or o toe the receptor for an important ligand. Examples are outlined in Table eera o markers have been examined in several autoimmune coditions and the expression has pCTIUSOO/11 2 9 6 WO 00/65472 41T elsfrom RA patients show higher levels of the been found to be variable. For example, T cells fro RA patients show higher levels o f the adhesion receptor LFA-1 (CD la/CD18) but no change in the expression o t t receptor (CD25), which is normally increased on activated cells, or a marke for activation and co-stimulation (CD80ustrating the kinds of indicators and cell populations that may be Some examples illustrating examined are discussed below. RA (Fox, D T cels. 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 associaion of A with MHC class II alleles that share a common sequence in the third hypervariable region (Weyand, C.M. and Goronzy, J.J. (1997) AnnN Y Acad Sci 815,353-6 and Weyand,

C.M.

and Goronzy, J.J. (1997) Med ClinNorth Am 81, 29-55). Since CD4 T cells recognize antigen bound to MHC class II antigens, the association ofRA with expression of specific class II molecules implies a role for CD4 T cels in A. In addition studies in animal models of RA, such as collagen induced arthritis or adjuvantarthritis, 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 liinating 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 and Lipsk P.E. (1991) Clin Ortho 9- 22). Increased numbers of activated T cells are detectable in the peripheral blood and synovial fluid of A patients. These T cells express CD3 and CD4 cell surface markers at synovial fluid of RA patients.T ctr l ar to the levels seen in mitogen a lower antigen density compared to controls, similar to the levels seen in mitogen activated T cells in vitro (Luyten, Suykens, Veys, Van Lerbeirghe,

J.,

Ackerman, 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 A e ncury, J, Murphy, of the early activation marker CD69 (Pitzalis, Kingsley, Lanchbury,

J

S

Murphy,

J-

of the early activation aer CD6(Peased numbers of CD CD29 and and Panayi, G.S. (1987) J Rheumatol 14, 662-6), ieased numbers of CD4CD 2 9 ad CD4 CD45RO memory cells, and increased expression of MHC class II products (Pitzalis, Kingsley, Murphy, J. and Panayi, G. (1987) Clin Immunol immunopatho 252-8). Expression of CD44-dependent primary adhesion strongly correlates with concurrent symptomatic disease in juvenile RA and systemic lupus erythematosus (Estess, pC1'1US00I11 296 WO 00/65472 DeGrendele, Pascual, V. and Siegelman, M.H. (1998) J Cli Invest 102, 1782) and may be important in adult RA. Some studies have shown increased u er o TCR T cells and increased HLA expression on these cells (Reme, Portier,

M.

Frayssinoux, Combe, Miossec, Favier, F. and Sany, J (1990) Arthritis Rheum Frayssinoux, Combe, B.,es associated with restricted 33, 485-92). An increase in CD857 cells in RA, sometimes associated with restricted 5 TCR33, 485-92)has also been reasported (Morley, Batliwalla, Hingorani, R. and Gregersen, TCR, has also been reported (Morley, J.K M Allen, Kolitz, J, P.K. (1995) J Immunol 154, 6182-90 and Serrano, D. M teiro., Chr and Schulman, Lichtman, Buchbinder, Vinciguee or VaP., Chan onitor rtritd Gregersen, P.K. (1997) J Immunol 158,1482-9).

vp expression on this specific T cell subset.d in RA patients A B cells. Phenotypic analysis of B cells has also been performen ate cell subpopulation expressing the pan T cell marker CD5 has been shown to be elevated (Sowden, Roberts-Thomson, P.J. and Zola, H. (1987) Rheumatol It 7,255-9, Hardy, Hayakawa, Shimizu, Yamasaki, K. and Kishimoto, T. (1987) Science 236, 81-3 and Casali, Burastero, Nakamura, M, ghirami 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 CD 5 B cells in the pathogenesis of autoimmunity in humans is still unclear, perhaps reflecting the pesence of activated B cells (Werner- Favre, Vischer, 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 13). Circulating B cells from RA Patien (Eliaou, Andary, M., DR molecules, again indicative of an activated B cell phenotype Eliaou, Anda, M Favier, Carayon, Poncelet, Sany, Brochier, J. and Clot, J. (1988) Autoimmunity 1,217-22) Three-color assay are able to monitor increased HLA class expression specifically on CD5 CD19+ B cells. antigen-presenting cells, Antientesentin cells. Several cell types can serve as in ce including monocytes, macrophage, dendritic cells, B cells and other cells induced to including monocytes, mnacrophage, den 1h n ieo demonstrated express class II antigens. In general these cells show an activated pheotype demotrated by increased expression levels of HLA class II antigens in patients with autoimmune disease (Lipsky, Davis, Cush, J.J. and OppenheimeMarks, N. (1989) Springer Semin Immunopathol 11, 123-62). Antigen-presenting cells are pCTIUSOO/11 2 9 6 WO 00/65472 43 compartent (Viner, N.J. (1995) Br Med Bull 51, 3 59-67) and blood-derived macrophages have been associated with human cartilage glycoprotein 39 expression in some studies have been associated with human Lysko, P.G. and Rosenberg, M- (Kirkpatrick, Emery, Connor, Dodds, R, ysko, P.G. and osenberg,

M

(1997) Exp Cell Res 237, 46-54). additional battery Soluble factor assays. Soluble factor assays provide an potential biological markers. There are many important soluble factors that have been identified in RA patients. These include levels of circulating cytokines such as TNF and IL-6, cytokine receptors, chemokines, rheumatoid factors of different isotypes, immunoglobulin with different forms of glycoslation hormones, acutephlecules, as proteins such as C-reactive protein and serum amyloid A, ad soluble adhesin ol eule f as we as matrix metalloproteinases and their inhibitors. Many of thes e aco a non to be present at varying levels in RA patients at different stages of disease (Choy, E and Scott, D.L. (1995) Drugs 50, 15-25, Feldmann, 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 historie. In addition to soluble factors, w information of patients are included in the database. The clinical parameters wil inlud e inorm and 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 CD5 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 CD5 1 9 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. Din atient o u lations. A review of the cellular assay literature as it relates to autoimmune disease reveals that there are apparently conflicting reports.

example, some reports indicate an increase in levels of CD5 B cells (Markeljevic,

J.,

Batinic, Uzarevic, Bozikov, Cikes, Babic-Naglic, Horvat, Z. and Marusic, PCTIUSOO11 2 9 6 WO 00/65472 44 studies do no (iu,

S.,

M. (1994) J Rheumatol 21, 2225-30) in A patients, while other studi n, Wang, Liu, Li, Lei, H.Y. and Chuang, C.Y. (1996) Clin Rheumatol Wang, Liu, Li, L e i, the cn ng fact that have 250-3). These publications suggest that there may be other confounding fctors that have important implications for cellular phenotypes, and perhaps cellular function, in RA ant pcaons for eular phenotypes, d for study, based on levels of soluble patients Segregating the patient populationsselected for study, based on levels of obe factors circulating in the serum, stage of disease, and therapy could in part explain the apparent discrepancy with respect to the CD5' 19 B celllevels n RA patients discussed apparent discrepancy with rpect t telaton between the levels of above. For example, it is known that there is a significant crrelation between the levels of IgM rheumatoid factor and the percentage ofCD5 B cells (Youinou, Mackenzie,

L.,

Katsikis, Merdrignac, Isenberg, Tuaillon, Lamour, Le Goff,

P.,

Jouquan, Drogou, A. and et al. (1990) Arthritis Rheum 33, 339-4). Furthermore the level of IgA rheumatoid factor is associated with the level of CD5 B cells as well as CD4eCD45R T cells (Arinbjamarson, Jonson, Steinsson, Sigfusson,

A.,

Jonsson, Geirsson, Thorsteinsson, J. and Valdimarsson, H. (1997) J Rheuatol 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 diseaseus The assays can be cog pleted 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 on a single blood sample and ensurescan accommodate a mixture can be acquired. The biological marker identification system ong o e as 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.

conducted are considered the technology of the present Relevant biological markers can be identified using the technology of the present invention.

Exmpl Tw rple inventionen bio markers for MultileSclerosis The present invention can be used to identify biological markers fo Multiple Sclerosis 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 elapsing and remitting episodes of neurologic dysfunction. The etiology of the disease remains unknown, however the pCTIUSOO11l2 9 6 WO 00/65472 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 hallmark of the MS lesion is an area o seen at the edges of the throughout the brain and spinal cord. Inflammatory cells are seenat ces include plaque and scattered oughout the white matter. The main inflammatory cells include plaque and scattered throughout the w ve macrophages. CD4 T cells accumulate at the activated lymphocytes and monocyte derived ac rophagesy in acve disease, buut are edges of the plaque; CD8 T cells are not found as requently in active disease, but ae present in longstanding lesions. Autoreactive T cells ronzing m lin ca protn aou other non-myelin self-antigens circulate in the blood and upon activation can pass tough lo the bood-brain barrier. Up- ciregulation of adhesion molecules, histocompatability antigens, theblood-brainbarer. Up-regulatino activation (ILR, FcR) are all connected and other markers of lymphocyte and monocyte activation (IL2R, FcR) are all connected and other markers of lypo d n Furthermore, there is in increase in with the activation and homing process. Fur the e is mmu ne rease oimm proinflammatory cytokines that serves t o amplify t he autoantibodies produced can response also includes pronounced B cell stimulation Th roughout the various activate the complement system and promotecules and cells in the CNS and the varibloodus stages of disease, there are changes in the molecules and cells in the CNS and the blood that have potential to be markers of diseasec and pronostic The present invention can identify disease markers of diagnostic and prognostic value for Multiple Sclerosis. Such markers are valuable for classifying different forms of value for Multiple Sclerosis. Such markers aredisease the disease, for example identifying the subset of patients with relapsing-re disease who are most likely to develop those secondary progressive disease. Furthermore, the o are ost likely for evaluating the efficacy of intervention and developing early, nonmarkers are valuable for evuatine y estigati ons have been made of cells and soluble toxic and successful therapies. Many investhat are candidate markers for the factors in blood, cerebrospinal fluid (CS) and e hae been inveate While some 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, factors, such as oligoclonal iomu scifc markers. There is a strong need to there is no consensus panel of MSspecimarker Thereis ongneedto simultaneously evaluate multiple candidate markers Such T ls. There are several lines of evidence that implicate T cells in MS Such evidence includes the association of MS with MHC class II (particularlY HLA DR) alleles (Hauser, Fleischnick, Weiner, Marcus, Awde, Yunis, E.J. and Aper, C.A. (1989) Neurology 39, 275-7) Since CD4 T cells recognize antigen bound to MHC class antigen, the association of MS with expression of pecific class molecules class 1I antigens, the association of MS with pCTJUSO11l 29 6 WO 00/65472 46 modelsofMSsuchas implies a role for CD4 T cels in MS. In addition, studiesin anim m elof such as mouse or rat experimental allergic encephalomyeliis 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.Hstudies, Canneila, 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 withT cell function can slow progression of MS.

Perhaps more relevant to the present invention, studies examining the phenote led to cells, either in the cerebrospinal fluid and/or perpheralblood of MS patients have led to some interesting findings. There is a reduction in CD8 T cells in the blood of MS patients.

some interesting findings. There is arethe CD8CD1b subset (11onen, J., The subset showing the most marked decrease was the CD8CD1b subset (Iloned, J1, Surcel, Jagerroos, Nurmi, T. and Reunanen, M. (1990) ActaNeurol Scand 81, 128-30 and Oksaranta, Tarvonen, Ilonen, Poikonen, Reunanen, aneius,

T

M. and Salonen, R. (1996) Neurology 47, 1542-5) There is also an increase in activated

T

15 cells bearing the CD71 and CD25 markers particularly in active MS (Genc, Dona,

D.L.

cells bearing the CD71 and Strauss, K, Hulstaert, F. Deneys, V., and Reder, A.T. (1997) J Clin Invest 99, 2664-71 and Strauss, Husta, Deneys,

V.,

Mazzon, Hannet, De Bruyere, Reichert, T. and Sindic, C.J. (1995)

J

Neuroimmunol 63,133-42). Furthermore, the maity of i cels in the cereoCD29 on and peripheral blood show a memory phenotype with high levels of CD45RO and CD29 on both the CD4 and CD8 T cell populations (Vrethem, Dahle, Ekerfelt, Forsberg, Danielsson, 0. and Ernerudh, J. (1998) Acta Neurol Scand 97, 215-20). This leads to a Danielsson, O. and Emerudh, J. DenesV, Mazzon, A.M., reduction in CD4+CD45RA (Strauss, Hulstaert, Deneys, on Hannet, De Bruyere, Reichert, T. and Sindic, C.J. (1995) J Neuroimmunol 63, 133- 42) and CD8 CD27

CD

45

R

A (Hintzen, Fiszer, Fredrikson, Rep, Polman, 42) and CD8 CD27-CD45RA (H ntzen, 1 56, 99-105) nalve T cells in the van Lier, R.A. and Link, H. (1995) Neurommuno 56, 99-105) nve T cells in the peripheral circulation. A recent study has concluded that CD4 CD4a SLAM+ and CD4CD7 cells (preferentially T helper 1 cytokine producing cells) are increased in MS patients relative to controls (Ferrante, Fusi, Saresella, Caputo, Biasin,

M.,

Trabattoni, Salvaggio, Clerici, de Vries, Aversa, Cazzullo, C.L. and Clerici, M. (1998) J Immunol 160, 1514-21). Furthermore some studies have shown Clerici, M. (1998) J Immunol 160, indicative of a skewed TCR variable beta usage in the peripheral blood of MS patients indicative of a restricted TCR repertoire (Gran, Gestri, Sottini, Quirs Roldan, Bettinardi, Signorini, Primi, Ballerini, Taiuti, Amaducci, L. and Massacesi, L. (1998) WO 00/65472 PCT/US00/11296 47 J Neuroimmunol 85, 22-32). A restricted pattern of gene rearrangement has also been described in the y8 T cell subset (Michalowska-Wender, Nowak, J. and Wender,

M.

(1998) Folia Neuropathol 36, B cells. Phenotypic analysis of B cells has also been performed in MS patients.

A

B cell subpopulation expressing the pan T cell marker CD5 has been shown to be elevated (Mix, Olsson, Correale, Baig, Kostulas, Olsson, 0. 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, Hayakawa, Shimizu, Yamasaki, K. and Kishimoto, T. (1987) Science 236, 81-3). In humans, however, CD5 B cells do not preferentially produce autobodies (Suzuki, 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, Vischer, Wohlwend, D. and Zubler, R.H. (1989) Eur J Immunol 19, 1209-13). Consistent with this conclusion, high levels of the memory marker CD45RO were found on circulating CD20 B cells from patients with MS (Yacyshyn, Meddings, 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, 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, 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, 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, Morgan, Aziz, Zecchini,

D.,

Berkowitz, J. and Paty, D.W. (1988) J Neuroimmunol 20, 15-23).

pCT/US00/1 1296 WO 00/65472 Soluble factor assas. Soluble factor assays provide an dditional batte of potential biological markers. There are many important soluble factors that have been identified in MS patients. For example, levels of soluble Apo A-/Fas (Ferrante, P, Fusi, Saresella, Caputo, Biasin, Trabattoni, Salvaggio, Clerici, de S e,,Cap BJ Immunol 160, 1514-21) is Vries, Aversa, Cazzullo, C.L. and Clerici, M. (1998)J Immunl 160, 1514-21) isr augmented in acute MS compared with the levels seen in patients with stable disease or healthy controls i n addition, levels of soluble adhesion molecules such as soluble cn na on,G Lai, Thorpe, Kidd, D., intracellular adhesion molecule 1 (ICAM-1) (Giovannni G Lai, Thorpe, J Kidd, D Chamoun, Thompson, Miller, Feldmann, M. and Thompson, E.J. (1997) Neurology 48,1557-65) and soluble E -selectin (Giovannoni, Thorpe, Kidd,

D.,

Kendall, Moseley, Thompson, Keir, Miller, Feldman, 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. roinamatory cytokines like TNFoL 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 stages of disease (Navikas, V chemokines, matrix relevant proteins, such as cytokines and cytokine receptors, chemokine, matrx 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 tmeasure these soluble factors and look for statistical correlations with the cell populations i dentified. itn le Medical histories and distinatint oulations. in the 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 therap and an concomitant drugs ortherapies. 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 In addition, the number of CD71+ and HLA DR+ lymphocytes and onocytes is increased in active MS. However therapy with IFN-b 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 pCTIUSOO/11 2 9 6 WO 00/65472 49 disease duration may also be following therapy Other clinical variables such as disse rtiona so be useful. For example it has been shown that in MS patients with ctho do nt he repertoires, the median disease duration is shorter than in patients who do not have a restricted repertoire (Gran, Gestri, Sottini, Quiros Roldan, Bettinadi, A., Signorini, Primi, Ballerini, Taiuti, Amaducci, L. and Massacesi, L. (1998) Neuroimmunol 85, 22-32).

t.ple ad Arthritis Patient to Control Patient Blood lor cellular assays was prepared for the Imagn In pilo study, a panel o. Half of the samples came from 2000 and evaluated blood samples from about 50 donors a t a c the Stanford Blood Bank and half came from the Rheumatology Clinic at Stanford University. The study was designed to evaluate and develop key components ofthe biomarker search engine of the invention: instruments, assays and analytical tools. was not necessarily designed to elucidate biomarkers. All assays were done on whole blood, without necessarily desig n bound 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 The panel of assaysis shown in Table 3. Each reagent is tested and titrated before preparing the reagent combinations in order to optimize assay performance.

Samle re ationhomogeneous For this study all cellular assays were applied to whole blood, in geneous mode (no post stain washing). Aliquots (20 uL) of fluorecently labeled atibody reagent combinations of DNA dye were distributed with a multi-channel pipette from prepared racks into discrete wells of a microtiter plate. Whole blood or diluted whole blood (30 uL) was added with a multi-channel pipette and the sample mixed. Cells were incubated for minutes followed by the addition of 100 uL of diluent and mixing. A portion of each miue followed b

PCTIUSOO/

1 1 2 9 6 WO 00/65472 blood) was added vlumetc stained sample (50 uL, corresponding to 10 uLf blood) added tO vmetne capillaries (VC120) and loaded into the modified Imagn 2000 instrument Scans were initiated and executed without operator intervention. Data files are trans o computer network and converted to the Flow Cytometry Standard format. FlowJO computer network and cn v e rt ed t o ul sand obtain numerical values for cytometry software was used to identify cell populations and obtain numensitycal alues fo cell counts (cells per uL), relative cell size, and relative fluorescenceintensity, which is an estimate of the antigen density for each gated (boxed) cell population.

Serum levels of C-reactive protein were measured on the Imagn 2000 with a beadbased noaayBeads coated with anti-CRP antibody were used to capture the banalyte. Cy5 conjugimmunassay ated anti-CRP antibody was used reveal the captured analyte.

Patient me cal io at An abreviat e l history, ncluding age, gender, parameters of disease Anabbreviated edi ionunaent Data for the severity, co-morbidities and medications was obtained from each patient. Data for the blood bank samples was limited to age and gender.

Database and statistics medical histories Data output from the celular assays, soluble factor assay and medical histoes Data output fro potetial biological markers (cell counts were combined into a single database. To identify otentih catgorlical arr(cia l covr es or staining intensity, serum concentration) associant te niusal inical il (such as diagnosis of disease) a variety of discriinant techniques was used including 0 (Fisher linear and quadratic discriminant analysis, logistic regression, and classification Fisher linear and quadratic disrmn t tued clinical variables (such as trees. To identify markers associated with continuousvalued clinical vaiables (such asluding erythrocyte sedimentation rate) we use a variet of regresso t crmnant and regression multivariate linear regression and regression trees For bo used to identify those analyses, stepwise variable selection and cross-validation was to ient thoe markers that are most closely associated with the clinical variable of interest Where appropriate demographic and clinical variables (such as age, gender, and concomitant appropriate, demographic andcnical b ese techniques were implemented and drugs) were included as covariates in the models. These techniqu kages mented and applied using the SAS and Statistica statistical aalysis oftware packages.

ss se a be used for ost atient sam les lthough one of the drawbacks of cell ation studies is often the variability among norf sps entcal aaclksis indows (gates) were used across all donors for among donor samples, identical analysis w ows (gates) WO 00/65472 PCTUSOO/11296 51 d consistency Of 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 CD8S T cells (MHC class I restricted) to be monitored, which are CD27 (activated) and CD2T. 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 among 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, CD5, 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 CD5. 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 PCT/IUS00/I 1296 52 coefficient was 0.97 for both CD3 vs. CD5 and CD19 vs. CD20. Examples of these correlations are given in Figure 5 and Figure 6.

Differences are observed amon RA and blood bank samles 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 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 ARAVA 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.

Cellua Lasa s 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/ fl 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 pCTIUSOO/I 1296 WO 00/65472 staining). This assay panel enables the identification of more than 150 different cell san3 dThisfassayrpaent c e populations. The reagent combinations and the cell populatio hat can be identified are provided in Table Sera are aliquotd and frozen for each blood sample for subsequent measurement of multiple soluble factors. These include levels of circulating cytokines such as TNFo and IL-6, cytokine receptors, chemokines, rheumatoid factors (RF) of different isotypes, unoglobulin, 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.

atient medical informationwith A medical history with more detailed disease-specifc information is included with each sample in the study.

Exame Five ellularssas on a 4-chaassay canbe run on the 4- More assays, with greater information content pe assay,inations.

channel SurroScan instrument. Assays are developed using 3 color reagent combinations Effective dye combinations include Cy5, Cy5.5 and Cy7 and CyS, Cy5.5 and Cy7-APC allow simultaneous and independent measurent of three target antigens. Three color combinations facilitate the acquisition of more information per capillary than 2 color combinations by 1) eliminating redundancy 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 (denf na e p es that epreboth CD45RA and CD62L). Given appropriate naive CD T cells that expressbothc is ossible to simutaneously monitor fluorescent dyes with distinct emission sp ectra, i is in the e x i s ting additional target antigens either in the fourthn e can th e eistin channels. Figure 7 provides the results of a 3-color assay on the SurroScan instrumenty 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 pCTIUSOO/l1 2 9 6 WO 00/65472 assays per 10 mL tube of blood. For RBC-lysed blo od sa es w a panel of 64 blood and lysed ormats. It should allow identification of more EamPle Six Intracellulainr m s be measured with MLSC technology. PBMC were Intracellular molecules can be measure Cells were stained with of PHA an d iotomycin. ^ed wi cultured for 5 hours in the presence of PHA and onomycin Ces wee sta anti-CD8 to identify cytoxic T cells, fixed, periablized, and stained with Cy5 antiinerferon-gamrnma (IFN-y) to detect the intracellular cytokine. Data in Figure 8 shows that cells. Among the CDS T cells, 20 express intracellular

IFN-Y.

Identi Biolo cal Markers for the Treatment of Aller and Ast prinan be used to identif biological markers for allergic The present invention can certain etiology It is characterized asthma. Asthma is common chronic lung disease of uncertain etiology charactezed by inflammation of the airways leading to symptoms of coughing, wheezing, chest tightness, and shortness of breath. These clinical symptoms re thought to be due to hyper responsiveness of the airways and a long-term inflammatory process causing obstruction of responsiveness of the airw at times be fatal in the absence of airflow. The disease causes extreme discomfo and can t tes e fl the appropate treatment. The clinical manifestations of asthma are thought to result from the appropriate treatment. The clinical manife actors on genetic predispositions that superimposition of a variety of environmenal f s on en itic i enoiio nental increase the likelihood of developing asthma. Atopy, the hypersensitivity to environmental easebut not all atopic individuals develop asthm. ere ve allergens, is common in asthma but notaaP1c understood. Corticosteroids (inhaled importance of allergic mechanisms is not completely ndertood Coioeoid inhalde and systemic) are efficacious in asthma but have associated with perceived and real side and systemic) are efficacious inerstanding of response to 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.

PCTIUS00/ 1296 WO 00/65472 thma and the A study has been designed to identify biological markers of atopy, asthma and response to corticosteroid therapy. Subjects are screened for four stud gro s who 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 have testednegativens n nontsthmatics who have tested negative to skin test allergens skin test allergens, and 4) non-asthm eaticsd onto a single-blinded, placebo (healthy subjects). Alleligible subjects are entered onto a singleblinded p ebo 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.

on the morning of day 1 and 12 hours er including detailed medical history and clinical Subjects undergo rigorous screeningncluding detailed m al h predicted, 2) tests for lung function and allergy. Mild asthmatics have a 1) FEV 8 documented diagnosis of asthma or history of any of the following: cough, worse particularly at night, recurrent wheeze, recurrent difficult breathing, recurrent chest ightess and 3) a positive methacholine challenge test (Cockcroft DW, et al Clin Allergy 977; 7235 and Juniper EF, et al Thorax 1984; 39:556). Non asthmatics have a 1) FEVI 1977; 7:235 and Juniper EF, et ale methacholine challenge test.

predicted 2) no history of asthma and 3) a negative methac llege Allergic subjects have a positive skin test to at least one blood cell count (WB red Examples of clinical data include Haematology white bl cell volume (MCV), blood cell count (RBC), hemoglobin hematocrit (HICT), mean (ce Hll volum e mean cell haemoglobin (MCH), mean cell haemoglobin concentration (MCC) platelet 20 cunt, neutropl count lymphocyte count, monocyte count, eosinophil count, basophil count, neutrophil count lymphocyte blood biochemistry: alkaline phosphatase, count and ESR erythrocyte sedimentation rate; blood biochemtidase, albumtase alanine transaminase, aspartate transaminase, gamaglutaml transpeidse albsi total protein, total bilirubin, urea, creatinine, sodium, potassium, glucose; urinalysis total protein, total bilirabin, urea, leucocytes; Hepatitis and HIV testing: HIV 1 protein, glucose, ketones, bilirubin, blood, leucocytes epatitis and IV testing and II, Hepatitis B surface antigen, Hepatitis C antibody. All clinical history and test arameers will be included in the master database for statistical analysis, evaluation as parameters will be included in the master covariates and data mining.

Atopica asthma i an imunologgic disease mediated by IgE antibodies. Exposure to Atopic asthma is an immunolog which binds to the high affinity receptor mast allergen causes B cells to synthesize IgE, w bns to the allergen, antigencells residing in the mucosa of the airways. On r ree to e aee, diators of antibody interactions on the surface of the mast cells triggers releeoetoro anaphylaxis stored in mast cell granules, including: histamine, tryptase,

P

pCT/US00/11296 WO 00/65472

C

4 and D 4 and patelet activating factor (PA56 These soluble factors induce contraction of air smooth muscle and cause an immediate fall in the FEV Re-expoure to allergens also leads to the synthesis and release of a variety of cytokines: IL-4,L-5, G-CSF,

TNF-,

aTGF-, from T cesh and mast cells. These cytokines attract and activate B cells, which TGF-P, from T cets and mast cells. e s ls, which produce leads to the production of more IgE, and eosinophils and neutrophils, which produce eosinophil cationic protein (ECP), major basic protein (MBP) and AF. These factors aeosnophi e cauionic hypen smooth muscle contraction and increase the bronchial reactivity that is typicallY associated with the late asthmatic response, indicated by a fall in FEVi about 4-6 hours after exposure. applied to the subject A broad panel of cellular and soluble factor measurements are applied to the subject blood samples with the goal of discovery bioarkers The study design updifferences i informationof inter-individual variability within groups, and ter-grou information of inter-igrovp differences allergic nonmarker expression. It is believed that the inter-group dern aerindivid asthmatic versus non-allergic non-asthmatic) will be greater than interindividua variability within groups. It is further believed that prednisone therapy will result in 15 variability within groups.

si o n significant intra-individual changes in marker expression.

Cellular assays, A panel of 64 three color cellular assay, focusing on immune and inflammatory pelof64the colocesc asthma. The panel parameters in the blood, has been prepared and tested for initial atopic asthma. The panel is given in Table 7.

The study will also look at a broad panel of soluble factors. Immunoassays, in the The study will alsthe following targets.

sandwich-based chemiluminescent ELISA format, are used for the following targets: Cytokines, chemokines and their soluble receptors: IL- alpha, IL- beta, IL- RA, IL-1 sRI, IL-1 sRII, IL-2, IL-2sR, IL-31L 4 ,IL-5, IL-, IL-6 sR, IL-8, IL-1, IL-12 p 4 0, IL-12 p 7 0, IL-13, IL-16, IL-17, MIF, MIP-1 alpha, MIP- beta, RANTES, s Falpha sTNFalpha RII TGF beta, TNF alpha, alpha, TGF beta2, TGF beta3, Oncostatin

M,

M-CSF, GM-CSF, IGF-, PDGF-BB, FGF-, FGF-6, FGF-7, Fas, VEGF, MCP PF-4, EOTAXIN, IFN gamma, Immunoglobulin: IgA1 Kappa, IgAl Lambda, IgA1, 2 Kappa, IgA1,2 Lambda, IgA2 Kappa, IgA2 Lambda, IgE total, IgG Kappa, IgGI Lambda, IgGI 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: PCTIUSOO/11 2 9 6 WO 00/65472 CR, SAA; Matrix metall57roteinases and the inhibitors: MMP-3, MMP-9, TIMP-1, CRP, SAA; Matrix metalloproteinase s D54 (ICAM-1), sCD62E, sCD62P.

TIMP-2; Soluble adhesion molecules: sCD5 4 (ICAM-)immunoassays or mass Additional soluble factors which are measured by immunos says or mass spectroscopy assay include, but are not limited to, Cytokines, chemokines and their soluble spectroscop assay IL IL-1, sCD23, eosinophil proteins: ECP, MBP, receptors: IL-9, IL-11, IL-14, IL- E, carbohydrate modified Ig; a variety of Immunoglobulin: Allergen specific IgE, carbohydrate m 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, concentrations, etc) associated with categonc of ANOVA and discriminant prednisone or placebo, before or after therapy) a variety of A and dic a techniques can be used. Where appropriate demographic and clinical variables (such as age, gender, specific history results) can be included as covarates in the models.

Techniques can be implemented with SAS, Statistica, Statview or similar statistical analysis software packages.

Examle Eight Useof the resent invention to identi bioloical arkersfoe administration of 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 eecsofdgadnistrtiononceluarwill make possible analysis of samples of peripheral blood. It is expected that these assays will make possible analysis the effects of different doses of drugs on cellular and soluble markers in human peripheral blod. In this exaple the widely used er-the-counter drug, aspirin (acetylsalicylic acid), blood. In this examp ifferent doses of the drug will be orally is administered to human volunteers. Dfferent de f te dr i e ra 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 t 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/antiinflammat agent. The mehanism nderlying 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 PCT/US00/11 2 9 6 WO 00/65472 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 product of lipid oxidation is malo with thiobarbituric acid (TBA colorimeterically or fluorometrically following interaction w A levels in Aspirin, by inhibiting prostaglandin synthesis, is expected to decrease MDin 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. on and the production of tumor E-type prostaglandins suppress lymphocyte activation and the production of tumor necrosis factor-a (TNF-oa) by the cells of the monocytemacrophage lineage If thersone is some level of lymphocyte activation and TNF-L production in normal healthy persos, 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.eters. Eligible 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 ofthree dosing schemes. Group I, 1 dose (325 mg tablet) after breakfast, Group II. 2 doses (1300 mg total) 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 antiinflammatory drugs nor currently under care, which requires the use of anti-inflammatory (steroidal or nonsteroidal) drugs.

Cellular assadys 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 These assays include direct measurements in diluted whole blood (WB, 1-9) as well as thrombin stimulation assays (TRT, 10-13) and stimulation controls NTT 14-17) P'\OPERUEH\Res Cms\2004\MarChU2471246 clms doc-29/03/4 -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.

The reference to any prior art in this specification is not, and should not be taken as, an acknowledgment or any form of suggestion that that prior art forms part of the common general knowledge in Australia.

Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" and "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

*o o o**o* WO 0065472PCTUSOO/ 1296 TABLE 1 B Cell: Adhesion7, ID8 iab/Dilt8,) CDS,CD9 CD2,9, CD, CDID, CD2

CD

CD23 CD72IO CD4a-, CD64ICM-) CDLFA3), C8,C8, 6ELpslC Is) 52CDI 2(CAM) CDlO4, CD0L3 B Cll:e~rsntn el Anteien: rceptors:ntgns)

ISCD

CD 19, CDl, CD21,~ CD33, CR:c3, 1,,spcfcTc ae NK el: CR: CDI6(FelcYRin, CD302(CARII), CDI6 CD5, C57, KB1 CD604FCD138 A g erasntnt Cyl' A tie receptors: CD2 c 5/ C VP panelR CD3, CDI, CDl6, CD33 TCD95(FaS), y8CDI s6pGcSF) NK cll: cRCD1aII3GI), C12(FL3R), CDwI 14(L4R), CD125(L5R),Cl2(L6) CDl 27(IL7R), CDwlI28(ILSR Nonlineage: CD9, CD35, CD4 CD45,

H-LA

class 11 DR, DP, DQ, PAN, CDwlI *So e cell surface antigens are in more than one category.

PCT11JS0011 1 2 9 6 WO 00/65472 TABLE 2 pCTIUSOO/1 1296 WO 00/65472 62 TABLE 3 Assay Panel iobO# Cv5 Cv5.5 potential oulations detected_ #ao Tclsuets (secondarily monocvtCs based ol CD4, NK b CD3ISRO CD4/SRO5 Total GD4 54 1Total CD3 CD3+4+ (CD4

T)

CD3+4- (CDS

T)

CD3-4+ (mono) 2CD3/SRO CD8/SRl2 Total CD3 54 3 Total CD8 CD3+8+ (CD8

T)

CD3+8- (CD4

T)

CD3-8+ 3CD27/SR CD4/SROS Total CD27 31L62 1 Total CD4 CD27+ 4 (CD4

T)

CD27+ 4 (CD8

T)

CD27- 4 (mono) 4CD27/SR CD8ISRl1 2 Total CD27 4162 3 Total CD8 CD27+ 8 (CD8

T)

CD27+ 8 (CD4

T)

CD27-8+ 5CD7/SRl CD4/SRO5 Total CD7 529 1 Total CD4 CD7+4+ (CD4

T)

CD7+4- (NK 8) CD7-4+ (mono) 6CD7/SRI CD8/SR12 Total CD7 629 3 Total COS CD7+8+ (CD8

T)

CD7+8- (NK 4) CD7-8+ CD7ISR13 Total CD7 752 6 Total

(T)

(NK)

8ccpTCR/S CD7ISR13 Total CD7 8R089 6 Total c43TCR CD7+c43TCR+

(T)

CD7+c43TCR-

(NK)

CD7-+c43TCR+ Minor T cell subsets (included are CD45RA, CD62L, 9 CD 45RAI CD4/SRO) CD4+45PRA- 9SR181 I CD4+45RA+ CD45RAJ' CD8/SRl 2 GD8+45R.A SR181 3 CD8+45RA+ (3 new pops) New CD7 (4 new pops) WO 00/65472 PTUO/l 9 63 TABLE 3 (CONTINUED) 1f I051 11CD62L/S CD4/SRO4 TCD4+62L- (2 new o )O) R098 I ICD4+62L+. In Pro-SOO] 12 CD62L/S CD8/SRl2 CD8+62L eWos R098 3 D+2+In Pro-50 0 1 13 CD69lSR CD4ISRO) CD4+69- e os CD2LA CD4/SRO5 Tota3PA11 new pps) 16 PA/SRI C19/R Total CD (2nw+OS 099 PANC 19++ (mono)8 19 CD80/SR CD19/PAN C (B)epos 101 50PA C 11-4-dNewY)S WO 00/65472 WO 0065472PCT/USOO/1 1296 64 TABLE 3 (CONTINUED) 26 HLA 2 CD4/SRO5 Total DR+ New DR DR/SR14 I Total CD4+ (3 new pops) 7 DR+4+ (mono) DR+4- (B) DR-4+ (T) 27 CD33/SR CD4/SRO5 Total GD4+ New way to detect 094 1 Total CD33+ mono CD33+4± (mono) (3 new pops) CD33-4+ (CD4 28 CDI4/SR CD4/SRO5 Total CD4+ New CD14 179 1 Total CD 14+ (2 new pops) CD4+14+ 29 CD 14/SR CD3/SRO5 Total CD 14+ Confirmation of CD3 179 5 Total CD3+ and CD14 subsets (no newv) CD8O/SR CD33/SRl Total CD33+ (2 new pops) 101 06 CD33+80- CD33+80+ 31 CD86/SR CD33/SRl Total CD33+ (2 new pops) 143 06 CD33+86- 32 CD45RAJ CD33/SRI Total CD33+ (2 new pops) SR181 06 CD33+45RA- _______CD33+45RA+ 33 CD62L/S CD33/SRl Total CD33+ (2 new pops) R098 06 CD33+62L- Granulocyte subsets Combo Cy5 Cy5.5 Potential populations Comments detected by this combination of stains 34 CDI6/SR CD45/SRl Total CD45+ (total wbc) In 065 39 Total CD 16+ (6 new pops) Large, small CD45+16+ small CD45+16- CD I6/SR CD11b/SR Total CD I Ilb+ In Pro-500l 065 070 Total CD 16+ (4 new pops) Large, small CD 16+11lb+ small CD 16-11lb+ 36 CD62L/S CD45/SRI Total CD45 New gran combo R098 39 Large, small CD45+62L+ (4 new pops.) Large, small1 CD345+62- 37 CD1 lb/S CD5/SRl Total CD45 New gran combo R102 39 Large, small CD45+1 Ilb+ (4 new pops) ~~Large, small CD45+1 I b 38 CD45RB/ -CD-4/SRO5 Total CD4+ New SR 144 I Total CD45RB+ (3 new pops?) CD4+45RB+ WO 00/65472 PCT/US00/11296 TABLE 3 (CONTINUED) Others 39 none none TCC 1 new pop total cells 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 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 Incorrect Single Marker sample assignment Single Markers 7 TCR-a3 T cells as a of leukocytes 9 CD7 cells as a of leukocytes CD3 cells as a of leukocytes CD5 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 cells (all leukocytes) CD8 T cells as a of leukocytes 14 CD20 intensity on B cells Marker Pairs CD4 T cells as a of leukocytes CD8 T cells as a of leukocytes intensity on B cells CD7 cells as a of leukocytes 6 CD45 cells (all leukocytes) CD8 T cells as a of leukocytes T cells as a of leukocytes CD7 T cells as a of leukocytes Most measurements are averages from 2 to 6 assays WO 00/65472 WO 0065472PCTUSOO/1 1296 67 TABLE Information on reagent combinations in panel for Pro-5003 No. Cy5 Cy5.5 Populations Comments 001 CD2/SR306 CD4/SRO51 3 populations CD2+4+ CD2-4+ CD2+4- 002 CD2/SR306 CD8/SR212 4 populations CD2+8+bright CD2+8+dulI CD2+8+total CD2+8- 003 CD3/SR054 CD4ISRO51 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/SRO51 3 populations Pro-5002 CD7+4+ CD7+4- CD7-4+ 006 CD7/SR208 CD8/SR212 3 populations Pro-5002 CD7+8+ CD7+8- 007 Oj3TCRJSRO CD7/SR21 1 5 populations Pro-5002 89 (P7 ap3-7+bright ccp-7+dull cp- 7+total 008 CD27/SR 162 CD4/SRO51 3 populations CD2 7+4± CD27+4- CD27-4± Pro-S 002 009 CD27/SR162 CD8/SR212 7 populations Pro-5002 CD2 7+8+bright CD2 7+8+dull CD2 7+8+total CD27+8- CD2 7-8+bright CD27-8+dull WO 00/65472 WO 0065472PCTLJSOO/1 1296 68 5 (CONTINUED) 010 CD6/SR364 CD4/SRO51 3 populations 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/SRO5I 3 populations CD26±4- CD26+4+ CD26-4+ 013 CD26/SR363 CD8/SR212 5 populations CD26+8- CD26+8+ CD26-8+bright CD26-8+dull 014 CDS7/SR197 CD4/SR051 2 populations 7+4+ CD5 7-4+ 015 CD571SR197 CD8/SR212 6 populations 7+8+bright 7+8+dull 7+8+total 7-8+bright 7-8+dull 7+8+total 016 NKBI/SR37 CD6/SR362 2 populations NKI+6+ 017 CD45RAISR CD4/SRO5I 3 populations Pro-5002 346 CD45RA-4+ CD45RA+4+ CD45RA+4- 018 CD45RA/SR CD8/SR212 3 populations Pro-5002 346 ICD45RA-8+ CD45RA±8+ 019 CD62L/SR2 CD4/SRO51 3 populations Pro-5002 27 CD62L+4+bright CD62L-4+bright ~~CW WO 00/65472 WO 0065472PCTIUSOO/1 1296 69 TABLE 5 (CONTINUED) 020 CD62L/SR2 CD8/SR212 6 populations IPro-5002 27 CD62L+8+bright CD62L±8+dull CD62L+8+total CD62L-8+bright I CD62L-8+dull ~~~CD62L-8+total 021 CD691SR099 CD4/SRO51 2 populations T Se lysed CD69+4+ CD69-4+ 022 CD69/SR099 CD8/SR2 12 2 populations Use lysed (IlOx) CD69+8+ CD69-8+I 023 CD25/SR231 CD4/SRO51 2 populations [Use lysed CD25+4+ CD25-4+ 024 CD25/SR231 CD8ISR212 2 populations Use lysed (IlOx) CD25+8+ CD25-8+ 025 TCR- CD8ISR212 3 populations jUse lysed (IlOx) VB3/SR215 TCRVB3+8+ TCRVB3-8+ TCRVB3+8-____ 026 TCR- CD8ISR212 3 populations Use lysed (IlOx) VB5/SR2 16 TCRVB35+8+ -8+ 027 TCR- CD8ISR2I2 3 populations 1Use lysed (l Ox) VB8/SR2-17 TCRVIB8+8+ TCRVB8-8+ TCRVB8+8- 028 NKBl/SR37 CD4/SRO51 3 populations Use lysed NKB 1+4+ NKBI±4- 1-4± 029 NKB1/SR37 CD8/SR212 3 populations 1:Use lysed (IlOx) NKB 1+8+ NKB 1+8- NKBl1-8+ 030 CD5/SR297 CD I9/SRO5O 3 populations IPro-5002 CD5-19+ CD5+19± CD5+1 9- 031 CD6/SR364 CDI9/SRO5O 3 populations CD6+19- CD6+19+ CD6-19+ WO 00/65472 WO 0065472PCTUSOO/1 1296 TABLE 5 (CONTINUED)_ 032 CD27/SR162 CD19/SRO5O 3 populations CD27-19+ CD27+19+ CD27+19- 0 3 3 CD2/SR306 CDI9/SRO5O 3 populations Stanford study CD2+l 9- CD2-19+ CD2+19+ 034 CD8O/SR228 CDl9ISRO50 2 populations Use lysed (IlOx) CD8O+19+ 035 CD86/SR236 CDl9/SRO5O 2 populations Use lysed (IlOx) CD86±19± CD86-19+ 036 CD25/SR231 CDI9/SRO5O 2 populations Use lysed CD25+19+ 037 CD69/SR099 CDI9/SR050 2 populations Use lysed (l Ox) CD69+19+ 038 CD62LISR2 CD2O/SR224 I population Pro-5002 27 ______CD62L+20+ 039 CD45RA/SR CD2O/SR224 2 populations Pro-5002 346 CD45RA+20+ 040 HLA CD2O/SR224 2 populations Pro-S 002 PAN/SR229 HLAPAN 11+20+ HLAPAN 11±20- 041 HLA CD2O/SR224 2 populations Pro-5002 2DR'SR230 042 HLA CD2O/SR224 2 populations DP/SR3 70 043 HLA CD4/SRO5I 3 populations Pro-5002 PAN/SR229 HLAPAN+4+ HLAPAN+4- HLAPAN-4+ 044 HLA CD4/SRO5l 3 populations Pro-5002 2DR/SR230 HLADR+4+ HLADR+4- HLADR-4+ 045 HLA CD4/SRO5 1 3 populations DP/SR370 HLADP+4+ HLADP+4- WO 00/65472 WO 0065472PCT/USOO/1 1296 71 TABLE 5 (CONTINUED) 046 CD33/SR2-32 CD14/SR366 4 populations Good crosscheck on CD33+14+ CD 3 3 CD33+14+total use doped down CD33dulll4+ CD14 CD3 3+14- 047 CD33/SR2?32 CD4/SR051 3 populations Pro-5002 CD33+4+ Second check on CD3 3-4+ CD33 CD3 3+-4- 048 CD16/SR065 CD14/SR366 5 populations CD 1 6+14+bright CD16+14+dulI CD 1 6+1 4+total CD 16-14+ 16+14- 049 CD64/SRI 82 CD4/SR05 1 2 populations CD64+4+ CD64-4+ 050 CD64/SR182 CDI6/SR072 3 populations Stanford study CD64+1 6+ CD64+1 6- CD64-1 6+ 051 CD45RAISR CD14/SR366 2 populations Doped down CD 14 346 CD45RA± 14+ CD45RA+1 4- 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 CD14/5R366 4 populations Nice breakdown of CD45+14-tetal lymphs, grans, mono 4- Stanford study CD45clulll4- Lysed, 1:4 diluted ____CD45+14+ 055 CD45/SR132 CDI6/SR072 3 populations Pro-5002 total CD45+ 1:4 diluted blood CD4S5+16+hi-sl CD45+16+lo-si 6- 056 CDI5/SR195 CD16/SR072 2 populations 1:4 diluted blood CD 15+16+ 057 CD18/5R374 CDI5/SR37 2pop .ulations 1:4 diluted blood CD 18+15+ WO 00/65472 PCTUSOO/I 1296 TABLE 5 (CONTINUED) 058 CD45/SR132 CD14/SR366 4 populations Nice breakdown of CD45+14-total lymphs, grans, mono CD45brightl4- Stanford study CD45dulll4- 1:4 diluted blood CD45+1 4+ 059 CDI lb/SRO6 CDI5/SR372 2 populations 1:4 diluted blood 3 CD1I lb+1 5- 060 CD32/SR18O CD15/SR372 2 populations 1:4 diluted blood CD32+15+ CD32+15- WO 00/65472 WO 0065472PCTIUSOO/1 1296 73 TABLE 6 Soluble Factor Immunoassavs No ASSAY 1 IL- I alpha 2 IL- I beta 3 IL- Ira 4 IL- IsRI IL- IsRII 6 IL-6 7 IL-8 8 IL-10 9 RF (all isotypes) RF IgM I1I RF IgG 12 RF IgA 13 CRP 14 SAA MMP-3 16 MMP-9 17 TIMP-1 18 TNF alpha 19 IEgamma TGF beta 21 sCD62E 22 sCD62P No ASSAY 23 IL-2 24 IL-3 25 IL-4 26 27 MMP-1 28 MMP-2 29 MMP13 30 TIMP-2 31 TIMP-3 32 sCD44 3 3 ScD54 ICAM-1 34 sCD62L 35 RANTES 36-51 Immunoglobulin H and L isotypes_(16_assays) S soluble WO 00/65472 WO 0065472PCTUSOO/1 1296 74 TABLE 7 Assav K Numbers D'e Antgen Dve ntigen Dve Antigen Format General Cy7-

WB

ASY3-149 Cy5) CD45 SR712 CN5.5 D14 SR503 AC CD16 SR433 O0.25x Cy7- ysed ASY3 -13 2 Cv5 CD45 PR712 v. D14 IR503 APC CD 16 SR433 O0.25x TCels_(llo both 4 and 8) ASY3-OO1 Cy5 CD4 SR349 j45. CD8 SR212 JAPC CD3 SR435 WB ]Cy7- ASY3-15O Cy5CD2 SR306 Cy55 CD4 SR506 IACCD8 SR529 WB Cy7- ASY3 -066 Cy5 TCRc43 SR660 gy5 .5 TCy SR663 IAC CD3 ISR435 fCy7- ASY3-151 Cy5 rCRc4p ISR660 Cy5.5 CD4 SR506 [APC CD8 SR529 WB CD8 Cells I Cy7- ASY3-055 C5 ICD62L SR227 Cy5. CD45RA SR453 AC CD8 SR529 WB Cy7- ASY3-008 x1 ICD57 SR342 Cy5. CD6 SR362 APC CD8 SR529 WB Cy7- ASY3-152 Cy ICD27 SR225 Cv5.5 CD45R-A SR453 AC CD8 SR529 Cy7- ASY3-1 78 CA ICD28 SR675 Cy. CD62L SR454 APC CD8 ISR529 WB iCy7- ASY3-179 Cy ICD28 SR675 Cy55 CD45 RA SR453 AC CD8 SR529 WB Cy7- ASY3-079 CY5 CD69 SR235 Cy. CD25 SR616 APC CD8 SR529 Lysed Cy7- ASY3-O8O Cy ICD71 SR654 Cy5.5 CD57 SR619 APC CD8 SR529 Lysed Cy7- ASY3-O89 Cyj ICD38 SR671 Cy5.5 CD72 SR592 ACTCD8 SR529 ICy7- ASY3-090 Cy5 CD28 SR675 Cy55 CD26 SR343 PC CD8 SR529 WB -y7- ASY3-091.Cy5 CCR5 R52 y5.5CD8 SR212 APC CD3 SR435 B Cy7- ASY3-142 Vy5 CD4 SR349 Cv5.5 CD7 SR21 1 APC CD8 SR529 WB [Cy7-

BJ-

ASY3-145ICy5 ICD44 SR58 Cy. D7 SR211 APC CD8 ISR5 29 WO 00/65472 WO 0065472PCTIUSOO/1 1296 Nu s vye Kntigen JSR### TABE 7 CONTINUED )ve jAntigen jDve Antigen SR#Yr ICD4 TCells 1 r 1- A -n S6 ~SR453 2v~ W~n62L SR227 ICv5.5 kD45RA AS306* IC62 SR227 C 5.5 D45 ASY3-0041 ~5 1 CD4 SR349 ICY5.5 D27 SR161 y

A.PC

ASY3-153 K 5 ICD26 3R363 1 Cy5.5 CD4 CD4 SR530 WB CD3 -SR435 B CD3 SR435 WB CD3 SR435 WB CD3 SR435 WB SR506 AjPC

~LI

7 n57 SR342Cy. D LyI SR506 APC A SSYA-I '4 CD5 ASY3-155 Cy5 jCD62L QC 11QQ C, 1 1(-T'17 SP)~7 CvS5 LD4 SR506 P i SR227 I+v 5

TC

T~ CD45RA SR453 SR530 Cy7- ASY3-18Ogy5 CD28 SR675 Cy5.5 CD45RA ISR453 AC CD4 SR530 WB Cy7- ASY3-063 Cy5 CD7 SR208 Cy5.5 CD6 SR362 AC CD4 SR530 WB Cy7- B- ASY3-156 C5 ICD44 SR558 Cy5.5 ICD4 SR506 APC CD3 SR435 0.25x Cy7-

WB-

ASY3-157 Ay D89 SR447 yS5CD4 ISR506. AC CD3 SR435 CD4 T and Mono y 7 ASY3-137Cy5 669 SR235 Cy5.5 CD14 SR503 APC CD4 SR530 Lysed-2x ASY3 -13 8 Cy5 625 SR231 Cy5.5 CD14 SR503 AC CD4 SR530 Lysed-2x y 7 ASY3-158Cy5 CR5 PR502 Cy5.5 CD4 SR506 AC CD14 -SR719 WB Cy 7 ASY3-159!gA CD38 SR671 Cy5.5 CD14 SR503 APC CD4 SR530 VB y 7 ASY3-160 y5 ICD86 S R2 36 L5.5 CD14 -SR503 C CD4 SR530 WB vS--13 9 CY5CD71 SR654 ICy5.5 ICD14 S53 CCD4 SR5 30 ysed-2x.

WO 00/65472 PCTUSOO/I 1296 76 TABLE 7 CONTINUED) Assa[L Numbers Dve LAntien LDve Antigen rve tigen ISR### ormat B Cells ASY3- Cy7- 143 Cy5 CD5 SR297 Cy5.5 CD19 SR050 APC CD20 SR718 WB ASY3- Cy7- 161 Cy5 CD72 SR100 Cy5.5 CD19 SR050 APC CD20 SR718 WB ASY3- Cy7- 162 £x5 CD80 SR228 Cy5.5 CD86 SR706 APC CD20 SR718 WB ASY3- Cy7- 163 Cy5 CD69 SR235 Cy5.5 CD71 SR655 APC CD20 SR729 B Cell and Mono ASY3- Cy7- 164 Cy5 HLADP SR370 Cy5.5 CD14 SR503 APC CD20 SR718 WB ASY3- Cy7- 165 Cy5 HLADQ SR500 Cy5.5 CD14 SR503 APC CD20 SR718 WB ASY3- Cy7- 166 Cy5 HLADR, SR230 Cy5.5, CD14 SR503 APC CD20 SR718 WB ASY3- HLAPA Cy7- 167 Cy N SR229 Cy5.5 CD14 SR503 APC CD20 SR718 WB ASY3- Cy7- 168 Cy5 CD14 SR179 Cy5.5 CD45RA SR453 APC CD20 SR729 WB ASY3- Cy7- 169 Cy5 CD40 SR634 Cy5.5 CD14 SR503 APC CD20 SR718 WB ASY3- I Cy7- 170 j£y5 CD62L SR227 y5.5 CD14 SR503 APC CD20 SR729 WB Monocyte ASY3- Cy7- 171 Cy5 CD33 SR232 Cy5.5 CD14 SR503 APC CD4 SR530 WB ASY3- Cy7- WB- 172 Cy5 CD4 SR349 Cy5.5 CD11b SR371 APC CD14 SR719 0.25x ASY3- Cy7- WB- 345 Cy5 CD16b SR359 Cy5.5 CD66b SR536 APC CD16 SR433 0.25x ASY3- Cy7- WB- 173 Cy5 CD64 SR365 Cy5.5 CD14 SR503 APC CD16 SR433 0.25x ASY3- Cy7- WB- 349 Cy5 CD32 SR379 C 5.5 CD15 SR372 APC CD16 SR433 0.25x ASY3- Cy7- WB- 347 Cy5 CD18 SR374 Cy5.5 CD1Ib SR371 APC CD16 SR433 0.25x ASY3- Cy7- WB- 174 Cy 5 CD44 SR558I 5.5 CD15 SR372 APC CD14 SR719 0.25x Cy7- WB- 175 Cy5 CD89 SR658 C5.51 CD15 SR372 APC CD14 SR719 0.25x WO 00/65472 WO 0065472PCTIUSOO/1 1296 77 7 (CONTINUED) A4ssay Numbers ye AntigeniSR### Dve jntge~ ISR### 1 DveAntigen Format ASY3- Cy D IS31 Cy7- Lysed 128 C G9S3O CD 15 SR372 IAPC CD16 SR433 0.25x ASY3- {Cy7-

WB

148 Cy5 D2 SR289 Cy. C D 32 SR704 jAPC CD16 SR433 0.25x ASY3- Cy7-

WB

147 Cy5 CD123 SR289 Cy5.5 CD1%I5 SR372 [APC CD16 SR433 0.25x NK ASY3- Cy7- 038 CyS N B1I SR3 75 Cy5.5 CD5 SR298 APC CD7 SR490 WB ASY3- Cy7- 071 Cy5 CD57 SR342 Cy5.5 CD5 SR298 APC CD7 SR490 WB ASY3- Cy7- 085 Cy5 CD56 SR676 Cy5.5 CD2 SR352, APC ,CD3 SR435 WB ASY3- C7 086 CySl CD56 ISR676,C5. CD5 S28APC CD7 SR490 WB Controls ASY3- Cy7- 050 Cy MOPC SR344 Cyx. MOPC SR350 APC- MOPC SR624 WB ASY3- Cy7- 176 Cy5~ CD5 SR297 Cy. CD14 SR503 APC CD20 SR729 WB ASY3- Cy7- 177 CySl CD5 SR297 Cy5.5 _CDI4 SR503, APC CD20 SR729 Lysed -Ix 4SY3- SR212 Cy7-

APC

CD4 SR349 I Cv5 .5 CD8 CD3 SR435 lx- 2 WO 00/65472 WO 0065472PCT[USOOI 1 1296 78 TABLE 8 BLOOD SAMPLING/DOSING SCHEDULE___ [DAY DAY JDAY DAY DAY DAY DAY Fri Mon ITue Wed Thurs Fri Thurs -3 0 1i 2? 4 SrengAspirin Aspiin Aspirin Aspirin Blo lo f____Blood Blood Blood Blood Blood draw between 8 and 9 am each day TABLE ChO ClIA Ch2___ Assay Dye Antigen SR## Dye Antige Dye Antigen FormI I#n Iat 1ASY3- Cy5 CD36 SR67 Cy5.5 CD9 SR678 Cy7 CDlSR641 WB 102 9

APC

2 ASY3- Gy5 CD42a SR68 Cy5.5 CD4la SR683 Cy7 CD61 SR641 WB 103 4 1APC 3 ASY3- Gy5 JCD42a SR68 Cy5.5 CD62p SR590 Cy7 CD61 SR641 JWB 104 4 4 JASY3- Cy5 CD42b SR68 Cy5.5 CD4 Ia SR683 Gy7 CD61 SR641 WB 105 15

APC

JASY3- Cy5 CD42b ISR68 Cy5.5 CD62p SR590 Cy7 CD61 SR641 WB 106

APC

6 JASY3- Gy5 GD62p SR68 Cy5.5 CD61 SR681 Cy7 CD41la SR640 WB 107 6

APC___

7 JASY3- Cy5 CD63 SR68 Cy5.5 CD61 SR681 Cy7 CD41la SR640 WB 108 7 1APC 8 JASY3- Cy5 PAC-1 SR67 Cy5.5 ICD9 SR678 Cy7 CD61 SR641 WB 109 13

APC___

9 JASY3- Cy5 CD29 SR15 Cy5.5 CD9 SR678 Cy7 CD41la SR640 WB 110 0 1 APC JASY3- Gy5 CD62p SR68 jCy5.5 CD61 SR681 jCy7 CD41la SR640 TRT 111 6 ___APC 11 ASY3- Cy5 ICD63 SR68 Cy5.5 CD61 SR681 Cy7 CD4la SR640 TRT 112 7 APC 12 JASY3- Gy5 PAC-1 SR67 Cy5.5 CD9 ISR678 Cy7 CD61 S R-641 TR 113 _3 APC_ 13 JASY3- Cy5 CD42b SR68 Cy5.5 CD62p SR590 Cy7 CD61 SR641 TRT 114 5 1APC 14 ASY3- Gy5 CD62p SR68 jCy5.5 CD61 SR681 Cy7 CD41 a SR640 NT RT 115 1 6 _APC_ WO 00/65472 PCT/US00/1 1296 79 TABLE 9 (CONTINUED) JASY3- Cy5 CD63 SR68 Cy5.5 CD61 SR681 Cy7 CD41a SR640 NTRT 1116 7

APC

16 jASY3- CyS PAC-1 SR67 Cy5.5 CD9 SR678 Cy7 CD61 SR641 NTRT 117 3 APC 171IASY3- Cy5 CD42b SR68 Cy5.5 CD62p SR590 Cy7 CD61 SR641 NTRT 1118 5 A 18 ASY3- SR66 Cy7A 066 Cy5 TCRab 0 Cy5.5 TCRgd SR663 PC CD3 SR435 WB 19 ASY3- SR66 Cy7A 151 Cv5 1TCRab 0 Cy5.5 CD4 SR506 PC CD8 SR529 WB IASY3- SR22 CD45 Cy7A 055 Cy5 CD62L 7 Cy5.5 RA SR453 PC CD8 SR529 WB 21 IASY3- SR67 Cy7A 178 Cy5 CD28 5 Cy5.5 CD62L SR454 PC CD8 SR529 WB 22 1ASY3- SR50 Cy7A 1091 Cy5 CCR5 2 C5.5 CD8 SR212 PC CD3 SR435 WB 23 JASY3- SR34 Cy7A 142 Cv5 CD4 9 Cy5.5 CD72 SR211 PC CD8 SR529 WB 24 1ASY3- SR22 CD45 Cy7A 056 Cy5 CD62L 7 Cy5.5 IRA SR453 PC CD4 SR530 WB IASY3- SR67 CD45 Cy7A 180 Cy5 CD28 5 Cy5.5 IRA SR453 PC CD4 SR530 WB 26 ASY3- SR50 Cy7A 158 Cy5 CCR5 2 Cy5.5 CD4 SR506 PC CD14 SR719 WB 27 IASY3- SR23 Cy7A 160 Cvy CD86 6 Cy5.5 CD14 SR503 PC CD4 SR530 WB 28 ASY3- SR29 Cy7A 186 Cy5 ICD5 7 Cy5.5 CD19 SR050 PC CD20 SR729 WB 29 IASY3- SR22 Cy7- SR729 181 Cy5 CD80 8 Cy5.5 CD86 SR706 APC CD20 WB ASY3- SR37 Cy7A SR729 182 Cv5 HLADPO 0 Cy5.5 CDI14 SR503 PC CD20 WB 31 IASY3- HLAD SR5 0 Cy7A SR729 183 Cy5 Q 10 Cy5.5 CD14 SR503 PC CD20 WB 32 ASY3- HLAD SR23 Cy7A SR729 184 Cy5 R 0 Cy5.5 CD14 SR503 PC CD20 WB 33 ASY3- SR63 Cy7A SR729 1185 CxL ICD40 4 CY5.51CD14 SR503 PC CD20 WB 34ASY3- SR23 Cy7A 171 Cv CD33 2 Cy5.5 CDI4 SR503 PC CD4 SR530 WB 1ASY3- SR37 I Cy7A 038 _C5 NKBI 5 C C5.5 CD- ISR298 PC CD7 SR490 WB WO 00/65472 WO 0065472PCTUSOO/1 1296 ___TABLE 9 (CONTINUED)_______ r T ~T F- 36 ASY3- CvS SR67 SR352 Cy7A

PC

SR435 CD3 37tASY3- SR34 Cy7A 050 Cy MOPC 4 Cy5.5 MOPC SR350 PC MOPC SR624 WB 38 ASY3- SR55 Cy7A

WB-

156 Cy5 ICD44 8 Cy5.5,CD4 -SR506 PC CD3 SR435 0.25x 39 ASY3- SR35 Cy7A

WB-

045 CyS CD16b 9 Cy5.5 ICD66b SR536 PC CD16 SR433 0.25x ASY3- SR36 Cy7A

WB-

173 Cy5 CD64 5 CY5.5 CD14 SR503 PC CD16 SR433 0.25xI 41 ASY3- SR28 Cy7-

WB

148 Cy5_ CD123 9 Cy5.5 CD32 SR704 APC CD16 SR433 0.25x 42 ASY3- SR28 Cy7-

WB

147 jCy5 ICD123 19 Cy5.5 ICD15 SR372 APC CD16 SR433 10.25x

Claims (7)

1. A biological marker identification system comprising: a) an integrated database comprising a plurality of data categories, said data categories comprising, i) levels of a plurality of cell populations and/or cell associated molecules in the biological fluid of an organism, and/or levels of a plurality of soluble factors in the biological fluid of an organism, and ii) information associated with a plurality of clinical parameters of an organism; b) data from a plurality of organisms corresponding to said data categories; and i) processing means for correlating data within the data categories, wherein correlation analysis of data categories can be made to identify the data category or categories indicating normal biologic processes, pathogenic processes, or pharmacological responses to therapeutic intervention, "wherein said identified category or categories are biological markers.

2. The biological marker identification system of claim 1 wherein said data for S.levels of cell populations and/or cell associated molecules are obtained by microvolume laser :i scanning cytometry.

3. The biological marker identification system of claims 1 or 2 comprising at least 20 cell population and/or cell associated molecules level data categories.

4. The biological marker identification system of claim 3 comprising at least cell population and/or cell associated molecules level data categories. The biological marker identification system of claim 3 comprising at least cell population and/or cell associated molecules level data categories.

6. The biological marker identification system of any one of claims 1 to 3 wherein the soluble factor is a soluble protein.

7. The biological marker identification system of claim 1 wherein the soluble factor is a small molecule. P \OPERUEWcs CImslU \MhrUhl47 1246 clms dm-29103/O4

82- 8. The biological marker identification system of claim 1 wherein said data for levels of soluble factors are obtained by microvolume laser scanning cytometry. 9. The biological marker identification system of claim 1 wherein said data for levels of soluble factors are obtained by immunoassays. The biological marker identification system of claim 1 comprising at least soluble factor level data categories. 11. The biological marker identification system of claim 10 comprising at least soluble factor level data categories. 12. The biological marker identification system of claim 10 comprising at least soluble factor level data categories. 13. The biological marker identification system of claim 1 wherein data from at least some of said organisms is included a plurality of times. 14. The biological marker identification system of claim 1 wherein said data categories further include: iii) genotype information associated with an organism. The biological marker identification system of claim 1 wherein said data for 6 levels of soluble factors are obtained by mass spectrometry. 0:00 S16. The biological marker identification system of claim 1 wherein said information associated with said clinical parameters is selected from the group consisting of 0:0. 0: age, gender, weight, height, body type, medical history, family history, environmental factors and manifestation and categorization of disease or medical condition. 17. The biological marker identification system of claim 1 wherein said data is obtained from organisms prior to and after the administration of a therapeutic treatment. 18. The biological marker identification system of claim 1 wherein at least some of said data is obtained from an organism having been previously diagnosed as having a 0 0 0000 0.0 predetermined disease or medical condition. S°19. The biological marker identification system of claim 1 wherein at least some of o. *0said data is obtained at a plurality of times from an organism having been previously diagnosed as having a predetermined disease or medical condition. The biological marker identification system of claims 18 or 19 wherein said predetermined disease or medical condition is rheumatoid arthritis. P\OPERVEHRec Cims\20044archh247 1246 chs dm.29/03104 -83- 21. The biological marker identification system of claims 18 or 19 wherein said predetermined disease or medical condition is selected from the group consisting of rheumatoid arthritis, asthma, allergy and multiple sclerosis. 22. The biological marker identification system of claim 1 wherein said data categories comprise levels of a plurality of cell populations and/or cell associated molecules in the biological fluid of an organism and levels of a plurality of soluble factors in the biological fluid of an organism. 23. A method for identifying a biological marker for a given disease or medical condition comprising: correlating information obtained from a plurality of organisms, at least some of said organisms having said disease or medical condition, wherein information is associated with a plurality of data categories, and wherein said data categories comprise, i) levels of a plurality of cell populations and/or cell associated molecules in the biological fluid of an organism, and/or levels of a plurality of soluble factors in the biological fluid of an organism, and ii) information associated with a plurality of clinical parameters of an organism; identifying a data category where organisms having said disease or medical condition may be differentiated from those organisms not having said disease or medical condition, wherein said identified category is a biological marker for said disease. 24. The method for identifying a biological marker of claim 23 wherein said data for levels of cell populations and/or cell associated molecules are obtained by microvolume laser scanning cytometry. The method for identifying a biological marker of claim 23 comprising at least cell population and/or cell associated molecules level data categories. 26. The method for identifying a biological marker of claim 25 comprising at least cell population and/or cell associated molecules level data categories. 27. The method for identifying a biological marker of claim 25 comprising at least 40 cell population and/or cell associated molecules level data categories. •f28. The method for identifying a biological marker of claim 23 wherein said data for levels of soluble factors are obtained by microvolume laser scanning cytometry. WO 00/65472 PCTIUS00/I 1296 84 29. The method for identifying a biological marker of claim 23 comprising at least soluble factor level data categories. The method for identifying a biological marker of claim 29 comprising at least soluble factor level data categories. 31. The method for identifying a biological marker of claim 29 comprising at least soluble factor level data categories. 32. The method for identifying a biological marker of claim 23 wherein said data categories further include: iii) genotype information associated with any organism. 33. The method for identifying a biological marker of claim 23 wherein said data for levels of soluble factors are obtained by mass spectrometry. 34. The method for identifying a biological maker of claim 23 wherein said data for levels of soluble factors are obtained by immunoassays. The method for identifying a biological marker of claim 23 wherein said information associated with said clinical parameters is selected from the group consisting of age, gender, weight, height, body type, medical history, family history, environmental factors and manifestation and categorization of disease or medical condition. 36. The method for identifying a biological marker of claim 23 wherein said disease is rheumatoid arthritis. 37. The method for identifying a biological marker of claim 23 wherein said disease is selected from the group consisting of rheumatoid arthritis, asthma, allergy and multiple sclerosis. 38. A phenotype of an organism comprising a plurality of biological parameters comprising: i) the results of at least 20 assays relating to cell populations and/or cell associated molecules; ii) the results of at least 20 assays relating to soluble factors; and iii) clinical parameters. 39. The phenotype of claim 38 comprising the results of at least 40 assays relating to cell populations and/or cell associated molecules. WO 00/65472 PCT/US00/1 1296 The phenotype of claim 38 comprising the results of at least 40 assays relating to soluble factors. 41. The phenotype of claim 38 further comprising genotype information of said organism. 42. A phenotype of a class or subclass of organisms comprising a plurality of biological parameters from a plurality of members of said class or subclass; wherein from each said member said biological parameters comprise: i) the results of at least 20 assays relating to cell populations and/or cell associated molecules; ii) the results of at least 20 assays relating to soluble factors; and iii) clinical parameters. 43. The phenotype of claim 42 comprising the results of at least 40 assays relating to cell populations and/or cell associated molecules. 44. The phenotype of claim 42 comprising the results of at least 40 assays relating to soluble factors. The phenotype of claim 42 further comprising genotype information of each said member. 46. A system for creating the phenotype of an organism comprising: i) obtaining biological parameters from said organism comprising: a) the results of at least 20 assays relating to cell populations and/or cell associated molecules; b) the results of at least 20 assays relating to soluble factors; and c) clinical parameters; and ii) entering said biological parameters into an integrated data base. 47. The system of claim 46 comprising the results of at least 40 assays relating to cell populations and/or cell associated molecules. 48. The system of claim 46 comprising the results of at least 40 assays relating to soluble factors. 49. The system of claim 46 wherein said biological parameters further comprise genotype information. WO 00/65472 PCT/US00/1 1296 86 A method for evaluating the effect of a perturbation on an organism comprising: i) obtaining the phenotype of said organism prior to and after said perturbation; and ii) comparing the information in said prior to and after phenotypes to identify changed parameters; wherein said phenotypes are comprised of: a) the results of at least 20 assays relating to cell populations and/or cell associated molecules; b) the results of at least 20 assays relating to soluble factors; and c) clinical parameters. 51. The method of claim 50, wherein said phenotypes comprise at least assays relating to cell populations and/or cell associated molecules. 52. The method of claim 50, wherein said phenotypes comprise at least assays relating to soluble factors. 53. The method of claim 50, wherein said phenotypes further comprise genotype information of said organism. 54. A method for evaluating the effect of a perturbation on a class or subclass of organisms comprising: i) obtaining the phenotype of a plurality of members of said class or subclass of organisms prior to and after said perturbation; ii) comparing the information in said prior to and after phenotype to identify changed parameters; wherein said phenotypes are comprised of: a) the results of at least 20 assays relating to cell populations and/or cell associated molecules; b) the results of at least 20 assays relating to soluble factors; and c) clinical parameters. A method for evaluating the effect of a perturbation on an organism or class or subclass or organisms comprising: PAOPER\VEH\s C, OU4M\M-,247 1246 ciI. dwc-29103104 -87- i) obtaining the phenotype of a plurality of said organisms who have not been effected by said perturbation and the phenotype of one or more of said organisms who have been effected by said perturbation; and ii) comparing the information in the phenotypes of said plurality of organisms who have not been effected by said perturbation with the phenotype of the one or more organisms who have been effected by said perturbation to identify changed parameters; wherein said phenotypes are comprised of; a) the results of at least 20 assays relating to cell populations and/or cell associated molecules; b) the results of at least 20 assays relating to soluble factors; and c) clinical parameters. 56. A system for the identification of biological markers of a disease or medical condition in an animal model of said disease or medical condition comprising: a) an integrated database comprising a plurality of data categories, said data categories comprising, i) levels of a plurality of cell populations and/or cell associated molecules in the biological fluid of an animal, and/or levels of a plurality of soluble factors in the Sbiological fluid of an animal, and 0**O Sii) information associated with a plurality of physical parameters of an animal; b) data from a plurality of animals corresponding to said data categories; and i) processing means for correlating data within the data categories, wherein correlation analysis of data categories can be made to identify the data category or ooo° °categories indicating normal biologic processes, pathogenic processes, or pharmacological responses to candidate therapeutic intervention; wherein said identified category or categories are biological markers. 57. The biological marker identification system of claim 56 wherein said data for levels of cell populations and/or cell associated molecules are obtained by microvolume laser scanning cytometry. 58. The biological marker identification system of claims 56 or 57 comprising at least 20 cell population and/or cell associated molecules level data categories. WO 00/65472 PCT/US00/I 1296 88 59. The biological marker identification system of claim 58 comprising at least cell population and/or cell associated molecules level data categories. The biological marker identification system of claim 56 comprising at least soluble factor level data categories. 61. The biological marker identification system of claim 56 comprising at least soluble factor level data categories. 62. The biological marker identification system of claim 56 wherein said data categories further include: genotype information associated with an animal. 63. A method for identifying a biological marker for a given disease or medical condition in an animal model of said disease or medical condition comprising: providing an animal model of said disease or medical condition; correlating information obtained from a plurality of individual animals, at least some of said individual animals having said disease or medical condition, wherein information is associated with a plurality of data categories, and wherein said data categories comprise, i) levels of a plurality of cell populations and/or cell associated molecules in the biological fluid of an individual, and/or levels of a plurality of soluble factors in the biological fluid of an individual animal; and ii) information associated with a plurality of physical parameters of an individual animal; identifying a data category where individual animals having said disease or medical condition may be differentiated from those individual animals not having said disease or medical condition, wherein said identified category is a biological marker for said disease in said animal model. 64. A method for identifying a biological marker for a given disease or medical in humans, comprising: providing an animal model of said disease or medical condition; identifying a biological marker for said disease or medical condition in the animal model of said disease or medical condition according to the method of claim 63; and P \OPER\JEF4\Rcs CIa 004\MarChU47 1246 lims doc.290304 -89- determining if said biological marker is diagnostic or prognostic of said disease or medical condition in humans. A method for assaying a candidate therapeutic agent directed against a human disease or medical condition, the method comprising: providing an animal model of said disease or medical condition; identifying at least one biological marker of said disease or medical condition in said animal model by the method of claim 63; treating said animal model with said candidate therapeutic; and monitoring the response of said biological markers in said animal model. 66. A method for monitoring the results of a clinical study in humans with a given medical disease or condition comprising: evaluating biological markers in humans that are homologues of biological markers identified by the method of claim 63 in animal models of said medical disease or condition. 67. A method for designing an improved animal model for a human disease or medical condition comprising: •identifying human biological markers relative to said disease or medical condition by the method of claim 64; tailoring the animal model to more accurately simulate said disease or medical condition by elevating or reducing the levels of the animal homologues of said human biological marker. 68. A method for identifying an animal model of a disease or medical condition comprising: i) obtaining the phenotype of a plurality of potential animal models of said .*disease or medical condition; ,•oo o ii) obtaining the phenotype of organism having said disease or medical condition; *:iii) comparing the potential animal model phenotypes with the phenotype of the organisms having said disease or medical condition to identify the animal model phenotype that most closely simulates the phenotype of the organisms having said disease or medical condition; wherein said phenotypes are comprised of: P \OPER\UEH\Rcs ChsQ20 4W2MrchW247 246 clms doc2910Y04 90 a) the results of at least 20 assays relating to cell populations and/or cell associated molecules; b) the results of at least 20 assays relating to soluble factors; and c) clinical parameters. 69. The phenotype of claim 38 wherein said organism is selected from the group consisting of a human, an animal, a plant, and a virus. The phenotype of claim 42 wherein said class or subclass or organisms is selected from the group consisting of humans, animals, plants and virus. 71. A method for evaluating the effects of a genetic alteration on a plant or animal comprising: i) obtaining the phenotype of said plant or animal that has been genetically altered and the phenotype of the non-genetically altered plant or animal; and ii) comparing the information in the genetically-altered and non-genetically altered phenotype to identify changed parameters; wherein said phenotypes are comprised of: a) the results of at least 20 assays relating to cell populations and/or cell S° associated molecules; 0 b) the results of at least 20 assays relating to soluble factors; and 0: c) clinical parameters. 72. A biological marker according to any one claims 1 to 22 or 57 to 62, a method according to any one of claims 23 to 37, 50 to 55, 63 to 68 or 71, a phenotype according to any one of claims 38 to 45 or 69 to 70 or a system according to any one of claims 46 to 49 or 56 substantially as hereinbefore described with reference to the Figures and/or Examples. DATED this 2 9 th day of March, 2004 SURROMED, INC. by DAVIES COLLISON CAVE Patent Attorneys for the Applicant(s)