Identification, monitoring and treatment of disease and characterization of biological condition using gene expression profiles

TECHNICAL FIELD AND BACKGROUND ART

The present invention relates to use of gene expression data, and in particular to use of gene expression data in identification, monitoring and treatment of disease and in characterization of biological condition of a subject.

The prior art has utilized gene expression data to determine the presence or absence of particular markers as diagnostic of a particular condition, and in some circumstances have described the cumulative addition of scores for over expression of particular disease markers to achieve increased accuracy or sensitivity of diagnosis. Information on any condition of a particular patient and a patient's response to types and dosages of therapeutic or nutritional agents has become an important issue in clinical medicine today not only from the aspect of efficiency of medical practice for the health care industry but for improved outcomes and benefits for the patients.

SUMMARY OF THE INVENTION

In a first embodiment, there is provided a method, for evaluating a biological condition of a subject, based on a sample from the subject. The method includes:

deriving from the sample a profile data set, the profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables evaluation of the biological condition; and

in deriving the profile data set, achieving such measure for each constituent under measurement conditions that are substantially repeatable.

There is a related embodiment for providing an index that is indicative of the state of a subject, as to a biological condition, based on a sample from the subject. This embodiment includes:

in deriving the profile data set, achieving such measure for each constituent under measurement conditions that are substantially repeatable; and

applying values from the profile data set to an index function that provides a mapping from an instance of a profile data set into a single-valued measure of biological condition, so as to produce an index pertinent to the biological condition of the subject. In further embodiments related to the foregoing, there is also included, in deriving the profile data set, achieving such measure for each constituent under measurement conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar. Similarly further embodiments include alternatively or in addition, in deriving the profile data set, achieving such measure for each constituent under measurement conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar.

In embodiments relating to providing the index a further embodiment also includes providing with the index a normative value of the index function, determined with respect to a relevant population, so that the index may be interpreted in relation to the normative value. Optionally providing the normative value includes constructing the index function so that the normative value is approximately 1. Also optionally, the relevant population has in common a property that is at least one of age group, gender, ethnicity, geographic location, diet, medical disorder, clinical indicator, medication, physical activity, body mass, and environmental exposure.

In another related embodiment, efficiencies of amplification, expressed as a percent, for all constituents lie within a range of approximately 2 percent, and optionally, approximately 1 percent.

In another related embodiment, measurement conditions are repeatable so that such measure for each constituent has a coefficient of variation, on repeated derivation of such measure from the sample, that is less than approximately 3 percent. In further embodiments, the panel includes at least three constituents and optionally fewer than approximately 500 constituents.

In another embodiment, the biological condition being evaluated is with respect to a localized tissue of the subject and the sample is derived from tissue or fluid of a type distinct from that of the localized tissue. In related embodiments, the biological condition may be any of the conditions identified in Tables 1 through 12 herein, in which case there are measurements conducted corresponding to constituents of the corresponding Gene Expression Panel. The panel in each case includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the corresponding Gene Expression Panel.

In another embodiment, there is provided a method of providing an index that is indicative of the inflammatory state of a subject based on a sample from the subject that includes: deriving from the sample a first profile data set, the first profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents, the panel including at least two of the constituents of the Inflammation Gene Expression Panel of Table 1; (although in other embodiments, at least three, four, five, six or ten constituents of the panel of Table 1 may be used in a panel) wherein, in deriving the first profile data set, such measure is performed for each constituent both under conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar and under substantially repeatable conditions; and applying values from the first profile data set to an index function that provides a mapping from an instance of a profile data set into a single-valued measure of biological condition (in an embodiment, this may be an inflammatory condition), so as to produce an index pertinent to the biological condition of the sample or the subject. The biological condition may be any condition that is assessable using an appropriate Gene Expression Panel; the measurement of the extent of inflammation using the Inflammation Gene Expression Panel is merely an example.

In additional embodiments, the mapping by the index function may be further based on an instance of a relevant baseline profile data set and values may be applied from a corresponding baseline profile data set from the same subject or from a population of subjects or samples with a similar or different biological condition. Additionally, the index function may be constructed to deviate from a normative value generally upwardly in an instance of an increase in expression of a constituent whose increase is associated with an increase of inflammation and also in an instance of a decrease in expression of a constituent whose decrease is associated with an increase of inflammation. The index function alternatively be constructed to weigh the expression value of a constituent in the panel generally in accordance with the extent to which its expression level is determined to be correlated with extent of inflammation. The index function may be alternatively constructed to take into account clinical insight into inflammation biology or to take into account experimentally derived data or to take into account relationships derived from computer analysis of profile data sets in a data base associating profile data sets with clinical and demographic data. In this connection, the construction of the index function may be achieved using statistical methods, which evaluate such data, to establish a model of constituent expression values that is an optimized predictor of extent of inflammation.

In another embodiment, the panel includes at least one constituent that is associated with a specific inflammatory disease.

The methods described above may further utilize the step wherein (i) the mapping by the index function is also based on an instance of at least one of demographic data and clinical data and (ii) values are applied from the first profile data set including applying a set of values associated with at least one of demographic data and clinical data.

In another embodiment of the above methods, a portion of deriving the first profile data set is performed at a first location and applying the values from the first profile data set is performed at a second location, and data associated with performing the portion of deriving the first profile data set are communicated to the second location over a network to enable, at the second location, applying the values from the first profile data set.

In an embodiment of the methods, the index function is a linear sum of terms, each term being a contribution function of a member of the profile data set. Moreover, the contribution function may be a weighted sum of powers of one of the member or its reciprocal, and the powers may be integral, so that the contribution function is a polynomial of one of the member or its reciprocal. Optionally, the polynomial is a linear polynomial. The profile data set may include at least three, four or all members corresponding to constituents selected from the group consisting of IL1A, IL1B, TNF, IFNG and IL10. The index function may be proportional to ¼{IL1A}+¼{IL1B}+¼{TNF}+¼{INFG}−1/{IL10} and braces around a constituent designate measurement of such constituent.

In an additional embodiment, a method is provided of analyzing complex data associated with a sample from a subject for information pertinent to inflammation, the method that includes: deriving a Gene Expression Profile for the sample, the Gene Expression Profile being based on a Signature Panel for Inflammation; and using the Gene Expression Profile to determine a Gene Expression Profile Inflammatory Index for the sample.

In an additional embodiment, a method is provided of monitoring the biological condition of a subject, that includes deriving a Gene Expression Profile for each of a series of samples over time from the subject, the Gene Expression Profile being based on a Signature Panel for Inflammation; and for each of the series of samples, using the corresponding Gene Expression Profile to determine a Gene Expression Profile Inflammatory Index.

In an additional embodiment, there is provided a method of determining at least one of (i) an effective dose of an agent to be administered to a subject and (ii) a schedule for administration of an agent to a subject, the method including: deriving a Gene Expression Profile for a sample from the subject, the Gene Expression Profile being based on a Signature Panel for Inflammation; using the Gene Expression Profile to determine a Gene Expression Profile Inflammatory Index for the sample; and using the Gene Expression Profile Inflammatory Index as an indicator in establishing at least one of the effective dose and the schedule.

In an additional embodiment, a method of guiding a decision to continue or modify therapy for a biological condition of a subject, is provided that includes: deriving a Gene Expression Profile for a sample from the subject, the Gene Expression Profile being based on a Signature Panel for Inflammation; and using the Gene Expression Profile to determine a Gene Expression Profile Inflammatory Index for the sample.

A method of predicting change in biological condition of a subject as a result of exposure to an agent, is provided that includes: deriving a first Gene Expression Profile for a first sample from the subject in the absence of the agent, the first Gene Expression Profile being based on a Signature Panel for Inflammation; deriving a second Gene Expression Profile for a second sample from the subject in the presence of the agent, the second Gene Expression Profile being based on the same Signature Panel; and using the first and second Gene Expression Profiles to determine correspondingly a first Gene Expression Profile Inflammatory Index and a second Gene Expression Profile Inflammatory Index. Accordingly, the agent may be a compound and the compound may be therapeutic.

In an additional embodiment, a method of evaluating a property of an agent is provided where the property is at least one of purity, potency, quality, efficacy or safety, the method including: deriving a first Gene Expression Profile from a sample reflecting exposure to the agent of (i) the sample, or (ii) a population of cells from which the sample is derived, or (iii) a subject from which the sample is derived; using the Gene Expression Profile to determine a Gene Expression Profile Inflammatory Index; and using the Gene Expression Profile Inflammatory Index in determining the property.

In accordance with another embodiment there is provided a method of providing an index that is indicative of the biological state of a subject based on a sample from the subject. The method of this embodiment includes:

deriving from the sample a first profile data set, the first profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents, the panel including at least two of the constituents of the Inflammation Gene Expression Panel of Table 1; and

applying values from the first profile data set to an index function that provides a mapping from an instance of a profile data set into a single-valued measure of biological condition, so as to produce an index pertinent to the biological condition of the sample or the subject.

In carrying out this method the index function also uses data from a baseline profile data set for the panel. Each member of the baseline data set is a normative measure, determined with respect to a relevant population of subjects, of the amount of one of the constituents in the panel. In addition, in deriving the first profile data set and the baseline data set, such measure is performed for each constituent both under conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar and under substantially repeatable conditions.

In another type of embodiment, there is provided a method, for evaluating a biological condition of a subject, based on a sample from the subject. In this embodiment, the method includes:

deriving from the sample a first profile data set, the first profile dataset including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables measurement of the biological condition; and

In this embodiment, each member of the baseline data set is a normative measure, determined with respect to a relevant population of subjects, of the amount of one of the constituents in the panel, and the calibrated profile data set provides a measure of the biological condition of the subject.

In a similar type of embodiment, there is provided a method, for evaluating a biological condition of a subject, based on a sample from the subject, and the method of this embodiment includes:

applying the first sample or a portion thereof to a defined population of indicator cells;

obtaining from the indicator cells a second sample containing at least one of RNAs or proteins;

deriving from the second sample a first profile data set, the first profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables measurement of the biological condition; and

producing a calibrated profile data set for the panel, wherein each member of the calibrated profile data set is a function of a corresponding member of the first profile data set and a corresponding member of a baseline profile data set for the panel, wherein each member of the baseline data set is a normative measure, determined with respect to a relevant population of subjects, of the amount of one of the constituents in the panel, the calibrated profile data set providing a measure of the biological condition of the subject.

Furthermore, another and similar, type of embodiment provides a method, for evaluating a biological condition affected by an agent. The method of this embodiment includes:

obtaining, from a target population of cells to which the agent has been administered, a sample having at least one of RNAs and proteins;

deriving from the sample a first profile data set, the first profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables measurement of the biological condition; and

In further embodiments based on these last three embodiments, the relevant population may be a population of healthy subjects. Alternatively, or in addition, the relevant population is has in common a property that is at least one of age group, gender, ethnicity, geographic location, diet, medical disorder, clinical indicator, medication, physical activity, body mass, and environmental exposure.

Alternatively or in addition, the panel includes at least two of the constituents of the Inflammation Gene Expression Panel of Table 1. (Other embodiments employ at least three, four, five, six, or ten of such constituents.) Also alternatively or in addition, in deriving the first profile data set, such measure is performed for each constituent both under conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar and under substantially repeatable conditions. Also alternatively, when such measure is performed for each constituent both under conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar and under substantially repeatable conditions, optionally one need not produce a calibrated profile data set, but may instead work directly with the first data set.

In another embodiment, there is provided a method, for evaluating the effect on a biological condition by a first agent in relation to the effect by a second agent. The method of this embodiment includes:

obtaining, from first and second target populations of cells to which the first and second agents have been respectively administered, first and second samples respectively, each sample having at least one of RNAs and proteins;

deriving from the first sample a first profile data set and from the second sample a second profile data set, the profile data sets each including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables measurement of the biological condition; and

producing for the panel a first calibrated profile data set and a second profile data set, wherein (i) each member of the first calibrated profile data set is a function of a corresponding member of the first profile data set and a corresponding member of a baseline profile data set for the panel, wherein each member of the baseline data set is a normative measure, determined with respect to a relevant population of subjects, of the amount of one of the constituents in the panel, and (ii) each member of the second calibrated profile data set is a function of a corresponding member of the second profile data set and a corresponding member of the baseline profile data set, the calibrated profile data sets providing a measure of the effect by the first agent on the biological condition in relation to the effect by the second agent.

In this embodiment, in deriving the first and second profile data sets, such measure is performed for each constituent both under conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar and under substantially repeatable conditions. In a further related embodiment, the first agent is a first drug and the second agent is a second drug. In another related embodiment, the first agent is a drug and the second agent is a complex mixture. In yet another related embodiment, the first agent is a drug and the second agent is a nutriceutical .

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of the invention will be more readily understood by reference to the following detailed description, taken with reference to the accompanying drawings, in which:

FIG. 1A shows the results of assaying 24 genes from the Source Inflammation Gene Panel (shown in Table 1) on eight separate days during the course of optic neuritis in a single male subject.

FIG. 1B illustrates use of an inflammation index in relation to the data of FIG. 1A, in accordance with an embodiment of the present invention.

FIG. 2 is a graphical illustration of the same inflammation index calculated at 9 different, significant clinical milestones.

FIG. 3 shows the effects of single dose treatment with 800 mg of ibuprofen in a single donor as characterized by the index.

FIG. 4 shows the calculated acute inflammation index displayed graphically for five different conditions.

FIG. 5 shows a Viral Response Index for monitoring the progress of an upper respiratory infection (URI).

FIGS. 6 and 7 compare two different populations using Gene Expression Profiles (with respect to the 48 loci of the Inflammation Gene Expression Panel of Table 1).

FIG. 8 compares a normal population with a rheumatoid arthritis population derived from a longitudinal study.

FIG. 9 compares two normal populations, one longitudinal and the other cross sectional.

FIG. 10 shows the shows gene expression values for various individuals of a normal population.

FIG. 11 shows the expression levels for each of four genes (of the Inflammation Gene Expression Panel of Table 1), of a single subject, assayed monthly over a period of eight months.

FIGS. 12 and 13 similarly show in each case the expression levels for each of 48 genes (of the Inflammation Gene Expression Panel of Table 1), of distinct single subjects (selected in each case on the basis of feeling well and not taking drugs), assayed, in the case of FIG. 12 weekly over a period of four weeks, and in the case of FIG. 13 monthly over a period of six months.

FIG. 14 shows the effect over time, on inflammatory gene expression in a single human subject., of the administration of an anti-inflammatory steroid, as assayed using the Inflammation Gene Expression Panel of Table 1.

FIG. 17A further illustrates the consistency of inflammatory gene expression in a population.

FIG. 17B shows the normal distribution of index values obtained from an undiagnosed population.

FIG. 18 plots, in a fashion similar to that of FIG. 17A, Gene Expression Profiles, for the same 7 loci as in FIG. 17A, two different (responder v. non-responder) 6-subject populations of rheumatoid arthritis patients.

FIG. 19 thus illustrates use of the inflammation index for assessment of a single subject suffering from rheumatoid arthritis, who has not responded well to traditional therapy with methotrexate.

FIG. 20 similarly illustrates use of the inflammation index for assessment of three subjects suffering from rheumatoid arthritis, who have not responded well to traditional therapy with methotrexate.

Each of FIGS. 21-23 shows the inflammation index for an international group of subjects, suffering from rheumatoid arthritis, undergoing three separate treatment regimens.

FIG. 24 illustrates use of the inflammation index for assessment of a single subject suffering from inflammatory bowel disease.

FIG. 26 illustrates how the effects of two competing anti-inflammatory compounds can be compared objectively, quantitatively, precisely, and reproducibly.

FIGS. 27 through 41 illustrate the use of gene expression panels in early identification and monitoring of infectious disease.

FIG. 27 uses a novel bacterial Gene Expression Panel of 24 genes, developed to discriminate various bacterial conditions in a host biological system.

FIG. 28 shows differential expression for a single locus, IFNG, to LTA derived from three distinct sources: S. pyogenes, B. subtilis, and S. aureus.

FIGS. 29 and 30 show the response after two hours of the Inflammation 48A and 48B loci respectively (discussed above in connection with FIGS. 6 and 7 respectively) in whole blood to administration of a Gram-positive and a Gram-negative organism.

FIGS. 31 and 32 correspond to FIGS. 29 and 30 respectively and are similar to them, with the exception that the monitoring here occurs 6 hours after administration.

FIG. 33 compares the gene expression response induced by E. coli and by an organism-free E. coli filtrate.

FIG. 34 is similar to FIG. 33, but here the compared responses are to stimuli from E. coli filtrate alone and from E. coli filtrate to which has been added polymyxin B.

FIG. 35 illustrates the gene expression responses induced by S. aureus at 2, 6, and 24 hours after administration.

FIGS. 36 through 41 compare the gene expression induced by E. coli and S. aureus under various concentrations and times.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Definitions

For purposes of this description and the following claims, the following terms shall have the meanings indicated unless the context otherwise requires:

“Algorithm” is a set of rules for describing a biological condition. The rule set may be defined exclusively algebraically but may also include alternative or multiple decision points requiring domain-specific knowledge, expert interpretation or other clinical indicators.

An “agent” is a “composition” or a “stimulus”, as those terms are defined herein, or a combination of a composition and a stimulus.

“Amplification” in the context of a quantitative RT-PCR assay is a function of the number of DNA replications that are tracked to provide a quantitative determination of its concentration. “Amplification” here refers to a degree of sensitivity and specificity of a quantitative assay technique. Accordingly, amplification provides a measurement of concentrations of constituents that is evaluated under conditions wherein the efficiency of amplification and therefore the degree of sensitivity and reproducibility for measuring all constituents is substantially similar.

A “baseline profile data set” is a set of values associated with constituents of a Gene Expression Panel resulting from evaluation of a biological sample (or population of samples) under a desired biological condition that is used for mathematically normative purposes. The desired biological condition may be, for example, the condition of a subject (or population of subjects) before exposure to an agent or in the presence of an untreated disease or in the absence of a disease. Alternatively, or in addition, the desired biological condition may be health of a subject or a population of subjects. Alternatively, or in addition, the desired biological condition may be that associated with a population subjects selected on the basis of at least one of age group, gender, ethnicity, geographic location, diet, medical disorder, clinical indicator, medication, physical activity, body mass, and environmental exposure.

A “biological condition” of a subject is the condition of the subject in a pertinent realm that is under observation, and such realm may include any aspect of the subject capable of being monitored for change in condition, such as health, disease including cancer; trauma; aging; infection; tissue degeneration; developmental steps; physical fitness; obesity, and mood. As can be seen, a condition in this context may be chronic or acute or simply transient. Moreover, a targeted biological condition may be manifest throughout the organism or population of cells or may be restricted to a specific organ (such as skin, heart, eye or blood), but in either case, the condition may be monitored directly by a sample of the affected population of cells or indirectly by a sample derived elsewhere from the subject. The term “biological condition” includes a “physiological condition”.

“Bodyfluid” of a subject includes blood, urine, spinal fluid, lymph, mucosal secretions, prostatic fluid, semen, haemolymph or any other body fluid known in the art for a subject.

“Calibrated profile data set” is a function of a member of a first profile data set and a corresponding member of a baseline profile data set for a given constituent in a panel.

A “clinical indicator” is any physiological datum used alone or in conjunction with other data in evaluating the physiological condition of a collection of cells or of an organism. This term includes pre-clinical indicators.

A “composition” includes a chemical compound, a nutriceutical, a pharmaceutical, a homeopathic formulation, an allopathic formulation, a naturopathic formulation, a combination of compounds, a toxin, a food, a food supplement, a mineral, and a complex mixture of substances, in any physical state or in a combination of physical states.

To “derive” a profile data set from a sample includes determining a set of values associated with constituents of a Gene Expression Panel either (i) by direct measurement of such constituents in a biological sample or (ii) by measurement of such constituents in a second biological sample that has been exposed to the original sample or to matter derived from the original sample.

“Distinct RNA or protein constituent” in a panel of constituents is a distinct expressed product of a gene, whether RNA or protein. An “expression” product of a gene includes the gene product whether RNA or protein resulting from translation of the messenger RNA.

A “Gene Expression Panel” is an experimentally verified set of constituents, each constituent being a distinct expressed product of a gene, whether RNA or protein, wherein constituents of the set are selected so that their measurement provides a measurement of a targeted biological condition.

A “Gene Expression Profile” is a set of values associated with constituents of a Gene Expression Panel resulting from evaluation of a biological sample (or population of samples).

A “Gene Expression Profile Inflammatory Index” is the value of an index function that provides a mapping from an instance of a Gene Expression Profile into a single-valued measure of inflammatory condition.

The “health” of a subject includes mental, emotional, physical, spiritual, allopathic, naturopathic and homeopathic condition of the subject.

“Index” is an arithmetically or mathematically derived numerical characteristic developed for aid in simplifying or disclosing or informing the analysis of more complex quantitative information. A disease or population index may be determined by the application of a specific algorithm to a plurality of subjects or samples with a common biological condition.

“Inflammation” is used herein in the general medical sense of the word and may be an acute or chronic; simple or supporative; localized or disseminated; cellular and tissue response, initiated or sustained by any number of chemical, physical or biological agents or combination of agents.

“Inflammatory state” is used to indicate the relative biological condition of a subject resulting from inflammation, or characterizing the degree of inflammation

A “large number” of data sets based on a common panel of genes is a number of data sets sufficiently large to permit a statistically significant conclusion to be drawn with respect to an instance of a data set based on the same panel.

A “normative” condition of a subject to whom a composition is to be administered means the condition of a subject before administration, even if the subject happens to be suffering from a disease.

A “panel” of genes is a set of genes including at least two constituents.

A “sample” from a subject may include a single cell or multiple cells or fragments of cells or an aliquot of body fluid, taken from the subject, by means including venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage sample, scraping, surgical incision or intervention or other means known in the art.

A “Signature Profile” is an experimentally verified subset of a Gene Expression Profile selected to discriminate a biological condition, agent or physiological mechanism of action.

A “Signature Panel” is a subset of a Gene Expression Panel, the constituents of which are selected to permit discrimination of a biological condition, agent or physiological mechanism of action.

A “subject” is a cell, tissue, or organism, human or non-human, whether in vivo, ex vivo or in vitro, under observation. When we refer to evaluating the biological condition of a subject based on a sample from the subject, we include using blood or other tissue sample from a human subject to evaluate the human subject's condition; but we also include, for example, using a blood sample itself as the subject to evaluate, for example, the effect of therapy or an agent upon the sample.

A “stimulus” includes (i) a monitored physical interaction with a subject, for example ultraviolet A or B, or light therapy for seasonal affective disorder, or treatment of psoriasis with psoralen or treatment of melanoma with embedded radioactive seeds, other radiation exposure, and (ii) any monitored physical, mental, emotional, or spiritual activity or inactivity of a subject.

“Therapy” includes all interventions whether biological, chemical, physical, metaphysical, or combination of the foregoing, intended to sustain or alter the monitored biological condition of a subject.

The PCT patent application publication number WO 01/25473, published Apr. 12, 2001, entitled “Systems and Methods for Characterizing a Biological Condition or Agent Using Calibrated Gene Expression Profiles,” filed for an invention by inventors herein, and which is herein incorporated by reference, discloses the use of Gene Expression Panels for the evaluation of (i) biological condition (including with respect to health and disease) and (ii) the effect of one or more agents on biological condition (including with respect to health, toxicity, therapeutic treatment and drug interaction).

In particular, Gene Expression Panels may be used for measurement of therapeutic efficacy of natural or synthetic compositions or stimuli that may be formulated individually or in combinations or mixtures for a range of targeted physiological conditions; prediction of toxicological effects and dose effectiveness of a composition or mixture of compositions for an individual or in a population; determination of how two or more different agents administered in a single treatment might interact so as to detect any of synergistic, additive, negative, neutral or toxic activity; performing pre-clinical and clinical trials by providing new criteria for pre-selecting subjects according to informative profile data sets for revealing disease status; and conducting preliminary dosage studies for these patients prior to conducting phase 1 or 2 trials. These Gene Expression Panels may be employed with respect to samples derived from subjects in order to evaluate their biological condition.

A Gene Expression Panel is selected in a manner so that quantitative measurement of RNA or protein constituents in the Panel constitutes a measurement of a biological condition of a subject. In one kind of arrangement, a calibrated profile data set is employed. Each member of the calibrated profile data set is a function of (i) a measure of a distinct constituent of a Gene Expression Panel and (ii) a baseline quantity.

We have found that valuable and unexpected results may be achieved when the quantitative measurement of constituents is performed under repeatable conditions (within a degree of repeatability of measurement of better than twenty percent, and preferably five percent or better, and more preferably three percent or better). For the purposes of this description and the following claims, we regard a degree of repeatability of measurement of better than twenty percent as providing measurement conditions that are “substantially repeatable”. In particular, it is desirable that, each time a measurement is obtained corresponding to the level of expression of a constituent in a particular sample, substantially the same measurement should result for the substantially the same level of expression. In this manner, expression levels for a constituent in a Gene Expression Panel may be meaningfully compared from sample to sample. Even if the expression level measurements for a particular constituent are inaccurate (for example, say, 30% too low), the criterion of repeatability means that all measurements for this constituent, if skewed, will nevertheless be skewed systematically, and therefore measurements of expression level of the constituent may be compared meaningfully. In this fashion valuable information may be obtained and compared concerning expression of the constituent under varied circumstances.

In addition to the criterion of repeatability, it is desirable that a second criterion also be satisfied, namely that quantitative measurement of constituents is performed under conditions wherein efficiencies of amplification for all constituents are substantially similar (within one to two percent and typically one percent or less). When both of these criteria are satisfied, then measurement of the expression level of one constituent may be meaningfully compared with measurement of the expression level of another constituent in a given sample and from sample to sample.

Present embodiments relate to the use of an index or algorithm resulting from quantitative measurement of constituents, and optionally in addition, derived from either expert analysis or computational biology (a) in the analysis of complex data sets; (b) to control or normalize the influence of uninformative or otherwise minor variances in gene expression values between samples or subjects; (c) to simplify the characterization of a complex data set for comparison to other complex data sets, databases or indices or algorithms derived from complex data sets; (d) to monitor a biological condition of a subject; (e) for measurement of therapeutic efficacy of natural or synthetic compositions or stimuli that may be formulated individually or in combinations or mixtures for a range of targeted physiological conditions; (f) for predictions of toxicological effects and dose effectiveness of a composition or mixture of compositions for an individual or in a population; (g) for determination of how two or more different agents administered in a single treatment might interact so as to detect any of synergistic, additive, negative, neutral of toxic activity (h) for performing pre-clinical and clinical trials by providing new criteria for pre-selecting subjects according to informative profile data sets for revealing disease status and conducting preliminary dosage studies for these patients prior to conducting phase 1 or 2 trials.

Gene expression profiling and the use of index characterization for a particular condition or agent or both may be used to reduce the cost of phase 3 clinical trials and may be used beyond phase 3 trials; labeling for approved drugs; selection of suitable medication in a class of medications for a particular patient that is directed to their unique physiology; diagnosing or determining a prognosis of a medical condition or an infection which may precede onset of symptoms or alternatively diagnosing adverse side effects associated with administration of a therapeutic agent; managing the health care of a patient; and quality control for different batches of an agent or a mixture of agents.

The Subject

The methods disclosed here may be applied to cells of humans, mammals or other organisms without the need for undue experimentation by one of ordinary skill in the art because all cells transcribe RNA and it is known in the art how to extract RNA from all types of cells.

Selecting Constituents of a Gene Expression Panel

The general approach to selecting constituents of a Gene Expression Panel has been described in PCT application publication number WO 01/25473. We have designed and experimentally verified a wide range of Gene Expression Panels, each panel providing a quantitative measure, of biological condition, that is derived from a sample of blood or other tissue. For each panel, experiments have verified that a Gene Expression Profile using the panel's constituents is informative of a biological condition. (We show elsewhere that in being informative of biological condition, the Gene Expression Profile can be used to used, among other things, to measure the effectiveness of therapy, as well as to provide a target for therapeutic intervention.) Examples of Gene Expression Panels, along with a brief description of each panel constituent, are provided in tables attached hereto as follows:

Table 1. Inflammation Gene Expression Panel

Table 2. Diabetes Gene Expression Panel

Table 3. Prostate Gene Expression Panel

Table 4. Skin Response Gene Expression Panel

Table 5. Liver Metabolism and Disease Gene Expression Panel

Table 6. Endothelial Gene Expression Panel

Table 7. Cell Health and Apoptosis Gene Expression Panel

Table 8. Cytokine Gene Expression Panel

Table 9. TNF/IL1 Inhibition Gene Expression Panel

Table 10. Chemokine Gene Expression Panel

Table 11. Breast Cancer Gene Expression Panel

Table 12. Infectious Disease Gene Expression Panel

Other panels may be constructed and experimentally verified by one of ordinary skill in the art in accordance with the principles articulated in the present application.

Design of Assays

We commonly run a sample through a panel in quadruplicate; that is, a sample is divided into aliquots and for each aliquot we measure concentrations of each constituent in a Gene Expression Panel. Over a total of 900 constituent assays, with each assay conducted in quadruplicate, we found an average coefficient of variation, (standard deviation/average)*100, of less than 2 percent, typically less than 1 percent, among results for each assay. This figure is a measure of what we call “intra-assay variability”. We have also conducted assays on different occasions using the same sample material. With 72 assays, resulting from concentration measurements of constituents in a panel of 24 members, and such concentration measurements determined on three different occasions over time, we found an average coefficient of variation of less than 5 percent, typically less than 2 percent. We regard this as a measure of what we call “inter-assay variability”.

We have found it valuable in using the quadruplicate test results to identify and eliminate data points that are statistical “outliers”; such data points are those that differ by a percentage greater, for example, than 3% of the average of all four values and that do not result from any systematic skew that is greater, for example, than 1%. Moreover, if more than one data point in a set of four is excluded by this procedure, then all data for the relevant constituent is discarded.

Measurement of Gene Expression for a Constituent in the Panel

For measuring the amount of a particular RNA in a sample, we have used methods known to one of ordinary skill in the art to extract and quantify transcribed RNA from a sample with respect to a constituent of a Gene Expression Panel. (See detailed protocols below. Also see PCT application publication number WO 98/24935 herein incorporated by reference for RNA analysis protocols). Briefly, RNA is extracted from a sample such as a tissue, body fluid, or culture medium in which a population of a subject might be growing. For example, cells may be lysed and RNA eluted in a suitable solution in which to conduct a DNAse reaction. First strand synthesis may be performed using a reverse transcriptase. Gene amplification, more specifically quantitative PCR assays, can then conducted and the gene of interest size calibrated against a marker such as 18S rRNA (Hirayama et al., Blood 92, 1998: 46-52). Samples are measured in multiple duplicates, for example, 4 replicates. Relative quantitation of the mRNA is determined by the difference in threshhold cycles between the internal control and the gene of interest. In an embodiment of the invention, quantitative PCR is performed using amplification, reporting agents and instruments such as those supplied commercially by Applied Biosystems (Foster City, Calif.). Given a defined efficiency of amplification of target transcripts, the point (e.g., cycle number) that signal from amplified target template is detectable may be directly related to the amount of specific message transcript in the measured sample. Similarly, other quantifiable signals such as fluorescence, enzyme activity, disintegrations per minute, absorbance, etc., when correlated to a known concentration of target templates (e.g., a reference standard curve) or normalized to a standard with limited variability can be used to quantify the number of target templates in an unknown sample.

Although not limited to amplification methods, quantitative gene expression techniques may utilize amplification of the target transcript. Alternatively or in combination with amplification of the target transcript, amplification of the reporter signal may also be used. Amplification of the target template may be accomplished by isothermic gene amplification strategies, or by gene amplification by thermal cycling such as PCR.

It is desirable to obtain a definable and reproducible correlation between the amplified target or reporter and the concentration of starting templates. We have discovered that this objective can be achieved by careful attention to, for example, consistent primer-template ratios and a strict adherence to a narrow permissible level of experimental amplification efficiencies (for example 99.0 to 100% relative efficiency, typically 99.8 to 100% relative efficiency). For example, in determining gene expression levels with regard to a single Gene Expression Profile, it is necessary that all constituents of the panels maintain a similar and limited range of primer template ratios (for example, within a 10-fold range) and amplification efficiencies (within, for example, less than 1%) to permit accurate and precise relative measurements for each constituent. We regard amplification efficiencies as being “substantially similar”, for the purposes of this description and the following claims, if they differ by no more than approximately 10%. Preferably they should differ by less than approximately 2% and more preferably by less than approximately 1%. These constraints should be observed over the entire range of concentration levels to be measured associated with the relevant biological condition. While it is thus necessary for various embodiments herein to satisfy criteria that measurements are achieved under measurement conditions that are substantially repeatable and wherein specificity and efficiencies of amplification for all constituents are substantially similar, nevertheless, it is within the scope of the present invention as claimed herein to achieve such measurement conditions by adjusting assay results that do not satisfy these criteria directly, in such a manner as to compensate for errors, so that the criteria are satisfied after suitable adjustment of assay results.

In practice, we run tests to assure that these conditions are satisfied. For example, we typically design and manufacture a number of primer-probe sets, and determine experimentally which set gives the best performance. Even though primer-probe design and manufacture can be enhanced using computer techniques known in the art, and notwithstanding common practice, we still find that experimental validation is useful. Moreover, in the course of experimental validation, we associate with the selected primer-probe combination a set of features:

The reverse primer should be complementary to the coding DNA strand. In one embodiment, the primer should be located across an intron-exon junction, with not more than three bases of the three-prime end of the reverse primer complementary to the proximal exon. (If more than three bases are complementary, then it would tend to competitively amplify genomic DNA.)

In an embodiment of the invention, the primer probe should amplify cDNA of less than 110 bases in length and should not amplify genomic DNA or transcripts or cDNA from related but biologically irrelevant loci.

A suitable target of the selected primer probe is first strand cDNA, which may be prepared, in one embodiment, is described as follows:

(a) Use of whole blood for ex vivo assessment of a biological condition affected by an agent.

Human blood is obtained by venipuncture and prepared for assay by separating samples for baseline, no stimulus, and stimulus with sufficient volume for at least three time points. Typical stimuli include lipopolysaccharide (LPS), phytohemagglutinin (PHA) and heat-killed staphylococci (HKS) or carrageean and may be used individually (typically) or in combination. The aliquots of heparinized, whole blood are mixed without stimulus and held at 37° C. in an atmosphere of 5% CO2 for 30 minutes. Stimulus is added at varying concentrations, mixed and held loosely capped at 37° C. for 30 min. Additional test compounds may be added at this point and held for varying times depending on the expected pharmacokinetics of the test compound. At defined times, cells are collected by centrifugation, the plasma removed and RNA extracted by various standard means.

Nucleic acids, RNA and or DNA are purified from cells, tissues or fluids of the test population or indicator cell lines. RNA is preferentially obtained from the nucleic acid mix using a variety of standard procedures (or RNA Isolation Strategies, pp. 55-104, in RNA Methodologies, A laboratory guide for isolation and characterization, 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press), in the present using a filter-based RNA isolation system from Ambion (RNAqueous™, Phenol-free Total RNA Isolation Kit, Catalog #1912, version 9908; Austin, Tex.).

In accordance with one procedure, the whole blood assay for Gene Expression Profiles determination was carried out as follows: Human whole blood was drawn into 10 mL Vacutainer tubes with Sodium Heparin. Blood samples were mixed by gently inverting tubes 4-5 times. The blood was used within 10-15 minutes of draw. In the experiments, blood was diluted 2-fold, i.e. per sample per time point, 0.6 mL whole blood +0.6 mL stimulus. The assay medium was prepared and the stimulus added as appropriate.

A quantity (0.6 mL) of whole blood was then added into each 12×75 mm polypropylene tube. 0.6 mL of 2× LPS (from E. coli serotye 0127:B8, Sigma#L3880 or serotype 055, Sigma #L4005, 10 ng/ml, subject to change in different lots) into LPS tubes was added. Next, 0.6 mL assay medium was added to the “control” tubes with duplicate tubes for each condition. The caps were closed tightly. The tubes were inverted 2-3 times to mix samples. Caps were loosened to first stop and the tubes incubated @ 37° C., 5% CO2 for 6 hours. At 6 hours, samples were gently mixed to resuspend blood cells, and 1 mL was removed from each tube (using a micropipettor with barrier tip), and transfered to a 2 mL “dolphin” microfuge tube (Costar #3213).

The samples were then centrifuged for 5 min at 500×g, ambient temperature (IEC centrifuge or equivalent, in microfuge tube adapters in swinging bucket), and as much serum from each tube was removed as possible and discarded. Cell pellets were placed on ice; and RNA extracted as soon as possible using an Ambion RNAqueous kit.

(b) Amplification Strategies.

Specific RNAs are amplified using message specific primers or random primers. The specific primers are synthesized from data obtained from public databases (e.g., Unigene, National Center for Biotechnology Information, National Library of Medicine, Bethesda, Md.), including information from genomic and cDNA libraries obtained from humans and other animals. Primers are chosen to preferentially amplify from specific RNAs obtained from the test or indicator samples, see, for example, RT PCR, Chapter 15 in RNA Methodologies, A laboratory guide for isolation and characterization, 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press; or Chapter 22 pp.143-151, RNA isolation and characterization protocols, Methods in molecular biology, Volume 86, 1998, R. Rapley and D. L. Manning Eds., Human Press, or 14 in Statistical refinement of primer design parameters, Chapter 5, pp.55-72, PCR applications: protocols for functional genomics, M. A. Innis, D. H. Gelfand and J. J. Sninsky, Eds., 1999, Academic Press). Amplifications are carried out in either isothermic conditions or using a thermal cycler (for example, a ABI 9600 or 9700 or 7700 obtained from Applied Biosystems, Foster City, Calif.; see Nucleic acid detection methods, pp. 1-24, in Molecular methods for virus detection, D. L. Wiedbrauk and D. H., Farkas, Eds., 1995, Academic Press). Amplified nucleic acids are detected using fluorescent-tagged detection primers (see, for example, Taqman™ PCR Reagent Kit, Protocol, part number 402823 revision A, 1996, Applied Biosystems, Foster City Calif.) that are identified and synthesized from publicly known databases as described for the amplification primers. In the present case, amplified DNA is detected and quantified using the ABI Prism 7700 Sequence Detection System obtained from Applied Biosystems (Foster City, Calif.). Amounts of specific RNAs contained in the test sample or obtained from the indicator cell lines can be related to the relative quantity of fluorescence observed (see for example, Advances in quantitative PCR technology: 5′ nuclease assays, Y. S. Lie and C. J. Petropolus, Current Opinion in Biotechnology, 1998, 9:43-48, or Rapid thermal cycling and PCR kinetics, pp. 211-229, chapter 14 in PCR applications: protocols for functional genomics, M. A. Innis, D. H. Gelfand and J. J. Sninsky, Eds., 1999, Academic Press).

As a particular implementation of the approach described here, we describe in detail a procedure for synthesis of first strand cDNA for use in PCR. This procedure can be used for both whole blood RNA and RNA extracted from cultured cells (i.e. THP-1 cells).

Materials

1. Applied Biosystems TAQMAN Reverse Transcription Reagents Kit (P/N 808-0234). Kit Components: 10× TaqMan RT Buffer, 25 mM Magnesium chloride, deoxyNTPs mixture, Random Hexamers, RNase Inhibitor, MultiScribe Reverse Transcriptase (50 U/mL) (2) RNase/DNase free water (DEPC Treated Water from Ambion (P/N 9915G), or equivalent)

Methods

1. Place RNase Inhibitor and MultiScribe Reverse Transcriptase on ice immediately. All other reagents can be thawed at room temperature and then placed on ice.

2. Remove RNA samples from −80° C. freezer and thaw at room temperature and then place immediately on ice.

3. Prepare the following cocktail of Reverse Transcriptase Reagents for each 100 mL RT reaction (for multiple samples, prepare extra cocktail to allow for pipetting error):

1 reaction (mL) 11X, e.g. 10 samples (mL)

10X RT Buffer
10.0
110.0

25 mM MgCl2
22.0
242.0

dNTPs
20.0
220.0

Random Hexamers
5.0
55.0

RNAse Inhibitor
2.0
22.0

Reverse Transcriptase
2.5
27.5

Water
18.5
203.5

Total:
80.0
880.0
(80 mL per sample)

4. Bring each RNA sample to a total volume of 20 mL in a 1.5 mL microcentrifuge tube (for example, for THP-1 RNA, remove 10 mL RNA and dilute to 20 mL with RNase/DNase free water, for whole blood RNA use 20 mL total RNA) and add 80 mL RT reaction mix from step 5, 2, 3. Mix by pipetting up and down.

5. Incubate sample at room temperature for 10 minutes.

6. Incubate sample at 37° C. for 1 hour.

7. Incubate sample at 90° C. for 10 minutes.

8. Quick spin samples in microcentrifuge.

9. Place sample on ice if doing PCR immediately, otherwise store sample at 20° C. for future use.

10. PCR QC should be run on all RT samples using 18S and b-actin (see SOP 200-020).

The use of the primer probe with the first strand cDNA as described above to permit measurement of constituents of a Gene Expression Panel is as follows:

Set up of a 24-gene Human Gene Expression Panel for Inflammation.

Materials

1. 20× Primer/Probe Mix for each gene of interest.

2. 20× Primer/Probe Mix for 18S endogenous control.

3. 2× Taqman Universal PCR Master Mix.

4. cDNA transcribed from RNA extracted from cells.

5. Applied Biosystems 96-Well Optical Reaction Plates.

6. Applied Biosystems Optical Caps, or optical-clear film.

7. Applied Biosystem Prism 7700 Sequence Detector.

Methods

1. Make stocks of each Primer/Probe mix containing the Primer/Probe for the gene of interest, Primer/Probe for 18S endogenous control, and 2× PCR Master Mix as follows. Make sufficient excess to allow for pipetting error e.g. approximately 10% excess. The following example illustrates a typical set up for one gene with quadruplicate samples testing two conditions (2 plates).

9X

1X (1 well)
(2 plates worth)

2X Master Mix
12.50
112.50

20X 18S Primer/Probe Mix
1.25
11.25

20X Gene of interest Primer/Probe Mix
1.25
11.25

Total
15.00
135.00

2. Make stocks of cDNA targets by diluting 95 μl of cDNA into 2000 μl of water. The amount of cDNA is adjusted to give Ct values between 10 and 18, typically between 12 and 13.

3. Pipette 15 μl of Primer/Probe mix into the appropriate wells of an Applied Biosystems 96-Well Optical Reaction Plate.

4. Pipette 10 μl of cDNA stock solution into each well of the Applied Biosystems 96-Well Optical Reaction Plate.

5. Seal the plate with Applied Biosystems Optical Caps, or optical-clear film.

6. Analyze the plate on the AB Prism 7700 Sequence Detector.

Methods herein may also be applied using proteins where sensitive quantitative techniques, such as an Enzyme Linked ImmunoSorbent Assay (ELISA) or mass spectroscopy, are available and well-known in the art for measuring the amount of a protein constituent. (see WO 98/24935 herein incorporated by reference).

Baseline Profile Data Sets

The analyses of samples from single individuals and from large groups of individuals provide a library of profile data sets relating to a particular panel or series of panels. These profile data sets may be stored as records in a library for use as baseline profile data sets. As the term “baseline” suggests, the stored baseline profile data sets serve as comparators for providing a calibrated profile data set that is informative about a biological condition or agent. Baseline profile data sets may be stored in libraries and classified in a number of cross-referential ways. One form of classification may rely on the characteristics of the panels from which the data sets are derived. Another form of classification may be by particular biological condition. The concept of biological condition encompasses any state in which a cell or population of cells may be found at any one time. This state may reflect geography of samples, sex of subjects or any other discriminator. Some of the discriminators may overlap. The libraries may also be accessed for records associated with a single subject or particular clinical trial. The classification of baseline profile data sets may further be annotated with medical information about a particular subject, a medical condition, a particular agent etc.

The choice of a baseline profile data set for creating a calibrated profile data set is related to the biological condition to be evaluated, monitored, or predicted, as well as, the intended use of the calibrated panel, e.g., as to monitor drug development, quality control or other uses. It may be desirable to access baseline profile data sets from the same subject for whom a first profile data set is obtained or from different subject at varying times, exposures to stimuli, drugs or complex compounds; or may be derived from like or dissimilar populations.

The profile data set may arise from the same subject for which the first data set is obtained, where the sample is taken at a separate or similar time, a different or similar site or in a different or similar physiological condition. For example, FIG. 5 provides a protocol in which the sample is taken before stimulation or after stimulation. The profile data set obtained from the unstimulated sample may serve as a baseline profile data set for the sample taken after stimulation. The baseline data set may also be derived from a library containing profile data sets of a population of subjects having some defining characteristic or biological condition. The baseline profile data set may also correspond to some ex vivo or in vitro properties associated with an in vitro cell culture. The resultant calibrated profile data sets may then be stored as a record in a database or library (FIG. 6) along with or separate from the baseline profile data base and optionally the first profile data set although the first profile data set would normally become incorporated into a baseline profile data set under suitable classification criteria. The remarkable consistency of Gene Expression Profiles associated with a given biological condition makes it valuable to store profile data, which can be used, among other things for normative reference purposes. The normative reference can serve to indicate the degree to which a subject conforms to a given biological condition (healthy or diseased) and, alternatively or in addition, to provide a target for clinical intervention.

Selected baseline profile data sets may be also be used as a standard by which to judge manufacturing lots in terms of efficacy, toxicity, etc. Where the effect of a therapeutic agent is being measured, the baseline data set may correspond to Gene Expression Profiles taken before administration of the agent. Where quality control for a newly manufactured product is being determined, the baseline data set may correspond with a gold standard for that product. However, any suitable normalization techniques may be employed. For example, an average baseline profile data set is obtained from authentic material of a naturally grown herbal nutriceutical and compared over time and over different lots in order to demonstrate consistency, or lack of consistency, in lots of compounds prepared for release.

Calibrated Data

Given the repeatability we have achieved in measurement of gene expression, described above in connection with “Gene Expression Panels” and “gene amplification”, we conclude that where differences occur in measurement under such conditions, the differences are attributable to differences in biological condition. Thus we have found that calibrated profile data sets are highly reproducible in samples taken from the same individual under the same conditions. We have similarly found that calibrated profile data sets are reproducible in samples that are repeatedly tested. We have also found repeated instances wherein calibrated profile data sets obtained when samples from a subject are exposed ex vivo to a compound are comparable to calibrated profile data from a sample that has been exposed to a sample in vivo. We have also found, importantly, that an indicator cell line treated with an agent can in many cases provide calibrated profile data sets comparable to those obtained from in vivo or ex vivo populations of cells. Moreover, we have found that administering a sample from a subject onto indicator cells can provide informative calibrated profile data sets with respect to the biological condition of the subject including the health, disease states, therapeutic interventions, aging or exposure to environmental stimuli or toxins of the subject.

Calculation of Calibrated Profile Data Sets and Computational Aids

The calibrated profile data set may be expressed in a spreadsheet or represented graphically for example, in a bar chart or tabular form but may also be expressed in a three dimensional representation. The function relating the baseline and profile data may be a ratio expressed as a logarithm. The constituent may be itemized on the x-axis and the logarithmic scale may be on the y-axis. Members of a calibrated data set may be expressed as a positive value representing a relative enhancement of gene expression or as a negative value representing a relative reduction in gene expression with respect to the baseline.

Each member of the calibrated profile data set should be reproducible within a range with respect to similar samples taken from the subject under similar conditions. For example, the calibrated profile data sets may be reproducible within one order of magnitude with respect to similar samples taken from the subject under similar conditions. More particularly, the members may be reproducible within 50%, more particularly reproducible within 20%, and typically within 10%. In accordance with embodiments of the invention, a pattern of increasing, decreasing and no change in relative gene expression from each of a plurality of gene loci examined in the Gene Expression Panel may be used to prepare a calibrated profile set that is informative with regards to a biological condition, biological efficacy of an agent treatment conditions or for comparison to populations. Patterns of this nature may be used to identify likely candidates for a drug trial, used alone or in combination with other clinical indicators to be diagnostic or prognostic with respect to a biological condition or may be used to guide the development of a pharmaceutical or nutriceutical through manufacture, testing and marketing.

The numerical data obtained from quantitative gene expression and numerical data from calibrated gene expression relative to a baseline profile data set may be stored in databases or digital storage mediums and may retrieved for purposes including managing patient health care or for conducting clinical trials or for characterizing a drug. The data may be transferred in physical or wireless networks via the World Wide Web, email, or internet access site for example or by hard copy so as to be collected and pooled from distant geographic sites (FIG. 8).

In an embodiment of the present invention, a descriptive record is stored in a single database or multiple databases where the stored data includes the raw gene expression data (first profile data set) prior to transformation by use of a baseline profile data set, as well as a record of the baseline profile data set used to generate the calibrated profile data set including for example, annotations regarding whether the baseline profile data set is derived from a particular Signature Panel and any other annotation that facilitates interpretation and use of the data.

Because the data is in a universal format, data handling may readily be done with a computer. The data is organized so as to provide an output optionally corresponding to a graphical representation of a calibrated data set.

For example, a distinct sample derived from a subject being at least one of RNA or protein may be denoted as P_I. The first profile data set derived from sample P_Iis denoted M_J, where M_Jis a quantitative measure of a distinct RNA or protein constituent of P_I. The record Ri is a ratio of M and P and may be annotated with additional data on the subject relating to, for example, age, diet, ethnicity, gender, geographic location, medical disorder, mental disorder, medication, physical activity, body mass and environmental exposure. Moreover, data handling may further include accessing data from a second condition database which may contain additional medical data not presently held with the calibrated profile data sets. In this context, data access may be via a computer network.

The above described data storage on a computer may provide the information in a form that can be accessed by a user. Accordingly, the user may load the information onto a second access site including downloading the information. However, access may be restricted to users having a password or other security device so as to protect the medical records contained within. A feature of this embodiment of the invention is the ability of a user to add new or annotated records to the data set so the records become part of the biological information.

The graphical representation of calibrated profile data sets pertaining to a product such as a drug provides an opportunity for standardizing a product by means of the calibrated profile, more particularly a signature profile. The profile may be used as a feature with which to demonstrate relative efficacy, differences in mechanisms of actions, etc. compared to other drugs approved for similar or different uses.

The various embodiments of the invention may be also implemented as a computer program product for use with a computer system. The product may include program code for deriving a first profile data set and for producing calibrated profiles. Such implementation may include a series of computer instructions fixed either on a tangible medium, such as a computer readable medium (for example, a diskette, CD-ROM, ROM, or fixed disk), or transmittable to a computer system via a modem or other interface device, such as a communications adapter coupled to a network. The network coupling may be for example, over optical or wired communications lines or via wireless techniques (for example, microwave, infrared or other transmission techniques) or some combination of these. The series of computer instructions preferably embodies all or part of the functionality previously described herein with respect to the system. Those skilled in the art should appreciate that such computer instructions can be written in a number of programming languages for use with many computer architectures or operating systems. Furthermore, such instructions may be stored in any memory device, such as semiconductor, magnetic, optical or other memory devices, and may be transmitted using any communications technology, such as optical, infrared, microwave, or other transmission technologies. It is expected that such a computer program product may be distributed as a removable medium with accompanying printed or electronic documentation (for example, shrink wrapped software), preloaded with a computer system (for example, on system ROM or fixed disk), or distributed from a server or electronic bulletin board over a network (for example, the Internet or World Wide Web). In addition, a computer system is further provided including derivative modules for deriving a first data set and a calibration profile data set.

The calibration profile data sets in graphical or tabular form, the associated databases, and the calculated index or derived algorithm, together with information extracted from the panels, the databases, the data sets or the indices or algorithms are commodities that can be sold together or separately for a variety of purposes as described in WO 01/25473.

Index Construction

In combination, (i) the remarkable consistency of Gene Expression Profiles with respect to a biological condition across a population and (ii) the use of procedures that provide substantially reproducible measurement of constituents in a Gene Expression Panel giving rise to a Gene Expression Profile, under measurement conditions wherein specificity and efficiencies of amplification for all constituents of the panel are substantially similar, make possible the use of an index that characterizes a Gene Expression Profile, and which therefore provides a measurement of a biological condition.

An index may be constructed using an index function that maps values in a Gene Expression Profile into a single value that is pertinent to the biological condition at hand. The values in a Gene Expression Profile are the amounts of each constituent of the Gene Expression Panel that corresponds to the Gene Expression Profile. These constituent amounts form a profile data set, and the index function generates a single value—the index—from the members of the profile data set.

The index function may conveniently be constructed as a linear sum of terms, each term being what we call a “contribution function” of a member of the profile data set. For example, the contribution function may be a constant times a power of a member of the profile data set. So the index function would have the form

I=ΣC_iM_i^P(i),

where I is the index, M_iis the value of the member i of the profile data set, C_iis a constant, and P(i) is a power to which M, is raised, the sum being formed for all integral values of i up to the number of members in the data set. We thus have a linear polynomial expression.

The values C_iand P(i) may be determined in a number of ways, so that the index I is informative of the pertinent biological condition. One way is to apply statistical techniques, such as latent class modeling, to the profile data sets to correlate clinical data or experimentally derived data, or other data pertinent to the biological condition. In this connection, for example, may be employed the software from Statistical Innovations, Belmont, Mass., called Latent Gold®. See the web pages at statisticalinnovations.com/lg/, which are hereby incorporated herein by reference.

Alternatively, other simpler modeling techniques may be employed in a manner known in the art. The index function for inflammation may be constructed, for example, in a manner that a greater degree of inflammation (as determined by the a profile data set for the Inflammation Gene Expression Profile) correlates with a large value of the index function. In a simple embodiment, therefore, each P(i) may be +1 or −1, depending on whether the constituent increases or decreases with increasing inflammation. As discussed in further detail below, we have constructed a meaningful inflammation index that is proportional to the expression

¼{ILIA}+¼{ILIB}+¼{TNF}+¼{INFG}−1/{IL10},

where the braces around a constituent designate measurement of such constituent and the constituents are a subset of the Inflammation Gene Expression Panel of Table 1.

Just as a baseline profile data set, discussed above, can be used to provide an appropriate normative reference, and can even be used to create a Calibrated profile data set, as discussed above, based on the normative reference, an index that characterizes a Gene Expression Profile can also be provided with a normative value of the index function used to create the index. This normative value can be determined with respect to a relevant population, so that the index may be interpreted in relation to the normative value. The relevant population may have in common a property that is at least one of age group, gender, ethnicity, geographic location, diet, medical disorder, clinical indicator, medication, physical activity, body mass, and environmental exposure.

As an example, the index can be constructed, in relation to a normative Gene Expression Profile for a population of healthy subjects, in such a way that a reading of approximately 1 characterizes normative Gene Expression Profiles of healthy subjects. Let us further assume that the biological condition that is the subject of the index is inflammation; a reading of 1 in this example thus corresponds to a Gene Expression Profile that matches the norm for healthy subjects. A substantially higher reading then may identify a subject experiencing an inflammatory condition. The use of 1 as identifying a normative value, however, is only one possible choice; another logical choice is to use 0 as identifying the normative value. With this choice, deviations in the index from zero can be indicated in standard deviation units (so that values lying between −1 and +1 encompass 90% of a normally distributed reference population. Since we have found that Gene Expression Profile values (and accordingly constructed indices based on them) tend to be normally distributed, the 0-centered index constructed in this manner is highly informative. It therefore facilitates use of the index in diagnosis of disease and setting objectives for treatment. The choice of 0 for the normative value, and the use of standard deviation units, for example, are illustrated in FIG. 17B, discussed below.

EXAMPLES
Example 1

Acute inflammatory index to assist in analysis of large, complex data sets.

In one embodiment of the invention the index value or algorithm can be used to reduce a complex data set to a single index value that is informative with respect to the inflammatory state of a subject. This is illustrated in FIGS. 1A and 1B.

FIG. 1A is entitled Source Precision Inflammation Profile Tracking of A Subject Results in a Large, Complex Data Set. The figure shows the results of assaying 24 genes from the Inflammation Gene Expression Panel (shown in Table 1) on eight separate days during the course of optic neuritis in a single male subject.

FIG. 1B shows use of an Acute Inflammation Index. The data displayed in FIG. 1A above is shown in this figure after calculation using an index function proportional to the following mathematical expression: (¼{IL1A}+¼{IL1B}+¼{TNF}+¼{INFG}−1/{IL10}).

Example 2

Use of acute inflammation index or algorithm to monitor a biological condition of a sample or a subject. The inflammatory state of a subject reveals information about the past progress of the biological condition, future progress, response to treatment, etc. The Acute Inflammation Index may be used to reveal such information about the biological condition of a subject. This is illustrated in FIG. 2.

The results of the assay for inflammatory gene expression for each day (shown for 24 genes in each row of FIG. 1A) is displayed as an individual histogram after calculation. The index reveals clear trends in inflammatory status that may correlated with therapeutic intervention (FIG. 2).

FIG. 2 is a graphical illustration of the acute inflammation index calculated at 9 different, significant clinical milestones from blood obtained from a single patient treated medically with for optic neuritis. Changes in the index values for the Acute Inflammation Index correlate strongly with the expected effects of therapeutic intervention. Four clinical milestones have been identified on top of the Acute Inflammation Index in this figure including (1) prior to treatment with steroids, (2) treatment with IV solumedrol at 1 gram per day, (3) post-treatment with oral prednisone at 60 mg per day tapered to 10 mg per day and (4) post treatment. The data set is the same as for FIG. 1. The index is proportional to 1/4{IL1A}+1/4{IL1B}+1/4{TNF}+1/4{INFG}−1/{IL10}. As expected, the acute inflammation index falls rapidly with treatment with IV steroid, goes up during less efficacious treatment with oral prednisone and returns to the pre-treatment level after the steroids have been discontinued and metabolized completely.

Example 3

Use of the acute inflammatory index to set dose, including concentrations and timing, for compounds in development or for compounds to be tested in human and non-human subjects as shown in FIG. 3. The acute inflammation index may be used as a common reference value for therapeutic compounds or interventions without common mechanisms of action. The compound that induces a gene response to a compound as indicated by the index, but fails to ameliorate a known biological conditions may be compared to a different compounds with varying effectiveness in treating the biological condition.

FIG. 3 shows the effects of single dose treatment with 800 mg of ibuprofen in a single donor as characterized by the Acute Inflammation Index. 800 mg of over-the-counter ibuprofen were taken by a single subject at Time=0 and Time=48 hr. Gene expression values for the indicated five inflammation-related gene loci were determined as described below at times=2, 4, 6, 48, 50, 56 and 96 hours. As expected the acute inflammation index falls immediately after taking the non-steroidal anti-inflammatory ibuprofen and returns to baseline after 48 hours. A second dose at T=48 follows the same kinetics at the first dose and returns to baseline at the end of the experiment at T=96.

Example 4

Use of the acute inflammation index to characterize efficacy, safety, and mode of physiological action for an agent, which may be in development and/or may be complex in nature. This is illustrated in FIG. 4.

FIG. 4 shows that the calculated acute inflammation index displayed graphically for five different conditions including (A) untreated whole blood; (B) whole blood treated in vitro with DMSO, an non-active carrier compound; (C) otherwise unstimulated whole blood treated in vitro with dexamethasone (0.08 ug/ml); (D) whole blood stimulated in vitro with lipopolysaccharide, a known pro-inflammatory compound, (LPS, 1 ng/ml) and (E) whole blood treated in vitro with LPS (1 ng/ml) and dexamethasone (0.08 ug/ml). Dexamethasone is used as a prescription compound that is commonly used medically as an anti-inflammatory steroid compound. The acute inflammation index is calculated from the experimentally determined gene expression levels of inflammation-related genes expressed in human whole blood obtained from a single patient. Results of mRNA expression are expressed as Ct's in this example, but may be expressed as, e.g., relative fluorescence units, copy number or any other quantifiable, precise and calibrated form, for the genes IL1A, IL1B, TNF, IFNG and IL10. From the gene expression values, the acute inflammation values were determined algebraically according in proportion to the expression ¼{IL1A}+¼{IL1B}+¼{TNF}+¼{INFG}−1/{IL10}.

Example 5

Development and use of population normative values for gene expression profiles. FIGS. 6 and 7 show the arithmetic mean values for gene expression profiles (using the 48 loci of the Inflammation Gene Expression Panel of Table 1) obtained from whole blood of two distinct patient populations. These populations are both normal or undiagnosed. The first population, which is identified as Bonfils (the plot points for which are represented by diamonds), is composed of 17 subjects accepted as blood donors at the Bonfils Blood Center in Denver, Colo. The second population is 9 donors, for which Gene Expression Profiles were obtained from assays conducted four times over a four-week period. Subjects in this second population (plot points for which are represented by squares) were recruited from employees of Source Precision Medicine, Inc., the assignee herein. Gene expression averages for each population were calculated for each of 48 gene loci of the Gene Expression Inflammation Panel. The results for loci 1-24 (sometimes referred to below as the Inflammation 48A loci) are shown in FIG. 6 and for loci 25-48 (sometimes referred to below as the Inflammation 48B loci) are shown in FIG. 7.

The consistency between gene expression levels of the two distinct populations is dramatic. Both populations show gene expressions for each of the 48 loci that are not significantly different from each other. This observation suggests that there is a “normal” expression pattern for human inflammatory genes, that a Gene Expression Profile, using the Inflammation Gene Expression Panel of Table 1 (or a subset thereof) characterizes that expression pattern, and that a population-normal expression pattern can be used, for example, to guide medical intervention for any biological condition that results in a change from the normal expression pattern.

In a similar vein, FIG. 8 shows arithmetic mean values for gene expression profiles (again using the 48 loci of the Inflammation Gene Expression Panel of Table 1) also obtained from whole blood of two distinct patient populations. One population, expression values for which are represented by triangular data points, is 24 normal, undiagnosed subjects (who therefore have no known inflammatory disease). The other population, the expression values for which are represented by diamond-shaped data points, is four patients with rheumatoid arthritis and who have failed therapy (who therefore have unstable rheumatoid arthritis).

As remarkable as the consistency of data from the two distinct normal populations shown in FIGS. 6 and 7 is the systematic divergence of data from the normal and diseased populations shown in FIG. 8. In 45 of the shown 48 inflammatory gene loci, subjects with unstable rheumatoid arthritis showed, on average, increased inflammatory gene expression (lower cycle threshold values; Ct), than subjects without disease. The data thus further demonstrate that is possible to identify groups with specific biological conditions using gene expression if the precision and calibration of the underlying assay are carefully designed and controlled according to the teachings herein.

FIG. 9, in a manner analogous to FIG. 8, shows the shows arithmetic mean values for gene expression profiles using 24 loci of the Inflammation Gene Expression Panel of Table 1) also obtained from whole blood of two distinct patient populations. One population, expression values for which are represented by diamond-shaped data points, is 17 normal, undiagnosed subjects (who therefore have no known inflammatory disease) who are blood donors. The other population, the expression values for which are represented by square-shaped data points, is 16 subjects, also normal and undiagnosed, who have been monitored over six months, and the averages of these expression values are represented by the square-shaped data points. Thus the cross-sectional gene expression-value averages of a first healthy population match closely the longitudinal gene expression-value averages of a second healthy population., with approximately 7% or less variation in measured expression value on a gene-to-gene basis.

FIG. 10 shows the shows gene expression values (using 14 loci of the Inflammation Gene Expression Panel of Table 1) obtained from whole blood of 44 normal undiagnosed blood donors (data for 10 subjects of which is shown). Again, the gene expression values for each member of the population are closely matched to those for the population, represented visually by the consistent peak heights for each of the gene loci. Other subjects of the population and other gene loci than those depicted here display results that are consistent with those shown here.

In consequence of these principles, and in various embodiments of the present invention, population normative values for a Gene Expression Profile can be used in comparative assessment of individual subjects as to biological condition, including both for purposes of health and/or disease. In one embodiment the normative values for a Gene Expression Profile may be used as a baseline in computing a “calibrated profile data set” (as defined at the beginning of this section) for a subject that reveals the deviation of such subject's gene expression from population normative values. Population normative values for a Gene Expression Profile can also be used as baseline values in constructing index functions in accordance with embodiments of the present invention. As a result, for example, an index function can be constructed to reveal not only the extent of an individual's inflammation expression generally but also in relation to normative values.

Example 6

Consistency of expression values, of constituents in Gene Expression Panels, over time as reliable indicators of biological condition. FIG. 11 shows the expression levels for each of four genes (of the Inflammation Gene Expression Panel of Table 1), of a single subject, assayed monthly over a period of eight months. It can be seen that the expression levels are remarkably consistent over time.

FIG. 14 also shows the effect over time, on inflammatory gene expression in a single human subject, of the administration of an anti-inflammatory steroid, as assayed using the Inflammation Gene Expression Panel of Table 1. In this case, 24 of 48 loci are displayed. The subject had a baseline blood sample drawn in a PAX RNA isolation tube and then took a single 60 mg dose of prednisone, an anti-inflammatory, prescription steroid. Additional blood samples were drawn at 2 hr and 24 hr post the single oral dose. Results for gene expression are displayed for all three time points, wherein values for the baseline sample are shown as unity on the x-axis. As expected, oral treatment with prednisone resulted in the decreased expression of most of inflammation-related gene loci, as shown by the 2-hour post-administration bar graphs. However, the 24-hour post-administration bar graphs show that, for most of the gene loci having reduced gene expression at 2 hours, there were elevated gene expression levels at 24 hr.

Although the baseline in FIG. 14 is based on the gene expression values before drug intervention associated with the single individual tested, we know from the previous example, that healthy individuals tend toward population normative values in a Gene Expression Profile using the Inflammation Gene Expression Panel of Table 1 (or a subset of it). We conclude from FIG. 14 that in an attempt to return the inflammatory gene expression levels to those demonstrated in FIGS. 6 and 7 (normal or set levels), interference with the normal expression induced a compensatory gene expression response that over-compensated for the drug-induced response, perhaps because the prednisone had been significantly metabolized to inactive forms or eliminated from the subject.

FIG. 15, in a manner analogous to FIG. 14, shows the effect over time, via whole blood samples obtained from a human subject, administered a single dose of prednisone, on expression of 5 genes (of the Inflammation Gene Expression Panel of Table 1). The samples were taken at the time of administration (t=0) of the prednisone, then at two and 24 hours after such administration. Each whole blood sample was challenged by the addition of 0.1 ng/ml of lipopolysaccharide (a Gram-negative endotoxin) and a gene expression profile of the sample, post-challenge, was determined. It can seen that the two-hour sample shows dramatically reduced gene expression of the 5 loci of the Inflammation Gene Expression Panel, in relation to the expression levels at the time of administration (t=0). At 24 hours post administration, the inhibitory effect of the prednisone is no longer apparent, and at 3 of the 5 loci, gene expression is in fact higher than at t=0, illustrating quantitatively at the molecular level the well-known rebound effect.

FIG. 16 also shows the effect over time, on inflammatory gene expression in a single human subject suffering from rheumatoid arthritis, of the administration of a TNF-inhibiting compound, but here the expression is shown in comparison to the cognate locus average previously determined (in connection with FIGS. 6 and 7) for the normal (i.e., undiagnosed, healthy) population. As part of a larger international study involving patients with rheumatoid arthritis, the subject was followed over a twelve-week period. The subject was enrolled in the study because of a failure to respond to conservative drug therapy for rheumatoid arthritis and a plan to change therapy and begin immediate treatment with a TNF-inhibiting compound. Blood was drawn from the subject prior to initiation of new therapy (visit 1). After initiation of new therapy, blood was drawn at 4 weeks post change in therapy (visit 2), 8 weeks (visit 3), and 12 weeks (visit 4) following the start of new therapy. Blood was collected in PAX RNA isolation tubes, held at room temperature for two hours and then frozen at −30° C.

Frozen samples were shipped to the central laboratory at Source Precision Medicine, the assignee herein, in Boulder, Colo. for determination of expression levels of genes in the 48-gene Inflammation Gene Expression Panel of Table 1. The blood samples were thawed and RNA extracted according to the manufacturer's recommended procedure. RNA was converted to cDNA and the level of expression of the 48 inflammatory genes was determined. Expression results are shown for 11 of the 48 loci in FIG. 16. When the expression results for the 11 loci are compared from visit one to a population average of normal blood donors from the United States, the subject shows considerable difference. Similarly, gene expression levels at each of the subsequent physician visits for each locus are compared to the same normal average value. Data from visits 2, 3 and 4 document the effect of the change in therapy. In each visit following the change in the therapy, the level of inflammatory gene expression for 10 of the 11 loci is closer to the cognate locus average previously determined for the normal (i.e., undiagnosed, healthy) population.

FIG. 17A further illustrates the consistency of inflammatory gene expression, illustrated here with respect to 7 loci of (of the Inflammation Gene Expression Panel of Table 1), in a population of 44 normal, undiagnosed blood donors. For each individual locus is shown the range of values lying within±2 standard deviations of the mean expression value, which corresponds to 95% of a normally distributed population. Notwithstanding the great width of the confidence interval (95%), the measured gene expression value (ΔCT)—remarkably—still lies within 10% of the mean, regardless of the expression level involved. As described in further detail below, for a given biological condition an index can be constructed to provide a measurement of the condition. This is possible as a result of the conjunction of two circumstances: (i) there is a remarkable consistency of Gene Expression Profiles with respect to a biological condition across a population and (ii) there can be employed procedures that provide substantially reproducible measurement of constituents in a Gene Expression Panel giving rise to a Gene Expression Profile, under measurement conditions wherein specificity and efficiencies of amplification for all constituents of the panel are substantially similar and which therefore provides a measurement of a biological condition. Accordingly, a function of the expression values of representative constituent loci of FIG. 17A is here used to generate an inflammation index value, which is normalized so that a reading of 1 corresponds to constituent expression values of healthy subjects, as shown in the right-hand portion of FIG. 17A.

In FIG. 17B, an inflammation index value was determined for each member of a population of 42 normal undiagnosed blood donors, and the resulting distribution of index values, shown in the figure, can be seen to approximate closely a normal distribution, notwithstanding the relatively small population size. The values of the index are shown relative to a 0-based median, with deviations from the median calibrated in standard deviation units. Thus 90% of the population lies within +1 and −1 of a 0 value. We have constructed various indices, which exhibit similar behavior.

FIG. 17C illustrates the use of the same index as FIG. 17B, where the inflammation median for a normal population has been set to zero and both normal and diseased subjects are plotted in standard deviation units relative to that median. An inflammation index value was determined for each member of a normal, undiagnosed population of 70 individuals (black bars). The resulting distribution of index values, shown in FIG. 17C, can be seen to approximate closely a normal distribution. Similarly, index values were calculated for individuals from two diseased population groups, (1) rheumatoid arthritis patients treated with methotrexate (MTX) who are about to change therapy to more efficacious drugs (e.g., TNF inhibitors)(hatched bars), and (2) rheumatoid arthritis patients treated with disease modifying anti-rheumatoid drugs (DMARDS) other than MTX, who are about to change therapy to more efficacious drugs (e.g., MTX). Both populations present index values that are skewed upward (demonstrating increased inflammation) in comparison to the normal distribution. This figure thus illustrates the utility of an index to derived from Gene Expression Profile data to evaluate disease status and to provide an objective and quantifiable treatment objective. When these two populations were treated appropriately, index values from both populations returned to a more normal distribution (data not shown here).

FIG. 18 plots, in a fashion similar to that of FIG. 17A, Gene Expression Profiles, for the same 7 loci as in FIG. 17A, two different 6-subject populations of rheumatoid arthritis patients. One population (called “stable” in the figure) is of patients who have responded well to treatment and the other population (called “unstable” in the figure) is of patients who have not responded well to treatment and whose therapy is scheduled for change. It can be seen that the expression values for the stable population, lie within the range of the 95% confidence interval, whereas the expression values for the unstable population for 5 of the 7 loci are outside and above this range. The right-hand portion of the figure shows an average inflammation index of 9.3 for the unstable population and an average inflammation index of 1.8 for the stable population, compared to 1 for a normal undiagnosed population. The index thus provides a measure of the extent of the underlying inflammatory condition, in this case, rheumatoid arthritis. Hence the index, besides providing a measure of biological condition, can be used to measure the effectiveness of therapy as well as to provide a target for therapeutic intervention.

FIG. 19 thus illustrates use of the inflammation index for assessment of a single subject suffering from rheumatoid arthritis, who has not responded well to traditional therapy with methotrexate. The inflammation index for this subject is shown on the far right at start of a new therapy (a TNF inhibitor), and then, moving leftward, successively, 2 weeks, 6 weeks, and 12 weeks thereafter. The index can be seen moving towards normal, consistent with physician observation of the patient as responding to the new treatment.

FIG. 20 similarly illustrates use of the inflammation index for assessment of three subjects suffering from rheumatoid arthritis, who have not responded well to traditional therapy with methotrexate, at the beginning of new treatment (also with a TNF inhibitor), and 2 weeks and 6 weeks thereafter. The index in each case can again be seen moving generally towards normal, consistent with physician observation of the patients as responding to the new treatment.

Each of FIGS. 21-23 shows the inflammation index for an international group of subjects, suffering from rheumatoid arthritis, each of whom has been characterized as stable (that is, not anticipated to be subjected to a change in therapy) by the subject's treating physician. FIG. 21 shows the index for each of 10 patients in the group being treated with methotrexate, which known to alleviate symptoms without addressing the underlying disease. FIG. 22 shows the index for each of 10 patients in the group being treated with Enbrel (an TNF inhibitor), and FIG. 23 shows the index for each 10 patients being treated with Remicade (another TNF inhibitor). It can be seen that the inflammation index for each of the patients in FIG. 21 is elevated compared to normal, whereas in FIG. 22, the patients being treated with Enbrel as a class have an inflammation index that comes much closer to normal (80% in the normal range). In FIG. 23, it can be seen that, while all but one of the patients being treated with Remicade have an inflammation index at or below normal, two of the patients have an abnormally low inflammation index, suggesting an immunosuppressive response to this drug. (Indeed, studies have shown that Remicade has been associated with serious infections in some subjects, and here the immunosuppressive effect is quantified.) Also in FIG. 23, one subject has an inflammation index that is significantly above the normal range. This subject in fact was also on a regimen of an anti-inflammation steroid (prednisone) that was being tapered; within approximately one week after the inflammation index was sampled, the subject experienced a significant flare of clinical symptoms.

Remarkably, these examples show a measurement, derived from the assay of blood taken from a subject, pertinent to the subject's arthritic condition. Given that the measurement pertains to the extent of inflammation, it can be expected that other inflammation-based conditions, including, for example, cardiovascular disease, may be monitored in a similar fashion.

FIG. 24 illustrates use of the inflammation index for assessment of a single subject suffering from inflammatory bowel disease, for whom treatment with Remicade was initiated in three doses. The graphs show the inflammation index just prior to first treatment, and then 24 hours after the first treatment; the index has returned to the normal range. The index was elevated just prior to the second dose, but in the normal range prior to the third dose. Again, the index, besides providing a measure of biological condition, is here used to measure the effectiveness of therapy (Remicade), as well as to provide a target for therapeutic intervention in terms of both dose and schedule.

FIG. 25 shows Gene Expression Profiles with respect to 24 loci (of the Inflammation Gene Expression Panel of Table 1) for whole blood treated with Ibuprofen in vitro in relation to other non-steroidal anti-inflammatory drugs (NSAIDs). The profile for Ibuprofen is in front. It can be seen that all of the NSAIDs, including Ibuprofen share a substantially similar profile, in that the patterns of gene expression across the loci are similar. Notwithstanding these similarities, each individual drug has its own distinctive signature.

FIG. 26 illustrates how the effects of two competing anti-inflammatory compounds can be compared objectively, quantitatively, precisely, and reproducibly. In this example, expression of each of a panel of two genes (of the Inflammation Gene Expression Panel of Table 1) is measured for varying doses (0.08-250 μg/ml) of each drug in vitro in whole blood. The market leader drug shows a complex relationship between dose and inflammatory gene response. Paradoxically, as the dose is increased, gene expression for both loci initially drops and then increases in the case the case of the market leader. For the other compound, a more consistent response results, so that as the dose is increased, the gene expression for both loci decreases more consistently.

FIGS. 27 through 41 illustrate the use of gene expression panels in early identification and monitoring of infectious disease. These figures plot the response, in expression products of the genes indicated, in whole blood, to the administration of various infectious agents or products associated with infectious agents. In each figure, the gene expression levels are “calibrated”, as that term is defined herein, in relation to baseline expression levels determined with respect to the whole blood prior to administration of the relevant infectious agent. In this respect the figures are similar in nature to various figures of our below-referenced patent application WO 01/25473 (for example, FIG. 15 therein). The concentration change is shown ratiometrically, and the baseline level of 1 for a particular gene locus corresponds to an expression level for such locus that is the same, monitored at the relevant time after addition of the infectious agent or other stimulus, as the expression level before addition of the stimulus. Ratiometric changes in concentration are plotted on a logarithmic scale. Bars below the unity line represent decreases in concentration and bars above the unity line represent increases in concentration, the magnitude of each bar indicating the magnitude of the ratio of the change. We have shown in WO 01/25473 and other experiments that, under appropriate conditions, Gene Expression Profiles derived in vitro by exposing whole blood to a stimulus can be representative of Gene Expression Profiles derived in vivo with exposure to a corresponding stimulus.

FIG. 27 uses a novel bacterial Gene Expression Panel of 24 genes, developed to discriminate various bacterial conditions in a host biological system. Two different stimuli are employed: lipotechoic acid (LTA), a gram positive cell wall constituent, and lipopolysaccharide (LPS), a gram negative cell wall constituent. The final concentration immediately after administration of the stimulus was 100 ng/mL, and the ratiometric changes in expression, in relation to pre-administration levels, were monitored for each stimulus 2 and 6 hours after administration. It can be seen that differential expression can be observed as early as two hours after administration, for example, in the IFNA2 locus, as well as others, permitting discrimination in response between gram positive and gram negative bacteria.

FIG. 28 shows differential expression for a single locus, IFNG, to LTA derived from three distinct sources: S. pyogenes, B. subtilis, and S. aureus. Each stimulus was administered to achieve a concentration of 100 ng/mL, and the response was monitored at 1, 2, 4, 6, and 24 hours after administration. The results suggest that Gene Expression Profiles can be used to distinguish among different infectious agents, here different species of gram positive bacteria.

FIGS. 29 and 30 show the response of the Inflammation 48A and 48B loci respectively (discussed above in connection with FIGS. 6 and 7 respectively) in whole blood to administration of a stimulus of S. aureus and of a stimulus of E. coli (in the indicated concentrations, just after administration, of 10⁷and 10⁶CFU/mL respectively), monitored 2 hours after administration in relation to the pre-administration baseline. The figures show that many of the loci respond to the presence of the bacterial infection within two hours after infection.

FIGS. 31 and 32 correspond to FIGS. 29 and 30 respectively and are similar to them, with the exception that the monitoring here occurs 6 hours after administration. More of the loci are responsive to the presence of infection. Various loci, such as IL2, show expression levels that discriminate between the two infectious agents.

FIG. 33 shows the response of the Inflammation 48A loci to the administration of a stimulus of E. coli (again in the concentration just after administration of 10⁶CFU/mL) and to the administration of a stimulus of an E. coli filtrate containing E. coli bacteria by products but lacking E. coli bacteria. The responses were monitored at 2, 6, and 24 hours after administration. It can be seen, for example, that the responses over time of loci IL1B, IL18 and CSF3 to E. coli and to E. coli filtrate are different.

FIG. 34 is similar to FIG. 33, but here the compared responses are to stimuli from E. coli filtrate alone and from E. coli filtrate to which has been added polymyxin B, an antibiotic known to bind to lipopolysaccharide (LPS). An examination of the response of IL1B, for example, shows that presence of polymyxin B did not affect the response of the locus to E. coli filtrate, thereby indicating that LPS does not appear to be a factor in the response of IL1B to E. coli filtrate.

FIG. 35 illustrates the responses of the Inflammation 48A loci over time of whole blood to a stimulus of S. aureus (with a concentration just after administration of 10⁷CFU/mL) monitored at 2, 6, and 24 hours after administration. It can be seen that response over time can involve both direction and magnitude of change in expression. (See for example, IL5 and IL18.)

FIGS. 36 and 37 show the responses, of the Inflammation 48A and 48B loci respectively, monitored at 6 hours to stimuli from E. coli (at concentrations of 10⁶and 10²CFU/mL immediately after administration) and from S. aureus (at concentrations of 10⁷and 10²CFU/mL immediately after administration). It can be seen, among other things, that in various loci, such as B7 (FIG. 36), TACI, PLA2G7, and C1QA (FIG. 37), E. coli produces a much more pronounced response than S. aureus. The data suggest strongly that Gene Expression Profiles can be used to identify with high sensitivity the presence of gram negative bacteria and to discriminate against gram positive bacteria.

FIGS. 38 and 39 show the responses, of the Inflammation 48B and 48A loci respectively, monitored 2, 6, and 24 hours after administration, to stimuli of high concentrations of S. aureus and E. coli respectively (at respective concentrations of 10⁷and 10⁶CFU/mL immediately after administration). The responses over time at many loci involve changes in magnitude and direction.

FIG. 40 is similar to FIG. 39, but shows the responses of the Inflammation 48B loci.

FIG. 41 similarly shows the responses of the Inflammation 48A loci monitored at 24 hours after administration to stimuli high concentrations of S. aureus and E. coli respectively (at respective concentrations of 10⁷and 10⁶CFU/mL immediately after administration). As in the case of FIGS. 20 and 21, responses at some loci, such as GRO1 and GRO2, discriminate between type of infection.

These data support our conclusion that Gene Expression Profiles with sufficient precision and calibration as described herein (1) can determine subpopulations of individuals with a known biological condition; (2) may be used to monitor the response of patients to therapy; (3) may be used to assess the efficacy and safety of therapy; and (4) may used to guide the medical management of a patient by adjusting therapy to bring one or more relevant Gene Expression Profiles closer to a target set of values, which may be normative values or other desired or achievable values. We have shown that Gene Expression Profiles may provide meaningful information even when derived from ex vivo treatment of blood or other tissue. We have also shown that Gene Expression Profiles derived from peripheral whole blood are informative of a wide range of conditions neither directly nor typically associated with blood.

Furthermore, in embodiments of the present invention, Gene Expression Profiles can also be used for characterization and early identification (including pre-symptomatic states) of infectious disease, such as sepsis. This characterization includes discriminating between infected and uninfected individuals, bacterial and viral infections, specific subtypes of pathogenic agents, stages of the natural history of infection (e.g., early or late), and prognosis. Use of the algorithmic and statistical approaches discussed above to achieve such identification and to discriminate in such fashion is within the scope of various embodiments herein.

TABLE 1

Inflammation Gene Expression Panel

Symbol
Name
Classification
Description

IL1A
Interleukin 1,
cytokines-
Proinflammatory; constitutively and

alpha
chemokines-growth
inducibly expressed in variety of cells.

factors
Generally cytosolic and released only

during severe inflammatory disease

IL1B
Interleukin 1,
cytokines-
Proinflammatory; constitutively and

beta
chemokines-growth
inducibly expressed by many cell types,

factors
secreted

TNFA
Tumor
cytokines-
Proinflammatory, TH1, mediates host

necrosis
chemokines-growth
response to bacterial stimulus, regulates

factor, alpha
factors
cell growth & differentiation

IL6
Interleukin 6
cytokines-
Pro- and antiinflammatory activity, TH2

(interferon,
chemokines-growth
cytokine, regulates hemotopoietic system

beta 2)
factors
and activation of innate response

IL8
Interleukin 8
cytokines-
Proinflammatory, major secondary

chemokines-growth
inflammatory mediator, cell adhesion,

factors
signal transduction, cell-cell signaling,

angiogenesis, synthesized by a wide

variety of cell types

IFNG
Interferon
cytokines-
Pro- and antiinflammatory activity, TH1

gamma
chemokines-growth
cytokine, nonspecific inflammatory

factors
mediator, produced by activated T-cells

IL2
Interleukin 2
cytokines-
T-cell growth factor, expressed by

chemokines-growth
activated T-cells, regulates lymphocyte

factors
activation and differentiation; inhibits

apoptosis, TH1 cytokine

IL12B
Interleukin
cytokines-
Proinflammatory; mediator of innate

12 p40
chemokines-growth
immunity, TH1 cytokine, requires co-

factors
stimulation with IL-18 to induce IFN-g

IL15
Interleukin
cytokines-
Proinflammatory; mediates T-cell

15
chemokines-growth
activation, inhibits apoptosis, synergizes

factors
with IL-2 to induce IFN-g and TNF-a

IL18
Interleukin
cytokines-
Proinflammatory, TH1, innate and

18
chemokines-growth
aquired immunity, promotes apoptosis,

factors
requires co-stimulation with IL-1 or IL-2

to induce TH1 cytokines in T- and NK-

cells

IL4
Interleukin 4
cytokines-
Antiinflammatory; TH2; suppresses

chemokines-growth
proinflammatory cytokines, increases

factors
expression of IL-1RN, regulates

lymphocyte activation

IL5
Interleukin 5
cytokines-
Eosinophil stimulatory factor; stimulates

chemokines-growth
late B cell differentiation to secretion of

factors
Ig

IL10
Interleukin
cytokines-
Antiinflammatory; TH2; suppresses

10
chemokines-growth
production of proinflammatory cytokines

factors

IL13
Interleukin
cytokines-
Inhibits inflammatory cytokine

13
chemokines-growth
production

factors

IL1RN
Interleukin 1
cytokines-
IL1 receptor antagonist;

receptor
chemokines-growth
Antiinflammatory; inhibits binding of IL-

antagonist
factors
1 to IL-1 receptor by binding to receptor

without stimulating IL-1-like activity

IL18BP
IL-18
cytokines-
Implicated in inhibition of early TH1

Binding
chemokines-growth
cytokine responses

Protein
factors

TGFB1
Transforming
cytokines-
Pro- and antiinflammatory activity, anti-

growth
chemokines-growth
apoptotic; cell-cell signaling, can either

factor, beta 1
factors
inhibit or stimulate cell growth

IFNA2
Interferon,
cytokines-
interferon produced by macrophages with

alpha 2
chemokines-growth
antiviral effects

factors

GRO1
GRO1
cytokines-
AKA SCYB1; chemotactic for

oncogene
chemokines-growth
neutrophils

(melanoma
factors

growth

stimulating

activity,

alpha)

GRO2
GRO2
cytokines-
AKA MIP2, SCYB2; Macrophage

oncogene
chemokines-growth
inflammatory protein produced by

factors
moncytes and neutrophils

TNFSF5
Tumor
cytokines-
ligand for CD40; expressed on the surface

necrosis
chemokines-growth
of T cells. It regulates B cell function by

factor
factors
engaging CD40 on the B cell surface

(ligand)

superfamily,

member 5

TNFSF6
Tumor
cytokines-
AKA FasL; Ligand for FAS antigen;

necrosis
chemokines-growth
transduces apoptotic signals into cells

factor
factors

(ligand)

superfamily,

member 6

CSF3
Colony
cytokines-
AKA GCSF; cytokine that stimulates

stimulating
chemokines-growth
granulocyte development

factor 3
factors

(granulocyte)

B7
B7 protein
cell signaling and
Regulatory protein that may be associated

activation
with lupus

CSF2
Granulocyte-
cytokines-
AKA GM-CSF; Hematopoietic growth

monocyte
chemokines-growth
factor; stimulates growth and

colony
factors
differentiation of hematopoietic precursor

stimulating

cells from various lineages, including

factor

granulocytes, macrophages, eosinophils,

and erythrocytes

TNFSF13B
Tumor
cytokines-
B cell activating factor, TNF family

necrosis
chemokines-growth

factor
factors

(ligand)

superfamily,

member 13b

TACI
Transmembrane
cytokines-
T cell activating factor and calcium

activator
chemokines-growth
cyclophilin modulator

and CAML
factors

interactor

VEGF
vascular
cytokines-
Producted by monocytes

endothelial
chemokines-growth

growth factor
factors

ICAM1
Intercellular
Cell Adhesion/
Endothelial cell surface molecule;

adhesion
Matrix Protein
regulates cell adhesion and trafficking,

molecule 1

upregulated during cytokine stimulation

PTGS2
Prostaglandin-
Enzyme/Redox
AKA COX2; Proinflammatory, member

endoperoxide

of arachidonic acid to prostanoid

synthase 2

conversion pathway; induced by

proinflammatory cytokines

NOS2A
Nitric oxide
Enzyme/Redox
AKA iNOS; produces NO which is

synthase 2A

bacteriocidal/tumoricidal

PLA2G7
Phospholipase
Enzyme/Redox
Platelet activating factor

A2, group

VII (platelet

activating

factor

acetylhydrolase,

plasma)

HMOX1
Heme
Enzyme/Redox
Endotoxin inducible

oxygenase

(decycling) 1

F3
F3
Enzyme/Redox
AKA thromboplastin, Coagulation Factor

3; cell surface glycoprotein responsible

for coagulation catalysis

CD3Z
CD3 antigen,
Cell Marker
T-cell surface glycoprotein

zeta

polypeptide

PTPRC
protein
Cell Marker
AKA CD45; mediates T-cell activation

tyrosine

phosphatase,

receptor

type, C

CD14
CD14
Cell Marker
LPS receptor used as marker for

antigen

monocytes

CD4
CD4 antigen
Cell Marker
Helper T-cell marker

(p55)

CD8A
CD8 antigen,
Cell Marker
Suppressor T cell marker

alpha

polypeptide

CD19
CD19
Cell Marker
AKA Leu 12; B cell growth factor

antigen

HSPA1A
Heat shock
Cell Signaling and
heat shock protein 70 kDa

protein 70
activation

MMP3
Matrix
Proteinase/
AKA stromelysin; degrades fibronectin,

metalloproteinase 3
Proteinase Inhibitor
laminin and gelatin

MMP9
Matrix
Proteinase/
AKA gelatinase B; degrades extracellular

metalloproteinase 9
Proteinase Inhibitor
matrix molecules, secreted by IL-8-

stimulated neutrophils

PLAU
Plasminogen
Proteinase/
AKA uPA; cleaves plasminogen to

activator,
Proteinase Inhibitor
plasmin (a protease responsible for

urokinase

nonspecific extracellular matrix

degradation)

SERPINE1
Serine (or
Proteinase/
Plasminogen activator inhibitor-1/PAI-1

cysteine)
Proteinase Inhibitor

protease

inhibitor,

clade B

(ovalbumin),

member 1

TIMP1
tissue
Proteinase/
Irreversibly binds and inhibits

inhibitor of
Proteinase Inhibitor
metalloproteinases, such as collagenase

metalloproteinase 1

C1QA
Complement
Proteinase/
Serum complement system; forms C1

component 1, q
Proteinase Inhibitor
complex with the proenzymes c1r and c1s

subcomponent,

alpha

polypeptide

HLA-DRB1
Major
Histocompatibility
Binds antigen for presentation to CD4+

histocompatibility

cells

complex,

class II, DR

beta 1

TABLE 2

Diabetes Gene Expression Panel

Symbol
Name
Classification
Description

G6PC
glucose-6-
Glucose-6-
Catalyzes the final step in the

phosphatase,
phosphatase/Glycogen
gluconeogenic and glycogenolytic

catalytic
metabolism
pathways. Stimulated by

glucocorticoids and strongly inhibited

by insulin. Overexpression (in

conjunction with PCK1 overexpression)

leads to increased hepatic glucose

production.

GCG
glucagon
pancreatic/peptide hormone
Pancreatic hormone which counteracts

the glucose-lowering action of insulin

by stimulating glycogenolysis and

gluconeogenesis. Underexpression of

glucagon is preferred. Glucagon-like

peptide (GLP-1) proposed for type 2

diabetes treatment inhibits glucag

GCGR
glucagon receptor
glucagon receptor
Expression of GCGR is strongly

upregulated by glucose. Deficiency

imbalance could play a role in NIDDM.

Has been looked as a potential for gene

therapy.

GFPT1
glutamine-fructose-
Glutamine amidotransferase
The rate limiting enzyme for glucose

6-phosphate

entry into the hexosamine biosynthetic

transaminase 1

pathway (HBP). Overexpression of

GFA in muscle and adipose tissue

increases products of the HBP which are

thought to cause insulin resistance

(possibly through defects to glucose

GYS1
glycogen synthase 1
Transferase/Glycogen
A key enzyme in the regulation of

(muscle)
metabolism
glycogen synthesis in the skeletal

muscles of humans. Typically

stimulated by insulin, but in NIDDM

individuals GS is shown to be

completely resistant to insulin

stimulation (decreased activity and

activation in muscle)

HK2
hexokinase 2
hexokinase
Phosphorylates glucose into glucose-6-

phosphate. NIDDM patients have lower

HK2 activity which may contribute to

insulin resistance. Similar action to

GCK.

INS
insulin
Insulin receptor ligand
Decreases blood glucose concentration

and accelerates glycogen synthesis in

the liver. Not as critical in NIDDM as

in IDDM.

IRS1
insulin receptor
signal
Positive regultion of insulin action. This

substrate 1
transduction/transmembrane
protein is activated when insulin binds

receptor protein
to insulin receptor - binds 85-kDa

subunit of PI 3-K. decreased in skeletal

muscle of obese humans.

PCK1
phosphoenolpyruvate
rate-limiting gluconeogenic
Rate limiting enzyme for

carboxykinase 1
enzyme
gluconeogenesis - plays a key role in the

regulation of hepatic glucose output by

insulin and glucagon. Overexpression in

the liver results in increased hepatic

glucose production and hepatic insulin

resistance to glycogen synthe

PIK3R1
phosphoinositide-3-
regulatory enzyme
Positive regulation of insulin action.

kinase, regulatory

Docks in IRS proteins and Gab1 -

subunit, polypeptide

activity is required for insulin stimulated

1 (p85 alpha)

translocation of glucose transporters

the plasma membrane and activation

glucose uptake.

PPARG
peroxisome
transcription factor/Ligand-
The primary pharmacological target for

proliferator-activated
dependent nuclear receptor
the treatment of insulin resistance in

receptor, gamma

NIDDM. Involved in glucose and lipid

metabolism in skeletal muscle.

PRKCB1
protein kinase C,
protein kinase C/protein
Negative regulation of insulin action.

beta 1
phosphorylation
Activated by hyperglycemia - increases

phosphorylation of IRS-1 and reduces

insulin receptor kinase activity.

Increased PKC activation may lead to

oxidative stress causing overexpression

of TGF-beta and fibronectin

SLC2A2
solute carrier family
glucose transporter
Glucose transporters expressed uniquely

2 (facilitated glucose

in b-cells and liver. Transport glucose

transporter), member 2

into the b-cell. Typically

underexpressed in pancreatic islet cells

of individuals with NIDDM.

SLC2A4
solute carrier family
glucose transporter
Glucose transporter protein that is final

2 (facilitated glucose

mediator in insulin-stimulated glucose

transporter), member 4

uptake (rate limiting for glucose uptake).

Underexpression not important, but

overexpression in muscle and adipose

tissue consistently shown to increase

glucose transport.

TGFB1
transforming growth
Transforming growth factor
Regulated by glucose - in NIDDM

factor, beta 1
beta receptor ligand
individuals, overexpression (due to

oxidative stress - see PKC) promotes

renal cell hypertrophy leading to

diabetic nephropathy.

TNF
tumor necrosis factor
cytokine/tumor necrosis
Negative regulation of insulin action.

factor receptor ligand
Produced in excess by adipose tissue of

obese individuals - increases IRS-1

phosphorylation and decreases insulin

receptor kinase activity.

TABLE 3

Prostate Gene Expression Panel

Symbol
Name
Classification
Description

ABCC1
ATP-binding cassette,
membrane transporter
AKA MRP1, ABC29:

sub-family C, member 1

Multispecific organic anion

membrane transporter;

overexpression confers tissue

protection against a wide

variety of xenobiotics due to

their removal from the cell.

ACPP
Acid phosphatase,
phosphatase
AKA PAP: Major

prostate

phosphatase of the prostate;

synthesized under androgen

regulation; secreted by the

epithelial cells of the prostrate

BCL2
B-cell CLL/lymphoma 2
apoptosis Inhibitor - cell
Blocks apoptosis by

cycle control -
interfering with the activation

oncogenesis
of caspases

BIRC5
Baculoviral IAP repeat-
apoptosis Inhibitor
AKA Survivin; API4: May

containing 5

counteract a default induction

of apoptosis in G2/M phase of

cell cycle; associates with

microtubules of the mitotic

spindle during apoptosis

CDH1
Cadherin 1, type 1, E-
cell-cell adhesion/
AKA ECAD, UVO: Calcium

cadherin
interaction
ion-dependent cell adhesion

molecule that mediates cell to

cell interactions in epithelial

cells

CDH2
Cadherin 2, type 1, N-
cell-cell adhesion/
AKA NCAD, CDHN:

cadherin
interaction
Calcium-dependent

glycoprotein that mediates

cell-cell interactions; may be

involved in neuronal

recognition mechanism

CDKN2A
Cyclin-dependent kinase
cell cycle control -
AKA p16, MTS1, INK4:

inhibitor 2A
tumor suppressor
Tumor suppressor gene

involved in a variety of

malignancies; arrests normal

diploid cells in late G1

CTNNA1
Catenin, alpha 1
cell adhesion
Binds cadherins and links

them with the actin

cytoskeleton

FOLH1
Folate Hydrolase
hydrolase
AKA PSMA, GCP2:

Expressed in normal and

neoplastic prostate cells;

membrane bound

glycoprotein; hydrolyzes

folate and is an N-acetylated

a-linked acidic dipeptidase

GSTT1
Glutathione-S-
metabolism
Catalyzes the conjugation of

Transferase, theta 1

reduced glutathione to a wide

number of exogenous and

endogenous hydrophobic

electrophiles; has an important

role in human carcinogenesis

HMGIY
High mobility group
DNA binding -
Potential oncogene with MYC

protein, isoforms I and Y
transcriptional
binding site at promoter

regulation - oncogene
region; involved in the

transcription regulation of

genes containing, or in close

proximity to a + t-rich regions

HSPA1A
Heat shock 70 kD protein
cell signalling and
AKA HSP-70, HSP70-1:

1A
activation
Molecular chaperone,

stabilizes AU rich mRNA

IGF1R
Insulin-like growth
cytokines - chemokines -
Mediates insulin stimulated

factor 1 receptor
growth factors
DNA synthesis; mediates

IGF1 stimulated cell

proliferation and

differentiation

IL6
Interleukin 6
cytokines - chemokines -
Pro- and anti-inflammatory

growth factors
activity, TH2 cytokine,

regulates hematopoiesis,

activation of innate response,

osteoclast development;

elevated in sera of patients

with metastatic cancer

IL8
Interleukin 8
cytokines - chemokines -
AKA SCYB8, MDNCF:

growth factors
Proinflammatory chemokine;

major secondary inflammatory

mediator resulting in cell

adhesion, signal transduction,

cell-cell signaling; regulates

angiogenesis in prostate

cancer

KAI1
Kangai 1
tumor suppressor
AKA SAR2, CD82, ST6:

suppressor of metastatic

ability of prostate cancer cells

KLK2
Kallikrein 2, prostatic
protease - kallikrein
AKA hGK-1: Glandular

kallikrein; expression

restricted mainly to the

prostate.

KLK3
Kallikrein 3
protease - kallikrein
AKA PSA: Kallikrein-like

protease which functions

normally in liquefaction of

seminal fluid. Elevated in

prostate cancer.

KRT19
Keratin 19
structural protein -
AKA K19: Type I epidermal

differentiation
keratin; may form

intermediate filaments

KRT5
Keratin 5
structural protein -
AKA EBS2: 58 kD Type II

differentiation
keratin co-expressed with

keratin 14, a 50 kD Type I

keratin, in stratified

epithelium. KRT5 expression

is a hallmark of mitotically

active keratinocytes and is the

primary structural component

of the 10 nm intermediate

filaments of the mitotic

epidermal basal cells.

KRT8
Keratin 8
structural protein -
AKA K8, CK8: Type II

differentiation
keratin; coexpressed with

Keratin 18; involved in

intermediate filament

formation

LGALS8
Lectin, Galactoside-
cell adhesion - growth
AKA PCTA-1: binds to beta

binding, soluble 8
and differentiation
galactoside; involved in

biological processes such as

cell adhesion, cell growth

regulation, inflammation,

immunomodulation, apoptosis

and metastasis

MYC
V-myc avian
transcription factor -
Transcription factor that

myelocytomatosis viral
oncogene
promotes cell proliferation

oncogene homolog

and transformation by

activating growth-promoting

genes; may also repress gene

expression

NRP1
Neuropilin 1
cell adhesion
AKA NRP, VEGF165R: A

novel VEGF receptor that

modulates VEGF binding to

KDR (VEGF receptor) and

subsequent bioactivity and

therefore may regulate VEGF-

induced angiogenesis;

calcium-independent cell

adhesion molecule that

function during the formation

of certain neuronal circuits

PART1
Prostate androgen-

Exhibits increased expression

regulated transcript 1

in LNCaP cells upon exposure

to androgens

PCA3
Prostate cancer antigen 3

AKA DD3: prostate specific;

highly expressed in prostate

tumors

PCANAP7
Prostate cancer

AKA IPCA7: unknown

associated protein 7

function; co-expressed with

known prostate cancer genes

PDEF
Prostate epithelium
transcription factor
Acts as an androgen-

specific Ets transcription

independent transcriptional

factor

activator of the PSA promoter;

directly interacts with the

DNA binding domain of

androgen receptor and

enhances androgen-mediated

activation of the PSA

promoter

PLAU
Urokinase-type
proteinase
AKA UPA, URK: cleaves

plasminogen activator

plasminogen to plasmin

POV1
Prostate cancer

RNA expressed selectively in

overexpressed gene 1

prostate tumor samples

PSCA
Prostate stem cell
antigen
Prostate-specific cell surface

antigen

antigen expressed strongly by

both androgen-dependent and

-independent tumors

PTGS2
Prostaglandin-
cytokines - chemokines -
AKA COX-2:

endoperoxide synthase 2
growth factors
Proinflammatory; member of

arachidonic acid to prostanoid

conversion pathway

SERPINB5
Serine proteinase
proteinase inhibitor -
AKA Maspin, PI5: Protease

inhibitor, clade B,
tumor suppressor
Inhibitor; Tumor suppressor,

member 5

especially for metastasis.

SERPINE1
Serine (or cystein)
proteinase inhibitor
AKA PAI1: regulates

proteinase inhibitor,

fibrinolysis; inhibits PLAU

clade E, member 1

STAT3
Signal transduction and
transcription factor
AKA APRF: Transcription

activator of transcription 3

factor for acute phase

response genes; rapidly

activated in response to

certain cytokines and growth

factors; binds to IL6 response

elements

TERT
Telomerase reverse

AKA TCS1, EST2:

transcriptase

Ribonucleoprotein which in

vitro recognizes a single-

stranded G-rich telomere

primer and adds multiple

telomeric repeats to its 3-

prime end by using an RNA

template

TGFB1
Transforming growth
cytokines - chemokines -
AKA DPD1, CED: Pro- and

factor, beta 1
growth factors
antiinflammatory activity;

anti-apoptotic; cell-cell

signaling, can either inhibit or

stimulate cell growth

TNF
Tumor necrosis factor,
cytokines - chemokines -
AKA TNF alpha:

member 2
growth factors
Proinflammatory cytokine that

is the primary mediator of

immune response and

regulation, associated with

TH1 responses, mediates host

response to bacterial stimuli,

regulates cell growth &

differentiation

TP53
Tumor protein 53
DNA binding protein -
AKA P53: Activates

cell cycle - tumor
expression of genes that

suppressor
inhibit tumor growth and/or

invasion; involved in cell

cycle regulation (required for

growth arrest at G1); inhibits

cell growth through activation

of cell-cycle arrest and

apoptosis

VEGF
Vascular Endothelial
cytokines - chemokines -
AKA VPF: Induces vascular

Growth Factor
growth factors
permeability, endothelial cell

proliferation, angiogenesis

TABLE 4

Skin Response Gene Expression Panel

Symbol
Name
Classification
Description

BAX
BCL2 associated X
apoptosis
Accelerates programmed cell death by

protein
induction-
binding to and antagonizing the apoptosis

germ
repressor BCL2; may induce caspase

cell
activation

development

BCL2
B-cell
apoptosis
Integral mitochondrial membrane protein

CLL/lymphoma 2
inhibitor -
that blocks the apoptotic death of some

cell
cells such as lymphocytes; constitutive

cycle
expression of BCL2 thought to be cause of

control-
follicular lymphoma

oncogenesis

BSG
Basignin
signal
Member of Ig superfamily; tumor cell-

transduction-
derived collagenase stimulatory factor;

peripheral
stimulates matrix metalloproteinase

plasma
synthesis in fibroblasts

membrane

protein

COL7A1
Type VII collagen,
collagen-
alpha 1 subunit of type VII collagen; may

alpha 1
differentiation-
link collagen fibrils to the basement

extracellular
membrane

matrix

CRABP2
Cellular Retinoic
retinoid
Low molecular weight protein highly

Acid Binding
binding-
expressed in skin; thought to be important

Protein
signal
in RA-mediated regulation of skin growth

transduction-
& differentiation

transcription

regulation

CTGF
Connective Tissue
insulin-
Member of family of peptides including

Growth Factor
like
serum-induced immediate early gene

growth
products expressed after induction by

factor-
growth factors; overexpressed in fibrotic

differentiation-
disorders

wounding

response

DUSP1
Dual Specificity
oxidative
Induced in human skin fibroblasts by

Phosphatase
stress
oxidative/heat stress & growth factors; de-

response-
phosphorylates MAP kinase crk2; may play

tyrosine
a role in negative regulation of cellular

phosphatase
proliferation

FGF7
Fibroblast growth
growth
aka KGF; Potent mitogen for epithelial

factor 7
factor-
cells; induced after skin injury

differentiation-

wounding

response-

signal

transduction

FN1
Fibronectin
cell
Major cell surface glycoprotein of many

adhesion -
fibroblast cells; thought to have a role in

motility-
cell adhesion, morphology, wound healing

signal
& cell motility

transduction

FOS
v-fos FBJ murine
transcription
Proto-oncoprotein acting with JUN,

osteosarcoma virus
factor-
stimulates transcription of genes with AP-1

oncogene homolog
inflammatory
regulatory sites; in some cases FOS

response-
expression is associated with apototic cell

cell
death

growth &

maintanence

GADD45A
Growth Arrest and
cell
Transcriptionally induced following

DNA-damage-
cycle-
stressful growth arrest conditions &

inducible alpha
DNA
treatment with DNA damaging agents;

repair-
binds to PCNA affecting it's interaction

apoptosis
with some cell division protein kinase

GRO1
GRO1 oncogene
cytokines-
AKA SCYB1; chemotactic for neutrophils

(melanoma growth
chemokines-

stimulating
growth

activity, alpha)
factors

IIMOX1
Heme Oxygenase 1
metabolism-
Essential enzyme in heme catabolism;

endoplasmic
HMOX1 induced by its substrate heme &

reticulum
other substances such as oxidizing agents &

UVA

ICAM1
Intercellular
Cell
Endothelial cell surface molecule; regulates

adhesion molecule 1
Adhesion/
cell adhesion and trafficking, upregulated

Matrix
during cytokine stimulation

Protein

IL1A
Interleukin 1, alpha
cytokines-
Proinflammatory; constitutively and

chemokines-
inducibly expressed in variety of cells.

growth
Generally cytosolic and released only

factors
during severe inflammatory disease

IL1B
Interleukin 1, beta
cytokines
Proinflammatory; constitutively and

chemokines
inducibly expressed by many cell types,

growth
secreted

factors

1L8
Interleukin 8
cytokines-
Proinflammatory, major secondary

chemokines-
inflammatory mediator, cell adhesion,

growth
signal transduction, cell-cell signaling,

factors
angiogenesis, synthesized by a wide variety

of cell types

IVL
Involucrin
structural
Component of the keratinocyte crosslinked

protein-
envelope; first appears in the cytosol

peripheral
becoming crosslinked to membrane

plasma
proteins by transglutaminase

membrane

protein

JUN
v-jun avian
transcription
Proto-oncoprotein; component of

sarcoma virus 17
factor-
transcription factor AP-1 that interacts

oncogene homolog
DNA
directly with target DNA sequences to

binding
regulate gene expression

KRT14
Keratin 14
structural
Type I keratin; associates with keratin 5;

protein-
component of intermediate filaments;

differentiation
several autosomal dominant blistering skin

cell
disorders caused by gene defects

shape

KRT16
Keratin 16
structural
Type I keratin; component of intermediate

protein-
filaments; induced in skin conditions

differentiation-
favoring enhanced proliferation or

cell
abnormal differentiation

shape

KRT5
Keratin 5
structural
Type II intermediate filament chain

protein-
expressed largely in stratified epithelium;

differentiation-
hallmark of mitotically active keratinocytes

cell

shape

MAPK8
Mitogen Activated
kinase-
aka JNK1; mitogen activated protein kinase

Protein Kinase 8
stress
regulates c-Jun in response to cell stress;

response -
UV irradiation of skin activates MAPK8

signal

transduction

MMP1
Matrix
Proteinase/
aka Collagenase; cleaves collagens types I-III;

Metalloproteinase 1
Proteinase
plays a key role in remodeling occuring

Inhibitor
in both normal & diseased conditions;

transcriptionally regulated by growth

factors, hormones, cytokines & cellular

transformation

MMP2
Matrix
Proteinase/
aka Gelatinase; cleaves collagens types IV,

Metalloproteinase 2
Proteinase
V, VII and gelatin type I; produced by

Inhibitor
normal skin fibroblasts; may play a role in

regulation of vascularization & the

inflammatory response

MMP3
Matrix
Proteinase/
aka Stromelysin; degrades fibronectin,

Metalloproteinase 3
Proteinase
laminin, collagens III, IV, IX, X, cartilage

Inhibitor
proteoglycans, thought to be involved in

wound repair; progression of

atherosclerosis & tumor initiation;

produced predominantly by connective

tissue cells

MMP9
Matrix
Proteinase/
AKA gelatinase B; degrades extracellular

metalloproteinase 9
Proteinase
matrix molecules, secreted by IL-8-

Inhibitor
stimulated neutrophils

NR1I2
Nuclear receptor
transcription
aka PAR2; Member of nuclear hormone

subfamily 1
activation
receptor family of ligand-activated

factor-
transcription factors; activates transcription

signal
of cytochrome P-450 genes

transduction-

xenobiotic

metabolism

PCNA
Proliferating Cell
DNA
Required for both DNA replication &

Nuclear Antigen
binding-
repair; processivity factor for DNA

DNA
polymerases delta and epsilon

replication-

DNA

repair-

cell

proliferation

PI3
Proteinase inhibitor
proteinase
aka SKALP; Proteinase inhibitor found in

3 skin derived
inhibitor-
epidermis of several inflammatory skin

protein
diseases; it's expression can be used as a

binding-
marker of skin irritancy

extracellular

matrix

PLAU
Plasminogen
Proteinase/
AKA uPA; cleaves plasminogen to plasmin

activator, urokinase
Proteinase
(a protease responsible for nonspecific

Inhibitor
extracellular matrix degradation)

PTGS2
Prostaglandin-
Enzyme/
aka COX2; Proinflammatory, member of

endoperoxide
Redox
arachidonic acid to prostanoid conversion

synthase 2

pathway; induced by proinflammatory

cytokines

S100A7
S100 calcium-
calcium
Member of S100 family of calcium binding

binding protein 7
binding-
proteins; localized in the cytoplasm &/or

epidermal
nucleus of a wide range of cells; involved

differentiation
in the regulation of cell cycle progression

& differentiation; markedly overexpressed

in skin lesions of psoriatic patients

TGFB1
Transforming
cytokines-
Pro- and antiinflammatory activity, anti-

growth factor, beta
chemokines-
apoptotic; cell-cell signaling, can either

growth
inhibit or stimulate cell growth

factors

TIMP1
Tissue Inhibitor of
metalloproteinase
Member of TIMP family; natural inhibitors

Matrix
inhibitor-
of matrix metalloproteinases;

Metalloproteinase 1
ECM
transcriptionally induced by cytokines &

maintenance-
hormones; mediates erythropoeisis in vitro

positive

control

cell

proliferation

TNF
Tumor necrosis
cytokines-
Proinflammatory, TH1, mediates host

factor, alpha
chemokines-
response to bacterial stimulus, regulates

growth
cell growth & differentiation

factors

TNFSF6
Tumor necrosis
ligand-
aka FASL; Apoptosis antigen ligand 1 is

factor (ligand)
apoptosis
the ligand for FAS; interaction of FAS with

superfamily,
induction-
its ligand is critical in triggering apoptosis

member 6
signal
of some types of cells such as lymphocytes;

transduction
defects in protein may be related to some

cases of SLE

TP53
tumor protein p53
transcription
Tumor protein p53, a nuclear protein, plays

factor-
a role in regulation of cell cycle; binds to

DNA
DNA p53 binding site and activates

binding-
expression of downstream genes that

tumor
inhibit growth and/or invasion of tumor

suppressor-

DNA

recombination/

repair

VEGF
vascular
cytokines-
Producted by monocytes

endothelial growth
chemokines-

factor
growth

factors

TABLE 5

Liver Metabolism and Disease Gene Expression Panel

Symbol
Name
Classification
Description

ABCC1
ATP-binding cassette,
Liver Health Indicator
AKA Multidrug

sub-family C, member 1

resistance protein

1; AKA CFTR/MRP;

multispecific organic

anion membrane

transporter; mediates

drug resistance by

pumping xenobiotics out

of cell

AHR
Aryl hydrocarbon
Metabolism
Increases expression of

receptor
Receptor/Transcription Factor
xenobiotic metabolizing

enzymes (ie P450) in

response to binding of

planar aromatic

hydrocarbons

ALB
Albumin
Liver Health Indicator
Carrier protein found in

blood serum,

synthesized in the liver,

downregulation linked to

decreased liver

function/health

COL1A1
Collagen, type 1, alpha 1
Tissue Remodelling
AKA Procollagen;

extracellular matrix

protein; implicated in

fibrotic processes of

damaged liver

CYP1A1
Cytochrome P450 1A1
Metabolism Enzyme
Polycyclic aromatic

hydrocarbon

metabolism;

monooxygenase

CYP1A2
Cytochrome P450 1A2
Metabolism Enzyme
Polycyclic aromatic

hydrocarbon

metabolism;

monooxygenase

CYP2C19
Cytochrome P450
Metabolism Enzyme
Xenobiotic metabolism;

2C19

monooxygenase

CYP2D6
Cytochrome P450 2D6
Metabolism Enzyme
Xenobiotic metabolism;

monooxygenase

CYP2E
Cytochrome P450 2E1
Metabolism Enzyme
Xenobiotic metabolism;

monooxygenase;

catalyzes formation of

reactive intermediates

from small organic

molecules (i.e. ethanol,

acetaminophen, carbon

tetrachloride)

CYP3A4
Cytochrome P450 3A4
Metabolism Enzyme
Xenobiotic metabolism;

broad catalytic

specificity, most

abundantly expressed

liver P450

EPHX1
Epoxide hydrolase 1,
Metabolism Enzyme
Catalyzes hydrolysis of

microsomal

reactive epoxides to

(xenobiotic)

water soluble

dihydrodiols

FAP
Fibroblast activation
Liver Health Indicator
Expressed in cancer

protein, □

stroma and wound

healing

GST
Glutathione S-
Metabolism Enzyme
Catalyzes glutathione

transferase

conjugation to metabolic

substrates to form more

water-soluble, excretable

compounds; primer-

probe set nonspecific for

all members of GST

family

GSTA1 and
Glutathione S-
Metabolism Enzyme
Catalyzes glutathione

A2
transferase 1A1/2

conjugation to metabolic

substrates to form more

water-soluble, excretable

compounds

GSTM1
Glutathione S-
Metabolism Enzyme
Catalyzes glutathione

transferase M1

conjugation to metabolic

substrates to form more

water-soluble, excretable

compounds

KITLG
KIT ligand
Growth Factor
AKA Stem cell factor

(SCF); mast cell growth

factor, implicated in

fibrosis/cirrhosis due to

chronic liver

inflammation

LGALS3
Lectin, galactoside-
Liver Health Indicator
AKA galectin 3; Cell

binding, soluble, 3

growth regulation

NR1I2
Nuclear receptor
Metabolism
AKA Pregnane X

subfamily 1, group I,
Receptor/Transcription Factor
receptor (PXR);

family 2

heterodimer with

retinoid X receptor

forms nuclear

transcription factor for

CYP3A4

NR1I3
Nuclear receptor
Metabolism
AKA Constitutive

subfamily 1, group I,
Receptor/Transcription Factor
androstane receptor beta

family 3

(CAR); heterodimer

with retinoid X receptor

forms nuclear

transcription factor;

mediates P450 induction

by phenobarbital-like

inducers.

ORM1
Orosomucoid 1
Liver Health Indicator
AKA alpha 1 acid

glycoprotein (AGP),

acute phase

inflammation protein

PPARA
Peroxisome proliferator
Metabolism Receptor
Binds peroxisomal

activated receptor □

proliferators (ie fatty

acids, hypolipidemic

drugs) & controls

pathway for beta-

oxidation of fatty acids

SCYA2
Small inducible
Cytokine/Chemokine
AKA Monocyte

cytokine A2

chemotactic protein 1

(MCP1); recruits

monocytes to areas of

injury and infection,

upregulated in liver

inflammation

UCP2
Uncoupling protein 2
Liver Health Indicator
Decouples oxidative

phosphorylation from

ATP synthesis, linked to

diabetes, obesity

UGT
UDP-
Metabolism Enzyme
Catalyzes glucuronide

Glucuronosyltransferase

conjugation to metabolic

substrates, primer-probe

set nonspecific for all

members of UGT1

family

TABLE 6

Endothelial Gene Expression Panel

Symbol
Name
Classification
Description

ADAMTS1
Disintegrin-like and
Protease
AKA METH1; Inhibits endothelial

metalloprotease (reprolysin

cell proliferation; may inhibit

type) with thrombospondin

angiogenesis; expression may be

type 1 motif, 1

associated with development of cancer

cachexia.

CLDN14
Claudin 14

AKA DFNB29; Component of tight

junction strands

ECE1
Endothelin converting
Metalloprotease
Cleaves big endothelin 1 to endothelin 1

enzyme 1

EDN1
Endothelin 1
Peptide hormone
AKA ET1; Endothelium-derived

peptides; potent vasoconstrictor

EGR1
Early growth response 1
Transcription
AKA NGF1A; Regulates the

factor
transcription of genes involved in

mitogenesis and differentiation

FLT1
Fms-related tyrosine kinase 1

AKA VEGFR1; FRT; Receptor for

(vascular endothelial growth

VEGF; involved in vascular

factor/vascular permeability

development and regulation of

factor receptor)

vascular permeability

GJA1
gap junction protein, alpha 1,

AKA CX43; Protein component of

43 kD

gap junctions; major component of

gap junctions in the heart; may be

important in synchronizing heart

contractions and in embryonic

development

GSR
Glutathione reductase 1
Oxidoreductase
AKA GR; GRASE; Maintains high

levels of reduced glutathione in the

cytosol

HIF1A
Hypoxia-inducible factor 1,
Transcription
AKA MOP1; ARNT interacting

alpha subunit
factor
protein; mediates the transcription of

oxygen regulated genes; induced by

hypoxia

HMOX1
Heme oxygenase (decycling) 1
Redox Enzyme
AKA HO1; Essential for heme

catabolism, cleaves heme to form

biliverdin and CO; endotoxin

inducible

ICAM1
Intercellular adhesion
Cell Adhesion/
Endothelial cell surface molecule;

molecule 1
Matrix Protein
regulates cell adhesion and trafficking

upregulated during cytokine

stimulation

IGFBP3
Insulin-like growth factor

AKA IBP3; Expressed by vascular

binding protein 3

endothelial cells; may influence

insulin-like growth factor activity

IL15
Interleukin 15
cytokines-
Proinflammatory; mediates T-cell

chemokines-
activation, inhibits apoptosis,

growth factors
synergizes with IL-2 to induce IFN-g

and TNF-a

IL1B
Interleukin 1, beta
cytokines-
Proinflammatory; constitutively and

chemokines-
inducibly expressed by many cell

growth factors
types, secreted

IL8
Interleukin 8
cytokines-
Proinflammatory, major secondary

chemokines-
inflammatory mediator, cell adhesion.

growth factors
signal transduction, cell-cell signaling

angiogenesis, synthesized by a wide

variety of cell types

MAPK1
mitogen-activated protein
Transferase
AKA ERK2; May promote entry into

kinase 1

the cell cycle, growth factor

responsive

NFKB1
Nuclear Factor kappa B
Transcription
AKA KBF1, EBP1; Transcription

Factor
factor that regulates the expression of

infolammatory and immune genes;

central role in Cytokine induced

expression of E-selectin

NOS2A
Nitric oxide synthase 2A
Enzyme/Redox
AKA iNOS; produces NO which is

bacteriocidal/tumoricidal

NOS3
EndothelialNitric Oxide

AKA ENOS, CNOS; Synthesizes

Synthase

nitric oxide from oxygen and arginine;

nitric oxide is implicated in vascular

smooth muscle relaxation, vascular

endothelial growth factor induced

angiogenesis, and blood clotting

through the activation of platelets

PLAT
Plasminogen activator, tissue
Protease
AKA TPA; Converts plasminogin to

plasmin; involved in fibrinolysis and

cell migration

PTGIS
Prostaglandin I2
Isomerase
AKA PGIS; PTG1; CYP8; CYP8A1;

(prostacyclin) synthase

Converts prostaglandin h2 to

prostacyclin (vasodilator); cytochrome

P450 family; imbalance of

prostacyclin may contribute to

myocardial infarction, stroke,

atherosclerosis

PTGS2
Prostaglandin-endoperoxide
Enzyme/Redox
AKA COX2; Proinflammatory,

synthase 2

member of arachidonic acid to

prostanoid conversion pathway;

induced by proinflammatory cytokines

PTX3
pentaxin-related gene,

AKA TSG-14; Pentaxin 3; Similar to

rapidly induced by IL-1 beta

the pentaxin subclass of inflammatory

acute-phase proteins; novel marker of

inflammatory reactions

SELE
selectin E (endothelial
Cell Adhesion
AKA ELAM; Expressed by cytokine-

adhesion molecule 1)

stimulated endothelial cells; mediates

adhesion of neutrophils to the vascular

lining

SERPINE1
Serine (or cysteine) protease
Proteinase
AKA PAI1; Plasminogen activator

inhibitor, clade B
Inhibitor
inhibitor type 1; interacts with tissue

(ovalbumin), member 1

plasminogen activator to regulate

fibrinolysis

TEK
tyrosine kinase, endothelial
Transferase
AKA TIE2, VMCM; Receptor for

Receptor
angiopoietin-1; may regulate

endothelial cell proliferation and

differentiation; involved in vascular

morphogenesis; TEK defects are

associated with venous malformations

VCAM1
vascular cell adhesion
Cell Adhesion/
AKA L1CAM; CD106; INCAM-100;

molecule 1
Matrix Protein
Cell surface adhesion molecule

specific for blood leukocytes and

some tumor cells; mediates signal

transduction; may be linked to the

development of atherosclerosis, and

rheumatoid arthritis

VEGF
Vascular Endothelial Growth
Growth factor
AKA VPF; Induces vascular

Factor

permeability and endothelial cell

growth; associated with angiogenesis

TABLE 7

Cell Health and Apoptosis Gene Expression Panel

Symbol
Name
Classification
Description

ABL1
V-abl Abelson murine leukemia
oncogene
Cytoplasmic and nuclear

viral oncogene homolog 1

protein tyrosine kinase

implicated in cell

differentiation, division,

adhesion and stress response.

Alterations of ABL1 lead to

malignant transformations.

APAF1
Apoptotic Protease Activating
protease
Cytochrome c binds to

Factor 1
activator
APAF1, triggering activation

of CASP3, leading to

apoptosis. May also facilitate

procaspase 9 autoactivation.

BAD
BCL2 Agonist of Cell Death
membrane
Heterodimerizes with BCLX

protein
and counters its death

repressor activity. This

displaces BAX and restores its

apoptosis-inducing activity.

BAK1
BCL2-antagonist/killer 1
membrane
In the presence of an

protein
apropriate stimulus BAK 1

accelerates programed cell

death by binding to, and

antagonizing the repressor

BCL2 or its adenovirus

homolog e1b 19 k protein.

BAX
BCL2-associated X protein
membrane
Accelerates apoptosis by

protein
binding to, and antagonizing

BCL2 or its adenovirus

homolog e1b 19 k protein. It

induces the release of

cytochrome c and activation of

CASP3

BCL2
B-cell CLL/lymphoma 2
membrane
Interferes with the activation

protein
of caspases by preventing the

release of cytochrome c, thus

blocking apoptosis.

BCL2L1
BCL2-like 1 (long form)
membrane
Dominant regulator of

protein
apoptotic cell death. The long

form displays cell death

repressor activity, whereas the

short isoform promotes

apoptosis. BCL2L1 promotes

cell survival by regulating the

electrical and osmotic

homeostasis of mitochondria.

BID
BH3-Interacting Death Domain

Induces ice-like proteases and

Agonist

apoptosis. counters the

protective effect of bcl-2 (by

similarity). Encodes a novel

death agonist that

heterodimerizes with either

agonists (BAX) or antagonists

(BCL2).

BIK
BCL2-Interacting Killer

Accelerates apoptosis.

Binding to the apoptosis

repressors BCL2L1, bhrf1,

BCL2 or its adenovirus

homolog e1b 19 k protein

suppresses this death-

promoting activity.

BIRC2
Baculoviral IAP Repeat-
apoptosis
May inhibit apoptosis by

Containing 2
suppressor
regulating signals required for

activation of ICE-like

proteases. Interacts with

TRAF1 and TRAF2.

Cytoplasmic

BIRC3
Baculoviral IAP Repeat-
apoptosis
Apoptotic suppressor.

Containing 3
suppressor
Interacts with TRAF1 and

TRAF2. Cytoplasmic

BIRC5
Survivin
apoptosis
Inhibits apoptosis. Inhibitor of

suppressor
CASP3 and CASP7.

Cytoplasmic

CASP1
Caspase 1
proteinase
Activates IL1B; stimulates

apoptosis

CASP3
Caspase 3
proteinase
Involved in activation cascade

of caspases responsible for

apoptosis - cleaves CASP6,

CASP7, CASP9

CASP9
Caspase 9
proteinase
Binds with APAF1 to become

activated; cleaves and activates

CASP3

CCNA2
Cyclin A2
cyclin
Drives cell cycle at G1/S and

G2/M phase; interacts with

cdk2 and cdc2

CCNB1
Cyclin B1
cyclin
Drives cell cycle at G2/M

phase; complexes with cdc2 to

form mitosis promoting factor

CCND1
Cyclin D1
cyclin
Controls cell cycle at G1/S

(start) phase; interacts with

cdk4 and cdk6; has oncogene

function

CCND3
Cyclin D3
cyclin
Drives cell cycle at G1/S

phase; expression rises later in

G1 and remains elevated in S

phase; interacts with cdk4 and

cdk6

CCNE1
Cyclin E1
cyclin
Drives cell cycle at G1/S

transition; major downstream

target of CCND1; cdk2-

CCNE1 activity required for

centrosome duplication during

S phase; interacts with RB

cdk2
Cyclin-dependent kinase 2
kinase
Associated with cyclins A, D

and E; activity maximal during

S phase and G2; CDK2

activation, through caspase-

mediated cleavage of CDK

inhibitors, may be instrumental

in the execution of apoptosis

following caspase activation

cdk4
Cyclin-dependent kinase 4
kinase
cdk4 and cyclin-D type

complexes are responsible for

cell proliferation during G1;

inhibited by CDKN2A (p16)

CDKN1A
Cyclin-Dependent Kinase
tumor
May bind to and inhibit cyclin-

Inhibitor 1A (p21)
suppressor
dependent kinase activity,

preventing phosphorylation of

critical cyclin-dependent

kinase substrates and blocking

cell cycle progression;

activated by p53; tumor

suppressor function

CDKN2B
Cyclin-Dependent Kinase
tumor
Interacts strongly with cdk4

Inhibitor 2B (p15)
suppressor
and cdk6; role in growth

regulation but limited role as

tumor suppressor

CHEK1
Checkpoint, S. pombe

Involved in cell cycle arrest

when DNA damage has

occurred, or unligated DNA is

present; prevents activation of

the cdc2-cyclin b complex

DAD1
Defender Against Cell Death
membrane
Loss of DAD1 protein triggers

protein
apoptosis

DFFB
DNA Fragmentation Factor, 40-
nuclease
Induces DNA fragmentation

KD, Beta Subunit

and chromatin condensation

during apoptosis; can be

activated by CASP3

FADD
Fas (TNFRSF6)-associated via
co-receptor
Apoptotic adaptor molecule

death domain

that recruits caspase-8 or

caspase-10 to the activated fas

(cd95) or tnfr-1 receptors; this

death-inducing signalling

complex performs CASP8

proteolytic activation

GADD45A
Growth arrest and DNA damage
regulator of
Stimulates DNA excision

inducible, alpha
DNA repair
repair in vitro and inhibits

entry of cells into S phase;

binds PCNA

K-ALPHA-1
Alpha Tubulin, ubiquitous
microtubule
Major constituent of

peptide
microtubules; binds 2

molecules of GTP

MADD
MAP-kinase activating death
co-receptor
Associates with TNFR1

domain

through a death domain-death

domain interaction;

Overexpression of MADD

activates the MAP kinase

ERK2, and expression of the

MADD death domain

stimulates both the ERK2 and

JNK1 MAP kinases and

induces the phosphorylation of

cytosolic phospholipase A2

MAP3K14
Mitogen-activated protein
kinase
Activator of NFKB1

kinase kinase kinase 14

MRE11A
Meiotic recombination (S.
nuclease
Exonuclease involved in DNA

cerevisiae) 11 homolog A

double-strand breaks repair

NFKB1
Nuclear factor of kappa light
nuclear
p105 is the precursor of the

polypeptide gene enhancer in B-
translational
p50 subunit of the nuclear

cells 1 (p105)
regulator
factor NFKB, which binds to

the kappa-b consensus

sequence located in the

enhancer region of genes

involved in immune response

and acute phase reactions; the

precursor does not bind DNA

itself

PDCD8
Programmed Cell Death 8
enzyme,
The principal mitochondrial

(apoptosis-inducing factor)
reductase
factor causing nuclear

apoptosis. Independent of

caspase apoptosis.

PNKP
Polynucleotide kinase 3′-
phosphatase
Catalyzes the 5-prime

phosphatase

phosphorylation of nucleic

acids and can have associated

3-prime phosphatase activity,

predictive of an important

function in DNA repair

following ionizing radiation or

oxidative damage

PTEN
Phosphatase and tensin homolog
tumor
Tumor suppressor that

(mutated in multiple advanced
suppressor
modulates G1 cell cycle

cancers 1)

progression through negatively

regulating the PI3-kinase/Akt

signaling pathway; one critical

target of this signaling process

is the cyclin-dependent kinase

inhibitor p27 (CDKN1B).

RAD52
RAD52 (S. cerevisiae) homolog
DNA binding
Involved in DNA double-

proteinsor
stranded break repair and

meiotic/mitotic

recombination

RB1
Retinoblastoma 1 (including
tumor
Regulator of cell growth;

osteosarcoma)
suppressor
interacts with E2F-like

transcription factor; a nuclear

phosphoprotein with DNA

binding activity; interacts with

histone deacetylase to repress

transcription

SMAC
Second mitochondria-derived
mitochondrial
Promotes caspase activation in

activator of caspase
peptide
cytochrome c/APAF-1/

caspase 9 pathway of apoptosis

TERT
Telomerase reverse transcriptase
transcriptase
Ribonucleoprotein which in

vitro recognizes a single-

stranded G-rich telomere

primer and adds multiple

telomeric repeats to its 3-prime

end by using an RNA template

TNF
Tumor necrosis factor
cytokines-
Proinflammatory, TH1,

chemokines-
mediates host response to

growth factors
bacterial stimulus, regulates

cell growth & differentiation

TNFRSF11A
Tumor necrosis factor receptor
receptor
Activates NFKB1; Important

superfamily, member 11a,

regulator of interactions

activator of NFKB

between T cells and dendritic

cells

TNFRSF12
Tumor necrosis factor receptor
receptor
Induces apoptosis and activates

superfamily, member 12

NF-kappaB; contains a

(translocating chain-association

cytoplasmic death domain and

membrane protein)

transmembrane domains

TOSO
Regulator of Fas-induced
receptor
Potent inhibitor of Fas induced

apoptosis

apoptosis; expression of

TOSO, like that of FAS and

FASL, increases after T-cell

activation, followed by a

decline and susceptibility to

apoptosis; hematopoietic cells

expressing TOSO resist anti-

FAS-, FADD-, and TNF-

induced apoptosis without

increasing expression of the

inhibitors of apoptosis BCL2

and BCLXL; cells expressing

TOSO and activated by FAS

have reduced CASP8 and

increased CFLAR expression,

which inhibits CASP8

processing

TP53
Tumor Protein 53
DNA binding
Activates expression of genes

protein - cell
that inhibit tumor growth

cycle - tumor
and/or invasion; involved in

suppressor
cell cycle regulation (required

for growth arrest at G1);

inhibits cell growth through

activation of cell-cycle arrest

and apoptosis

TRADD
TNFRSF1A-associated via
co-receptor
Overexpression of TRADD

death domain

leads to 2 major TNF-induced

responses, apoptosis and

activation of NE-kappa-B

TRAF1
TNF receptor-associated factor 1
co-receptor
Interact with cytoplasmic

domain of TNFR2

TRAF2
TNF receptor-associated factor 2
co-receptor
Interact with cytoplasmic

domain of TNFR2

VDAC1
Voltage-dependent anion
membrane
Functions as a voltage-gated

channel 1
protein
pore of the outer mitochondrial

membrane; proapoptotic

proteins BAX and BAK

accelerate the opening of

VDAC allowing cytochrome c

to enter, whereas the

antiapoptotic protein BCL2L1

closes VDAC by binding

directly to it

XRCC5
X-ray repair complementing
helicase
Functions together with the

defective repair in Chinese

DNA ligase IV-XRCC4

hamster cells 5

complex in the repair of DNA

double-strand breaks

TABLE 8

Cytokine Gene Expression Panel

Symbol
Name
Classification
Description

CSF3
Colony Stimulating
Cytokines/
AKA G-CSF; Cytokine that

Factor 3 (Granulocyte)
Chemokines/Growth
stimulates granulocyte

Factors
development

IFNG
Interferon, Gamma
Cytokines/
Pro- and antiinflammatory

Chemokines/Growth
activity; TH1 cytokine;

Factors
nonspecific inflammatory

mediator; produced by

activated T-cells.

Antiproliferative effects on

transformed cells.

IL1A
Interleukin 1, Alpha
Cytokines/
Proinflammatory;

Chemokines/Growth
constitutively and inducibly

Factors
expressed in variety of cells.

Generally cytosolic and

released only during severe

inflammatory disease

IL1B
Interleukin 1, Beta
Cytokines/
Proinflammatory; constitutively

Chemokines/Growth
and inducibly expressed by

Factors
many cell types, secreted

IL1RN
Interleukin 1 Receptor
Cytokines/
IL1 receptor antagonist;

Antagonist
Chemokines/Growth
Antiinflammatory; inhibits

Factors
binding of IL-1 to IL-1

receptor by binding to receptor

without stimulating IL-1-like

activity

IL2
Interleukin 2
Cytokines/
T-cell growth factor, expressed

Chemokines/Growth
by activated T-cells, regulates

Factors
lymphocyte activation and

differentiation; inhibits

apoptosis, TH1 cytokine

IL4
Interleukin 4
Cytokines/
Antiinflammatory; TH2;

Chemokines/Growth
suppresses proinflammatory

Factors
cytokines, increases expression

of IL-1RN, regulates

lymphocyte activation

IL5
Interleukin 5
Cytokines/
Eosinophil stimulatory factor;

Chemokines/Growth
stimulates late B cell

Factors
differentiation to secretion of

Ig

IL6
Interleukin 6
Cytokines/
AKA Interferon, Beta 2; Pro-

Chemokines/Growth
and anti-inflammatory activity,

Factors
TH₂cytokine, regulates

hematopoiesis, activation of

innate response, osteoclast

development; elevated in sera

of patients with metastatic

cancer

IL10
Interleukin 10
Cytokines/
Antiinflammatory; TH₂;

Chemokines/Growth
Suppresses production of

Factors
proinflammatory cytokines

Interleukin 12 (p40)
Cytokines/
Proinflammatory; mediator of

Chemokines/Growth
innate immunity, TH₁

Factors
cytokine, requires co-

stimulation with IL-18 to

induce IFN-y

IL13
Interleukin 13
Cytokines/
Inhibits inflammatory cytokine

Chemokines/Growth
production

Factors

IL15
Interleukin 15
Cytokines/
Proinflammatory; mediates T-

Chemokines/Growth
cell activation, inhibits

Factors
apoptosis, synergizes with IL-2

to induce IFN-g and TNF-a

IL18
Interleukin 18
Cytokines/
Proinflammatory, TH1, innate

Chemokines/Growth
and aquired immunity,

Factors
promotes apoptosis, requires

co-stimulation with IL-1 or IL-

2 to induce TH1 cytokines in

T- and NK-cells

IL18BP
IL-18 Binding Protein
Cytokines/
Implicated in inhibition of

Chemokines/Growth
early TH1 cytokine responses

Factors

TGFA
Transforming Growth
Transferase/Signal
Proinflammatory cytokine that

Factor, Alpha
Transduction
is the primary mediator of

immune response and

regulation, Associated with

TH₁responses, mediates host

response to bacterial stimuli,

regulates cell growth &

differentiation; Negative

regulation of insulin action

TGFB1
Transforming Growth
Cytokines/
AKA DPD1, CED; Pro- and

Factor, Beta 1
Chemokines/Growth
antiinflammatory activity;

Factors
Anti-apoptotic; cell-cell

signaling, Can either inhibit or

stimulate cell growth;

Regulated by glucose in

NIDDM individuals,

overexpression (due to

oxidative stresS promotes

renal cell hypertrophy leading

to diabetic nephropathy

TNFSF5
Tumor Necrosis Factor
Cytokines/
Ligand for CD40; Expressed

(Ligand) Superfamily,
Chemokines/Growth
on the surface of T-cells;

Member 5
Factors
Regulates B-cell function by

engaging CD40 on the B-cell

surface

TNFSF6
Tumor Necrosis Factor
Cytokines/
AKA FASL; Apoptosis

(Ligand) Superfamily,
Chemokines/Growth
antigen ligand 1 is the ligand

Member 6
Factors
for FAS antigen; Critical in

triggering apoptosis of some

types of cells such as

lymphocytes; Defects in

protein may be related to some

cases of SLE

TNFSF13B
Tumor Necrosis Factor
Cytokines/
B-cell activating factor, TNF

(Ligand) Superfamily,
Chemokines/Growth
family

Member 13B
Factors

TABLE 9

TNF/IL1 Inhibition Gene Expression Panel

HUGO Symbol
Name
Classification
Description

CD14
CD14
Cell Marker
LPS receptor used as marker for

Antigen

monocytes

GRO1
GRO1
Cytokines/Chemokines/
AKA SCYB1, Melanoma

Oncogene
Growth factors
growth stimulating activity,

Alpha; Chemotactic for

neutrophils

HMOX1
Heme
Enzyme: Redox
Enzyme that cleaves heme to

Oxygenase

form biliverdin and CO;

(Decycling) 1

Endotoxin inducible

ICAM1
Intercellular
Cell Adhesion: Matrix
Endothelial cell surface

Adhesion
Protein
molecule; Regulates cell

Molecule 1

adhesion and trafficking; Up-

regulated during cytokine

stimulation

IL1B
Interleukin 1,
Cytokines/Chemokines/
Pro-inflammatory;

Beta
Growth factors
Constitutively and inducibly

expressed by many cell types;

Secreted

IL1RN
Interleukin 1
Cytokines/Chemokines/
Anti-inflammatory; Inhibits

Receptor
Growth factors
binding of IL-1 to IL-1 receptor

Antagonist

by binding to receptor without

stimulating IL-1-like activity

IL10
Interleukin 10
Cytokines/Chemokines/
Anti-inflammatory; TH₂

Growth factors
cytokine; Suppresses production

of pro-inflammatory cytokines

MMP9
Matrix
Proteinase/Proteinase
AKA Gelatinase B; Degrades

Metalloproteinase 9
Inhibitor
extracellular matrix molecules;

Secreted by IL-8 stimulated

neutrophils

SERPINE1
Serine (or
Proteinase/Proteinase
AKA Plasminogen activator

Cysteine)
Inhibitor
inhibitor-1, PAI-1; Regulator of

Protease

fibrinolysis

Inhibitor,

Clade E

(Ovalbumin),

Member 1

TGFB1
Transforming
Cytokines/Chemokines/
Pro- and anti-inflammatory

Growth
Growth factors
activity; Anti-apoptotic; Cell-cell

Factor, Beta 1

signaling; Can either inhibit or

stimulate cell growth

TIMP1
Tissue
Proteinase/Proteinase
Irreversibly binds and inhibits

Inhibitor of
Inhibitor
metalloproteinases such as

Metalloproteinase 1

collagenase

TNFA
Tumor
Cytokines/Chemokines/
Pro-inflammatory; TH₁cytokine;

Necrosis
Growth factors
Mediates host response to

Factor, Alpha

bacterial stimulus; Regulates cell

growth & differentiation

TABLE 10

Chemokine Gene Expression Panel

Symbol
Name
Classification
Description

CCR1
chemokine (C-C
Chemokine receptor
A member of the beta

motif) receptor 1

chemokine receptor family

(seven transmembrane

protein). Binds SCYA3/MIP-

1a, SCYA5/RANTES, MCP-3,

HCC-1, 2, and 4, and MPIF-1.

Plays role in dendritic cell

migration to inflammation sites

and recruitment of monocytes.

CCR3
chemokine (C-C
Chemokine receptor
C-C type chemokine receptor

motif) receptor 3

(Eotaxin receptor) binds to

Eotaxin, Eotaxin-3, MCP-3,

MCP-4, SCYA5/RANTES and

mip-1 delta thereby mediating

intracellular calcium flux.

Alternative co-receptor with

CD4 for HIV-1 infection.

Involved in recruitment of

eosinophils. Primarily a Th2

cell chemokine receptor.

CCR5
chemokine (C-C
Chemokine receptor
Member of the beta chemokine

motif) receptor 5

receptor family (seven

transmembrane protein).

Binds to SCYA3/MIP-1a and

SCYA5/RANTES. Expressed

by T cells and macrophages,

and is an important co-receptor

for macrophage-tropic virus,

including HIV, to enter host

cells. Plays a role in Th1 cell

migration. Defective alleles of

this gene have been associated

with the HIV infection

resistance.

CX3CR1
chemokine (C-X3-C)
Chemokine receptor
CX3CR1 is an HIV coreceptor

receptor 1

as well as a leukocyte

chemotactic/adhesion receptor

for fractalkine. Natural killer

cells predominantly express

CX3CR1 and respond to

fractalkine in both migration

and adhesion.

CXCR4
chemokine (C-X-C
Chemokine receptor
Receptor for the CXC

motif), receptor 4

chemokine SDF1. Acts as a

(fusin)

co-receptor with CD4 for

lymphocyte-tropic HIV-1

viruses. Plays role in B cell,

Th2 cell and naive T cell

migration.

GPR9
G protein-coupled
Chemokine receptor
CXC chemokine receptor

receptor 9

binds to SCYB10/IP-10,

SCYB9/MIG, SCYB11/I-

TAC. Binding of chemokines

to GPR9 results in integrin

activation, cytoskeletal

changes and chemotactic

migration. Prominently

expressed in in vitro cultured

effector/memory T cells and

plays a role in Th1 cell

migration.

GRO1
GRO1 oncogene
Chemokine
AKA SCYB1; chemotactic for

(melanoma growth

neutrophils. GRO1 is also a

stimulating activity,

mitogenic polypeptide secreted

alpha)

by human melanoma cells.

GRO2
GRO2 oncogene
Chemokine
AKA MIP2, SCYB2;

(MIP-2)

Macrophage inflammatory

protein produced by moncytes

and neutrophils. Belongs to

intercrine family alpha (CXC

chemokine).

IL8
interleukin 8
Chemokine
Proinflammatory, major

secondary inflammatory

mediator, cell adhesion, signal

transduction, cell-cell

signaling, angiogenesis,

synthesized by a wide variety

of cell types

PF4
Platelet Factor4
Chemokine
PF4 is released during platelet

(SCYB4)

aggregation and is chemotactic

for neutrophils and monocytes.

PF4's major physiologic role

appears to be neutralization of

heparin-like molecules on the

endothelial surface of blood

vessels, thereby inhibiting

local antithrombin III activity

and promoting coagulation.

SCYA2
small inducible
Chemokine
Recruits monocytes to areas of

cytokine A2 (MCP1)

injury and infection.

Stimulates IL-4 production;

implicated in diseases

involving monocyte, basophil

infiltration of tissue (ie.g.,

psoriasis, rheumatoid arthritis,

atherosclerosis).

SCYA3
small inducible
Chemokine
A “monokine” involved in the

cytokine A3 (MIP1a)

acute inflammatory state

through the recruitment and

activation of

polymorphonuclear

leukocytes. A major HIV-

suppressive factor produced by

CD8-positive T cells.

SCYA5
small inducible
Chemokine
Binds to CCR1, CCR3, and

cytokine A5

CCR5 and is a chemoattractant

(RANTES)

for blood monocytes, memory

t helper cells and eosinophils.

A major HIV-suppressive

factor produced by CD8-

positive T cells.

SCYB10
small inducible
Chemokine
A CXC subfamily chemokine.

cytokine subfamily B

Binding of SCYB10 to

(Cys-X-Cys),

receptor CXCR3/GPR9 results

member 10

in stimulation of monocytes,

natural killer and T-cell

migration, and modulation of

adhesion molecule expression.

SCYB10 is Induced by IFNg

and may be a key mediator in

IFNg response.

SDF1
stromal cell-derived
Chemokine
Belongs to the CXC subfamily

factor 1

of the intercrine family, which

activate leukocytes. SDF1 is

the primary ligand for CXCR4,

a coreceptor with CD4 for

human immunodeficiency

virus type 1 (HIV-1). SDF1 is

a highly efficacious

lymphocyte chemoattractant.

TABLE 11

Breast Cancer Gene Expression Panel

Symbol
Name
Classification
Description

ACTB
Actin, beta
Cell Structure
Actins are highly conserved

proteins that are involved in cell

motility, structure and integrity.

ACTB is one of two non-muscle

cytoskeletal actins. Site of action

for cytochalasin B effects on cell

motility.

BCL2
B-cell
membrane protein
Interferes with the activation of

CLL/lymphoma 2

caspases by preventing the release

of cytochrome c, thus blocking

apoptosis.

CD19
CD19 antigen
Cell Marker
AKA Leu 12; B cell growth factor

CD34
CD34 antigen
Cell Marker
AKA: hematopoietic progenitor

cell antigen. Cell surface antigen

selectively expressed on human

hematopoietic progenitor cells.

Endothelial marker.

CD44
CD44 antigen
Cell Marker
Cell surface receptor for

hyaluronate. Probably involved in

matrix adhesion, lymphocyte

activation and lymph node

homing.

DC13
DC13 protein

unknown function

DSG1
Desmoglein 1
membrane protein
Calcium-binding transmembrane

glycoprotein involved in the

interaction of plaque proteins and

intermediate filaments mediating

cell-cell adhesion. Interact with

cadherins.

EDR2
Early

The specific function in human

Development

cells has not yet been determined.

Regulator 2

May be part of a complex that may

regulate transcription during

embryonic development.

ERBB2
v-erb-b2
Oncogene
Oncogene. Overexpression of

erythroblastic

ERBB2 confers Taxol resistance

leukemia viral

in breast cancers. Belongs to the

oncogene

EGF tyrosine kinase receptor

homolog 2

family. Binds gp130 subunit of

the IL6 receptor in an IL6

dependent manner. An essential

component of IL-6 signalling

through the MAP kinase pathway.

ERBB3
v-erb-b2
Oncogene
Oncogene. Overexpressed in

Erythroblastic

mammary tumors. Belongs to the

Leukemia Viral

EGF tyrosine kinase receptor

Oncogene

family. Activated through

Homolog 3

neuregulin and ntak binding.

ESR1
Estrogen
Receptor/
ESR1 is a ligand-activated

Receptor 1
Transcription Factor
transcription factor composed of

several domains important for

hormone binding, DNA binding,

and activation of transcription.

FGF18
Fibroblast
Growth Factor
Involved in a variety of biological

Growth Factor 18

processes, including embryonic

development, cell growth,

morphogenesis, tissue repair,

tumor growth, and invasion.

FLT1
Fms-related
Receptor
Receptor for VEGF; involved in

tyrosine kinase 1

vascular development and

regulation of vascular

permeability.

FOS
V-fos FBJ murine
Oncogene/
Leucine zipper protein that forms

osteosarcoma
Transcriptional
the transcription factor AP-1 by

viral oncogene
Activator
dimerizing with JUN. Implicated

homolog

in the processes of cell

proliferation, differentiation,

transformation, and apoptosis.

GRO1
GRO1 oncogene
Chemokine/Growth
Proinflammatory; chemotactic for

Factor/Oncogene
neutrophils. Growth regulator

that modulates the expression of

metalloproteinase activity.

IFNG
Interferon,
Cytokine
Pro- and antiinflammatory

gamma

activity; TH1 cytokine;

nonspecific inflammatory

mediator; produced by activated

T-cells. Antiproliferative effects

on transformed cells.

IRF5
Interferon
Transcription Factor
Regulates transcription of

regulatory factor 5

interferon genes through DNA

sequence-specific binding.

Diverse roles, include virus-

mediated activation of interferon,

and modulation of cell growth,

differentiation, apoptosis, and

immune system activity.

KRT14
Keratin 14
Cytoskeleton
Type I keratin, intermediate

filament component; KRT14 is

detected in the basal layer, with

lower expression in more apical

layers, and is not present in the

stratum corneum. Together with

KRT5 forms the cytoskeleton of

epithelial cells.

KRT19
Keratin 19
Cytoskeleton
Type I epidermal keratin; may

form intermediate filaments.

Expressed often in epithelial cells

in culture and in some carcinomas

KRT5
Keratin 5
Cytoskeleton
Coexpressed with KRT14 to form

cytoskeleton of epithelial cells.

KRT5 expression is a hallmark of

mitotically active keratinocytes

and is the primary structural

component of the 10 nm

intermediate filaments of the

mitotic epidermal basal cells.

MDM2
Mdm2,
Oncogene/
Inhibits p53- and p73-mediated

transformed 3T3
Transcription Factor
cell cycle arrest and apoptosis by

cell double

binding its transcriptional

minute 2, p53

activation domain, resulting in

binding protein

tumorigenesis. Permits the nuclear

export of p53 and targets it for

proteasome-mediated proteolysis.

MMP9
Matrix
Proteinase/
Degrades extracellular matrix by

metalloproteinase 9
Proteinase Inhibitor
cleaving types IV and V collagen.

Implicated in arthritis and

metastasis.

MP1
Metalloprotease 1
Proteinase/
Member of the pitrilysin family.

Proteinase Inhibitor
A metalloendoprotease. Could

play a broad role in general

cellular regulation.

N33
Putative prostate
Tumor Suppressor
Integral membrane protein.

cancer tumor

Associated with homozygous

suppressor

deletion in metastatic prostate

cancer.

OXCT
3-oxoacid CoA
Transferase
OXCT catalyzes the reversible

transferase

transfer of coenzyme A from

succinyl-CoA to acetoacetate as

the first step of ketolysis (ketone

body utilization) in extrahepatic

tissues.

PCTK1
PCTAIRE protein

Belongs to the SER/THR family of

kinase 1

protein kinases; CDC2/CDKX

subfamily. May play a role in

signal transduction cascades in

terminally differentiated cells.

SERPINB5
Serine proteinase
Proteinase/
Protease Inhibitor; Tumor

inhibitor, clade B,
Proteinase Inhibitor/
suppressor, especially for

member 5
Tumor Suppressor
metastasis. Inhibits tumor

invasion by inhibiting cell

motility.

SRP19
Signal

Responsible for signal-

recognition

recognition-particle assembly.

particle 19 kD

SRP mediates the targeting of

proteins to the endoplasmic

reticulum.

STAT1
Signal transducer
DNA-Binding
Binds to the IFN-Stimulated

and activator of
Protein
Response Element (ISRE) and to

transcription 1,

the GAS element; specifically

91 kD

required for interferon signaling.

STAT1 can be activated by IFN-

alpha, IFN-gamma, EGF, PDGF

and IL6. BRCA1-regulated genes

overexpressed in breast

tumorigenesis included STAT1

and JAK1.

TGFB3
Transforming
Cell Signalling
Transmits signals through

growth factor,

transmembrane serine/threonine

beta 3

kinases. Increased expression of

TGFB3 may contribute to the

growth of tumors.

TLX3
T-cell leukemia,
Transcription Factor
Member of the homeodomain

homeobox 3

family of DNA binding proteins.

May be activated in T-ALL

leukomogenesis.

VWF
Von Willebrand
Coagulation Factor
Multimeric plasma glycoprotein

factor

active in the blood coagulation

system as an antihemophilic factor

(VIIIC) carrier and platelet-vessel

wall mediator. Secreted by

endothelial cells.

TABLE 12

Infectious Disease Gene Expression Panel

Symbol
Name
Classification
Description

C1QA
Complement
Proteinase/
Serum complement system; forms C1

component 1, q
Proteinase
complex with the proenzymes c1r and

subcomponent, alpha
Inhibitor
c1s

polypeptide

CASP1
Caspase 1
proteinase
Activates IL1B; stimulates apoptosis

CD14
CD14 antigen
Cell Marker
LPS receptor used as marker for

monocytes

CSF2
Granulocyte-
cytokines-
AKA GM-CSF; Hematopoietic

monocyte colony
chemokines-
growth factor; stimulates growth and

stimulating factor
growth factors
differentiation of hematopoietic

precursor cells from various lineages,

including granulocytes, macrophages,

eosinophils, and erythrocytes

EGR1
Early growth
cell signaling
master inflammatory switch for

response-1
and activation
ischemia-related responses including

chemokine sysntheis, adhesion

moelcules and macrophage

differentiation

F3
F3
Enzyme/
AKA thromboplastin, Coagulation

Redox
Factor 3; cell surface glycoprotein

responsible for coagulation catalysis

GRO2
GRO2 oncogene
cytokines-
AKA MIP2, SCYB2; Macrophage

chemokines-
inflammatory protein produced by

growth factors
moncytes and neutrophils

HMOX1
Heme oxygenase
Enzyme/
Endotoxin inducible

(decycling) 1
Redox

HSPA1A
Heat shock protein 70
Cell Signaling
heat shock protein 70 kDa

and activation

ICAM1
Intercellular adhesion
Cell Adhesion/
Endothelial cell surface molecule;

molecule 1
Matrix Protein
regulates cell adhesion and trafficking,

upregulated during cytokine

stimulation

IFI16
gamma interferon
cell signaling
Transcriptional repressor

inducible protein 16
and activation

IFNG
Interferon gamma
cytokines-
Pro- and antiinflammatory activity,

chemokines-
TH1 cytokine, nonspecific

growth factors
inflammatory mediator, produced by

activated T-cells

IL10
Interleukin 10
cytokines-
Antiinflammatory; TH2; suppresses

chemokines-
production of proinflammatory

growth factors
cytokines

IL12B
Interleukin 12 p40
cytokines-
Proinflammatory; mediator of innate

chemokines-
immunity, TH1 cytokine, requires co-

growth factors
stimulation with IL-18 to induce IFN-g

IL13
Interleukin 13
cytokines-
Inhibits inflammatory cytokine

chemokines-
production

growth factors

IL18
Interleukin 18
cytokines-
Proinflammatory, TH1, innate and

chemokines-
aquired immunity, promotes

growth factors
apoptosis, requires co-stimulation with

IL-1 or IL-2 to induce TH1 cytokines

in T- and NK-cells

IL18BP
IL-18 Binding
cytokines-
Implicated in inhibition of early TH1

Protein
chemokines-
cytokine responses

growth factors

IL1A
Interleukin 1, alpha
cytokines-
Proinflammatory; constitutively and

chemokines-
inducibly expressed in variety of cells.

growth factors
Generally cytosolic and released only

during severe inflammatory disease

IL1B
Interleukin 1, beta
cytokines-
Proinflammatory; constitutively and

chemokines-
inducibly expressed by many cell

growth factors
types, secreted

IL1R1
interleukin 1
receptor
AKA: CD12 or IL1R1RA

receptor, type I

IL1RN
Interleukin 1 receptor
cytokines-
IL1 receptor antagonist;

antagonist
chemokines-
Antiinflammatory; inhibits binding of

growth factors
IL-1 to IL-1 receptor by binding to

receptor without stimulating IL-1-like

activity

IL2
Interleukin 2
cytokines-
T-cell growth factor, expressed by

chemokines-
activated T-cells, regulates

growth factors
lymphocyte activation and

differentiation; inhibits apoptosis,

TH1 cytokine

IL4
Interleukin 4
cytokines-
Antiinflammatory; TH2; suppresses

chemokines-
proinflammatory cytokines, increases

growth factors
expression of IL-1RN, regulates

lymphocyte activation

IL6
Interleukin 6
cytokines-
Pro- and antiinflammatory activity,

(interferon, beta 2)
chemokines-
TH2 cytokine, regulates

growth factors
hemotopoietic system and activation

of innate response

IL8
Interleukin 8
cytokines-
Proinflammatory, major secondary

chemokines-
inflammatory mediator, cell adhesion,

growth factors
signal transduction, cell-cell signaling,

angiogenesis, synthesized by a wide

variety of cell types

MMP3
Matrix
Proteinase/
AKA stromelysin; degrades

metalloproteinase 3
Proteinase
fibronectin, laminin and gelatin

Inhibitor

MMP9
Matrix
Proteinase/
AKA gelatinase B; degrades

metalloproteinase 9
Proteinase
extracellular matrix molecules,

Inhibitor
secreted by IL-8-stimulated

neutrophils

PLA2G7
Phospholipase A2,
Enzyme/
Platelet activating factor

group VII (platelet
Redox

activating factor

acetylhydrolase,

plasma)

PLAU
Plasminogen
Proteinase/
AKA uPA; cleaves plasminogen to

activator, urokinase
Proteinase
plasmin (a protease responsible for

Inhibitor
nonspecific extracellular matrix

degradation)

SERPINE1
Serine (or cysteine)
Proteinase/
Plasminogen activator inhibitor-1/

protease inhibitor,
Proteinase
PAI-1

clade B (ovalbumin),
Inhibitor

member 1

SOD2
superoxide dismutase
Oxidoreductase
Enzyme that scavenges and destroys

2, mitochondrial

free radicals within mitochondria

TACI
Tumor necrosis factor
cytokines-
T cell activating factor and calcium

receptor superfamily,
chemokines-
cyclophilin modulator

member 13b
growth factors

TIMP1
tissue inhibitor of
Proteinase/
Irreversibly binds and inhibits

metalloproteinase 1
Proteinase
metalloproteinases, such as

Inhibitor
collagenase

TLR2
toll-like receptor 2
cell signaling
mediator of petidoglycan and

and activation
lipotechoic acid induced signalling

TLR4
toll-like receptor 4
cell signaling
mediator of LPS induced signalling

and activation

TNF
Tumor necrosis
cytokines-
Proinflammatory, TH1, mediates host

factor, alpha
chemokines-
response to bacterial stimulus,

growth factors
regulates cell growth & differentiation

TNFSF13B
Tumor necrosis factor
cytokines-
B cell activating factor, TNF family

(ligand) superfamily,
chemokines-

member 13b
growth factors

TNFSF5
Tumor necrosis factor
cytokines-
ligand for CD40; expressed on the

(ligand) superfamily,
chemokines-
surface of T cells. It regulates B cell

member 5
growth factors
function by engaging CD40 on the B

cell surface

TNFSF6
Tumor necrosis factor
cytokines-
AKA FasL; Ligand for FAS antigen;

(ligand) superfamily,
chemokines-
transduces apoptotic signals into cells

member 6
growth factors

VEGF
vascular endothelial
cytokines-
Producted by monocytes

growth factor
chemokines-

growth factors

IL5
Interleukin 5
Cytokines-
Eosinophil stimulatory factor;

chemokines-
stimulates late B cell differentiation to

growth factors
secretion of Ig

IFNA2
Interferon alpha 2
Cytokines-
interferon produced by macrophages

chemokines-
with antiviral effects

Number	Name	Date	Kind
5643765	Willey	Jul 1997	A
5696130	Jones et al.	Dec 1997	A
5811231	Farr et al.	Sep 1998	A
5846720	Foulkes et al.	Dec 1998	A
5866330	Kinzier et al.	Feb 1999	A
5955269	Ghai et al.	Sep 1999	A
5968784	Spinella et al.	Oct 1999	A
5994076	Chenchik et al.	Nov 1999	A
6132969	Stoughton et al.	Oct 2000	A
6146828	Lapidus et al.	Nov 2000	A
6150169	Smith et al.	Nov 2000	A
6165709	Friend et al.	Dec 2000	A
6185561	Balaban et al.	Feb 2001	B1
6203987	Friend et al.	Mar 2001	B1
6203988	Kambara et al.	Mar 2001	B1
6218122	Friend et al.	Apr 2001	B1
6222093	Marton et al.	Apr 2001	B1
6232065	Robinson et al.	May 2001	B1
6245517	Chen et al.	Jun 2001	B1
20010018182	Friend et al.	Aug 2001	A1
20010029018	Danenberg et al.	Oct 2001	A1
20010051344	Shalon et al.	Dec 2001	A1
20020012932	Wang	Jan 2002	A1
20020045197	Friend et al.	Apr 2002	A1

Number	Date	Country
10-261029	Sep 1998	JP
WO 9423023	Oct 1994	WO
WO 9741261	Jun 1997	WO
WO 9824935	Jun 1998	WO
WO 9824935	Nov 1998	WO
WO 9904251	Jan 1999	WO
WO 9944063	Feb 1999	WO
WO 9946403	Sep 1999	WO
WO 9954510	Oct 1999	WO
WO 9954510	Oct 1999	WO
WO 9911822	Nov 1999	WO
WO 9957130	Nov 1999	WO
WO 9958720	Nov 1999	WO
WO 0011208	Feb 2000	WO
WO 0022172	Apr 2000	WO
WO 0028092	May 2000	WO
WO 0071756	Nov 2000	WO
WO 0111082	Feb 2001	WO
WO 0112851	Feb 2001	WO
WO 0120998	Mar 2001	WO
WO 0123614	Apr 2001	WO
WO 0125473	Apr 2001	WO
WO 0127321	Apr 2001	WO
WO 0129261	Apr 2001	WO
WO 0129269	Apr 2001	WO
WO 0130972	May 2001	WO
WO 0132928	May 2001	WO
WO 03040404	May 2003	WO

Number	Date	Country
60348213	Nov 2001	US
60340881	Dec 2001	US
60369633	Apr 2002	US
60376997	Apr 2002	US
60141542	Jun 1999	US
60195522	Apr 2000	US

	Number	Date	Country
Parent	09821850	Mar 2001	US
Child	10291225		US
Parent	09605581	Jun 2000	US
Child	09821850		US

Identification, monitoring and treatment of disease and characterization of biological condition using gene expression profiles

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