CA2466502A1 - Identification, monitoring and treatment of disease and characterization of biological condition using gene expression profiles - Google Patents

Abstract

A method provides an index that is indicative of the state of a subject, as to a biological condition, based on a sample from the subject. An embodiment of this method includes: deriving from the sample a profile data set, the profi le 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 tha t 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 a n index pertinent to the biological condition of the subject.

Description

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 Tnvention 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:

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; 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. Gptionally 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 Iess 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 statisticale 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 perfoW xitng 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, 1FNG and IL10. The index~function may be proportional to 1/4{IL1A} + 1/4-{IL1B
} +
1/4 { TNF } + 1/4 { INFG } -1 / { IL 10 } 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 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.
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 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 as affected by the agent.
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.
Erief Descripti~n ~f the Da~avvin~s 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. lA 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.
1B illustrates use of an inflammation index in relation to the data of Fig.
lA, 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 (URl~.
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.
5 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 genes (of the Inflammation Gene Expression Panel of Table 1), of distinct single subjects 10 (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. 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).
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.
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. 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.
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. 2I-23 shows the inflammation index for an international group of subjects, suffering from rheumatoid arthritis, undergoing three separate treatment regimens.
IO Fig. 24 illustrates use of the inflammation index for assessment of a single subject suffering from inflammatory bowel disease.
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 Tbuprofen in vitro in relation to other non-steroidal anti-inflammatory drugs (NSAIDs).
Fig. 26 illustrates how the effects of two competing anti-inflammatory compounds can be compared objectively, quantitatively, precisely, and reproducibly.
Figs. 27 thxough 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 ILTA
derived from three distinct sources: S. pyogenes, l3. 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 4lcompare the gene expression induced by E. coli and S.
aureus under various concentrations and times.
Detailed Descri!~tion of Specific Embodiments Definitions 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 "baselifze 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 cofaditioh 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 ox 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 coradition" includes a "physiological condition".
"Body fluid" 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 proj tle 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 tJerae 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 RN~4 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 "Gerze 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 ar 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 "nonnative" 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 5 which are selected to permit discrimination of a biological eondition, 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 10 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 "stirrculus" includes (i) a monitored physical interaction with a subject, for example ultraviolet A or B, or light therapy fox seasonal affective disorder, or treatment 15 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 April 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 Pmels 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 theix 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 coridiiions 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 ~5 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 Ol/ 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 thexapy, 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/ILl 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 assa ~s 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, CA). 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 5 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 10 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 15 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 20 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 (HISS) 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% C02 for 30 minutes.
Stimulus is added at varying concentrations, mixed and held loosely capped at 37°C for 30 min. Additional test compounds rnay 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~uide 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 TM, Phenol-free Total RNA
Isolation I~it, Catalog #1912, version 9908; Austin, Texas).
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 x 75 mm polypropylene tube. 0.6 mL of 2X LPS (from E. coli serotye 0127:88, Sigma#L3880 or serotype 055, Sigma #I~005, lOng/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%
C02 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 x g, ambient temperature (IEC centrifuge or equivalent, in microfuge tube adapters in swinging bucket), and as much serum from eac-Fi tube vas 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 wide 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, Faster City, CA; 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, TaqmanTM PCR Reagent Kit, Protocol, part number 402823 revision A, 1996, Applied Biosystems, Foster City CA.) 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, CA). 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.

cells).
Materials 1. Applied Biosystems TAQMAN Reverse Transcription Reagents Kit (P/N
808-0234). Kit Components: lOX 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 (fox multiple samples, prepare extra cocktail to allow for pipetting error) 1 reaction (mL) 11X, e.g. 10 samples (mL) lOX 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, fox 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 -20oC 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 Hurnan Gene Expression Panel for Inflammation.
Materials 1. 20X Primer/Probe Mix for each gene of interest.
2. 20X Primer/Probe Mix for 18S endogenous control.
3. 2X Taqman Universal PCR Master Mix.
4. cDNA transcribed from RNA extracted from cells.
S. 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, PrimerlProbe for 18S endogenous control, and 2X PCR Master Mix as follows. Make sufficient excess to allow for pipetting error e.g.
approximately 10°l0 excess. The following example illustrates a typical set up for one gene with quadruplicate samples testing two conditions (2 plates).
1X(1 well) 9X (2 plates worth) 5 2X Master Mix 12.50 112.50 20X 18S PrimerlProbe Mix 1.25 11.25 20X Gene of interest Primer/Probe Mix 1.25 11.25 Total 15.00 135.00 10 2. Make stocks of cDNA targets by diluting 95p,1 of cDNA into 2000p,1 of water. The amount of cDNA is adjusted to give Ct values between 10 and 18, typically between 12 and 13.
3. Pipette 15p,1 of Primer/Probe mix into the appropriate wells of an Applied Biosystems 96-Well Optical Reaction Plate.
15 4. Pipette lOp,l 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.
20 Methods herein may also be applied using proteins where sensitive quantitative techniques, such as an Enzyme Linked TmmunoSorbent Assay (ELISA) or mass spectroscopy, are available and well-known in the art for measuring the amount of a protein constituent. (see WO 98124935 herein incorporated by reference).
Baseline profile data sets 25 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 same ex vivo or in vitro properties associated with an i~ 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 malces 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 d.
in databases or digital storage mediums and may retrieved for purposes including managing patient health care or for conducting clinical trials or for charact~rizii~g 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 PI. The first profile data set derived from sample PI is denoted M~, where M~ is a quantitative measure of a distinct RNA or protein constituent of PI. 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 demonsixate relative efficacy, differences in mechanisms of actions, etc. compared to other dnigs 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 fox 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, CI~-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. T he 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 5 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 10 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 15 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 20 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 25 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 30 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 1= ~' C~Mrp~'~ , where I is the index, M; is the value of the member i of the profile data set, Ct is 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 Ct and 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, fox example, may be employed the software from Statistical Innovations, Belmont, Massachusetts, called Latent Gold°. See the web pages at www.statisticalinnovations.corr~ll~/, which axe 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 1/4{ILlA} + 1/4{IL1B} + 1/4.{TNF} + 1/4{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 lZesults 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.
lA
above is shown in this figure after calculation using an index function proportional to the following mathematical expression: (1/4{IL1A} + 1/4{IL1B} + 1/4{TNF} +
1/4{INFG}
-1/{IL10}).
Example 2: Use of acute inflammation index or algorithm to monitor a biological condition of a sample or a sub,~ect. 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. lA) 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{ILIB} + 1l4{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 efficac. ,y 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 1/4{ILlA} + 1/4{IL1B} + 1/4{TNF} + 1/4{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, Colorado. 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 fox 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 5 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 10 (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 15 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 popula~i~ns 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 20 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.
25 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) 30 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 axe 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 andlor 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: Consistenc~pression 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 axe remarkably consistent over time.
Figs. 12 and 13 similarly show in each case the expression levels for each of 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. In each case, again the expression levels are remarkably consistent over time, and also similar across individuals.
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, Colorado 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 fox 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 (OCT)-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 (T~MARDS) 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 5 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 fox the unstable 10 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, 15 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 20 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 25 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.
30 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 twQ genes (of the Inflammation Gene Expression Panel of Table 1) is measured for varying doses (0.08 - 250 p,g/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.
l2atiometric 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,1FNG, 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 107 and 106 CFU/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 106 CFUImL) 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 107 CFU/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 106 and 102 CFU/mL immediately after administration) and from S. aureus (at concentrations of 107 and 102 CFU/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 107 and 106 CFU/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 107 and 106 CFU/mL immediately after administration). As in the case of Figs. 20 and 21, responses at some loci, such as GRO1 and GR02, 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 1 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 5 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 10 achieve such identification and to discriminate in such fashion is within the scope of various embodiments herein.
02331/00119 224730.1 Table 1. Inflammation Gene Expression Panel Symbol Name Classification Description IL 1 A Interleukincytokines- Proinflammatory; constitutively 1, and alpha chemokines-growthinducibly expressed in variety of cells.

factors Generally cytosolic and released only during severe inflammatory disease IL1B Interleukincytokines- Proinflammatory;constitutively l, and beta chemokines-growthinducibly expressed by many cell types, factors secreted TNFA Tumor cytokines- Proinflammatory, TH1, mediates host necrosis chemokines-growthxesponse to bacterial stimulus, regulates factor, factors cell growth & differentiation alpha IL6 Interleukincytokines- Pro- and antiinflammatory activity, (interferon,chemokines-growthcytokine, regulates hemotopoietic system beta 2) factors and activation of innate response ILS Interleukincytokines- Proinflammatory, major secondary ~

chemokines-growthinflammatory mediator, cell adhesion, factors signal transduction, cell-cell signaling, angiogenesis, synthesized by a wide variety of cell types Interferoncytokines- Pro- and antiinflammatory activity, gamma chemokines-growthcytokine, nonspecific inflammatory factors mediator, produced by activated T-cells IFNG

IL2 Interleukincytol~ines- T-cell growth factor, expressed 2 by chemokines-growthactivated T-cells, regulates lymphocyte factors activation and differentiation;
inhibits apoptosis, TH1 cytokine IL 12B Interleul~incytokines- Proinflammatory; mediator of innate 12 p40 chemokines-growthimmunity, TH 1 cytokine, requires co-factors stimulation with IL-18 to induce IFN-g IL15 Interleukincytolcines- Proinflammatory; mediates T-cell 15 chemokines-growthactivation, inhibits apoptosis, synergizes factors with IL-2 to induce IFN-g and TNF-a 1L18 Interleukincytokines- Proinflammatory, TH1, innate and 18 chemokines-growthaquired immunity, promotes apoptosis, factors requires co-stimulation with IL-1 or IL-2 to induce THl cytokines in T- and NK-cells IL,4 Interleukincytokines- Antiinflammatory; TH2; suppresses chemokines-growthproinflammatory cytokines, increases factors expression of IL-1RN, regulates lymphocyte activation ILS Interleukincytokines- Eosinophil stimulatory factor;
5 stimulates chemokines-growthlate B cell differentiation to secretion of factors Ig IL10 Interleukincytokines- Antiinflammatory; TH2; suppresses 10 chemokines-growthproduction of proinflammatory cytokines factors IL13 Interleukincytokines- Inhibits inflammatory cytokine 13 ~ chemokines-growthproduction factors IL1RN Interleukincytokines- IL1 receptor antagonist;

receptor chemokines-growthAntiinflammatory; inhibits binding of IL-antagonist factors 1 to IL-1 receptor by binding to receptor without stimulating IL-1-like activity IL18BP II,-18 cytokines- Implicated in inhibition of early TH1 Binding chemokines-growtheytolcine responses Protein factors TGFB i Transformincytokines- Pro- and antiinflammatory activity, anti-g growth chemokines-growthapoptotic; cell-cell signaling, can either factor, factors inhibit or stimulate cell growth beta 1 IFNA2 Interferon,cytokines- interferon produced by macrophages with alpha 2 chemokines-growthantiviral effects factors GRO 1 GRO 1 cytokines- AKA S CYB 1; chemotactic for oncogene chemokines-growthneutrophils (melanoma factors growth stimulating activity, alpha) GR02 GR02 cytokines- AKA MIP2, SCYB2; Macrophage oncogene chemokines-growthinflammatory protein produced by factors moncytes and neutrophils TNFSFS Tumor cytokines- ligand for CD40; expressed on the surface necrosis chemokines-growthof 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 Fast; Ligand for FAS antigen;

necrosis chemokines-growthtransduces apoptotic signals into cells factor factors (ligand) superfamily, member 6 CSF3 Colony cytol~ines- AKA GCSF;cytokine that stimulates stimulatingchemokines-growthgranulocyte development factor 3 factors (granulocyte) B7 B7 protein cell signaling Regulatory protein that may and be associated activation with lupus CSF2. Granulocyte-cytokines- AKA GM-CSF; Hematopoietic growth monocyte chemokines-growthfactor; 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 Transmembrcytokines- T cell activating factor and calcium ane activatorchemokines-growthcyclophilin modulator and CAML factors interactor VEGF vascular cytokines- Producted by monocytes endothelialchemokines-growth growth factors factor ICAM1 IntercellularCell Adhesion Endothelial cell surface molecule;
/

adhesion Matrix Protein regulates cell adhesion and trafficking, molecule upregulated during cytolcine 1 stimulation PTGS2 ProstaglandiEnzyme l IZedox AKA COX2.; Proinflammatory, member n- of arachidonic acid to prostanoid endoperoxide conversion pathway; induced by synthase proinflammatory cytokines NOS2.A Nitric oxideEnzyme / Redox AKA iNOS; produces NO which is synthase bacteriocidal/tumoricidal PLA2G7 PhospholipasEnzyme / Redox Platelet activating factor a A2, group VII (platelet activating factor acetylhydrola se, plasma) HMOX1 Heme Enzyme / Redox Endotoxin inducible oxygenase (decycling) F3 Enzyme l 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

CD 14 CD 14 Cell Marl~er LPS receptor used as marker for antigen monocytes CD4 CD4 antigenCell Marker Helper T-cell marker (p55) CDBA 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 heat shock protein 70 kDa and protein activation MMP3 Matrix Proteinase / AKA stromelysin; degrades fibronectin, metalloproteiProteinase Inhibitorlaminin and gelatin nase 3 MMP9 Matrix Proteinase / AKA gelatinase B; degrades extracellular metalloproteiProteinase Inhibitormatrix molecules, secreted by IL-8-nase 9 stimulated neutrophils PLAU . PlasminogenProteinase / AKA uPQ;.cleaves plasminogen to ..

activator, Proteinase Inhibitorplasmin (a protease responsible for urokinase nonspecific extracellular matrix degradation) SERPINEI Serine (or Proteinase / Plasminogen activator inhibitor-1 cysteine) Proteinase Inhibitor protease inhibitor, Glade B

(ovalbumin), member 1 TIMP 1 tissue Proteinase / Irreversibly binds and inhibits inhibitor Proteinase Inhibitormetalloproteinases, such as of collagenase metalloprotei nase 1 C_ 1 QA ComplementProteinase / Serum complement system; forms component Proteinase Inhibitorcomplex with the proenzymes l, c lr and c 1 s q subcompone nt, alpha polypeptide HLA-DRB 1 Major HistocompatibilityBinds antigen for presentation to CD4+

histocompati cells bility complex, class II, DR

beta 1 Table 2.
Diabetes Gene Expression Panel Classification Symbol Name Description Catalyzes the final step in the gluconeogenic and glycogenolyti glucose-6- Glucose-6- pathways. Stimulated by G6PC phosphatase, phosphatase/Glycogenglucocorticoids and strongly inhi catalytic metabolism by insulin. Overexpression (in conjunction with PCKl overexp~

leads to increased hepatic glucos roduction.

Pancreatic hormone which count the glucose-lowering action of in by stimulating glycogenolysis an GCG glucagon pancreatic/peptide gluconeogenesis. Underexpressi hormone glucagon is preferred.
Glucagon . peptide (GLP-1) proposed for tyl diabetes treatment inhibits luca;

Expression of GCGR is strongly upregul_ated by ~lncose.
.Deficie~

GCGR glucagon receptorglucagon receptor imbalance could play a role in N

Has been looked as a potential fc thera y.

The rate limiting enzyme fox glu entry into the hexosaxnine biosyr glutamine-fructose- pathway (HBP). Overexpressior GFPT1 6-phosphate Glutamine amidotransferaseGFA in muscle and adipose tissi transaminase increases products of 1 the HBP w thought to cause insulin resistant ( ossibly through defects to glut A key enzyme in the regulation i glycogen synthesis in the skeleta muscles of humans. Typically GYS 1 glycogen synthaseTransferase/Glycogenstimulated by insulin, 1 but in NII

(muscle) metabolism individuals GS is shown to be completely resistant to insulin stimulation (decreased activity a activation in muscle) Phosphorylates glucose into glw phosphate. NIDDM patients ha' HK2 hexokinase 2 hexokinase HK2 activity which may contril insulin resistance.
Similar actin GCK.

Decreases blood glucose concenr~

and accelerates glycogen 1NS insulin Insulin receptor ligandsynthe~

the liver. Not as critical in NI17I

in IDDM.

Positive regultion of insulin attic insulin receptorsl~~ protein is activated when insulin IRS 1 substrate 1 transduction/transmembraneto insulin receptor - binds 85-kD

receptor protein subunit of PI 3-K. decreased in s muscle of obese humans.

Rate limiting enzyme for gluconeogenesis - plays a key ro:

phosphoenolpyruvaterate-limiting gluconeogenicregulation of hepatic PCK1 glucose out c~.boxykinase enzyme insulin and glucagon.
1 Overexpre the liver results in increased hep~

glucose production and hepatic i~

resistance to lyco en s nthe Positive regulation of insulin act:

phosphoinositide-3- Docks in 1RS proteins and Gab 1 PIK3R1 ~nase, regulatoryregulatory enzyme activity is required for insulin sti subunit, polypeptide translocation of glucose transpor 1 (p85 alpha) the plasma membrane and activa lucose a take.

' The primary pharmacological peroxisome tar ~anscription ~actor/Ligand-the treatment of insulin PPARG proliferator-activated-. resistant dependent nuclear NIDDM. Involved in glucose receptor, gamma receptor any metabolism in skeletal muscle.

Negative regulation of insulin ac Activated by hyperglycemia - ins protein kinase protein kinase C/proteinphosphorylation of IRS-l PRKCB C, and re.

beta 1 , phosphorylation insulin receptor kinase activity.

Increased PKC activation may le oxidative stress causing overexp of TGF-beta and fibronectin solute carrier Glucose transporters family expressed t 2 (facilitated in b-cells and liver.
glucose Transport g:

SLC2A2 transporter), glucose transporter into the b-cell. Typically member underexpressed in pancreatic isle of individuals with NR?DM.

Glucose transporter protein that solute carrier mediator in insulin-stimulated family g:

2 (facilitated uptalce (rate limiting glucose for glucose SLC2.A4 glucose transporter LTnderex ression not im ortant transporter), p member p , overexpression in muscle and ac tissue consistently shown to inc~

glucose trans ort.

Regulated by glucose - in NIDL

transforming Transforming growth individuals, overexpression TGFB 1 growth factor (due oxidative stress - see factor, beta beta receptor ligand PKC) pror.

renal cell hypertrophy leading tc diabetic nephropathy.

Negative regulation of insulin ac cytokineltumor necrosis Produced in excess by adipose ti TNF tumor necrosis factor factor receptor ligand obese individuals - increases II2;
phosphorylation and decreases ii receptor kinase activity Table 3.
Prostate Gene Expression Panel S mbol Name Classification Y Description AKA MRP1, ABC29:

Multispecific organic anion ATP-binding cassette membrane transporter;

ABCC1 , membrane transporteroverexpression confers sub-family C, member tissue protection against a wide variety of xenobiotics due to their removal from the cell.

AKA PAP: Major phosphatase of the Acid phosphatase prostate;

ACPP , phosphatase synthesized under prostate androgen regulation; secreted by the a ithelial cells of the rostrate B-cell CLL / lymphomaapoptosis InhibitorBlocks apoptosis by - cell BCL2 ~ cycle control - interfering with the activation onco enesis of cas ases AKA Survivin; API4:
May counteract a default induction BIRC5 Baculoviral IAP apoptosis Inhibitorof apoptosis in G2/M
repeat- phase of containing 5 cell cycle; associates with microtubules of the nutotic s indle Burin a o tosis AKA ECAD, UVO: Calcium Cadherin 1, type cell-cell adhesionion-dependent cell CDHl 1, E- / adhesion cadherin interaction molecule that mediates cell to cell interactions in epithelial cells AKA NCAD, CDHN:

Calcium-dependent CDH2 Cadherin 2, type cell-cell adhesionglycoprotein that 1, N- l mediates cadherin interaction cell-cell interactions;
rnay be involved in neuronal reco nition mechanism AKA p16, MTS 1, INK4:

Tumor suppressor gene CD CYclin-dependent cell cycle control kinase -KN2A involved in a variet of inhibitor 2A tumor suppressor y m~ignancies; arrests normal di loid cells in late Binds cadherins and links CTNNA1 Catenin, alpha 1 cell adhesion them with the actin cytoskeleton AKA PSMA, GCP2:

Expressed in normal and neoplastic prostate cells;

FOLH1 Folate Hydrolase hydrolase membrane bound glycoprotein; hydrolyzes folate and is an N-acetylated a-linked acidic di a tidase Catalyzes the conjugation of reduced glutathione to a wide GSTTl Glutathione-S- metabolism number of exogenous and Transferase, theta endogenous hydrophobic electrophiles; has an important role in human carcino enesis Potential oncogene with MYC

High mobility groupDNA binding - binding site at promoter HMGIY protein, isoforms transcriptional region; involved in I and the Y regulation - oncogenetranscription regulation of genes containing, or in close roximity to a+t-rich re ions Heat shock 70kD cell signalling A~ HSP-70, HSP70-1:
HSPAlA protein and IA activation Molecular chaperone, stabilizes AU rich mRNA

Mediates insulin stimulated Insulin-like growthcytokines - chemokinesDNA synthesis; mediates IGFII~~

factor 1 receptor - growth factors IGF1 stimulated cell proliferation and differentiation Pro- and anti-inflammatory activity, TH2 cytokine, cytokines - chemokinesregulates hematopoiesis, IL6 Interleukin 6 _ growth factors activation of innate response, osteoclast development;

elevated in sera of patients with metastatic cancer AKA SCYBB, MDNCF:

Proinflammatory chemokine;

major secondary inflaminator5 II,B Interleukin 8 cytokines - chemolcinesmediator resulting in cell = growth factors adhesion, signal t ransduction, cell-cell signaling;
regulates angiogenesis in prostate cancer AKA SAR2, CD82, ST6:

KAIl Kangai I tumor suppressor suppressor of metastatic ability of prostate cancer cells AKA hGK-1: Glandular KLK2 Kallikrein 2, prostaticprotease - kallikreinkallikrein; expression restricted mainly to the rostate.

AKA PSA: Kallikrein-like protease which functions KLK3 Kallikrein 3 protease - kallikreinnormally in liquefaction of seminal fluid. Elevated in rostate cancer.

structural protein~'~ K19: Type I epidermal KRT 19 Keratin 19 -differentiation keratin; may form intermediate filaments AKA EBS2: 58 kD Type II

keratin co-expressed with keratin 14, a 50 kD
Type I

keratin, in stratified structural proteinepithelium. KRTS expression KRTS Keratin 5 -differentiation is a hallmark of mitotically active keratinocytes and is the ' primary structural component of the 10 nm intermediate filaments of the mitotic a idermal basal cells.

AKA K8, CKB: Type II

structural proteinkeratin; coexpressed KRT8 Keratin 8 - with differentiation Keratin 18; involved in intermediate filament formation - AID PCTA-1: binds to beta galactoside; involved in Lectin, Galactoside-cell adhesion - biological processes LGALS8 growth such as binding, soluble and differentiationcell adhesion, cell 8 growth regulation, inflammation, immunomodulation, apoptosis and metastasis Transcription factor that promotes cell proliferation V-myc avian transcription factorand transformation MAC myelocytomatosis - by viral oncogene activating growth-promoting oncogene homolog genes; may also repress gene ex cession AKA NRP, VEGF165R:
A

novel VEGF receptor that modulates VEGF binding to KDR (VEGF receptor) and subsequent bioactivity and NRP1 Neuropilin 1 cell adhesion 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 in LNCaP cells upon 1 exposure to androgens AKA DD3: prostate specific;

PCA3 Prostate cancer highly expressed in antigen 3 prostate tumors Prostate cancer AKA IPCA7: unknown PCANAP7 associated protein function; co-expressed 7 with known rostate cancer enes Acts as an androgen-independent transcriptional activator of the PSA
promoter;

Prostate epithelium directly interacts with the PDEF specific Ets transcriptiontranscription factorDNA binding domain of factor androgen receptor and enhances androgen-mediated activation of the PSA

romoter PLAU Urokinase-type proteinase A~ UPA, URK: cleaves lasmino en activator lasminogen to lasmin POV1 Prostate cancer RNA expressed selectively in overex ressed gene rostate tumor sam 1 les Prostate-specific cell surface PSCA Prostate stem cellanti en antigen expressed g strongly by antigen _~ both androgen-dependent a_nd -inde endent tumors AKA COX-2:

PTGS2 Prostaglandin- cytokines - chemokinesProinflammatory; member of endoperoxide synthase- growth factors arachidonic acid to 2 prostanoid conversion athway Serine proteinase proteinase inhibitorAKA Maspin, PIS: Protease -SERPINB5 inhibitor, Glade Inhibitor; Tumor suppressor, B, member 5 tumor suppressor es ecially for metastasis.

Serine (or cystein) AKA PAI1: regulates SERPINE proteinase inhibitor,proteinase inhibitor fibrinolysis; inhibits PLAU

Glade E, member AKA APRF: Transcription factor for acute phase Signal transduction response genes; rapidly and STAT3 activator of transcriptiontranscription factoractivated in response to 3 certain cytokines and growth factors; binds to IL6 response elements AKA TCS l, EST2:

Ribonucleoprotein which in vitro recognizes a single-TERT Telomerase reverse stranded G-rich telomere transcriptase primer and adds multiple telomeric repeats to its 3-prime end by using an RNA

tem late AKA DPD 1, CED: Pro-and Transforming growthcytokines - chemokinesantainflammatory activity;

factor, beta 1 - growth factors ~ti-apoptotic; cell-cell signaling, can either inhibit or stimulate cell rowth AKA TNF alpha:

Proinflammatory cytokine that is the primary mediator of immune response and TNF Tumor necrosis cytokines - chemokines factor, regulation, associated member 2 - growth factors with TH1 responses, mediates host ' response to bacterial stimuli, regulates cell growth &

differentiation AKA P53: Activates expression of genes that inhibit tumor growth and/or DNA binding proteininvasion; involved - in cell TP53 Tumor protein 53 cell cycle - tumorcycle regulation (required for suppressor growth arrest at Gl);
inhibits cell growth through activation of cell-cycle arrest and a o tosis Vascular Endothelialcytokines - chemokinesAKA VPF: Induces vascular VEGF

Growth Factor - growth factors permeability, endothelial' cell roliferation,-angiogenesis Table 4.
Skin Response Gene Expression Panel Symbol Name ClassificDescription ation apoptosi s Accelerates programmed cell death by inductio BCL2 associated binding to and antagonizing X the apoptosis BAX protein Cegerm repressor BCL2; may induce caspase activation develop ment apoptosi s Integral mitochondrial membrane protein inhibitorthat blocks the apoptotic death of some BCL2 B-cell - cell cells such as lymphocytes; constitutive CLLllymphoma cycle expression of BCLZ thought to 2 be cause of control-follicular lymphoma oncogen esis Signal transduc~~

ion- Member of Ig superfamily; tumor cell-peripherderived collagenase stimulatory factor;

BSG Basignin ~ stimulates matrix metalloproteinase plasma Synthesis in fibroblasts membra ne rotein collagen-differenti~pha 1 subunit of type ~TII
collagen; may CGL~A1 Type III collagen,ation- link collagen fibrils to the basement alpha 1 extxacell membrane, ular matrix retinoid binding-signal Low molecular weight protein highly Cellular Retinoictransductexpressed in skin; thought to be important CRABP2 Acid Binding ion- in RA-mediated regulation of skin growth Protein transcrip & differentiation tion regulatio n Connective Tissueinsulin-Member of family of peptides including CTGF Growth Factor hke serum-induced immediate early gene rowth roducts ex ressed after induction by factor-growth factors; overexpressed in fibrotic differentidisorders ation-woundin g res onse oxidativeeduced in human skin fibroblasts by stress oxidative/heat stress & growth Dual Specificityres factors; de-onse DUSP1 p phosphorylates MAP kinase erk2;
Phosphatase -tyrosinemay play a role in negative regulation phosphatof cellular proliferation ase growth factor-differenti ation-FGF7 Fibroblast growthwoundinaka KGF; Potent mitogen for epithelial factor 7 g cells; induced after skin injury response -signal transduct ion cell adhesionMajor cell surface glycoprotein of many FN1 Fibronectin motility-fibroblast cells; thought to have a role in signal cell adhesion, morphology, wound healing transduct& cell motility ion transcrip tion factor-inflammProto-oncoprotein acting with JUN, v-fos F13J murineatory stimulates transcription of genes with AP-1 FOS osteosarcoma responseregulatory sites; in some cases virus FOS

oncogene homolog-cell expression is associated with apototic cell growth death maintane nce cell Transcriptionally induced following cycle-Growth Arrest stressful growth arrest conditions and DNA &

GADD45A DNA-damage- treatment with DNA damaging agents;

inducible alpharepair-binds to PCNA affecting it's interaction apoptosiwith some cell division protein kinase s GRO1 oncogene cytokine GROl melanoma growths- A~ SCYB 1; chemotactic for neutrophils stimulating chemoki activity, alpha)nes-growth factors metaboli sm- Essential enzyme in heme catabolism;

HMOX1 Heme Oxygenase endoplasHMOX1 induced by its substrate 1 heme &

mic other substances such as oxidizing agents &

reticuluUVA

m Cell Intercellular AdhesioEndothelial cell surface molecule;
regulates ICAM1 adhesion moleculen / cell adhesion and trafficking, upregulated 1 Matrix during cytokine stimulation Protein cytokine s- Proinflammatory; constitutively and IL1A Interleukin chernokiinducibly expressed in variety 1, alpha of cells.

nes- Generally cytosolic and released only growth during severe inflammatory disease factors cytokine s Proinflammatory;constitutively m~ and IL1B Interleukin ri S inducibly expressed by many cell l, beta types, secreted growth _._ factors cytokineproinflammatory, major secondary s inflammatory mediator, cell adhesion, chemoki ILS Interleukin signal transduction, cell-cell 8 signaling, n es- angiogenesis, synthesized by growth a wide variety of cell types factors' structure 1 protein-peripherComponent of the lceratinocyte crosslinked I~JI, Involucrin ~ envelope; first appears in the cytosol plasma becoming crosslinlced to membrane membra proteins by transglutaminase ne rotein transcripproto-oncoprotein; component of v-jun avian tion transcription factor AP-1 that interacts JUN sarcoma virus factor-directly with target DNA sequences 17 to oncogene homologDNA regulate gene expression bindin structureType I keratin; associates with keratin 5;

1 protein-component of intermediate filaments;

KRT14 Keratin 14 differentiseveral autosomal dominant blistering skin ation- disorders caused by gene defects cell 64 .. ..
shape j structura 1 protein-Type I keratin; component of intermediate KRT 16 Keratin 16 ~fferentifilaments; induced in skin conditions ation- favoring enhanced proliferation or cell abnormal differentiation sha a structura 1 protein-Type II intermediate filament differentichain KRT5 Keratin 5 expessed largely in stratified ation- epithelium;

hallmark of mitotically active cell keratinocytes sha a kinase-stress ~a JNKl; nutogen activated protein Mitogen Activatedres kinase onse MAPK8 p regulates c-Jun in response to Protein Kinase - signalcell stress;

UV irradiation of skin activates transductMAPK8 ion Proteinasaka Collagenase; cleaves collagens types I-Matrix a / III; plays a key role in remodeling occuring MMP 1. MetalloproteinaseProteinasin both normal & diseased conditions;

1 a transcriptionally regulated by growth Inhibitorfactors, hormones, cytokines & cellular transformation Proteinasaka Gelatinise; cleaves collagens types IV, Matrix a / V, VTI and gelatin type I; produced by MMP2 MetalloproteinaseProteinasnormal skin fibroblasts; may play a role in a regulation of vascularization & the Inhibitorinflammatory res once aka Stromelysin; degrades fibronectin, Proteinaslaminin, collagens III, IV, IX, X, cartilage Matrix a / proteoglycans, thought to be involved in MMP3 MetalloproteinaseProteinaswound repair; progression of a atherosclerosis & tumor initiation;

Inhibitorproduced predominantly by connective tissue cells Proteinas Matrix a / AKA gelatinise B; degrades extracellular MMP9 metalloproteinaseProteinasmatrix molecules, secreted by a stimulated neutrophils Inhibitor transcrip tion aka PAR2; Member of nuclear hormone NRlI2 Nuclear receptoractivatioreceptor family of ligand-activated subfamily 1 n factor-transcription factors; activates transcription signal of cytochrome P-450 genes transduct ion-xenobiot is metaboli sm DNA

binding-DNA

Proliferating replicatiRequired for both DNA replication Cell &

PCNA Nuclear Antigenon-DNA repair; processivity factor for DNA

repair- polymerases delta and epsilon cell proliferat ion proteinas a inhibitoraka SKALP; Proteinase inhibitor found in PI3 Proteinase inhibitor-proteinepidermis of several inflammatory skin 3 skin derived binding-diseases; it's expression can be used as a extracellmarker of skin irritancy ular matrix Proteinas Plasminogen a / AKA uPA; cleaves plasxninogen to plasmin PLAU . Proteinas(a protease responsible for -activator, nonspecific urokinase a extracellular matrix degradation) Inhibitor Prostaglandin- aka COX2.; Proinflammatory, member of PTGS2 endoperoxide Enzyme arachidonic acid to prostanoid conversion synthase 2 / Redox pathway; induced by proinflammatory cytokines calcium Member of S 100 family of calcium binding binding-proteins; localized in the cytoplasm &/or S 100A7 S 100 calcium- epidermanucleus of a wide range of cells;
involved binding protein1 in the regulation of cell cycle 7 progression differenti& differentiation; markedly overexpressed ation in shin lesions of soriatic atients cytol~ine s Pro- and antiinflammatory activity, Transforming chemoki anti-TGFB 1 apoptotic; cell-cell signaling, growth factor, nes- can either beta inhibit or stimulate cell growth growth factors metallop Tissue InhibitorroteinaseMember of TIMP family; natural of inhibitorinhibitors Matrix of matrix metalloproteinases;
11MP 1 EClvt Metalloproteinase transcriptionally induced by cytokines ~

1 maintenahormones; mediates erythropoeisis in vitro nce-ositive control cell proliferat ion cytokine' s Proinflammatory, TH1, mediates Tumor necrosis chemoki host TNF response to bacterial stimulus, factor nes- regulates alpha , cell growth & differentiation growth factors ligand- ~a FASL; Apoptosis antigen ligand 1 is apoptosi Tumor necrosis the ligand for FAS; interaction of FAS with TNFSF6 factor (ligand)nductio its ligand is critical in triggering apoptosis superfamily, n-signalof some types of cells such as lymphocytes;

member 6 transductdefects in protein may be related to some cases of SLE

ion transcrip tion factor-DNA Tumor protein p53, a nuclear protein, plays binding-a role in regulation of cell cycle; binds to TP53 tumor protein tumor DNA p53 binding site and activates p53 suppressexpression of downstream genes that or-DNA inhibit growth andlor invasion of tumor recombi nation/re air cytokine vascular s moki VEGF endothelial ri ~ producted by monocytes growth factor growth factors Table 5.
Liver Metabolism and Disease Gene Expression Panel Symbol Name Classification Description AKA Multidrug resistance protein l;AKA CFTR/MRP;

ATP-binding cassette, multispecific organic ABCC1 sub-family C, Liver Health Indicatoranion membrane member 1 transporter; mediates drug resistance by pumping xenobiotics our of cell Increases expression of xenobiotic metabolizing ~R Aryl hydrocarbon Metabolism enzymes (ie P450) in receptor Receptor/Transcriptionresponse to binding Factor of planar aromatic h drocarbons Carrier protein found in - ~ -- blood serum, synthesized in ALB Albumin Liver Health Indicatorthe liver, downregulation linked tc decreased liver function/health AKA Procollagen;

extracellular Collagen, type matrix l, alpha COLlAI Tissue Remodelling protein; implicated in fibrotic processes of damaged liver Polycyclic aromatic CYPlAl Cytochrome P4.50 Metabolism Enzyme hydrocarbon lAl metabolism;

monooxy enase Polycyclic aromatic CYP1A2 Cytochrome P450 Metabolism Enzyme hydrocarbon lA2 metabolism;

monooxy enase CYP2C19 Cytochrome P450 Metabolism Enzyme Xenobiotic metabolism;

ZC 19 monooxygenase CYP2D6 Cytochrome P450 Metabolism Enzyme Xenobiotic metabolism.

monooxy enase Xenobiotic metabolism CYP2E Cytochrome P450 Metabolism Enzyme ~ monooxygenase;

catalyzes formation of reactive intermediates from small organic molecules (i.e.
ethanol, acetaminophen, carbon tetrachloride) Xenobiotic metabolism;

broad catalytic CYP3A4 Cytochrome P450 Metabolism Enzyme specificity, most abundantly expressed liver P450 Epoxide hydrolase Catalyzes hydrolysis 1, of EPHXl microsomal Metabolism Enzyme reactive epoxides to (xenobiotic) water soluble dihydrodiols Fibroblast activation Expressed in cancer FAP protein, ~ Liver Health Indicatorstroma and wound healing Catalyzes glutathione conjugation to metabolic substrates to form more GST Glutathione S- Metabolism Enzyme water-soluble, excretable transferase compounds; primer-probe set nonspecific for _ all. members of GST

family Catalyzes glutathione conjugation to GSTAl and Glutathione S- metabolic Metabolism Enzyme substrates to A2 transferase lAl/2 form more w ater-soluble, excretablc com ounds Catalyzes glutathione Glutathione S- conjugation to metabolic GSTM1 transferase M1 Metabolism Enzyme substrates to form more water-soluble, excretabl~

com ounds AKA Stem cell factor (SCF); mast cell growth KITLG KIT ligand Growth Factor factor, implicated in fibrosis/cirrhosis due to chronic liver inflammation LGALS3 Lectin, galactoside-Liver Health IndicatorA~ g~ectin 3;
Cell bindin , soluble, rowth regulation AKA Pregnane X

receptor (PXR);
Nuclear receptor Metabolism heterodimer with NRlI2 subfamily l, group I, Receptor/Transcriptionretinoid X receptor family 2 Factor forms nuclear transcri tion factor for AKA Constitutive androstane receptor beta (CAR); heterodimer Nuclear receptor with retinoid Metabolism X receptor NRlI3 subfamil 1 rou forms nuclear I, Receptor/Transcription y , g p Factor family 3 ~.~scription factor;

mediates P450 induction by phenobarbital-lilee inducers.

AKA alpha 1 acid GRM l Orosomucoid 1 Liver health IndicatorglYcoprotein (AGP), acute phase inflammation rotein Binds peroxisomal proliferators (ie fatty PPARA Peroxisome proliferatorMetabolism Receptor acids, hypolipidemic activated receptor drugs) & controls ~

pathway for beta-oxidation of fatt acids AKA Monocyte chemotactic protein Small inducible (MCPl); recruits SCYA2 cytokine A2 CYtokine/Chemokine monocytes to areas of injury and infection, upregulated in liver inflammation Decouples oxidative UCP2 Uncoupling proteinLiver health Indicatorphosphorylation 2 from ATP synthesis, linked to diabetes, obesity Catalyzes glucuronide UDP- conjugation to metabolic substrates, primer=probe UGT GlucuronosyltransferasMetabolism Enzyme set nonspecific for all members of UGTl family Table 6.
Endothelial Gene Expression Panel Symbol Name ClassificationDescription Disintegrin-Iike and ~ 1~TH1; Inhibits endotheli metalloprotease (reprolysin cell proliferation;
may inhibit ADAMTS type) with thrombospondinProtease angiogenesis; expression 1 may be associated with development type 1 motif, 1 of c cachexia.

CLDN14 Claudin 14 AKA DFNB29; Component of tip junction strands ECEl Endothelin convertingMetalloproteaseCleaves big endothelin 1 to endot enzyme 1 1 EDN1 Endothelin 1 Peptide hormone~ ET1; Endothelium-derived peptides; otent vasoconstrictor Transcription p'~ NGF1A; Regulates the EGR1 Early growth responsefactor transcription of genes 1 involved in mito enesis and differentiation Fms-related tyrosine AKA VEGFR1; FRT; Receptor kinase 1 f FLTl (vascular endothelial VEGF; involved in vascular growth factorlvascular permeability development and rcgulatior~
ci factor rece tor) vascular ermeability AKA CX43; Protein component gap junctions; major component GJA1 gap junction protein, gap junctions in the alpha 1, heart; may be 43kD important in synchronizing heart contractions and in embryonic develo meat AKA GR; GRASE; Maintains hi GSR Glutathione reductaseOxidoreductaselevels of reduced glutathione 1 in t cytosol AKA MOP1; ARNT interacting HIFlA Hypoxia-inducible Transcription protein; mediates the factor 1, transcriptio alpha subunit factor oxygen regulated genes;
induced by oxia AKA HOl; Essential for heme HMOX1 Heme oxygenase (decycling)Redox Enzyme catabolism, cleaves heme to fore 1 biliverdin and CO; endotoxin inducible Endothelial cell surface molecttl~

ICAM1 ~tercellular adhesionCell Adhesion regulates cell adhesion / and traffi molecule 1 Matrix Proteinupregulated during cytokine stimulation Insulin-like growth AI~ IBP3; Expressed factor by vascu:

IGFBP3 binding protein 3 endothelial cells; may influence insulin-like growth factor activit cytokines- Proinflammatory; mediates T-cel~

IL15 Interleukin 15 chemokines-activation, inhibits apoptosis, growth factorssynergizes with IL-2 to induce lF

and TNF-a cytokines- Proinflammatory;constitutively a~

IL1B Interleukin 1, beta chemokines- inducibly expressed by many cell owth factors ty es, secreted Proinflammatory, major secondar cytokines- inflammatory mediator, cell adhe IL8 Interleukin 8 chemokines- signal transduction, cell-cell sign:

growth factorsangiogenesis, synthesized by a wi variet ofcellty es mitogen-activated _ protein A~ ERK2; May promote entry MAPK1 ~nase 1 Transferase the cell cycle, growth factor res onsive AKA KBF1, EBP1 ; Transcriptio Transcription factor that regulates the expressio NFKB 1 Nuclear Factor kappa Factor infolammatory and immune B gene;

central role in Cytokine induced ex ression of E-selectin NOS2A Nitric oxide synthaseEnzyme / RedoxA~ SOS; produces NO
2A which bacteriocidal/tumoricidal AKA ENDS, CNOS; Synthesizes nitric oxide from oxygen and argi EndothelialNitric nitric oxide is implicated Oxide in vascL

NOS3 Synthase smooth muscle relaxation, vascul endothelial growth factor induces angiogenesis, and blood clotting throu h the activation of latelet~

AKA TPA; Converts plasminogi~

PLAT Plasminogen activator,Protease plasmin; involved in tissue fibrinolysis cell mi ration AKA PGIS; PTGI; CYPB;

Converts prostaglandin h2 to Prostaglandin I2 prostacyclin (vasodilator);
cytocl PTGIS (prostacyclin) synthaseIsomerase P450 family; imbalance of prostacyclin may contribute to myocardial infarction, stroke, atherosclerosis AKA COX2; Proinflammatory, PTGS2. Prostaglandin-endoperoxideEnzyme / Redoxmember of arachidonic acid to synthase 2 prostanoid conversion pathway;

induced by roinflammatory cytc AKA TSG-14; Pentaxin 3; Simil pentaxin-related gene, the pentaxin subclass PTX3 of inflamrr rapidly induced by acute-phase proteins;
IL-1 beta novel marl inflammatory reactions selectin E (endothelial AKA SLAM; Expressed by cytc SELE adhesion molecule Cell Adhesion stimulated endothelial 1) cells; mec adhesion of neutro hils to the v~

_71 _ lining Serine (or cysteine) AKA PAI1; Plasminogen protease activat SERPINEl inhibitor, Glade B Proteinase inhibitor type l; interacts with tis~

(ovalbumin), member ~bitor plasminogen activator 1 to regulate fibrinolysis AKA TIE2, VMCM; Receptor fo angiopoietin-l; may regulate TEK tyrosine kinase, endothelialTransferase endothelial cell proliferation and Receptor differentiation; involved in vascu:

morphogenesis; TEK defects are associated with venous malforma AKA L 1 CAM; CD 106;
INCAM-Cell surface adhesion molecule vascular cell adhesionCell Adhesion specific for blood leukocytes l and VCAM1 molecule 1 Matrix Proteinsome tumor cells; mediates signal transduction; may be linked to the development of atherosclerosis, a rheumatoid arthritis Vascular Endothelial A~ VPF; Induces vascular Growth VEGF Factor Growth factor permeability and endothelial cell growth; associated with angiogen Table 7.
Cell Health and Apoptosis Gene Expression Panel Symbol Name ClassificationDescription Cytoplasmic and nuclear protein tyrosine kinase V-abl Abelson murine implicated in cell leukemia ABLl viral oncogene homolog oncogene differentiation, division, adhesion and stress response.

Alterations of ABL1 lead to mali pant transformations.

Cytochrome c binds to Apoptotic Protease Activatingprotease p'P~l, triggering activation APAF1 of CASP3, leading to Factor 1 activator apoptosis. May also facilitate rocas ase 9 autoactivation.

Heterodimerizes with BCLX

membrane and counters its death BAD BCL2 Agonist of Cell repressor activity.
Death This protein displaces BAX and restores its a o tosis-inducing activity.

In the presence of an apropriate stimulus membrane accelerates programed cell BAKl BCL2-antagonist/killer death by binding to, 1 and protein antagonizing the repressor BCL2 or its adenovirus homolog a lb 19k rotein.

Accelerates apoptosis by binding to, and antagonizing ' membrane BCL2 or its adenovirus BAX BCLZ-associated X proteinprotein homolog elb 19k protein.
It induces the release of cytochrome c and activation of Interferes with the activation BCL2 B-cell CLL/lymphoma membrane of caspases by preventing 2 the protein release of cytochrome c, thus blocl~ing a o tosis.

Dominant regulator of apoptotic cell death.
The long membrane form displays cell death BCL2L1 BCL2-like 1 (long form) repressor activity, whereas the protein short isoform promotes apoptosis. BCL2L1 promotes cell survival by regulatin the electrical and osmotic homeostasis of mitochondria.

Induces ice-like proteases and apoptosis. counters the protective effect of bcl-2 (by B~ BH3-Interacting Death similarity). Encodes Domain a novel Agonist death agonist that heterodimerizes with either agonists (BAX) or antagonists (BCL2).

Accelerates apoptosis.

Binding to the apoptosis repressors BCL2L1, bhrfl, BIK BCL2-Interacting Filler BCL2 or its adenovirus homolog elb 19k protein suppresses this death-romotin activit .

May inhibit apoptosis by regulating signals required for BIRC2 Baculoviral IAP Repeat-apoptosis activation of ICE-like Containing 2 suppresser proteases. Interacts with TRAFl and TRAF2.

Cyto lasmic Apoptotic suppresser.
Baculoviral IAP Repeat-apoptosis BIRC3 Interacts with TRAF1 Containing 3 suppresser and TRAF2.C to lasmic apoptosis ~hibits apoptosis. Inhibitor of BIRC5 Survivin CASP3 and CASP7.

suppresser Cyto lasmic CASP1 Caspase 1 proteinase Activates IL1B; stimulates apoptosis Involved in activation cascade CASP3 Caspase 3 proteinase of caspases responsible for apoptosis - cleaves CASP6, CASP7, CASP9 Binds with APAF1 to become CASP9 Caspase 9 proteinase activated; cleaves and activates Drives cell cycle at GlIS and CCNA2 Cyclin A2 cyclin G2/M phase; interacts with cdk2 and cdc2 Drives cell cycle at G2lM

CCNB 1 Cyclin B 1 cyclin phase; complexes with cdc2 to form mitosis romoting factor Controls cell cycle at G1/S

(start) phase; interacts CCND 1 Cyclin D 1 cyclin with cdk4 and cdk6; has oncogene function Drives cell cycle at phase; expression rises later in CCND3 Cyclin D3 cyclin G1 and remains elevated in S

phase; interacts with cdk4 and cdk6 Drives cell cycle at transition; major downstream CCNE 1 Cyclin E 1 cyclin tat'get of CCND 1; cdk2-CCNEl activity required for centrosome duplication during S base; interacts with RB

Associated with cyclins A, D

and E; activity maximal during S phase and G2; CDK2 cdk2 Cyclin-dependent kinasekinase activation, through 2 caspase-mediated cleavage of CDI~

inhibitors, may be instrumental . in the execution of apoptosis following eas ase activation cdk4 and cyclin-D type cdk4 Cyclin-dependent kinasekinase complexes are responsible 4 for cell proliferation during G 1;

inhibited by CDKN2A
( 16) May bind to and inhibit cyclin-dependent kinase activity, preventing phosphorylation of CDKNlA Cyclin-Dependent I~inasetumor critical cyclin-dependent Inhibitor lA (p21) suppressor kinase substrates and blocking cell cycle progression;

activated by p53; tumor su ressor function Interacts strongly with cdl~4 CDKN2B CYclin-Dependent I~inasetumor and cdlc6; role in growth Inhibitor 2B (p15) suppressor regulation but limited role as tumor su ressor Involved in cell cycle arrest when DNA damage has CHEKl Checkpoint, S.pombe occurred, or unligated DNA is present; prevents activation of the cdc2-cyclin b com lex DADl Defender Against Cell membrane boss of DAD1 protein Death triggers protein apoptosis Induces DNA fragmentation DNA Fragmentation Factor,nuclease and chromatin condensation KD, Beta Subunit during apoptosis; can be activated by CASP3 Fas (TNFRSF6)-associated via Apoptotic adaptor molecule FADD death domain co-receptorthat recruits caspase-8 or caspase-10 to the activated fas (cd95) or tnfr-1 receptors;
this death-inducing signalling complex performs CASPB

roteolytic activation Stimulates DNA excision GADD45A Growth arrest and DNA regulator repair in vitro and damage of inhibits inducible, alpha DNA repair entry of cells into S phase;

binds PCNA

microtubuleMajor constituent of K-ALPHA-1 Alpha Tubulin, ubiquitous ~crotubules binds 2 peptide ;
molecules of GTP

Associates with TNFR1 through a death domain-death domain interaction;

Overexpression of MADD

MAP-kinase activating activates the MAP kinase MADD death domain co-receptorERK2, and expression of the MADD death domain stimulates both the ERK2 and JNKl MAP kinases and induces the phosphorylation of cytosolic hos holi ase MAP3K14 Mitogen-activated proteinkinase Activator of NFKB 1 kinase kinase kinase MRElIA Meiotic recombination nuclease Exonuclease involved (S. in DNA

cerevisiae) 11 homolog double-strand breaks A repair p105 is the precursor of the p50 subunit of the nuclear factor NFKB, which binds to Nuclear factor of kappanuclear the kappa-b consensus light NFKB 1 polypeptide gene enhancertranslationalsequence located in in B- the cells 1 (p105) regulator enhancer region of genes involved in immune response and acute phase reactions;
the precursor does not bind DNA

itself The principal mitochondria) PDCDB Programmed Cell Death enzyme, factor causing nuclear (apoptosis-inducing reductase apoptosis. Independent factor) of cas ase a o tosis.

Catalyzes the 5-prime phosphorylation of nucleic acids and can have associated PNKP Polynucleotide kinase phosphatase3-prime phosphatase 3'- activity, phosphatase predictive of an important function in DNA repair following ionizing radiation or oxidative damage _76_ Tumor suppressor that modulates G1 cell cycle Phosphatase and tensin progression through homolog negatively PTEN (mutated in multiple tumor regulating the PI3-kinase/Akt advanced cancers 1) suppressor signaling pathway; one critical target of this signaling process is the cyclin-dependent kinase inhibitor 27 (CDK1V1B).

Involved in DNA double-RAD52 RAD52 (S. cerevisiae) DNA bindingstranded break repair homolog and proteinsor meiotic / mitotic recombination Regulator of cell growth;

interacts with E2F-like transcription factor;
~ 1 Retinoblastoma 1 (includingtumor a nuclear phosphoprotein with osteosarcoma) suppressor DNA

binding activity; interacts with histone deacetylase to repress transcri tion Second mitochondria-derivedmitochondria)Promotes caspase activation SMAC in activator of caspase peptide cytochrome c / APAF-1 l cas ase 9 athway of a o tosis Ribonucleoprotein which in vitro recognizes a single-TERT Telomerase revexse transcriptasetranscriptasestranded G-rich telomere - - w primer and adds multiple telomeric repeats to its 3-prime end b using an RNA tem late cytokines- Proinflammatory, TH1, TNF Tumor necrosis factor chemokines-mediates host response to growth factorsbacterial stimulus, regulates cell rowth & differentiation Tumor necrosis factor Activates NFKB 1; Important receptor TNFRSF11A superfamily rece regulator of interactions member l la tor , p , activator of NFKB between T cells and dendritic cells Tumor necrosis factor Induces apoptosis and receptor activates TNFRSF12 superfamily, member NF-kappaB; contains 12 receptor a (translocating chain-association cytoplasmic death domain and membrane protein) transmembrane domains Potent inhibitor of Fas induced apoptosis; expression of TOSO, like that of FAS
and FASL, increases after T-cell TOSO Regulator of Fas-induced activation, followed by a a o tosis receptor p P decline and susceptibility to apoptosis; hematopoietic cells expressing TOSO resist anti-FAS-, FADD-, and TNF-' induced apoptosis without - 77 _ increasing expression of the inhibitors of apoptosis and BCLXL; cells expressing TOSO and activated by FAS

have reduced CASPB and increased CFLAR expression, which inhibits CASP8 rocessin Activates expression of genes that inhibit tumor growth DNA bindingand/or invasion; involved in TP53 Tumor Protein 53 protein cell cycle regulation - cell (required cycle - for growth arrest at tumor G 1 );

suppressor inhibits cell growth through activation of cell-cycle arrest and a o tosis Overexpression of TRADD

TRADD T~SF1A-associated via co_receptorleads to 2 major TNF-induced death domain responses, apoptosis and activation of NF-ka a-B

TRAFl TNF receptor-associated ~teract with cytoplasmic factor co_receptordomain of TNFR2 TRAF2 TNF receptor-associated. ~teract with cytoplasmic factor co ~ domain of TNFR2.
e:;eptor Functions as a voltage-gated pore of the outer mitochondrial membrane; proapoptotic proteins BAX and BAIL

VDAC 1 Voltage-dependent anionmembrane accelerate the opening of channel 1 protein VDAC allowing cytochrome c to enter, whereas the antiapoptotic protein BCL2.L1 closes VDAC by binding directly to it Functions together with X-ray repair complementing the DNA ligase IV-XRCC4.
XRCCS defective repair in helicase Chinese complex in the repair hamster cells 5 of DNA

double-strand breaks 79 _.... _... ..
Table 8.
Cytokine Gene Ex ression Panel Symbol Name Classification Description Colony StimulatingCytokines l AKA G-CSF; Cytokine that CSF3 Factor 3 (Granulocyte)Chemokines / Growthstimulates granulocyte Factors development Pro- and antiinflammatory activity; TH 1 cytokine;

Cytokines l nonspecific inflammatory IFNG Interferon, GammaChemokines l Growthmediator; produced by Factors activated T-cells.

Antiproliferative effects on transformed cells.

Proinflammatory;

Cytokines / constitutively and inducibly ILlA Interleukin 1, Chemokines / Growthexpressed in variety Alpha of cells.

Factors Generally cytosolic and released only during severe inflammatory disease Cytokines / Proinflammatory;constitutively IL1B Interleukin 1, Chemokines / Growthand inducibly expressed Beta by Factors many cell types, secreted IL1 receptor antagonist;

CYtokines / ~'' inhibits .

Interleukin 1 b ding of IL-1 to IL1RN Receptor Chemokines / GrowthIL-1 Antagonist Factors receptor by binding to receptor without stimulating IL-1-like activity T-cell growth factor, expressed Cytokines / by activated T-cells, regulates IL,2 Interleul~in 2 Chemokines / Growthlymphocyte activation and Factors differentiation; inhibits a o tosis, TH 1 cytokine Antiinflammatory; TH2;

Cytol~ines / suppresses proinflammatory IL,4 Interleul~in 4 Chemokines l Growthcytokines, increases expression Factors of IL-1RN, regulates lym hocyte activation Cytokines / Eosinophil stimulatory factor;

ILS Interleukin 5 Chemokines / Growthstimulates late B cell Factors differentiation to secretion of I

AKA Interferon, Beta 2; Pro-Cytokines / and anti-inflammatory activity, IL6 Interleukin 6 Chemokines / GrowthTHZ cytokine, regulates Factors hematopoiesis, activation of - innate response, osteoclast development; elevated in sera of patients with metastatic cancer Cytokines / Antiinflammatory; TH2;

IL10 Interleukin 10 Chemokines / Suppresses production Growth of Factors proinflammatory cytokines Proinflammatory; mediator of Cytokines l innate immunity, THl Interleukin 12 Chemokines / cytokine, requires co-(p40) Growth Factors stimulation with IL-18 to induce IFN-Cytokines / ~hibits inflammatory cytokine IL13 Interleukin 13 Chemokines / production Growth Factors Cytokines / Proinflammatory; mediates T-IL15 Interleukin 15 Chemokines / cell activation, inhibits Growth Factors apptosis, synergizes with IL-2 to induce IFN-g and TNF-a Proinflammatory, TH1, innate Cytokines / ~d aquired immunity, IL18 Interleukin 18 Chemokines / promotes apoptosis, Growth requires Factors co-stimulation with IL-1 or IL-2 to induce THl cytokines in T- and~NK-cells Cytokines / ~plicated in inhibition of IL18BP IL-18 Binding Chemokines / early TH1 cytokine responses Protein Growth Factors Proinflammatory cytol~ine that is the primary mediator of immune response and Transforming GrowthTransferase / regulation, Associated Signal with ' TGFA Factor, Alpha Transduction H~ responses, mediates host q response to bacterial stimuli, regulates cell growth &

differentiation; Negative re ulation of insulin action AIWA DPD1, CIED; Pro-and antiinflammatory activity;

Anti-apoptotic; cell-cell signaling, Can either inhibit or Transforming GrowthC~~nes / stimulate cell growth;

TGFB 1 Factor Chemol~ines / Regulated by glucose Beta 1 Growth in , Factors NIDDM individuals, overexpression (due to oxidative stress promotes renal cell hypertrophy leading to diabetic ne hro athy Ligand for CD40; Expressed TNFSF5 Tumor Necrosis Cytokines l on the surface of T-cells;
Factor Chemokines / Regulates B-cell function (Ligand) Superfamily,Growth by Member 5 Factors engaging CD40 on the B-cell surface AKA FASL; Apoptosis antigen ligand 1 is the ligand Tumor Necrosis Cytokines l for FAS antigen; Critical Factor in TNFSF6 (Ligand) Superfamily,Chemokines / ~lggering apoptosis Growth of some Member 6 Factors types of cells such as lymphocytes; Defects in protein may be related to some cases of SLE

Tumor Necrosis Cytokines /
Factor B-cell activating factor, TNFSF13B (Ligand) Superfamily,Chemokines / TNF
Growth family Member 13B Factors Table 9. TNF
l IL1 Inhibition Gene Expression Panel HUGO Symbol Name Classification Description CD14 CD14 Cell Marker LpS receptor used as marker for Antigen monocytes AKA SCYB 1, Melanoma GRO1 GRO1 Cytokines / Chemokinesgrowth stimulating activity, Oncogene / Growth factors Alpha; Chemotactic for neutro hill Heme ~ Enzyme that cleaves heme to HMOXl Oxygenase Enzyme: Redox form biliverdin and CO;

(Decycling) Endotoxin inducible Endothelial cell surface IntercellularCell Adhesion: Matrixmolecule; Regulates cell ICAM 1 Adhesion protein adhesion and trafficking;
Up-Molecule regulated during cytokine stimulation Pro-inflammatory;

IL1B InterleukinCytokines l ChemokinesConstitutively and inducibl;J
1, ._ Beta / Growth factors expressed by many cell types;

Secreted Interleukin Anti-inflammatory; Inhibits IL1RN Receptor CYtokmes / Chemokinesbinding of IL-1 to IL-1 receptor Antagonist / Growth factors by binding to receptor without stimulatin Il,-1-like activity Cytokines / Chemokines~ti-inflammatory; TH2 IL10 Interleukin/ Growth factors cytokine; Suppresses 10 production of pro-inflammatory cytokines Matrix AKA Gelatinase B; Degrades MMP9 Metalloproteiproteinase / Proteinaseextracellular matrix molecules;

nase 9 ~hibitor Secreted by IL,-8 stimulated .

neutro hils Serine (or Cysteine) Protease proteinase / Proteinase~'~ Plasminogen activator SERPINE1 Inhibitor, ~ibitor inhibitor-l, PAI-1; Regulator of Clade E fibrinolysis (Ovalbumin), Member 1 Transforming Pro- and anti-inflammatory TGFB 1 Growth Cytokines / Chemokinesactivity; Anti-apoptotic;
Cell-cell Factor, / Growth factors signaling; Can either Beta 1 inhibit or stimulate cell growth _8~,_ Tissue Irreversibly binds and inhibits Inhibitor Proteinase / Proteinase TIMPl of rnetalloproteinases such as MetalloproteiInhibitor collagenase nase 1 Tumor Pro-inflammatory; THt cytokine;

TNFA Necrosis C3'tokines / ChemokinesMediates host response . to Factor, / CTrowth factors bacterial stimulus; Regulates Alpha cell owth & differentiation Table 10.
Chemokine Gene Expression Panel Symbol Name Classification Description A member of the beta chemokine receptor family (seven transmembrane chemokine (C-C protein). Binds SCYA3/MIP-CCR1 motif) receptor Chemokine receptorla, SCYAS/RANTES, MCP-3, HCC-1, 2, and 4, and MPIF-1.

Plays role in dendritic cell migration to inflammation sites and recruitment of monocytes.

C-C type chemokine receptor (Eotaxin receptor) binds to Eotaxin, Eotaxin-3, MCP-3, MCP-4, SCYA5/RANTES
and chemokine (C-C mip-1 delta thereby mediating CCR3 motif) receptor Chemokine receptorintracellular calcium 3 flux.

Alternative co-receptor with CD4 for HIV-1 infection.

Involved in recruitment of eosinophils. Primarily a Th2 cell chemokine rece tor.

Member of the beta chemokine receptor family (seven transmembrane protein).

Binds to SCYA3/MIP-1a and SCYAS/RANTES. Expressed by T cells and macrophages, chemokine (C-C and is an important CCRS Chemokine receptorco-receptor motif) receptor for macrophage-tropic 5 virus, including HIV, to enter host cells. Plays a role in Th 1 cell migration. Defective alleles of this gene have been associated with the HIV infection resistance.

CX3CR1 is an HIV coreceptor as well as a leukocyte chemotactic/adhesion receptor CX3CR1 chemokine (C-X3-C)Chemokine receptorfor fractalkine. Natural killer receptor 1 cells predominantly express CX3CR1 and respond to fractalkine in both migration and adhesion.

~4 -Receptor for the CXC

chemokine SDF1. Acts as a chemokine (C-X-C co-receptor with CD4 for CXCR4 motif), receptor Chemokine receptorlymphocyte-tropic HIV-1 (fusin) viruses. Plays role in B cell, Th2 cell and naive T
cell mi ration.

CXC chemokine receptor binds to SCYB 10/IP-10, SCYB9/MIG, SCYB11/I-TAC. Binding of chemokines to GPR9 results in integrin GPR9 G protein-coupledChemokine receptoractivation, cytoskeletal receptor 9 changes and chemotactic migration. Prominently expressed in in vitro cultured effector/memory T cells and plays a role in Thl cell mi ration.

GROl oncogene AKA SCYB l; chemotactic for (melanoma growth neutrophils. GROl is GRO1 Chemokine also a stimulating activity, ~togenic polypeptide secreted al ha) by human melanoma cells.

AKA MIP2, SCYB2;

' Macrophage inflammatory GR02, GR02 oncogene Chemokine protein produced by moncytes (MIP-2) and neutrophils. Belongs to intercrine family alpha (CXC

chemokine).

Proinflammatory, major secondary inflammatory mediator, cell adhesion, signal IL8 interleul~in 8 Chemol~ine transduction, cell-cell signaling, angiogenesis, synthesized by a wide variety of cell ty es PF4 is released during platelet aggregation and is chemotactic for neutrophils and monocytes.

PF4's major physiologic role PF4 Platelet Factor Chemokine appears to be neutralization 4 of (SCYB4) heparin-like molecules on the endothelial surface of blood vessels, thereby inhibiting local antithrombin III
activity and romoting coa ulation.

Recruits monocytes to areas of SCYA2 small inducible Chemolcine injury and infection.

cytokine A2 (MCP1 Stimulates IL-4 production;
) implicated in diseases involving monocyte, basophil infiltration of tissue (ie.g., psoriasis, rheumatoid arthritis, atherosclerosis).
A "monokine" involved in the acute inflammatory state through the recruitment and SCYA3 small inducible Chemokine activation of cytokine A3 (M1P polymorphonuclear 1 a) leukocytes. A major HIV-suppressive factor produced by CD8- ositive T cells.

Binds to CCR1, CCR3, and CCRS and is a chemoattractant small inducible for blood monocytes, memory SCYAS cytokine AS Chemokine t helper cells and eosinophils.

(RANTES) A major HIV-suppressive factor produced by ositive T cells.

A CXC subfamily chemokine.

Binding of SCYB10 to receptor CXCR3lGPR9 results small inducible in stimulation of monocytes, cytokine subfamily natural killer and SCYB 10 B Chemokine T-cell ~Cys-X-Cys), nv,gration, and modulation of member 10 adhesion molecule expression.

SCYB 10 is Induced by IFNg and may be a key mediator in IFNg res onse.

Belongs to the CXC
subfamily of the intercrine family, which activate leukocytes.
SDFl is stromal cell-derived the primary ligand for CXCR4, I SDF1 Chemokine a coreceptor with CD4 factor 1 for human immunodeficiency virus type 1 (HIV-1).
SDF1 is a highly efficacious lym hocyte chemoattractant.

Table 11.
Breast Cancer Gene Expression Panel Symbol Name Classification Description Actins are highly conserved proteins that are involved in cell motility, structure and integrity.

ACTB Actin, beta Cell Structure ACTB is one of two non-muscle cytoskeletal actins. Site of action for cytochalasin B effects on cell motility.

Interferes with the activation of BCL2 B-cell membrane proteincaspases by preventing the release CLL/lymphoma of cytochrome c, thus 2 blocking a o tosis.

CD19 CD19 antigen Cell Marker AKA Leu 12; B cell growth factor AKA: hematopoietic progenitor cell antigen. Cell surface antigen CD34 CD34 antigen Cell Marker selectively expressed on human hematopoietic progenitor cells.

Endothelial marker.

Cell surface receptor for hyaluronate. Probably involved in CD44 CD44 antigen Cell Marker matrix adhesion, lymphocyte activation and lymph node homing.

DC 13 DC 13 protein unknown function Calcium-binding transmembrane glycoprotein involved in the DSG1 Desmoglein membrane proteininteraction of plaque 1 proteins and intermediate filaments mediating cell-cell adhesion. Interact with cadherins.

The specific function in human Early cells has not yet been determined.

EDR2 Development May be part of a complex that may Regulator 2 regulate transcription during embryonic develo ment.

Oncogene. Overexpression of v-erb-b2 ERBB2 confers Taxol resistance erythroblastic in breast cancers. Belongs to the ERBBZ leukemia viralOncogene EGF tyrosine kinase receptor oncogene family. Binds gp130 subunit of homolog 2 the IL6 receptor in an dependent manner. An essential component of IL-6 signalling through the MAP kinase pathway.

v-erb-b2 Oncogene. Overexpressed in Erythroblastic mammary tumors. Belongs to the ERBB3 Leukemia ViralOncogene , EGF tyrosine kinase receptor Oncogene family. Activated through Homolog 3 neuregulin and ntak binding.

ESRl is a ligand-activated Estrogen Receptor / transcription factor composed of ESR1 Receptor 1 Transcription several domains important Factor for hormone binding, DNA binding, and activation of transcri tion.

Involved in a variety of biological Fibroblast processes, including embryonic FGF18 Growth Factor Growth Factor development, cell growth, morphogenesis, tissue repair, tumor rowth, and invasion.

Receptor for VEGF; involved in FLTl Fms-related Receptor vascular development and tyrosine kinase regulation of vascular ermeabilit .

Leucine zipper protein that forms V-fos FBJ murine the transcription factor - Oncogene / AP-1 by osteosarcoina dimerizing with JUN. Implicated FOS Transcriptional viral oncogene in the processes of cell Activator homolo proliferation, differentiation, g transformation, and a o tosis.

Proinflammatory; chemotactic for GR01 GRO1 oncogene Chemokine / Growthneutrophils. Growth regulator Factor / Oncogenethat modulates the expression of metallo roteinase activit .

Pro- and antiinflammatory activity; TH 1 cytol~ine;

1FNG Interferon, C okine nonspecific inflammatory ~

gamma mediator; produced by activated T-cells. Antiproliferative effects on transformed cells.

Regulates transcription of interferon genes through DNA

Interferon sequence-specific binding.

IRFS regulatory Transcription Diverse roles, include factor Factor virus-mediated activation of interferon, and modulation of cell growth, differentiation, apoptosis, and immune system activit .

Type I lceratin, intermediate KRT14 Keratin 14 Cytoskeleton filament component; KRT14 is detected in the basal layer, with lower expression in more apical _88_ layers, and is not present in the stratum comeum. Together with KRTS forms the cytoskeleton of a ithelial cells.

Type I epidermal keratin;
may KRT19 Keratin 19 Cytoskeleton form intermediate filaments.

Expressed often in epithelial cells in culture and in some carcinomas Coexpressed with KRT14 to form cytoskeleton of epithelial cells.

KRTS expression is a hallmark of KRTS Keratin 5 Cytoskeleton ~totically active keratinocytes and is the primary structural component of the 10 nm intermediate filaments of the mitotic a idermal basal cells.

Inhibits p53- and p73-mediated Mdm2, cell cycle arrest and apoptosis by transformed binding its transcriptional 3T3 Oncogene /

MDM2 cell double activation domain, resulting Transcription in Factor minute 2, tumorigenesis. Permits p53 the nuclear binding protein export of p53 and targets it for roteasome-mediated roteol sis.

Matrix Degrades extracellular matrix by MMP9 metalloproteinaseProteinase / cleaving types IV and V collagen:

Proteinase InhibitorImplicated in arthritis and metastasis.

Member of the pitrilysin family.

MPl MetalloproteaseProteinase / A metalloendoprotease.
1 Could Proteinase Inhibitorplay a broad role in general cellular regulation.

Putative prostate Integral membrane protein.

N33 cancer tumor Tumor Suppressor Associated with homozygous deletion in metastatic prostate suppressor cancer.

~XCT catalyzes the reversible transfer of coenzyme A
from ~XCT 3-oxoacid Transferase succinyl-CoA to acetoacetate CoA as transferase the first step of ketolysis (ketone body utilization) in extrahepatic tissues.

Belongs to the SER/THR
family of PCTAIRE protein protein kinases; CDC2/CDKX

PCTKl ~nase 1 subfamily. May play a role in signal transduction cascades in terminally differentiated cells.

Serine proteinaseProteinase / Protease Inhibitor; Tumor Proteinase Inhibitorsuppressor, especially SERPINBS inhibitor, / for Glade B, metastasis. Inhibits tumor member 5 Tumor Suppressor invasion by inhibiting cell motility.

Responsible for signal-Signal recognition-particle assembly.

SRP19 recognition SRP mediates the targeting of particle l9kD proteins to the endoplasmic reticulum.

Binds to the IFN-Stimulated Response Element (ISRE) and to the GAS element; specifically Signal transducer required for interferon signaling.

STAT1 and activatorDNA-Binding STATl can be activated of by IFN-transcriptionProtein alpha, IFN-gamma, EGF, 1, PDGF

9lkD and IL6. BRCAl-regulated genes overexpressed in breast tumorigenesis included and JAK 1.

Transmits signals through Transforming transmembrane serine/threonine TGFB3 growth factor,Cell Signalling kinases. Increased expression of beta 3 TGFB3 may contribute to the owth of tumors.

Member of the homeodomain TLX3 T-cell leukemia,Transcription family of DNA binding Factor proteins.

homeobox 3 May be activated in T-ALL

leukomo enesis.

Multimeric plasma glycoprotein active in the blood coagulation Von WillebrandCoagulation Factorsystem as an antihemophilic factor factor (VIIIC) carrier and platelet-vessel wall mediator. Secreted by endothelial cells.

Table 12.
Infectious Disease Gene Expression Panel Symbol Name ClassificationDescription Complement component 1, Proteinase Serum complement system;
q / forms C1 C10_A Proteinase complex with the proenzymes subcomponent, clr and alpha ~ibitor cls oly a tide CASP1 Caspase 1 proteinase Activates 1L1B; stimulates apoptosis CD14 CD14 antigen Cell Marker LPS receptor used as marker for monocytes AKA GM-CSF; Hematopoietic Granulocyte- cytokines- growth factor; stimulates growth and CSF2 monocyte colony chemokines- differentiation of hematopoietic stimulating factorgrowth factorsprecursor cells from various lineages, including granulocytes, macrophages, eosino hils, and erythrocytes master inflammatory switch for Early growth cell signalingischemia-related responses ~ including EGR1 response-1 and activationchemokine sysntheis, adhesion' -- ' ' moelcules and macrophage differentiation Enzyme / A~ thromboplastin, Coagulation F3 F3 Factor 3; cell surface glycoprotein R edox res onsible for coa ulation catalysis ' cytokines- AKA MIP2, SCYB2; Macrophage GR02 GR02 oncogene chemokines- inflammatory protein produced by growth factorsmoncytes and neutro hils HMOXl Heme oxygenase Enzyme / Endotoxin inducible (decycling) 1 Redox HSPAlA Heat shock proteinCell Signalingheat shock protein 70 kDa and activation Endothelial cell surface molecule;

ICAM1 Intercellular Cell Adhesionregulates cell adhesion and adhesion / trafficl~ing, molecule 1 Matrix Proteinupregulated during cytokine stimulation IFI16 gamma interferoncell signalingTranscriptional repressor inducible roteinand activation cytokines- Pro- and antiinflammatory activity, TH1 cytokine, nonspecific IFNG Interferon gammachemokines-inflammatory mediator, produced growth factorsby activated T-cells cytokines- Antiinflammatory; TH2; suppresses IL10 Interleukin 10 chemokines- production of proinflammatory rowth factorscytokines cytokines- Proinflammatory; mediator of innate II,12B Interleukin 12 chemokines- l~unity, TH1 cytokine, requires p40 co-growth factorsstimulation with 1L-18 to induce IFN-cytokines- ~hibits inflammatory cytokine IL13 Interleukin 13 chemokines-production owth factors Proinflammatory, TH1, innate and cytokines- aquired immunity, promotes IL18 Interleukin 18 chemokines- apoptosis, requires co-stimulation with growth factorsIL-1 or IL-2 to induce TH1 cytokines in T- and NK-cells IL-18 Bindin cytokines-IL18BP g chemokines- ~Plicated in inhibition of early THl Protein rowth factorscYtokine responses cytokines- Proinflammatory; constitutively and IL1A Interleukin 1, chemokines- inducibly expressed in variety alpha of cells.

growth factorsGenerally cytosolic and released only durin severe inflammatory disease cytokines- Proinflammatory;constitutively and IL1B Interleukin l, chemokines- inducibly expressed by many beta cell owth factorsty es, secreted IL1R1 interleukin 1 receptor AKA: CD12 or IL1R1RA
receptor, type I

ILl receptor antagonist;

Interleukin 1 cYtokines- Antiinflammatory; inhibits receptor binding of B-1~ chemokines- IL-1 to IL-1 receptor by antagonist binding to growth factorsreceptor without stimulating TL-1-like activit T-cell growth factor, expressed by cytokines- activated T-cells, regulates IL2, Interleulcin chernokines-lymphocyte activation and growth factorsdifferentiation; inhibits apoptosis, TH 1 cytolcine cytokines- Antiinflammatory; TH2,; suppresses IY-44 Interleukin 4 chemokines- proinflammatory cytokines, increases growth factorsexpression of IL-1RN, regulates lym hocyte activation cytokines- Pro- and antiinflammatory activity, Interleukin 6 chemokines- TH2 cytokine, regulates (interferon, growth factorshemotopoietic system and beta 2) activation of innate res onse Proinflammatory, major secondary cytokines- inflammatory mediator, cell adhesion, 1L8 Interleukin 8 chemokines- signal t ransduction, cell-cell signaling, growth factorsangiogenesis, synthesized by a wide variety of cell ty es Matrix Proteinase A~ stromel sin; de rades MMP3 / Y g Proteinase metalloproteinase~hibitor fibronectin, laminin and 3 gelatin Proteinase A~ gelatinase B; degrades /

Matrix extracellular matrix molecules, MMP9 Proteinase metalloproteinase secreted by IL-8-stimulated 9 Inhibitor neutro hils Phospholipase A2, group VII (plateletEnzyme /

PLA2.G7 activating factorRedox Platelet activating factor acetylhydrolase, lasma) Proteinase Ate' uPA; cleaves plasminogen / to Plasminogen plasmin (a protease responsible PLAU Proteinase for activator, urokinase nonspecific extracellular Inhibitor matrix degradation) Serine (or cysteine)proteinase /

protease inhibitor, Plasminogen activator inhibitor-1 SERPINE1 proteinase /

Glade B (ovalbumin), pAI-1 Inhibitor member 1 SODZ superoxide dismutaseOxidoreductaseEnzyme that scavenges and destroys 2, mitochondrial free radicals within mitochondria Tumor necrosis cytokines-factor T cell activating factor TACI receptor superfamily,chemokines- and calcium cyclophilin modulator member 13b owth factors tissue inhibitorProteinase Irreversibly binds and inhibits of /

TIMP 1 Proteinase metalloproteinases, such --- metalloproteinase as I~ibitor colla enase TLR2 toll-like receptorcell signalingmediator of petidoglycan 2 and and activationlipotechoic acid induced signalling TLR4 toll-like receptorcell signalingmediator of LPS induced signalling and activation Tumor necrosis cytokines- Proinflammatory, TH1, mediates host TNF factor, alpha chemokines- response to bacterial stimulus, rowth factorsregulates cell rowth & differentiation Tumor necrosis cytokines-factor TNFSF13B (ligand) superfamily,chemokines- B cell activating factor TNF famil , y member 13b growth factors Tumor necrosis cytokines- ligand for CD40; expressed factor ~ on the TNFSFS (ligand) superfamily,chemokines- surface of T cells. It regulates B cell member 5 growth factorsfunction by engaging CD40 on the B

cell surface Tumor necrosis cytokines-factor AKA Fast; Ligand for FAS
TNFSF6 (ligand) superfamily,chemokines- antigen;

transducer apoptotic signals member 6 growth factorsinto cells vascular endothelialcytokines-VEGF growth factor chemokines- Producted by monocytes growth factors Interleukin 5 Cytokines- Eosinophil stimulatory factor;

ILS chemokines- stimulates late B cell differentiation to growth factorssecretion of Ig 1FNA2 ~terferon alpha Cytokines- interferon produced by macrophages chemokines- with antiviral effects growth factors TREM-1 Triggering Receptor / Cell Signaling and TREM 1 Receptor Activation Expressed on Myeloid Cells small inducible Chemokine A CXC subfamily chemokine.

cytokine subfamily Binding of SCYB10 to receptor B

(Cys-X-Cys), CXCR3/GPR9 results in stimulation SCYB 10 member 10 of monocytes, natural killer and T-cell migration, and modulation of adhesion molecule expression. SCYB
10 is Induced by IFNg and may be a key mediator in IFN res once.

Chemokine (C-C Chemokine A member of the beta chemokine motif) receptor receptor receptor family (seven transmembrane protein). Binds SCYA3/MIP'-la, CCR1 SCYAS/RANTES, MCP-3, HCC-1, 2, and 4, and MPIF-1. Plays role in dendritic cell migration to inflammation sites and recruitment of monocytes.

Chemokine (C-C Chemokine C-C type chemokine receptor motif) receptor receptor (Eotaxin receptor) binds 3 to Eotaxin, EotaXin-3, MCP=3; MCP-4, SCYAS/RANTES and mip-1 delta thereby mediating intracellular CCR3 calcium flux. Alternative co-receptor with CD4 for HIV-1 infection.

Involved in recruitment of eosinophils.

Primarily a Th2 cell chemokine rece tor.

Small inducile Chemol~ine A "monol~ine" involved in the acute cytokine A3 (MIl'la) inflammatory state through the SCYA3 recruitment and activation of polymorphonuclear leukocytes.
A

major HIV-suppressive factor roduced b CD8- ositive T
cells.

Chemokine (C-X3-Chemol~ine CX3CR1 is an HIV coreceptor as C) receptor 1 receptor well as a leukocyte chemotactic/adhesion receptor for CX3CR1 fractalkine. Natural killer cells predominantly express CX3CR1 and respond to fractalkine in both mi ration and adhesion.

02331/00119 225558.1

Claims (77)

What is claimed is:

1. A method, for evaluating a biological condition of a subject, based on a sample from the subject, comprising:
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.

2. A method of providing an index that is indicative of the state of a subject, as to a biological condition, based on a sample from the subject, the method comprising:
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; 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.

3. A method according to claim 1, further comprising 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.

4. A method according to claim 2, further comprising 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.

5. A method according to claims 2, further comprising 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.

6. A method according to claim 4, further comprising 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.

7. A method according to claim 5, wherein providing the normative value includes constructing the index function so that the normative value is approximately 1.

8. A method according to claim 6, wherein providing the normative value includes constructing the index function so that the normative value is approximately 0 and deviations in the index function from 0 are expressed in standard deviation units.

9. A method according to claim 5, wherein 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.

10. A method according to claim 6, wherein 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.

11. A method according to any of claims 1 through 10, wherein efficiencies of amplification, expressed as a percent, for all constituents lie within a range of approximately 2 percent.

12. A method according to any of claims 1through 10, wherein efficiencies of amplification, expressed as a percent, for all constituents lie within a range of approximately 1 percent.

13. A method according to any of claims 1through 10, wherein 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.

14. A method according to claim 11, wherein 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.

15. A method according to claim 12, wherein 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.

16. A method according to any of claims 1 through 10, wherein the panel includes at least three constituents.

17. A method according to any of claims 1 through 10, wherein the panel has fewer than approximately 500 constituents.

18. A method according to claim 1 through 10, wherein 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.

19. A method according to any of claims 1 through 10, wherein the biological condition is inflammation and the panel of constituents includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the Inflammation Gene Expression Panel of Table 1.

20. A method according to any of claims 1 through 10, wherein the biological condition is diabetes and the panel of constituents includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the Diabetes Gene Expression Panel of Table 2.

21. A method according to any of claims 1 through 10, wherein the biological condition is prostate health or disease and the panel of constituents includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the Prostate Gene Expression Panel of Table 3.

22. A method according to any of claims 1 through 10, wherein the biological condition is manifested in skin and the panel of constituents' includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the Skin Response Gene Expression Panel of Table 4.

23. A method according to any of claims 1 through 10, wherein the biological condition is liver metabolism and disease and the panel of constituents includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the Liver Metabolism and Disease Gene Expression Panel of Table 5.

24. A method according to any of claims 1 through 10, wherein the biological condition is vascular and the panel of constituents includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the Endothelial Gene Expression Panel of Table 6.

25. A method according to any of claims 1 through 10, wherein the biological condition is abnormal cell development and the panel of constituents includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the Cell Health and Apoptosis Gene Expression Panel of Table 7.

26. A method according to any of claims 1 through 10, wherein the biological condition is inflammation and the panel of constituents includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the Cytokine Gene Expression Panel of Table 8.

27. A method according to any of claims 1 through 10, wherein the biological condition is inflammation and the panel of constituents includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the TNF/IL1 Inhibition Gene Expression Panel of Table 9.

28. A method according to any of claims 1 through 10, wherein the biological condition is inflammation and the panel of constituents includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the Chemokine Gene Expression Panel of Table 10.

29. A method according to any of claims 1 through 10, wherein the biological condition is cancer and the panel of constituents includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the Breast Cancer Gene Expression Panel of Table 11.

30. A method according to any of claims 1 through 10, wherein the biological condition is infectious disease and the panel of constituents includes at least two, and optionally of least three, four, five, six, seven, eight, nine or ten, of the constituents of the Infectious Disease Gene Expression Panel of Table 12.

31. 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 comprising:
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;
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, so as to produce an index pertinent to the biological condition of the sample or the subject.

32. A method according to claim 31, wherein the panel includes at least three of the constituents in the Inflammation Gene Expression Panel.

33. A method according to claim 31, wherein the panel includes at least four of the constituents in the Inflammation Gene Expxession Panel.

34. A method according to claim 31, wherein the panel includes at least five of the constituents in the Inflammation Gene Expression Panel.

35. A method according to claim 31, wherein the panel includes at least six of the constituents in the Inflammation Gene Expression Panel.

36. A method according to claim 31, wherein the panel includes at least 10 of the constituents in the Inflammation Gene Expression Panel.

37. A method according to claim 31, wherein the biological condition is an inflammatory condition.

38. A method according to claim 31, wherein (i) the mapping by the index function is further based on an instance of a relevant baseline profile data set; and (ii) applying values from the first profile data set further includes applying values 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.

39. A method according to claim 31, wherein the index function is 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.

40. A method according to claim 39, wherein the index function is 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.

41. A method according to claim 39, wherein the index function is constructed to take into account clinical insight into inflammation biology.

42. A method according to claim 39, wherein the index function is constructed to take into account experimentally derived data.

43. A method according to claim 39, wherein the index function is constructed 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.

44. A method according to claim 31, wherein the panel includes at least one constituent that is associated with a specific inflammatory disease.

45. A method according to claim 31, 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) applying values from the first profile data set also includes applying a set of values associated with at least one of demographic data and clinical data.

46. A method according to claim 31, wherein 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.

47. A method according to claim 31, wherein the index function is a linear sum of terms, each term being a contribution function of a member of the profile data set.

48. A method according to claim 47, wherein the contribution function is a weighted power of the member.

49. A method according to claim 48, wherein the power is integral, so that the index function is a linear polynomial.

50. A method according to claim 49, wherein the profile data set includes at least three members corresponding to constituents selected from the group consisting of IL1A, IL1B, TNF, IFNG and IL10.

51. A method according to claim 49, wherein the profile data set includes at least four members corresponding to constituents selected from the group consisting of IL1A, IL1B, TNF, IFNG and IL10.

52. A method according to claim 51, wherein the index function is approximately proportional to 1/4{IL1A} + 1/4{IL1B} + 1/4{TNF} + 1/4{INFG} -1/{IL10}and braces around a constituent designate measurement of such constituent.

53. A method of analyzing complex data associated with a sample from a subject for information pertinent to inflammation, the method comprising:

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.

54. A method of monitoring the biological condition of a subject, the method comprising:
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.

55. 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 comprising:
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.

56. A method of guiding a decision to continue or modify therapy for a biological condition of a subject, the method comprising:
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.

57. A method of predicting change in biological condition of a subject as a result of exposure to an agent, the method comprising:
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.

58. A method according to claim 57, wherein the agent is a compound.

59. A method according to claim 58, wherein the compound is therapeutic.

60. A method of evaluating a property of an agent, the property being at least one of purity, potency, quality, efficacy or~safety, the method comprising:
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.

61. A method, for evaluating a biological condition of a subject, based on a sample from the subject, comprising:
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 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.

62. A method, for evaluating a biological condition of a subject, based on a sample from the subject, comprising:
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.

63. A method, for evaluating a biological condition affected by an agent, the method comprising:
obtaining, from a target population of cells to which the agent has been administered, a sample having at least one of RNA s 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 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 as affected by the agent.

64. A method according to any of claims 61 through 63, wherein the relevant population is a population of healthy subjects.

65. A method according to any of claims 61 through 63, wherein 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.

66. A method, for evaluating a biological condition of a subject, based on a sample from the subject, comprising:
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;
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.

67. A method according to claim 66, wherein the panel includes at least two, and optionally, at least three, at least four, at least five, or at least six of the constituents of the Inflammation Gene Expression Panel of Table 1.

68. A method according to any of claims 61 through 63, the panel including at least two of the constituents of the Inflammation Gene Expression Panel of Table 1 and 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.

69. A method according to any of claims 61 through 63, the panel including at least three of the constituents of the Inflammation Gene Expression Panel of Table 1 and wherein, in deriving the first profile data set, such measure i.s performed for each constituent both under conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar and under substantially repeatable conditions.

70. A method according to any of claims 61 through 63, the panel including at least four of the constituents of the Inflammation Gene Expression Panel of Table 1 and 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.

71. A method according to any of claims 61 through 63, the panel including at least five of the constituents of the Inflammation Gene Expression Panel of Table 1 and 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.

72. A method according to any of claims 61 through 63, the panel including at least six of the constituents of the Inflammation Gene Expression Panel of Table 1 and 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.

73. 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 comprising:
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 wherein, 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.

74. A method according to claim 73, wherein the first agent is a first drug and the second agent is a second drug.

75. A method according to claim 73, wherein the first agent is a drug and the second agent is a complex mixture.

76. A method according to claim 74, wherein the first agent is a drug and the second agent is a nutriceutical.

77. A method of providing an index that is indicative of the inflammatory state of a subject based on a sample from the subject, the method comprising:
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;
wherein the index function also uses data from 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 wherein, 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.