Patient classification Number:6,763,307 from the United States Patent and Trademark Office (PTO) owispatent

Title: Patient classification

Abstract: Clinical patient tissue samples are classified according to the physiological status of cells present in the sample. In some embodiments of the invention, such cells are classified according to their ability to respond to therapeutic agents and treatments. In other embodiments, the cells or tissue samples are classified according to their status with respect to the activity of pathways of interest. The information thus derived is useful in prognosis and diagnosis, and can further be used develop surrogate markers for disease states, and to investigate the effect of genetic polymorphisms in the responsiveness and state of cells involved in disease.

Patent Number: 6,763,307 Issued on 07/13/2004 to Berg,   et al.

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Inventors:	Berg; Ellen L. (Palo Alto, CA), Butcher; Eugene C. (Portola Valley, CA), Melrose; Jennifer (La Honda, CA)
Assignee:	BioSeek, Inc. (Burlingame, CA)
Appl. No.:	09/952,744
Filed:	September 13, 2001

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Related U.S. Patent Documents

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	Application Number	Filing Date	Patent Number	Issue Date
	800605	Mar., 2001	6656695

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Current U.S. Class:	702/19 ; 435/6; 435/7.1; 435/7.24
Current International Class:	G01N 33/50 (20060101); G01N 33/68 (20060101)
Field of Search:	701/19 435/6,7.1,7.24 702/19

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References Cited [Referenced By]

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U.S. Patent Documents

	4568649		February 1986		Bertoglio-Matte
	5569588		October 1996		Ashby et al.
	5631153		May 1997		Capecchi et al.
	5777888		July 1998		Rine et al.
	5994076		November 1999		Chenchik et al.
	6004755		December 1999		Wang
	6013437		January 2000		Luria et al.
	6146830		November 2000		Friend et al.
	6656695		December 2003		Berg et al.

Other References

Altschul et al. , Issues in Searching Molecular Sequence Databases. Nature Genetics, vol. 6, pp. 119-129 (1994).* . Blackstock et al. Proteomics: Quantitative and Physical Mapping of Cellular Proteins. TIBTECH vol. 17, pp. 121-127 (1999).* . Hatzimanikatis et al. Proteomics, Theoretical and Experimental Considerations. Biotechnol. Prog. vol. 15, pp. 312-318 (1999).* . Nellen et al. What Makes an mRNA Anti-Sensi-itive? TIBS vol. 18, pp. 419-423 (1993).* . Furukawa et al. Clinical Applications of the Histoculture Drug Response Assay. Clin. Cancer Res. vol. 1, pp. 305-311 (1995).* . Heller et al., Discovery and Analysis of Inflammatory Disease-Related Genes Using CDNA Microarrays, Proc. Natl. Acad. Sci., (1997), 94: 2150-2155..

Primary Examiner: Brusca; John S.
Attorney, Agent or Firm: Sherwood; Pamela J. Bozicevic, Field & Francis LLP

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Parent Case Text

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CROSS-REFERENCE TO RELATED APPLICATIONS

The application is a continuation-in-part of U.S. patent application Ser. No. 09/800,605 U.S. Pat. No. 6,656,695, filed Mar. 6, 2001, which claims benefit of U.S. Provisional Application Nos. 60/186,976, filed Mar. 6, 2000 and 60/195,672, filed Apr. 7, 2000.

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Claims

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What is claimed is:

1. A method of preparing a biomap for the classification of a patient sample according to pathways associated with a disease state, the method comprising: contacting said patient sample in a test cell culture with a plurality of factors in an amount and incubating for a time sufficient to induce a plurality of pathways active in said cell culture; measuring at least four parameters selected from the group consisting of ICAM, VCAM, E-selectin, IL-8, Mig, HLA-DR, MCP-1, CD69, CD14, CD142, CD40, Eotaxin3, IL-1alpha, M-CSF, and CD38 and comparing the measurement of said at least four parameters with the measurement from a control cell culture, and recording said measurements of said at least four parameters to produce a biomap, wherein said biomap is indicative of the pathways that are active in said cell culture.

2. The method according to claim 1, wherein said patient sample comprises cells, and wherein the response of said cells is measured for said at least four parameters.

3. The method according to claim 1, wherein said cell culture comprises a mixture of patient cells and non-patient indicator cells.

4. The method according to claim 1, wherein said patient sample is distributed in a panel of cell culture assay combinations, wherein at least one of said assay combinations is a control cell culture differing in at least one component from said test cell culture; wherein said component can be a factor, a biologically active agent or other environmental condition.

5. The method according to claim 1, wherein said test cell culture comprises a therapeutic agent, and wherein said biomap is indicative of the responsiveness of said patient tissue sample to said therapeutic agent.

6. The method according to claim 1, wherein said patient tissue sample comprises two or more distinct types of cells.

7. The method according to claim 6, wherein said cells are peripheral blood mononuclear cells.

8. The method according to claim 1, wherein cells in said patient sample are separated according to phenotype prior to said contacting step.

9. The method according to claim 1, further comprising the step of analyzing said patient tissue sample for the presence of nucleic acid polymorphisms.

10. The method according to claim 1, further comprising the step of correlating said biomap with patient history and clinical diagnosis.

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Description

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BACKGROUND OF THE INVENTION

In disease, as in health, there is a complex and changing cast of cells playing different roles. Functional capabilities of these cells can be altered, depending on the course of disease; as a result of underlying genetic differences; or due to drug exposure or other treatments. Even cancers, sometimes characterized as simple overgrowths of a single cell type, frequently show progression from one cell type to another. For example, in cancers of the breast and prostate there is a clear distinction between the steroid dependent and steroid independent cells, where the latter can emerge from the course of drug treatments. Similarly, the use of chemotherapeutics can select for resistant tumor cells, which are then able to persist through treatment. In other diseases, such as degenerative diseases, the loss of specific cell types is observed. For example, a key indicator of the severity of diabetes is the number of functioning islet cells that remain.

Apart from these diseased cells, normal cells in the body may be present, including the mobile cells of the immune system and angiogenic cells of the vascular system. Inflammatory diseases, as well as responses to infections, tumors and the like, are characterized by the presence of a variety of leukocytes, including B cells, T cells, polymorphonuclear cells (eosinophils, basophils and neutrophils), macrophages, natural killer cells, megakaryocytes, and the like. Even within one of these groups, there can be substantial variation in the function of the involved cells, for example a Th1 type T cells and a Th2 type T cells can have opposite effects on the course of a disease; and genetic and environmental effects can determine the onset and course of T cell-mediated diseases.

Angiogenesis is a process critical to both tumor growth and metastasis, and can be characterized by the presence of functionally distinct endothelial cells, which can vary in their responsiveness to cytokines and other growth and regulatory factors. Although angiogenesis is a continuous process, different consecutive steps can be identified, including release of pro-angiogenic factors and proteolytic enzymes, and endothelial cell migration, morphogenesis and proliferation. Under normal circumstances, the microvasculature is maintained in a quiescent state. The acquisition of the angiogenic phenotype depends on the outcome of stimulatory and inhibitory regulation by the tumur and its microenvironment, features which are modified by genetic differences.

In addition to the development and localization of cells, there is also genotypic variation, which can have important ramifications in an individuals response to therapy. Pharmacogenetics seeks to determine the linkage between an individual's genotype and that individual's ability to metabolize or react to a therapeutic agent. The use of pharmacogenetics is reviewed in Annu Rev Pharmacol Toxicol (2001);41:101-121. Differences in metabolism or target sensitivity can lead to severe toxicity or therapeutic failure by altering the relation between bioactive dose and blood concentration of the drug. However, given the complex networks of interacting elements that confer an individuals responses to environmental or therapeutic or pathologic influences, simply predicting responses from genotype may be difficult. Thus, more direct means of assessing relevant patient phenotypes are required.

A need exists for methods that give detailed information about the "physiotype", embodying cellular events that occur in response to differences in cell's genetic makeup, changes in a cell, its environment, and other events that influence the biology of the host. The present invention satisfies this need and provides additional advantages.

Related Literature

Cell based assays include a variety of methods to measure metabolic activities of cells including: uptake of tagged molecules or metabolic precursors, receptor binding methods, incorporation of tritiated thymidine as a measure of cellular proliferation, uptake of protein or lipid biosynthesis precursors, the binding of radiolabeled or otherwise labeled ligands; assays to measure calcium flux, and a variety of techniques to measure the expression of specific genes or their gene products.

Compounds have also been screened for their ability to inhibit the expression of specific genes in gene reporter assays. For example, Ashby et al. U.S. Pat. No. 5,569,588; Rine and Ashby U.S. Pat. No. 5,777,888 describe a genome reporter matrix approach for comparing the effect of drugs on a panel of reporter genes to reveal effects of a compound on the transcription of a spectrum of genes in the genome.

Methods utilizing genetic sequence microarrays allow the detection of changes in expression patterns in response to stimulus. A few examples include U.S. Pat. No. 6,013,437; Luria et al., "Method for identifying translationally regulated genes"; U.S. Pat. No. 6,004,755, Wang, "Quantitative microarray hybridization assays"; and U.S. Pat. No. 5,994,076, Chenchik et al., "Methods of assaying differential expression". U.S. Pat. No. 6,146,830, Friend et al. "Method for determining the presence of a number of primary targets of a drug".

Proteomics techniques have potential for application to pharmaceutical drug screening.

These methods require technically complex analysis and comparison of high resolution two-dimensional gels or other separation methods, often followed by mass spectrometry (for reviews see Hatzimanikatis et al. (1999) Biotechnol Prog 15(3):312-8; Blackstock et al. (1999) Trends Biotechnol 17(3):121-7. A discussion of the uses of proteomics in drug discovery may be found in Mullner et al. (1998) Arzneimittelforschung 48(1):93-5.

SUMMARY OF THE INVENTION

Methods and compositions are provided for the classification of clinical samples, e.g. patient tissue samples, according to the physiological status of cells or constituents present in the sample. The information thus derived is useful in prognosis and diagnosis, and can further be used develop surrogate markers for disease states, and to investigate the effect of genetic polymorphisms in the responsiveness and state of cells involved in disease. In some embodiments of the invention, such cells are classified according to their ability to respond to therapeutic agents and treatments. In other embodiments, the cells are classified according to their status with respect to the activity of pathways of interest. In another embodiment, patient tissue samples are evaluated for the presence of biologically active molecules, e.g. secreted factors and the like, by adding the patient sample to a cell culture responsive to the molecules.

Patient samples are cultured in a panel of environments, where each environment can comprise combinations of factors, cells and therapeutically active agents. Generally at least one environment contains multiple factors that affect pathways of interest. The effect of altering the culture environment is assessed by monitoring multiple output parameters. The cells may also be treated with therapeutic agents in the presence or absence of factors, and the profile of output parameters determined. A sufficient number of markers are selected to provide a high confidence level that the pathways of interest are being monitored. When factors are employed, a sufficient number of factors are used to involve one or a plurality of pathways and a sufficient number of markers are determined to insure the cellular status is accurately being monitored.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Assay combinations for charactering cells. A. Expression of selected readout parameters on selected assay combinations of HUVEC treated with proinflammatory cytokines. Confluent cultures of HUVEC cells were treated with TNF.alpha. (5 ng/ml), IFN.gamma. (200 ng/ml) and or IL-1.beta. (1 ng/ml). After 24 hours, cultures were washed and evaluated for the presence of the parameters ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7) by cell-based ELISA. For this, plates were inverted until dry, blocked with 1% Blotto for 1 hr, and treated with primary antibodies (obtained from Pharmingen and Becton Dickinson) at 1 ng/ml for 1 hr. Plates were washed and secondary peroxidase-conjugated anti-mouse IgG antibody (Promega) at 1:2500 was applied for 1 hr. After washing, TMB substrate (Kierkegaard & Perry) was added and color developed. Development was stopped by addition of H2SO4 and the absorbance at 450 nm (subtracting the background absorbance at 650 nm) with a Molecular Devices plate reader. The relative expression levels of each parameter are indicated by the OD at 450 nm shown along the y-axis. The mean +/-SD from triplicate samples is shown. B. Visual representation of the data from FIG. 1A. The measurement obtained for each parameter is classified according to its relative change from the value obtained in the optimized assay combination (containing IL-1+TNF-.alpha. +IFN-.gamma.), and represented by shaded squares. For each parameter and assay combination, the square is shaded by a checkerboard if the parameter measurement is unchanged (<20% above or below the measurement in the first assay combination (IL-1+TNF-.alpha.+IFN-.gamma.)) or p>0.05, n=3; slanted lines indicates that the parameter measurement is moderately increased (>20% but <50%); white indicates the parameter measurement is strongly increased (>50%); vertical lines indicates that the parameter measurement is moderated decreased (>20% but <50%); hatched lines indicates that the parameter measurement is strongly decreased (>50% less than the level measured in the first assay combination).

FIG. 2. Assay combinations for characterizing cells. Confluent cultures of HUVEC cells were treated with TNF.alpha. (5 ng/ml), IFN.gamma. (200 ng/ml) and IL-1.beta. (1 ng/ml) in the presence or absence of neutralizing anti-TNF.alpha. (R&D Systems) or control Goat anti-IgG. After 24 hours, cultures were washed and evaluated for the cell surface expression of ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7) by cell-based ELISA performed as described in FIG. 1. A. The relative expression of each parameter is shown along the y-axis as average value of the OD measured at 450 nm of triplicate samples. The mean +/-SD from triplicate samples are shown. * indicates p<0.05 comparing results obtained with anti-TNF.alpha. to the control. B. Visual representation of the data from FIG. 2A. The measurement obtained for each parameter is classified according to its relative change from the value obtained in the optimized assay combination (containing IL-1+TNF-.alpha.+IFN-.gamma.), and represented by shaded squares. For each parameter and assay combination, the square is shaded by a checkerboard if the parameter measurement is unchanged (<20% above or below the measurement in the first assay combination (IL-1+TNF-.alpha.+IFN-.gamma.)) or p>0.05, n=3; slanted lines indicates that the parameter measurement is moderately increased (>20% but <50%); white indicates the parameter measurement is strongly increased (>50%); vertical lines indicates that the parameter measurement is moderated decreased (>20% but <50%); hatched lines indicates that the parameter measurement is strongly decreased (>50% less than the level measured in the first assay combination).

FIG. 3. Effect of NF.kappa.B inhibitors NHGA and PDTC, MAP kinase inhibitor PD098059, or ibuprofen on the expression of readout parameters in the inflammatory BioMAP assay combination containing three factors (IL-1+TNF-.alpha.+IFN-.gamma.). Confluent cultures of HUVEC cells were treated with TNF.alpha. (5 ng/ml), IFN.gamma. (200 ng/ml) and IL-1.beta. (1 ng/ml) in the presence or absence of (A) 10 .mu.M NHGA, 200 .mu.M PDTC or 9 .mu.M PD098059; (B) 125-500 .mu.M ibuprofen. Compounds were tested at the highest concentration at which they were soluble, and/or did not result in loss of cells from the plate. After 24 hours, cultures were washed and evaluated for the cell surface expression of ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7) by cell-based ELISA performed as described in FIG. 1. The measurement obtained for each parameter is classified according to its relative change from the value obtained in the optimized assay combination (containing IL-1+TNF-.alpha.+IFN-.gamma.), and represented by shaded squares. For each parameter and assay combination, the square is shaded by a checkerboard if the parameter measurement is unchanged (<20% above or below the measurement in the first assay combination (IL-1+TNF-.alpha.+IFN-.gamma.)) or p>0.05, n=3; slanted lines indicates that the parameter measurement is moderately increased (>20% but <50%); white indicates the parameter measurement is strongly increased (>50%); vertical lines indicates that the parameter measurement is moderated decreased (>20% but <50%); hatched lines indicates that the parameter measurement is strongly decreased (>50% less than the level measured in the assay combination without compounds). C. Effect of compounds on the reference readout pattern in the inflammatory BioMAP assay combination containing three factors (IL-1+TNF-.alpha.+IFN-.gamma.). Confluent cultures of HUVEC cells were treated with TNF.alpha. (5 ng/ml), IFN.gamma. (200 ng/ml) and IL-1.beta. (1 ng/ml) in the presence or absence of compounds as listed in Table I. After 24 hours, cultures were washed and evaluated for the cell surface expression of parameters of ICAM-1, VCAM-1, E-selectin, IL-8, CD31, HLA-DR and MIG by cell-based ELISA performed as described in FIG. 1. The resulting BioMAPs were compared and correlation coefficients employed in clustering analysis (Clustal X program). Readout patterns are as visualized by a tree diagram in which a) each terminal branch point represents the readout pattern from one assay combination in one experiment; b) the length of the vertical distance from the upper horizontal line (no change and control patterns) to the termini are related to the extent of difference in the readout pattern from the control pattern (without drug); and c) the distance along the branches from one terminal pattern value to another reflects the extent of difference between them. Similar patterns are thus clustered together. The figure illustrates the reproducibility of patterns resulting from treatment with a single drug in multiple experiments, and those resulting from multiple drugs that target the same signaling pathway.

FIG. 4. Assay combinations containing HUVEC and T cell co-cultures. Confluent cultures of HUVEC were incubated with media (No Cells), TNF-.alpha., (5 ng/ml), IFN-.gamma. (100 ng/ml) or KIT255 T cells with and without IL-2 (10 ng/ml) and/or IL-12 (10 ng/ml). After 24 hours cultures were washed and evaluated for the cell surface expression of ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6) and MIG (7) by cell-based ELISA performed as described in FIG. 1. The relative expression of each parameter is shown along the y-axis as average value of the OD measured at 450 nm.BioMAP

FIG. 5. Assay combinations for characterizing patient blood samples. Expression of selected readout parameters on selected assay combinations of HUVEC with and without normal blood cells and proinflammatory cytokines. Human peripheral blood buffy coat cells, washed and resuspended to 1/16 volume were added to confluent cultures of HUVEC cells treated with TNF-.alpha. (5 ng/ml), IFN-.gamma. (100 ng/ml), IL-1 (1 ng/ml) and or base media. After 24 hours, cultures were washed and evaluated for the presence of the parameters ICAM-1 (1), VCAM-1 (2), E-selectin (3), IL-8 (4), CD31 (5), HLA-DR (6), MIG (7), CD40 (8) or MCP-1 (9) by cell-based ELISA. For this, plates were blocked with 1% Blotto for 1 hr, and treated with primary antibodies (obtained from Pharmingen and Becton Dickinson) at 1 ng/ml for 1 hr. Plates were washed and secondary biotin-conjugated anti-mouse IgG antibody (Jackson Immunoresearch) at 1:2500 was applied for 1 hr. Plates were washed and strepavidin-HRP (Jackson Immunoresearch) was applied for 1 hr. After washing, TMB substrate (Kierkegaard & Perry) was added and color developed. Development was stopped by addition of H.sub.2 SO.sub.4 and and the absorbance at 450 nm (subtracting the background absorbance at 650 nm) with a Molecular Devices plate reader. The relative expression levels of each parameter are indicated by the OD at 450 nm shown along the y-axis. A. Parameter readouts are shown from BioMAPs prepared from assays performed without blood or with blood and with or without one or more of TNF.alpha., IL-1 and/or IFN.gamma.. B. Parameter readouts are shown from BioMAPs prepared from assays performed with (closed symbols) and without (open symbols) blood and (a) IL-1, (b) TNF.alpha., (c) IFNg, (d) IL1+TNF+IFNg, and (e) no added cytokine. C. Visual representation of the data from FIG. 5B. The measurement obtained for each parameter is classified according to its relative change from the value obtained in the indicated assay combination (containing no cytokine; IL-1, TNF-.alpha., IFN-.gamma. or IL-1+TNF-.alpha.+IFN-.gamma.), and represented by shaded squares. For each parameter and assay combination, the square is shaded by a checkerboard if the parameter measurement is unchanged (<20% above or below the measurement in the first assay combination) or p>0.05, n=3; slanted lines indicates that the parameter measurement is moderately increased (>20% but <50%); white indicates the parameter measurement is strongly increased (>50%); vertical lines indicates that the parameter measurement is moderated decreased (>20% but <50%); hatched lines indicates that the parameter measurement is strongly decreased (>50% less than the level measured in the assay combination without blood cells).

DETAILED DESCRIPTION OF THE EMBODIMENTS

Methods and compositions are provided for the classification of clinical samples, e.g. patient tissue samples, cells, fluids, extracts of tissues, etc., according to the physiological status of cells present in the sample. The information thus derived is useful in prognosis and diagnosis, including susceptibility to disease(s), status of a diseased state and response to changes in the environment, such as the passage of time, treatment with drugs or other modalities. The state of the cells provided in the clinical sample may be classified according to the activation of pathways of interest, for example T cells can be classified as Th1, Th2, or Th3 type cells. The cells can also be classified as to their ability to respond to therapeutic agents and treatments. Where the sample is being evaluated for the presence of biologically active molecules, the sample is added to a culture of potentially responsive cells, where the response of such cells is then monitored.

Based on changes in parameters in response to factors, information is derived that is useful in determining what pathways or cellular functionality is present in a tissue. Changes in parameters in response to therapeutic agents provides information that is informative of a patient's ability to respond to a drug in the context of a physiologically relevant microenvironment. Changes in parameters in response to therapeutic agents can be correlated with databases of BioMAPs for classification or to BioMAPs from control samples. In addition to classification, BioMAPs derived from clinical samples and therapeutic agents can be used to compare drugs that act on different pathways is a physiologically relevant environment.

The clinical samples can be further characterized by genetic analysis, proteomics, cell surface staining, or other means, in order to determine the presence of markers that are useful in classification. For example, genetic polymorphisms, such as single nucleotide polymorphisms or microsatellite repeats, can be causative of disease susceptibility or drug responsiveness, or can be linked to such phenotypes. Analysis of the genotype of a cell can be correlated to a BioMAP classification, and the information used in the development of genetic markers. Similarly, such markers as expression of cell surface proteins, presence of lipids, carbohydrates, etc. can be directly or indirectly associated with disease associated phenotypes, and with responsiveness to therapeutic agents and treatments.

The invention is also useful for screening compounds for drug interactions. Drug interactions can be problematic in cancer therapy. For example, while steroids control the edema that occurs with glioma, they also interfere with chemotherapy efficacy. Cytotoxic drugs form the basis of many cancer therapies, thus interference with chemotherapy efficacy may offset any anti-tumor effects of angiogenesis inhibitors. Most cytotoxic drugs effect both normal and neoplastic cells, although at different concentrations, therefore, screening compounds in the presence of cytotoxic drugs can be performed and reveal unexpected interference or beneficial synergies. Interactions between a cytotoxic drug and any test compound is detected by the observation of BioMAPs obtained in the presence of both drugs that are inconsistent with additive effects.

Patient samples are cultured in a panel of environments, where each environment can comprise combinations of factors, cells and therapeutically active agents. Generally at least one environment contains multiple factors that affect pathways of interest. The effect of altering the culture environment is assessed by monitoring multiple output parameters. The cells may also be treated with therapeutic agents in the presence or absence of factors, and the profile of output parameters determined. A sufficient number of markers are selected to provide a high confidence level that the pathways of interest are being monitored. When factors are employed, a sufficient number of factors are used to involve one or a plurality of pathways and a sufficient number of markers are determined to insure the cellular status is accurately being monitored.

For convenience, a clinical sample comprising cells may be referred to as "test cells", and will comprise one or more types of cells present in a clinical sample. For example, a tissue sample may include endothelial cells, a variety of lymphocytes and other hematopoietic cells, tumor cells which may be clonal or polyclonal in origin, and the like. The test cells need not be directly involved in a disease of interest.

A clinical sample may also be evaluated for the presence of biologically active factors and other molecules, where such non-cellular material is indicative of the physiological state of the tissue from which it is obtained. Such samples are evaluated for their effect on one or a panel of cells, as described in co-pending patent application Ser. No. 09/800,605.

The term "assay combinations" refers to such cultures, where test cells are contacted with medium and multiple combination of factors, agents and other culture variations. These cell cultures are created by the addition of a sufficient number of different factors to provoke a response that simulates cellular physiology of a state of interest, and to allow for the status of cells in culture to be determined in relation to a change in an environment. The state of interest will normally involve a plurality of pathways where the pathways regulate a plurality of parameters or markers identifying a phenotype associated with the state of interest. In a preferred embodiment, one or more assay combinations are provided that simulate physiological cell states of interest, particularly physiological cell states in vivo, usually using the same type of cells or combinations of cells. Such a simulation will usually include at least three different regulated features (parameters) shared with in vivo cell counterparts in normal or diseased states. Alternatively, the simulation will include a cell culture system that allows discrimination of modifications in at least three different signaling pathways or cell functions operative in vivo under conditions of interest.

A phenotype of the test cells that is useful for monitoring output parameters can be generated by including a plurality of factors, and optionally additional cells, e.g. stromal cells, endothelial cells, fibroblasts, etc., that may interact with the patient tissue. The factors and cells inducing pathways induce a response in the test cells in vitro. Such factors are naturally occurring compounds, e.g. known compounds that have surface membrane receptors and induce a cellular signal that results in a modified phenotype; or synthetic compounds that mimic such naturally occurring factors. In some instances, factors will act intracellularly by passing through the cell surface membrane and entering the cytosol with binding to components in the cytosol, nucleus or other organelle. In referring to factors, it is understood that it is the activities of the factors that are of interest and not necessarily a particular naturally occurring factor itself.

For each test cell there are a number of markers that can be measured, which relate to specific pathways associated with the cell type and condition. As described in co-pending U.S. patent application Ser. No. 09/800,605, which disclosure is specifically incorporated by reference, at least about 4 markers are identified that allow for evaluating the up or down regulation of at least 2 pathways, generally three or more pathways, where the total number of markers will usually not exceed 8. The markers are selected to provide a robust picture of the status of the cell, due to its condition, e.g. pro-inflammatory, immunosuppressive, neoplastic, etc., its response to therapy, its response to a drug, or the like. Each set of markers will define a set of cell pathways and their response. However, there will normally be at least 2, usually at least 3, common markers for the particular determination.

The nature and number of parameters measured generally reflects the response of a plurality of pathways. The subject approach provides for robust results having enhanced predictability in relation to the status of the test cells. The results may be compared to the basal condition, tissue matched normal controls, and/or the condition in the presence of one or more of the factors, particularly in comparison to all of the factors used in the presence and absence of agent. The effects of different environments are conveniently provided in BioMAPs, where the results can be mathematically compared.

BioMAP

A BioMAP is prepared from values obtained by measuring parameters or markers of the test cells in the presence and absence of different factors, and/or by comparing the presence of an agent of interest and at least one other state, usually the control state, which may include the state without agent or with a different agent. Parameters include cellular products or epitopes thereof, as well as functional states, whose levels vary in the presence of the factors. Desirably, the results are normalized against a standard, usually a "control value or state," to provide a normalized data set. Values obtained from test conditions can be normalized by subtracting the unstimulated control values from the test values, and dividing the corrected test value by the corrected stimulated control value. Other methods of normalization can also be used; and the logarithm or other derivative of measured values or ratio of test to stimulated or other control values may be used. Data is normalized to control data on a cell type under control conditions, but a BioMAP may comprise normalized data from one, two or multiple cell types and assay conditions.

By referring to a BioMAP is intended that the dataset will comprise values of the levels of at least two sets of parameters obtained under different assay combinations. Depending on the use of the BioMAP, the BioMAP may also include the parameter values for each the factors included in the assay combination, individually and/or together with fewer than the entire assay combination. The parameter values are usually created electronically and stored in a data processor for comparison with other BioMAPs and databases compiled from the BioMAPs.

A graph of a BioMAP can be presented visually as numerical values, symbols, color gradations, or the like, indicating the parameter values. The graph is conveniently presented where color and/or design provide an indication of the level of the particular marker. The indicators may be vertical or horizontal as to the individual markers and the assay combinations, so that by looking at the graph, one can immediately compare the levels of the different markers for each of the combinations and discern patterns related to the assay combinations and the differences between assay combinations. In this way, one can rapidly relate different candidate pharmacologic agents, the pathways they affect and their efficacy in modulating the individual pathways.

Optionally, a BioMAP can be annotated to indicate information about the sources of information for the dataset. Annotations may include, for example, the number of assay conditions in a panel (n); controls used for normalization (N); parameters (P), which may be designated for the number and identity of the parameters; environmental changes, such as the addition of factors and/or agents or a change in the physical conditions (V); cell type (C); and the like. The annotation may further specify specific factors or conditions present in one of the assay combinations, e.g. n1, n2, n3, etc., where the presence of factors in the assay combination is designated (F), temperature may be designated (T), pH, etc. The parameters may also be designated in this as, e.g. P1=ICAM-1, P2=VCAM-1, P3=E-selectin, etc. Written out, the annotation may be set forth as: (v) B {n; N; P; C; F}.

A database of BioMAPs can be compiled from sets of experiments, for example, a database can contain BioMAPs obtained from clinical samples such as sites of inflammation, tumors, etc., each in a panel of assay combinations, with multiple different environmental changes, where each change can be a series of related compounds, or compounds representing different classes of molecules. In another embodiment, a database comprises BioMAPs from one compound, with multiple different cell panels.

Mathematical systems can be used to compare BioMAPs, and to provide quantitative measures of similarities and differences between them. For example, the BioMAPs in the database can be analyzed by pattern recognition algorithms or clustering methods (e.g. hierarchical or k-means clustering, etc.) that use statistical analysis (correlation coefficients, etc.) to quantify relatedness of BioMAPs. These methods can be modified (by weighting, employing classification strategies, etc.) to optimize the ability of a BioMAP to discriminate different functional effects. For example, individual parameters can be given more or less weight when analyzing the dataset of the BioMAP, in order to enhance the discriminatory ability of the BioMAP. The effect of altering the weights assigned each parameter is assessed, and an iterative process is used to optimize pathway or cellular function discrimination.

In many cases the literature has sufficient information to establish assay combinations to provide a useful BioMAP. Where the information is not available, by using the procedures described in the literature for identifying markers for diseases, microarrays for RNA transcription comparisons, proteomic or immunologic comparisons, between normal cells and cells in the disease state, one can ascertain the endogenous factors associated with the disease state and the markers that are produced by the cells associated with the disease state.

Biomap analysis can be used to optimize cell culture conditions. Additional markers can be deduced and added as a marker to the map. The greater the number of individual markers that vary independently of each other, the more robust the BioMAP. If desired, the parameters of the BioMAP can be optimized by obtaining BioMAP parameters within an assay combination or panel of assay combinations using different sets of readout, and using pattern recognition algorithms and statistical analyses to compare and contrast different BioMAPs of different parameter sets. Parameters are selected that provide a BioMAP that discriminates between changes in the environment of the cell culture known to have different modes of action, i.e. the BioMAP is similar for agents with a common mode of action, and different for agents with a different mode of action. The optimization process allows the identification and selection of a minimal set of parameters, each of which provides a robust readout, and that together provide a BioMAP that enables discrimination of different modes of action of stimuli or agents. The iterative process focuses on optimizing the assay combinations and readout parameters to maximize efficiency and the number of signaling pathways and/or functionally different cell states produced in the assay configurations that can be identified and distinguished, while at the same time minimizing the number of parameters or assay combinations required for such discrimination.

Clinical Samples

Clinical samples for use in the methods of the invention may be obtained from a variety of sources, including blood, lymph, cerebrospinal fluid, synovial fluid, tissue biopsies, skin, saliva, lavage, and the like. Such samples can comprise complex populations of cells, which can be assayed as a population, or separated into sub-populations, and can also comprise acellular samples. Such cellular and acellular sample can be separated by centrifugation, elutriation, density gradient separation, apheresis, affinity selection, panning, FACS, centrifugation with Hypaque, etc. By using antibodies specific for markers identified with particular cell types, a relatively homogeneous population of cells may be obtained. Alternatively, a heterogeneous cell population can be used. Acellular samples can be separated according to immunologic or biochemical criteria, for example by various electrophoretic and chromatographic means, as is known in the art.

Once a sample is obtained, it can be used directly, frozen, or maintained in appropriate culture medium for short periods of time. Various media can be employed to maintain cells during the classification process. There are established protocols for the culture of diverse cell types that reflect their in vivo counterparts. Protocols may require the use of special conditions and selective media to enable cell growth or expression of specialized cellular functions.

Such methods are described in the following: Animal Cell Culture Techniques (Springer Lab Manual), Clynes (Editor), Springer Verlag,1998; Animal Cell Culture Methods (Methods in Cell Biology, Vol 57, Barnes and Mather, Eds, Academic Press, 1998; Harrison and Rae, General Techniques of Cell Culture (Handbooks in Practical Animal Cell Biology), Cambridge University Press, 1997; Endothelial Cell Culture (Handbooks in Practical Animal Cell Biology), Bicknell (Editor), Cambridge University Press, 1996; Human Cell Culture, Cancer Cell Lines Part I: Human Cell Culture, Masters and Palsson, eds., Kluwer Academic Publishers, 1998; Human Cell Culture Volume II--Cancer Cell Lines Part 2 (Human Cell Culture Volume 2), Masters and Palsson, eds., Kluwer Academic Publishers, 1999; Wilson, Methods in Cell Biology: Animal Cell Culture Methods (Vol 57), Academic Press, 1998; Current Protocols in Immunology, Coligan et al., eds, John Wiley & Sons, New York, N.Y., 2000; Current Protocols in Cell Biology, Bonifacino et al., eds, John Wiley & Sons, New York, N.Y., 2000.

The methods of the invention find use in a wide variety of animal species, including mammalian species. Animal models, particularly small mammals, e.g. murine, lagomorpha, etc. are of interest for experimental investigations. Humans are of particular interest for both diagnostic and prognostic applications of the method.

The samples may come from any organ or compartment of the body, to the extent the cells can be obtained by any convenient procedure, such as the drawing of blood, venipuncture, biopsy, or the like. Cell types that can find use in the subject invention, include endothelial cells, muscle cells, myocardial, smooth and skeletal muscle cells, mesenchymal cells, epithelial cells; hematopoietic cells, such as lymphocytes, including T-cells, such as Th1 T cells, Th2 T cells, Th0 T cells, cytotoxic T cells; B cells, pre-B cells, etc.; monocytes; dendritic cells; neutrophils; and macrophages; natural killer cells; mast cells;, etc.; adipocytes, cells involved with particular organs, such as thymus, endocrine glands, pancreas, brain, such as neurons, glia, astrocytes, dendrocytes, etc. and in some instances, may even involve genetically modified cells thereof. Hematopoietic cells will be associated with inflammatory processes, autoimmune diseases, etc., endothelial cells, smooth muscle cells, myocardial cells, etc. may be associated with cardiovascular diseases; almost any type of cell may be associated with neoplasias, such as sarcomas, carcinomas and lymphomas; liver diseases with hepatic cells; kidney diseases with kidney cells; etc. Usually a sample will comprise at least about 10.sup.2 cells, more usually at least about 10.sup.3 cells, and preferable 10.sup.4 or more cells.

Assay Combinations

The term "environment," or "culture condition", as used in the assay combinations of the subject methods encompasses cells, media, factors, time and temperature. Environments may also include drugs and other compounds, particular atmospheric conditions, pH, salt composition, minerals, etc. The conditions will be controlled and the BioMAP will reflect the similarities and differences between each of the assay combinations involving a different environment or culture condition.

Culture of cells is typically performed in a sterile environment, for example, at 37.degree. C. in an incubator containing a humidified 92-95% air/5-8% CO.sub.2 atmosphere. Cell culture may be carried out in nutrient mixtures containing undefined biological fluids such as fetal calf serum, or media which is fully defined and serum free.

Culture protocols may require the use of special conditions and selective media to enable cell growth or expression of specialized cellular functions. Such methods are described in the following: Animal Cell Culture Techniques (Springer Lab Manual), Clynes (Editor), Springer Verlag,1998; Animal Cell Culture Methods (Methods in Cell Biology, Vol 57, Barnes and Mather, Eds, Academic Press, 1998; Harrison and Rae, General Techniques of Cell Culture (Handbooks in Practical Animal Cell Biology), Cambridge University Press, 1997; Endothelial Cell Culture (Handbooks in Practical Animal Cell Biology), Bicknell (Editor), Cambridge University Press, 1996; Human Cell Culture, Cancer Cell Lines Part I: Human Cell Culture, Masters and Paisson, eds., Kluwer Academic Publishers, 1998; Human Cell Culture Volume II--Cancer Cell Lines Part 2 (Human Cell Culture Volume 2), Masters and Palsson, eds., Kluwer Academic Publishers, 1999; Wilson, Methods in Cell Biology: Animal Cell Culture Methods (Vol 57), Academic Press, 1998; Current Protocols in Immunology, Coligan et al., eds, John Wiley & Sons, New York, N.Y., 2000; Current Protocols in Cell Biology, Bonifacino et al., eds, John Wiley & Sons, New York, N.Y., 2000.

Some preferred environments include environments that discriminate or emphasize cell or tissue states associated with pathology in one or more diseases, for example, Th1 versus Th2 polarization of effector T cells; prothrombotic; inflammatory (e.g. NF.kappa.B, upregulated TNF-.alpha. cytokine production, downregulated IL-10, TGF.beta., etc.; dysregulated proliferation (neoplasia); angiogenesis; etc.) Environments that facilitate discrimination of specific signaling pathways implicated in disease states are also of interest, e.g. NF.kappa.B, classic Th1 or Th2 induction environments, etc.

A assay combinations are used in classifying and investigating complex states of cells, frequently resulting from cellular interactions, which may frequently involve at least about two, frequently three, or more different cell types and/or will involve a plurality of soluble factors that are present in a physiological fluid, particularly as the result of a physiological event, e.g. infection, neoplasia, autoimmune, etc. that is, frequently involving more than one cell type and more than one factor. The measured parameters may be obtained from one or more of the cell types. The cells in the assay combination, either one or up to each of the different cell types, can have identifying characteristics allowing them to be distinguished during analysis. Various techniques may be employed to identify the cells in the assay combination for analysis of the parameters of interest.

Conditions of interest include inflammatory processes that occur in response to infection, trauma, etc., autoimmune diseases, such as diabetes, lupus, arthritis, etc., cardiovascular diseases, such as stroke, atherosclerosis, etc., neoplasia, hyperplasia, addiction, infection, obesity, cellular degeneration, apoptosis, senescence, differentiation, and the like.

Multifactorial, usually involving multicellular, assay combinations, may reflect many of the conditions indicated above, such as inflammatory processes; autoimmune diseases; cardiovascular diseases; tumors, etc. That is, a multiplicity of factors are employed to influence a plurality of cellular pathways and a multiplicity of parameters are measured that reflect the status of the pathways. Degenerative diseases, including affected tissues and surrounding areas, may be exploited to determine both the response of the affected tissue, and the interactions with other cell types or other parts of the body.

Factors added to the cultures can be the products of other cell types, for example, expressed proteins associated with a disease, can be compounds that simulate naturally occurring factors, can be surface membrane proteins free of the membrane or as part of microsomes, or other reagent that induces the appropriate pathway to aid in the simulation of the phenotype or provides the appropriate environment to simulate the physiological condition. Factors (including mimetics thereof) can be added individually or in combination, from feeder cells, may be added as a bolus or continuously, where the factor is degraded by the culture, etc.

Illustrative naturally occurring factors include cytokines, chemokines, and other factors, e.g. growth factors, such factors include GM-CSF, G-CSF, M-CSF, TGF, FGF, EGF, TNF-.alpha., GH, corticotropin, melanotropin, ACTH, etc., extracellular matrix components, surface membrane proteins, such as integrins and adhesins, and other components that are expressed by the targeted cells or their surrounding milieu in vivo, etc., that may be isolated from natural sources or produced by recombinant technology or synthesis, compounds that mimic the action of other compounds or cell types, e.g. an antibody which acts like a factor or mimics a factor, such as synthetic drugs that act as ligands for target receptors. For example, in the case of the T cell receptor, the action of an oligopeptide processed from an antigen and presented by an antigen-presenting cell, etc. can be employed. Where a family of related factors are referred to with a single designation, e.g. IL-1, VEGF, IFN, etc., in referring to the single description, any one or some or all of the members of the group are intended, where the literature will be aware of how the factors are to be used in the context of the assay combination. Components may also include soluble or immobilized recombinant or purified receptors, or antibodies against receptors or ligand mimetics.

Cancer cells may be cultured with different factors based on the different cells in the environment of the tumor, as well as other factors in the blood induced by factors secreted by the neoplastic cells. Many of these factors will be the same factors described above, but additional factors include factors associated with angiogenesis, such as angiogenin, angiopoietin-1, HGF, PDGF, TNF-.alpha., VEGF, IL-1, IL-4, IL-6, IL-8 and fibronectin.

Panels

For the most part, the BioMAP dataset will comprise data from a panel of assay combinations. The panel will be related to the purpose of the BioMAP and may include not only the information that has been developed substantially concurrently with the study, but also information that has been previously developed under comparable conditions. Frequently a panel will be used that is comprised of at least one assay combination that provides for simulation of multiple pathways of interest, while other assay combinations in the panel are variants thereof. The number of combinations in a panel may vary with the particular use. For example, the minimum number of assay combinations will be two for a panel for initial screening that would comprise a single assay combination. A panel for characterizing the mechanism of action of an active compound will usually comprise a plurality of assay combinations, usually at least about 4, more usually at least 6, frequently at least about 10 and may be as many as 20 or more unique combinations.

In another embodiment, the panel comprises culture conditions where multiple specific changes are made to the culture environment, e.g. two or more changes, usually not more than about 6, more usually not more than about 4. Such changes are associated with the additional information that is engendered by the indicated variations. The variations can include the addition of known inhibitors of specific pathways.

Parameters

Parameters are quantifiable components of cells, particularly components that can be accurately measured, desirably in a high throughput system. A parameter can be any cell component or cell product including cell surface determinant, receptor, protein or conformational or posttranslational modification thereof, lipid, carbohydrate, organic or inorganic molecule, nucleic acid, e.g. mRNA, DNA, etc. or a portion derived from such a cell component or combinations thereof. While most parameters will provide a quantitative readout, in some instances a semi-quantitative or qualitative result will be acceptable. Readouts may include a single determined value, or may include mean, median value or the variance, etc. Characteristically a range of parameter readout values will be obtained for each parameter from a multiplicity of the same assay combinations, usually at least about 2 of the same assay combination will be performed to provide a value. Variability is expected and a range of values for each of the set of test parameters will be obtained using standard statistical methods with a common statistical method used to provide single values.

Markers are selected to serve as parameters based on the following criteria, where any parameter need not have all of the criteria: the parameter is modulated in the physiological condition that one is simulating with the assay combination; the parameter is modulated by a factor that is available and known to modulate the parameter in vitro analogous to the manner it is modulated in vivo; the parameter has a robust response that can be easily detected and differentiated and is not too sensitive to concentration variation, that is, it will not substantially differ in its response to an over two-fold change; the parameter is secreted or is a surface membrane protein or other readily measurable component; the parameter desirably requires not more than two factors to be produced; the parameter is not co-regulated with another parameter, so as to be redundant in the information provided; and in some instances, changes in the parameter are indicative of toxicity leading to cell death. The set of parameters selected is sufficiently large to allow distinction between reference patterns, while sufficiently selective to fulfill computational requirements.

For each assay combination, certain parameters will be functionally relevant and will be altered in response to test or reference agents or conditions, while other parameters may remain static in that particular combination. Biomaps will generally comprise only functionally relevant parameter information, although a static parameter may serve as an internal control. A typical BioMAP will comprise data from at least 3 functionally relevant parameters, more usually at least about 5 functionally relevant parameters, and may include 10 or more functionally relevant parameters, usually not more than about 30, more usually not more than about 20, parameters. In analyzing the data from the BioMAP, all of the parameters need not be weighed equally. Those parameters that are closely functionally associated with the disease state or pathophysiologic response, and/or with modulation of cell pathways of interest may be given greater weight in evaluating a candidate drug or a readout, as compared to other parameters that are suggestive, but do not have as strong an association.

Parameters of interest include detection of cytoplasmic, cell surface or secreted biomolecules, frequently biopolymers, e.g. polypeptides, polysaccharides, polynucleotides, lipids, etc. Cell surface and secreted molecules are a preferred parameter type as these mediate cell communication and cell effector responses and can be more readily assayed. In one embodiment, parameters include specific epitopes. Epitopes are frequently identified using specific monoclonal antibodies or receptor probes. In some cases the molecular entities comprising the epitope are from two or more substances and comprise a defined structure; examples include combinatorially determined epitopes associated with heterodimeric integrins. A parameter may be detection of a specifically modified protein or oligosaccharide, e.g. a phosphorylated protein, such as a STAT transcriptional protein; or sulfated oligosaccharide, or such as the carbohydrate structure Sialyl Lewis x, a selectin ligand. The presence of the active conformation of a receptor may comprise one parameter while an inactive conformation of a receptor may comprise another, e.g. the active and inactive forms of heterodimeric integrin .alpha..sub.M.beta..sub.2 or Mac-1.

A parameter may be defined by a specific monoclonal antibody or a ligand or receptor binding determinant. Parameters may include the presence of cell surface molecules such as CD antigens (CD1-CD247), cell adhesion molecules including .alpha..sub.4.beta..sub.7 and other integrins, selectin ligands, such as CLA and Sialyl Lewis x, and extracellular matrix components. Parameters may also include the presence of secreted products such as lymphokines, including IL-2, IL-4, IL-6, growth factors, etc. (Leukocyte Typing VI, T. Kishimoto et al., eds., Garland Publishing, London, England, 1997); Chemokines in Disease: Biology and Clinical Research (Contemporary Immunology), Hebert, Ed., Humana Press, 1999.

For activated T cells these parameters may include IL-1R, IL-2R, IL4R, IL-12R.beta., CD45RO, CD49E, tissue selective adhesion molecules, homing receptors, chemokine receptors, CD26, CD27, CD30 and other activation antigens. Additional parameters that are modulated during activation include MHC class II; functional activation of integrins due to clustering and/or conformational changes; T cell proliferation and cytokine production, including chemokine production. Of particular importance is the regulation of patterns of cytokine production, the best-characterized example being the production of IL-4 by Th2 cells, and interferon-.gamma. by Th1 T cells. The ability to shift cytokine production patterns in vivo is a powerful means of modulating pathologic immune responses, for example in models of EAE, diabetes, inflammatory bowel disease, etc. Thus, the expression of secreted cytokines may be a preferred class of parameters, detectable, for example, by ELISA analysis of the supernatants, etc.

Therapeutic Agents

In many cases, it will be of interest to determine whether a patient is responsive to a particular therapeutic agent or regimen. In particular, it is of interest to determine the efficacy of such therapies in a biologically relevant context, i.e. in the presence of factors and interacting cells. Included are pharmacologically active drugs, genetically active molecules, etc. Compounds of interest include chemotherapeutic agents, anti-inflammatory agents, hormones or hormone antagonists, ion channel modifiers, and neuroactive agents. Exemplary of pharmaceutical agents suitable for this invention are those described in, "The Pharmacological Basis of Therapeutics," Goodman and Gilman, McGraw-Hill, New York, N.Y., (1996), Ninth edition, under the sections: Drugs Acting at Synaptic and Neuroeffector Junctional Sites; Drugs Acting on the Central Nervous System; Autacoids: Drug Therapy of Inflammation; Water, Salts and Ions; Drugs Affecting Renal Function and Electrolyte Metabolism; Cardiovascular Drugs; Drugs Affecting Gastrointestinal Function; Drugs Affecting Uterine Motility; Chemotherapy of Parasitic Infections; Chemotherapy of Microbial Diseases; Chemotherapy of Neoplastic Diseases; Drugs Used for Immunosuppression; Drugs Acting on Blood-Forming organs; Hormones and Hormone Antagonists; Vitamins, Dermatology; and Toxicology, all incorporated herein by reference.

Classification Methods

Cells and samples are classified by adding a therapeutic agent or treatment; and/or by culturing cells in combinations of factors, in at least one and usually a plurality of assay combinations to form a panel of assay combinations, usually in conjunction with positive and negative controls. The change in parameter readout in response to an agent, sample or factors is measured, desirably normalized, and the resultin