Patent Application
20080183395
The present invention relates generally to the identification of biological markers associated with the identification of multiple sclerosis. More specifically, the invention relates to the use of gene expression data in the identification, monitoring and treatment of subjects receiving anti-TNF therapy.
Multiple sclerosis (MS) is an autoimmune disease that affects the central nervous system (CNS). The CNS consists of the brain, spinal cord, and the optic nerves. Surrounding and protecting the nerve fibers of the CNS is a fatty tissue called myelin, which helps nerve fibers conduct electrical impulses. In MS, myelin is lost in multiple areas, leaving scar tissue called sclerosis. These damaged areas are also known as plaques or lesions. Sometimes the nerve fiber itself is damaged or broken. Myelin not only protects nerve fibers, but makes their job possible. When myelin or the nerve fiber is destroyed or damaged, the ability of the nerves to conduct electrical impulses to and from the brain is disrupted, and this produces the various symptoms of MS. People with MS can expect one of four clinical courses of disease, each of which might be mild, moderate, or severe. These include Relapsing-Remitting, Primary-Progressive, Secondary-Progressive, and Progressive-Relapsing
Individuals Progressive-Relapsing MS experience clearly defined flare-ups (also called relapses, attacks, or exacerbations). These are episodes of acute worsening of neurologic function. They are followed by partial or complete recovery periods (remissions) free of disease progression.
Individuals with Primary-Progressive MS experience a slow but nearly continuous worsening of their disease from the onset, with no distinct relapses or remissions. However, there are variations in rates of progression over time, occasional plateaus, and temporary minor improvements.
Individuals with Secondary-Progressive MS experience an initial period of relapsing-remitting disease, followed by a steadily worsening disease course with or without occasional flare-ups, minor recoveries (remissions), or plateaus.
Individuals with Progressive-Relapsing MS experience a steadily worsening disease from the onset but also have clear acute relapses (attacks or exacerbations), with or without recovery. In contrast to relapsing-remitting MS, the periods between relapses are characterized by continuing disease progression.
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. Thus a need exists for better ways to diagnose and monitor the progression of multiple sclerosis.
Currently, the characterization of disease condition related to MS (including diagnosis, staging, monitoring disease progression, monitoring treatment effects on disease activity) is imprecise. Imaging that detects what appears to be plaques in CNS tissue is typically insufficient, by itself, to give a definitive diagnosis of MS. Diagnosis of MS is often made only after both detection of plaques and of clinically evident neuropathy. It is clear that diagnosis of MS is usually made well after initiation of the disease process; i.e., only after detection of a sufficient number of plaques and of clinically evident neurological symptoms. Additionally, staging of MS is typically done by subjective measurements of exacerbation of symptoms, as well of other clinical manifestations. There are difficulties in diagnosis and staging because symptoms vary widely among individuals and change frequently within the individual. Thus, there is the need for tests which can aid in the diagnosis, monitor the progression and staging of MS. This is of particular importance in patients who are recommended to receive anti-TNF therapy as it is known that anti-TNF therapy exacerbates the clinical manifestations of multiple sclerosis.
The invention is based in part upon the identification of gene expression profiles associated with multiple sclerosis (MS). Theses genes are referred to herein as MS-associated genes. More specifically, the invention is based upon the surprising discovery that detection of as few as two MS-associated genes is capable of identifying individuals with or without MS with at least 75% accuracy.
In various aspects the invention provides a method for determining a profile data set for characterizing a subject with multiple sclerosis or an inflammatory condition related to multiple sclerosis based on a sample from the subject, the sample providing a source of RNAs, by using amplification for measuring the amount of RNA in a panel of constituents including at least 2 constituents from any of Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 and arriving at a measure of each constituent. The profile data set contains the measure of each constituent of the panel.
Also provided by the invention is a method of characterizing multiple sclerosis or inflammatory condition related to multiple sclerosis in a subject, based on a sample from the subject, the sample providing a source of RNAs, by assessing a profile data set of a plurality of members, each member being a quantitative measure of the amount of a distinct RNA constituent in a panel of constituents selected so that measurement of the constituents enables characterization of the presumptive signs of a multiple sclerosis.
In yet another aspect the invention provides a method of characterizing multiple sclerosis or an inflammatory condition related to multiple sclerosis in a subject, based on a sample from the subject, the sample providing a source of RNAs, by determining a quantitative measure of the amount of at least one constituent from any of Tables 1-10. In yet another aspect the invention provides a method for predicting an adverse effect from anti-TNF therapy in a subject, based on a sample from the subject, the sample providing a source of RNAs, said method comprising: a) assessing a profile data set of a plurality of members, each member being a quantitative measure of the amount of a distinct RNA constituent in a panel of constituents selected so that measurement of the constituents enables characterization of the presumptive signs of multiple sclerosis or an inflammatory condition related to multiple sclerosis, wherein such measure for each constituent is obtained under measurement conditions that are substantially repeatable to produce a patient data set; and b) comparing the patient data set to a baseline profile data set, wherein the baseline profile data set is related to said multiple sclerosis or inflammatory condition related to multiple sclerosis; wherein a similarity between the patient data set and the baseline profile data set indicates a risk of an adverse effect from anti-TNF therapy in the subject.
In still another embodiment, the present invention provides a method for predicting an increased risk to an adverse effect from anti-TNF therapy in a subject, based on a sample from the subject, the sample providing a source of RNAs, said method comprising: a) determining a quantitative measure of the amount of at least one constituent of Table 4 or 10 as a distinct RNA constituent, wherein such measure is obtained under measurement conditions that are substantially repeatable to produce a patient data set; and b) comparing the patient data set to a baseline profile data set, wherein the baseline profile data set is related to said multiple sclerosis or inflammatory condition related to multiple sclerosis; wherein a similarity between the patient data set and the baseline profile data set indicates a risk of an adverse effect from anti-TNF therapy in the subject. In one embodiment, the method of predicting an adverse effect of anti-TNF therapy is performed on a subject suffering from an inflammatory condition, including but not limited to rheumatoid arthritis, psoriasis, ankylosing spondylitis, psoriatic arthritis and Crohn's diseases. The method is performed prior to, during, or after administering an anti-TNF therapeutic or anti-TNF therapeutic regimen to the subject. In a preferred embodiment, said method is performed prior to administering an anti-TNF therapeutic to the subject. By increased risk it is meant that treatment with anti-TNF therapy is contraindicated.
The panel of constituents are selected so as to distinguish from a normal and a MS-diagnosed subject. The MS-diagnosed subject is washed out from therapy for three or more months. Preferably, the panel of constituents are selected so as to distinguish from a normal and a MS-diagnosed subject with at least 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater accuracy. By “accuracy” is meant that the method has the ability to distinguish between subjects having multiple sclerosis or an inflammatory condition associated with multiple sclerosis and those that do not. Accuracy is determined for example by comparing the results of the Gene Expression Profiling to standard accepted clinical methods of diagnosing MS, e.g. MRI, sign and symptoms such as blurred vision, fatigue, loss or balance.
Alternatively, the panel of constituents is selected as to permit characterizing severity of MS in relation to normal over time so as to track movement toward normal as a result of successful therapy and away from normal in response to symptomatic flare.
The panel contains 10, 8, 5, 4, 3 or fewer constituents. Optimally, the panel of constituents includes ITGAM, HLADRA, CASP9, ITGAL or STAT3. Alternatively, the panel includes ITGAM and i) CD4 and MMP9, ii) ITGA4 and MMP9, iii) ITGA4, MMP9 and CALCA, iv) ITGA4, MMP9 and NFKB1B, v) ITGA4, MMP9, CALCA and CXCR3, or vi) ITGA4, MMP9, NFKB1B and CXCR3. The panel includes two or more constituents from any of Tables 1-10. In one preferred embodiment, the panel includes two or more constituents from Table 4 or 10. In another preferred embodiment, the panel includes three constituents in any combination shown on Table 7. In yet another preferred embodiment, the panel includes any 2, 3, 4, or 5 genes in the combination shown in Tables 6, 7, 8 and 9 respectively. For example the panel contains i) HLADRA and one or more or the following: ITGAL, CASP9, NFKB1B, STAT2, NFKB1, ITGAM, ITGAL, CD4, IL1B, HSPA1A, ICAM1, IFI16, or TGFBR2; ii) CASP9 and one or more of the following VEGFB, CD14 or JUN; iii) ITGAL and one or more of the following: P13, ITGAM or TGFBR2; and iv) STAT3 and CD14.
Optionally, assessing may further include comparing the profile data set to a baseline profile data set for the panel. The baseline profile data set is related to the multiple sclerosis or an inflammatory condition related to multiple sclerosis to be characterized. The baseline profile data set is derived from one or more other samples from the same subject, taken when the subject is in a biological condition different from that in which the subject was at the time the first sample was taken, with respect to at least one of age, nutritional history, medical condition, clinical indicator, medication, physical activity, body mass, and environmental exposure, and the baseline profile data set may be derived from one or more other samples from one or more different subjects. In addition, the one or more different subjects may have in common with the subject at least one of age group, gender, ethnicity, geographic location, nutritional history, medical condition, clinical indicator, medication, physical activity, body mass, and environmental exposure. A clinical indicator may be used to assess multiple sclerosis or am inflammatory condition related to multiple sclerosis of the one or more different subjects, and may also include interpreting the calibrated profile data set in the context of at least one other clinical indicator, wherein the at least one other clinical indicator such as blood chemistry, urinalysis, X-ray or other radiological or metabolic imaging technique, other chemical assays, and physical findings.
The baseline profile data set may be derived from one or more other samples from the same subject taken under circumstances different from those of the first sample, and the circumstances may be selected from the group consisting of (i) the time at which the first sample is taken, (ii) the site from which the first sample is taken, (iii) the biological condition of the subject when the first sample is taken.
The subject has one or more presumptive signs of a multiple sclerosis. Presumptive signs of multiple sclerosis includes for example, altered sensory, motor, visual or proprioceptive system with at least one of numbness or weakness in one or more limbs, often occurring on one side of the body at a time or the lower half of the body, partial or complete loss of vision, frequently in one eye at a time and often with pain during eye movement, double vision or blurring of vision, tingling or pain in numb areas of the body, electric-shock sensations that occur with certain head movements, tremor, lack of coordination or unsteady gait, fatigue, dizziness, muscle stiffness or spasticity, slurred speech, paralysis, problems with bladder, bowel or sexual function, and mental changes such as forgetfulness or difficulties with concentration, relative to medical standards. Alternatively, the subject is at risk of developing multiple sclerosis, for example the subject has a family history of multiple sclerosis or another autoimmune disorder such as for example rheumatoid arthritis, Crohn's disease, or lupus. Optionally, subject is a candidate for anti-TNF therapy.
By multiple sclerosis or an inflammatory condition related to multiple sclerosis is meant that the condition is an autoimmune condition, an environmental condition, a viral infection, a bacterial infection, a eukaryotic parasitic infection, or a fungal infection.
The sample is any sample derived from a subject which contains RNA. For example the sample is blood, a blood fraction, body fluid, and a population of cells or tissue from the subject.
Optionally one or more other samples can be taken over an interval of time that is at least one month between the first sample and the one or more other samples, or taken over an interval of time that is at least twelve months between the first sample and the one or more samples, or they may be taken pre-therapy intervention or post-therapy intervention. In such embodiments, the first sample may be derived from blood and the baseline profile data set may be derived from tissue or body fluid of the subject other than blood. Alternatively, the first sample is derived from tissue or body fluid of the subject and the baseline profile data set is derived from blood.
All of the forgoing embodiments are carried out wherein the measurement conditions are substantially repeatable, particularly within a degree of repeatability of better than five percent or more particularly within a degree of repeatability of better than three percent, and/or wherein efficiencies of amplification for all constituents are substantially similar, more particularly wherein the efficiency of amplification is within two percent, and still more particularly wherein the efficiency of amplification for all constituents is less than one percent.
Additionally the invention includes storing the profile data set in a digital storage medium. Optionally, storing the profile data set includes storing it as a record in a database.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.
Other features and advantages of the invention will be apparent from the following detailed description and claims.
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:
1B illustrates use of an inflammation index in relation to the data of
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.
“Accuracy” is measure of the strength of the relationship between true values and their predictions. Accordingly, accuracy provided a measurement on how close to a true or accepted value a measurement lies
A “baseline profile data set” is a set of values associated with constituents of a Gene Expression Panel resulting from evaluation of a biological sample (or population or set of samples) under a desired biological condition that is used for mathematically normative purposes. The desired biological condition may be, for example, the condition of a subject (or population or set 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 or set of subjects. Alternatively, or in addition, the desired biological condition may be that associated with a population or set of subjects selected on the basis of at least one of age group, gender, ethnicity, geographic location, nutritional history, medical condition, 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; autoimmune condition; trauma; aging; infection; tissue degeneration; developmental steps; physical fitness; obesity, and mood. As can be seen, a condition in this context may be chronic or acute or simply transient. Moreover, a targeted biological condition may be manifest throughout the organism or population of cells or may be restricted to a specific organ (such as skin, heart, eye or blood), but in either case, the condition may be monitored directly by a sample of the affected population of cells or indirectly by a sample derived elsewhere from the subject. The term “biological condition” includes a “physiological condition”.
“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 profile data set” is a function of a member of a first profile data set and a corresponding member of a baseline profile data set for a given constituent in a panel.
A “clinical indicator” is any physiological datum used alone or in conjunction with other data in evaluating the physiological condition of a collection of cells or of an organism. This term includes pre-clinical indicators.
A “composition” includes a chemical compound, a nutraceutical, a pharmaceutical, a homeopathic formulation, an allopathic formulation, a naturopathic formulation, a combination of compounds, a toxin, a food, a food supplement, a mineral, and a complex mixture of substances, in any physical state or in a combination of physical states.
To “derive” a profile data set from a sample includes determining a set of values associated with constituents of a Gene Expression Panel either (i) by direct measurement of such constituents in a biological sample or (ii) by measurement of such constituents in a second biological sample that has been exposed to the original sample or to matter derived from the original sample.
“Distinct RNA or protein constituent” in a panel of constituents is a distinct expressed product of a gene, whether RNA or protein. An “expression” product of a gene includes the gene product whether RNA or protein resulting from translation of the messenger RNA.
A “Gene Expression Panel” is an experimentally verified set of constituents, each constituent being a distinct expressed product of a gene, whether RNA or protein, wherein constituents of the set are selected so that their measurement provides a measurement of a targeted biological condition.
A “Gene Expression Profile” is a set of values associated with constituents of a Gene Expression Panel resulting from evaluation of a biological sample (or population or set 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 suppurative; localized or disseminated; cellular and tissue response, initiated or sustained by any number of chemical, physical or biological agents or combination of agents.
“Inflammatory state” is used to indicate the relative biological condition of a subject resulting from inflammation, or characterizing the degree of inflammation
A “large number” of data sets based on a common panel of genes is a number of data sets sufficiently large to permit a statistically significant conclusion to be drawn with respect to an instance of a data set based on the same panel.
“Multiple sclerosis” (MS) is a debilitating wasting disease. The disease is associated with degeneration of the myelin sheaths surrounding nerve cells which leads to a loss of motor and sensory function.
A “normar” subject is a subject who has not been diagnosed with multiple sclerosis, or one who is not suffering from multiple sclerosis.
A “normative” condition of a subject to whom a composition is to be administered means the condition of a subject before administration, even if the subject happens to be suffering from a disease.
A “panel” of genes is a set of genes including at least two constituents.
A “population of cells” refers to any group of cells wherein there is an underlying commonality or relationship between the members in the population of cells, including a group of cells taken from an organism or from a culture of cells or from a biopsy, for example.
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 “set” or “population” of samples or subjects refers to a defined or selected group of samples or subjects wherein there is an underlying commonality or relationship between the members included in the set or population of samples or subjects.
A “Signature Profile” is an experimentally verified subset of a Gene Expression Profile selected to discriminate a biological condition, agent or physiological mechanism of action.
A “Signature Panel” is a subset of a Gene Expression Panel, the constituents of which are selected to permit discrimination of a biological condition, agent or physiological mechanism of action.
A “subject” is a cell, tissue, or organism, human or non-human, whether in vivo, ex vivo or in vitro, under observation. When we refer to evaluating the biological condition of a subject based on a sample from the subject, we include using blood or other tissue sample from a human subject to evaluate the human subject's condition; but we also include, for example, using a blood sample itself as the subject to evaluate, for example, the effect of therapy or an agent upon the sample.
A “stimulus” includes (i) a monitored physical interaction with a subject, for example ultraviolet A or B, or light therapy for seasonal affective disorder, or treatment of psoriasis with psoralen or treatment of melanoma with embedded radioactive seeds, other radiation exposure, and (ii) any monitored physical, mental, emotional, or spiritual activity or inactivity of a subject.
“Therapy” includes all interventions whether biological, chemical, physical, metaphysical, or combination of the foregoing, intended to sustain or alter the monitored biological condition of a subject.
The PCT patent application publication number WO 01/25473, published Apr. 12, 2001, entitled “Systems and Methods for Characterizing a Biological Condition or Agent Using Calibrated Gene Expression Profiles,” filed for an invention by inventors herein, and which is herein incorporated by reference, discloses the use of Gene Expression Panels for the evaluation of (i) biological condition (including with respect to health and disease) and (ii) the effect of one or more agents on biological condition (including with respect to health, toxicity, therapeutic treatment and drug interaction).
In particular, Gene Expression Panels may be used for measurement of therapeutic efficacy of natural or synthetic compositions or stimuli that may be formulated individually or in combinations or mixtures for a range of targeted biological conditions; prediction of toxicological effects and dose effectiveness of a composition or mixture of compositions for an individual or for a population or set of individuals or for a population of cells; determination of how two or more different agents administered in a single treatment might interact so as to detect any of synergistic, additive, negative, neutral or toxic activity; performing pre-clinical and clinical trials by providing new criteria for pre-selecting subjects according to informative profile data sets for revealing disease status; and conducting preliminary dosage studies for these patients prior to conducting phase 1 or 2 trials. These Gene Expression Panels may be employed with respect to samples derived from subjects in order to evaluate their biological condition.
The present invention provides Gene Expression Panels for the evaluation of multiple sclerosis and inflammatory condition related to multiple sclerosis. In addition, the Gene Expression Profiles described herein also provided the evaluation of the effect of one or more agents for the treatment of multiple sclerosis and inflammatory condition related to multiple sclerosis.
The Gene Expression Panels (Precision Profile™) are referred to herein as the “Precision Profile™ for Multiple Sclerosis or Inflammatory Conditions Related to Multiple Sclerosis. A Precision Profile™ for Multiple Sclerosis or Inflammatory Conditions Related to Multiple Sclerosis includes one or more genes, e.g., constituents, listed in 1-9. Each gene of the Precision Profile™ for Multiple Sclerosis or Inflammatory Conditions Related to Multiple Sclerosis is referred to herein as a multiple sclerosis associated gene or a multiple sclerosis associated constituent.
The evaluation or characterization of multiple sclerosis is defined to be diagnosing multiple sclerosis, assessing the risk of developing multiple sclerosis or assessing the prognosis of a subject with multiple sclerosis. Similarly, the evaluation or characterization of an agent for treatment of multiple sclerosis includes identifying agents suitable for the treatment of multiple sclerosis. The agents can be compounds known to treat multiple sclerosis or compounds that have not been shown to treat multiple sclerosis.
Multiple sclerosis and conditions related to multiple sclerosis is evaluated by determining the level of expression (e.g., a quantitative measure) of one or more multiple sclerosis genes. The level of expression is determined by any means known in the art, such as for example quantitative PCR. The measurement is obtained under conditions that are substantially repeatable. Optionally, the qualitative measure of the constituent is compared to a baseline level (e.g. baseline profile set). A baseline level is a level of expression of the constituent in one or more subjects known not to be suffering from multiple sclerosis (e.g., normal, healthy individual(s)). Alternatively, the baseline level is derived from one or more subjects known to be suffering from multiple sclerosis. Optionally, the baseline level is derived from the same subject from which the first measure is derived. For example, the baseline is taken from a subject at different time periods during a course of treatment. Such methods allow for the evaluation of a particular treatment for a selected individual. Comparison can be performed on test (e.g., patient) and reference samples (e.g., baseline) measured concurrently or at temporally distinct times. An example of the latter is the use of compiled expression information, e.g., a gene expression database, which assembles information about expression levels of multiple sclerosis genes.
A change in the expression pattern in the patient-derived sample of a multiple sclerosis gene compared to the normal baseline level indicates that the subject is suffering from or is at risk of developing multiple sclerosis. In contrast, when the methods are applied prophylacticly, a similar level compared to the normal control level in the patient-derived sample of a multiple sclerosis gene indicates that the subject is not suffering from or is at risk of developing multiple sclerosis. Whereas, a similarity in the expression pattern in the patient-derived sample of a multiple sclerosis gene compared to the multiple sclerosis baseline level indicates that the subject is suffering from or is at risk of developing multiple sclerosis.
Expression of an effective amount of a multiple sclerosis gene also allows for the course of treatment of multiple sclerosis to be monitored. In this method, a biological sample is provided from a subject undergoing treatment, e.g., if desired, biological samples are obtained from the subject at various time points before, during, or after treatment. Expression of an effective amount of a multiple sclerosis gene is then determined and compared to baseline profile. The baseline profile may be taken or derived from one or more individuals who have been exposed to the treatment. Alternatively, the baseline level may be taken or derived from one or more individuals who have not been exposed to the treatment. For example, samples may be collected from subjects who have received initial treatment for multiple sclerosis and subsequent treatment for multiple sclerosis to monitor the progress of the treatment.
Differences in the genetic makeup of individuals can result in differences in their relative abilities to metabolize various drugs. Accordingly, the Precision Profile™ for Multiple Sclerosis and Inflammatory Conditions Related to Multiple Sclerosis, disclosed herein, allows for a putative therapeutic or prophylactic to be tested from a selected subject in order to determine if the agent is a suitable for treating or preventing multiple sclerosis in the subject. Additionally, other genes known to be associated with toxicity may be used. By suitable for treatment is meant determining whether the agent will be efficacious, not efficacious, or toxic for a particular individual. By toxic it is meant that the manifestations of one or more adverse effects of a drug when administered therapeutically. For example, a drug is toxic when it disrupts one or more normal physiological pathways.
To identify a therapeutic that is appropriate for a specific subject, a test sample from the subject is exposed to a candidate therapeutic agent, and the expression of one or more of multiple sclerosis genes is determined. A subject sample is incubated in the presence of a candidate agent and the pattern of multiple sclerosis gene expression in the test sample is measured and compared to a baseline profile, e.g., a multiple sclerosis baseline profile or a non-multiple sclerosis baseline profile or an index value. The test agent can be any compound or composition. For example, the test agent is a compound known to be useful in the treatment of multiple sclerosis. Alternatively, the test agent is a compound that has not previously been used to treat multiple sclerosis.
If the reference sample, e.g., baseline is from a subject that does not have multiple sclerosis a similarity in the pattern of expression of multiple sclerosis genes in the test sample compared to the reference sample indicates that the treatment is efficacious. Whereas a change in the pattern of expression of multiple sclerosis genes in the test sample compared to the reference sample indicates a less favorable clinical outcome or prognosis.
By “efficacious” is meant that the treatment leads to a decrease of a sign or symptom of multiple sclerosis in the subject or a change in the pattern of expression of a multiple sclerosis gene in such that the gene expression pattern has an increase in similarity to that of a normal baseline pattern. Assessment of multiple sclerosis is made using standard clinical protocols. Efficacy is determined in association with any known method for diagnosing or treating multiple sclerosis.
Agents that are toxic for a specific subject are identified by exposing a test sample from the subject to a candidate agent, and the expression of one or more of multiple sclerosis genes is determined. A subject sample is incubated in the presence of a candidate agent and the pattern of multiple sclerosis gene expression in the test sample is measured and compared to a baseline profile, e.g., a multiple sclerosis baseline profile or a non-multiple sclerosis baseline profile or an index value. The test agent can be any compound or composition. For example, the test agent is a compound known to be useful in the treatment of multiple sclerosis. Alternatively, the test agent is a compound that has not previously been used to treat multiple sclerosis.
If the reference sample, e.g., baseline is from a subject in whom the candidate agent is not toxic a similarity in the pattern of expression of multiple sclerosis genes in the test sample compared to the reference sample indicates that the candidate agent is not toxic for the particular subject. Whereas a change in the pattern of expression of multiple sclerosis genes in the test sample compared to the reference sample indicates that the candidate agent is toxic.
A Gene Expression Panel (Precision Profile™) 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 (Precision Profile™) and (ii) a baseline quantity.
It has been discovered 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, preferably ten percent or better, more preferably five percent or better, and more preferably three percent or better). For the purposes of this description and the following claims, a degree of repeatability of measurement of better than twenty percent may be used as providing measurement conditions that are “substantially repeatable”. In particular, it is desirable that each time a measurement is obtained corresponding to the level of expression of a constituent in a particular sample, substantially the same measurement should result for substantially the same level of expression. In this manner, expression levels for a constituent in a Gene Expression Panel (Precision Profile™) may be meaningfully compared from sample to sample. Even if the expression level measurements for a particular constituent are inaccurate (for example, say, 30% too low), the criterion of repeatability means that all measurements for this constituent, if skewed, will nevertheless be skewed systematically, and therefore measurements of expression level of the constituent may be compared meaningfully. In this fashion valuable information may be obtained and compared concerning expression of the constituent under varied circumstances.
In addition to the criterion of repeatability, it is desirable that a second criterion also be satisfied, namely that quantitative measurement of constituents is performed under conditions wherein efficiencies of amplification for all constituents are substantially similar as defined herein. 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.
Additional 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 biological conditions; (f) for predictions of toxicological effects and dose effectiveness of a composition or mixture of compositions for an individual or for a population or set of individuals or for a population of cells; (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 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.
A subject can include those who have not been previously diagnosed as having multiple sclerosis or an inflammatory condition related to multiple sclerosis. Alternatively, a subject can also include those who have already been diagnosed as having multiple sclerosis or an inflammatory condition related to multiple sclerosis. Diagnosis of multiple sclerosis is made for example, by clinical data (e.g., episodes of neurologic symptoms characteristic of MS and abnormalities upon physical examination), magnetic resonance imaging of the brain and spine to identify lesions and plaques, testing of cerebral spinal fluid for oligoclonal bands, and measurements of antibodies against myelin proteins (e.g., myelin oligodendrocyte glycoprotein (MOG) and myelin basic protein (MBP).
Optionally, the subject has been previously treated with therapeutic agents, or with other therapies and treatment regimens for multiple sclerosis or an inflammatory condition related to multiple sclerosis. A subject can also include those who are suffering from, or at risk of developing multiple sclerosis or an inflammatory condition related to multiple sclerosis, such as those who exhibit known risk factors for multiple sclerosis or an inflammatory condition related to multiple sclerosis. Known risk factors for multiple sclerosis include but are not limited to viral infection, decrease exposure to sunlight, vitamin-D deficiency, chronic infection with spirochetal bacteria and/or Chlamydophila pneumonia, exposure to Epstein-Barr virus, severe stress, and smoking. A subject can include those who are candidates for anti-TNF therapy.
The general approach to selecting constituents of a Gene Expression Panel has been described in PCT application publication number WO 01/25473. A wide range of Gene Expression Panels have been designed and experimentally verified, 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. (It has also been demonstrated hat in being informative of biological condition, the Gene Expression Profile can be used to used, among other things, to measure the effectiveness of therapy, as well as to provide a target for therapeutic intervention).
Tables 1, 2, 3, 4, 5, 6, 7, 8, or 9 listed below, include relevant genes which may be selected for a given Gene Expression Panel, such as the Gene Expression Panels demonstrated herein to be useful in the evaluation of multiple sclerosis and inflammatory condition related to multiple sclerosis.
Tables 1-2 were derived from a study of the gene expression patterns described in Example 12 below. Tables 3 and 5-9 were derived from a study of gene expression patterns described in Example 13 below. Table 4 is a panel of 104 genes whose expression is associated with Multiple Sclerosis or Inflammatory Conditions related to Multiple Sclerosis, referred to herein as the Precision Profile™ for Multiple Sclerosis and Inflammatory Genes Related to Multiple Sclerosis. Table 5 shows a ranking of p-values (from most to least significant) of a subset of genes from Table 4. Table 6 describes 2-gene model based on genes from the Precision Profile™ for Multiple sclerosis derived from latent class modeling of the subjects from this study to distinguish between subjects suffering from multiple sclerosis and normal subjects. Two gene models capable of correctly classifying multiple sclerosis-afflicted and/or normal subjects with at least 75% accuracy are indicated. For example, in Table 6, 2-gene model, ITGAL and HLADRA, correctly classifies multiple sclerosis-afflicted subjects with 85.4% accuracy, and normal subjects with 82.9% accuracy.
The 2-gene model, CASP9 and HLADRA, correctly classifies multiple sclerosis-afflicted subjects with 78.5% accuracy, and normal subjects with 84.2% accuracy. Table 7 describes 3-gene models based on genes from the Precision Profile™ for Multiple Sclerosis, capable of correctly classifying multiple sclerosis-afflicted and/or normal subjects with at least 75% accuracy are indicated. For example, the three-gene model, ITGAL, HLADRA, and CASP9, correctly classifies multiple sclerosis-afflicted subjects with 85.4% accuracy, and normal subjects with 86.8% accuracy. Table 8 describes a 4-gene model based on genes from the Precision Profile™ for Multiple Sclerosis, capable of correctly classifying multiple sclerosis-afflicted and/or normal subjects with at least 75% accuracy are indicated. For example, the 4-gene model, CASP9, HLADRA, ITGAL, and CCR3, correctly classifies multiple sclerosis-afflicted subjects with 85.4% accuracy, and normal subjects with 83.6% accuracy. Table 9 describes a 5-gene model based on genes from the Precision Profile™ for Multiple Sclerosis, capable of correctly classifying multiple sclerosis-afflicted and/or normal subjects with at least 75% accuracy are indicated. For example, the 4-gene model, CASP9, HLADRA, ITGAL, CCR3, and TGFBR2, correctly classifies multiple sclerosis-afflicted subjects with 86.9% accuracy, and normal subjects with 84.2% accuracy. Table 10 is a panel of 96 genes whose expression is associated with Inflammation referred to herein as the Precision Profile™ for Inflammatory Response.
In general, 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.
Typically, a sample is run through a panel in replicates of three for each target gene (assay); that is, a sample is divided into aliquots and for each aliquot the concentrations of each constituent in a Gene Expression Panel (Precision Profile™) is measured. From over a total of 900 constituent assays, with each assay conducted in triplicate, an average coefficient of variation was found (standard deviation/average)*100, of less than 2 percent among the normalized ΔCt measurements for each assay (where normalized quantitation of the target mRNA is determined by the difference in threshold cycles between the internal control (e.g., an endogenous marker such as 18S rRNA, or an exogenous marker) and the gene of interest. This is a measure called “intra-assay variability”. Assays have also been conducted on different occasions using the same sample material. This is a measure of “inter-assay variability”. Preferably, the average coefficient of variation of intra-assay variability or inter-assay variability is less than 20%, more preferably less than 10%, more preferably less than 5%, more preferably less than 4%, more preferably less than 3%, more preferably less than 2%, and even more preferably less than 1%.
It has been determined that it is valuable to use the quadruplicate or triplicate 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 three or four values. Moreover, if more than one data point in a set of three or four is excluded by this procedure, then all data for the relevant constituent is discarded.
For measuring the amount of a particular RNA in a sample, methods known to one of ordinary skill in the art were used to extract and quantify transcribed RNA from a sample with respect to a constituent of a Gene Expression Panel (Precision Profile™). (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 any tissue, body fluid, cell, or culture medium in which a population of cells 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. Subsequent to RNA extraction, first strand synthesis may be performed using a reverse transcriptase. Gene amplification, more specifically quantitative PCR assays, can then be conducted and the gene of interest calibrated against an internal marker such as 18S rRNA (Hirayama et al., Blood 92, 1998: 46-52). Any other endogenous marker can be used, such as 28S-25S rRNA and 5S rRNA. Samples are measured in multiple replicates, for example, 3 replicates. In an embodiment of the invention, quantitative PCR is performed using amplification, reporting agents and instruments such as those supplied commercially by Applied Biosystems (Foster City, Calif.). Given a defined efficiency of amplification of target transcripts, the point (e.g., cycle number) that signal from amplified target template is detectable may be directly related to the amount of specific message transcript in the measured sample. Similarly, other quantifiable signals such as fluorescence, enzyme activity, disintegrations per minute, absorbance, etc., when correlated to a known concentration of target templates (e.g., a reference standard curve) or normalized to a standard with limited variability can be used to quantify the number of target templates in an unknown sample.
Although not limited to amplification methods, quantitative gene expression techniques may utilize amplification of the target transcript. Alternatively or in combination with amplification of the target transcript, quantitation of the reporter signal for an internal marker generated by the exponential increase of amplified product may also be used. Amplification of the target template may be accomplished by isothermic gene amplification strategies or by gene amplification by thermal cycling such as PCR.
It is desirable to obtain a definable and reproducible correlation between the amplified target or reporter signal, i.e., internal marker, and the concentration of starting templates. It has been 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 90.0 to 100%+/−5% relative efficiency, typically 99.8 to 100% relative efficiency). For example, in determining gene expression levels with regard to a single Gene Expression Profile, it is necessary that all constituents of the panels, including endogenous controls, maintain similar amplification efficiencies, as defined herein, to permit accurate and precise relative measurements for each constituent. Amplification efficiencies are regarded as being “substantially similar”, for the purposes of this description and the following claims, if they differ by no more than approximately 10%, preferably by less than approximately 5%, more preferably by less than approximately 3%, and more preferably by less than approximately 1%. Measurement conditions are regarded as being “substantially repeatable, for the purposes of this description and the following claims, if they differ by no more than approximately +/−10% coefficient of variation (CV), preferably by less than approximately +/−5% CV, more preferably +/−2% CV. 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, tests are run to assure that these conditions are satisfied. For example, the design of all primer-probe sets are done in house, experimentation is performed to determine which set gives the best performance. Even though primer-probe design can be enhanced using computer techniques known in the art, and notwithstanding common practice, it has been found that experimental validation is still useful. Moreover, in the course of experimental validation, the selected primer-probe combination is associated with 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 four bases of the three-prime end of the reverse primer complementary to the proximal exon. (If more than four bases are complementary, then it would tend to competitively amplify genomic DNA.)
In an embodiment of the invention, the primer probe set should amplify cDNA of less than 110 bases in length and should not amplify, or generate fluorescent signal from, 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 in one embodiment may be prepared from whole blood 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 exogenous stimulus, and pro-cancer stimulus with sufficient volume for at least three time points. Typical pro-cancer stimuli include for example, ionizing radiation, free radicals or DNA damaging agents, and may be used individually or in combination. The aliquots of heparinized, whole blood are mixed with additional test therapeutic compounds and held at 37° C. in an atmosphere of 5% CO2 for 30 minutes. Stimulus is added at varying concentrations, mixed and held loosely capped at 37° C. for the prescribed timecourse. At defined time-points, cells are lysed and RNA extracted by various standard means.
Nucleic acids, RNA and or DNA are purified from cells, tissues or fluids of the test population of cells or indicator cell lines. RNA is preferentially obtained from the nucleic acid mix using a variety of standard procedures (or RNA Isolation Strategies, pp. 55-104, in RNA Methodologies, A laboratory guide for isolation and characterization, 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press), in the present using a filter-based RNA isolation system from Anibion (RNAqueous™, Phenol-free Total RNA Isolation Kit, Catalog #1912, version 9908; Austin, Tex.).
In accordance with one procedure, the whole blood assay for Gene Expression Profiles determination was carried out as follows: Human whole blood was drawn into 10 mL Vacutainer tubes with Sodium Heparin. Blood samples were mixed by gently inverting tubes 4-5 times. The blood was used within 10-15 minutes of draw. In the experiments, blood was diluted 2-fold, i.e. per sample per time point, 0.6 mL whole blood +0.6 mL stimulus. The assay medium was prepared and the stimulus added as appropriate.
A quantity (0.6 mL) of whole blood was then added into each 12×75 mm polypropylene tube. 0.6 mL of 2×LPS (from E. coli serotype 0127:B8, Sigma#L3880 or serotype 055, Sigma #L4005, 10 ng/mL, subject to change in different lots) into LPS tubes was added. Next, 0.6 mL assay medium was added to the “control” tubes. 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 at 37° C., 5% CO2 for 6 hours. At 6 hours, samples were gently mixed to resuspend blood cells, and 0.15 mL was removed from each tube (using a micropipettor with barrier tip), and transferred to 0.15 mL of lysis buffer and mixed. Lysed samples were extracted using an ABI 6100 Nucleic Acid Prepstation following the manufacturer's recommended protocol.
The samples were then centrifuged for 5 min at 500×g, ambient temperature (IEC centrifuge or equivalent, in microfuge tube adapters in swinging bucket), and as much serum from each tube was removed as possible and discarded. Cell pellets were placed on ice; and RNA extracted as soon as possible using an Ambion RNAqueous kit.
(b) Amplification Strategies.
Specific RNAs are amplified using message specific primers or random primers. The specific primers are synthesized from data obtained from public databases (e.g., Unigene, National Center for Biotechnology Information, National Library of Medicine, Bethesda, Md.), including information from genomic and cDNA libraries obtained from humans and other animals. Primers are chosen to preferentially amplify from specific RNAs obtained from the test or indicator samples (see, for example, RT PCR, Chapter 15 in RNA Methodologies, A laboratory guide for isolation and characterization, 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press; or Chapter 22 pp. 143-151, RNA isolation and characterization protocols, Methods in molecular biology, Volume 86, 1998, R. Rapley and D. L. Manning Eds., Human Press, or 14 in statistical refinement of primer design parameters, Chapter 5, pp. 55-72, PCR applications: protocols for functional genomics, M. A. Innis, D. H. Gelfand and J. J. Sninsky, Eds., 1999, Academic Press). Amplifications are carried out in either isothermic conditions or using a thermal cycler (for example, a ABI 9600 or 9700 or 7900 obtained from Applied Biosystems, Foster City, Calif.; see Nucleic acid detection methods, pp. 1-24, in Molecular methods for virus detection, D. L. Wiedbrauk and D. H., Farkas, Eds., 1995, Academic Press). Amplified nucleic acids are detected using fluorescent-tagged detection oligonucleotide probes (see, for example, Taqman™ PCR Reagent Kit, Protocol, part number 402823, Revision A, 1996, Applied Biosystems, Foster City Calif.) that are identified and synthesized from publicly known databases as described for the amplification primers. In the present case, amplified cDNA is detected and quantified using the ABI Prism 7900 Sequence Detection System obtained from Applied Biosystems (Foster City, Calif.). Amounts of specific RNAs contained in the test sample or obtained from the indicator cell lines can be related to the relative quantity of fluorescence observed (see for example, Advances in quantitative PCR technology: 5′ nuclease assays, Y. S. Lie and C. J. Petropolus, Current Opinion in Biotechnology, 1998, 9:43-48, or Rapid thermal cycling and PCR kinetics, pp. 211-229, chapter 14 in PCR applications: protocols for functional genomics, M. A. Innis, D. H. Gelfand and J. J. Sninsky, Eds., 1999, Academic Press).
As a particular implementation of the approach described here in detail is a procedure for synthesis of first strand cDNA for use in PCR. This procedure can be used for both whole blood RNA and RNA extracted from cultured cells (i.e. THP-1 cells).
Materials
1. Applied Biosystems TAQMAN Reverse Transcription Reagents Kit (P/N 808-0234). Kit Components: 10× TaqMan RT Buffer, 25 mM Magnesium chloride, deoxyNTPs mixture, Random Hexamers, RNase Inhibitor, MultiScribe Reverse Transcriptase (50 U/mL) (2) RNase/DNase free water (DEPC Treated Water from Ambion (P/N 9915G), or equivalent).
Methods
1. Place RNase Inhibitor and MultiScribe Reverse Transcriptase on ice immediately. All other reagents can be thawed at room temperature and then placed on ice.
2. Remove RNA samples from −80° C. freezer and thaw at room temperature and then place immediately on ice.
3. Prepare the following cocktail of Reverse Transcriptase Reagents for each 100 mL RT reaction (for multiple samples, prepare extra cocktail to allow for pipetting error):
4. Bring each RNA sample to a total volume of 20 μL in a 1.5 mL microcentrifuge tube (for example, for THP-1 RNA, remove 10 μL RNA and dilute to 20 μL with RNase/DNase free water, for whole blood RNA use 20 μL total RNA) and add 80 μL RT reaction mix from step 5, 2, 3. Mix by pipetting up and down.
5. Incubate sample at room temperature for 10 minutes.
6. Incubate sample at 37° C. for 1 hour.
7. Incubate sample at 90° C. for 10 minutes.
8. Quick spin samples in microcentrifuge.
9. Place sample on ice if doing PCR immediately, otherwise store sample at −20° C. for future use.
10. PCR QC should be run on all RT samples using 18S and β-actin.
The use of the primer probe with the first strand cDNA as described above to permit measurement of constituents of a Gene Expression Panel (Precision Profile™) is as follows:
Materials
1. 20× Primer/Probe Mix for each gene of interest.
2. 20× Primer/Probe Mix for 18S endogenous control.
3. 2× Taqman Universal PCR Master Mix.
4. cDNA transcribed from RNA extracted from cells.
5. Applied Biosystems 96-Well Optical Reaction Plates.
6. Applied Biosystems Optical Caps, or optical-clear film.
7. Applied Biosystem Prism 7700 or 7900 Sequence Detector.
Methods
1. Make stocks of each Primer/Probe mix containing the Primer/Probe for the gene of interest, Primer/Probe for 18S endogenous control, and 2×PCR Master Mix as follows. Make sufficient excess to allow for pipetting error e.g., approximately 10% excess. The following example illustrates a typical set up for one gene with quadruplicate samples testing two conditions (2 plates).
2. Make stocks of cDNA targets by diluting 95 μL of cDNA into 2000 μL of water. The amount of cDNA is adjusted to give Ct values between 10 and 18, typically between 12 and 16.
3. Pipette 9 μL of Primer/Probe mix into the appropriate wells of an Applied Biosystems 384-Well Optical Reaction Plate.
4. Pipette 10 μL of cDNA stock solution into each well of the Applied Biosystems 384-Well Optical Reaction Plate.
5. Seal the plate with Applied Biosystems Optical Caps, or optical-clear film.
6. Analyze the plate on the ABI Prism 7900 Sequence Detector.
In another embodiment of the invention, the use of the primer probe with the first strand cDNA as described above to permit measurement of constituents of a Gene Expression Panel (Precision Profile™) is performed using a QPCR assay on Cepheid SmartCycler® and GeneXpert® Instruments as follows:
I. To run a QPCR assay in duplicate on the Cepheid SmartCycler® instrument containing three target genes and one reference gene, the following procedure should be followed.
A. With 20× Primer/Probe Stocks.
Materials
Methods
B. With Lyophilized SmartBeads™.
Materials
Methods
Materials
Methods
In other embodiments, any tissue, body fluid, or cell(s) may be used for ex vivo assessment of a biological condition affected by an agent.
Methods herein may also be applied using proteins where sensitive quantitative techniques, such as an Enzyme Linked ImmunoSorbent Assay (ELISA) or mass spectroscopy, are available and well-known in the art for measuring the amount of a protein constituent. (see WO 98/24935 herein incorporated by reference).
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, e.g., multiple sclerosis. 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, and/or a particular agent.
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 or sets of subjects. The baseline profile data set may be normal, healthy baseline.
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 biological condition. For example, a sample may be taken before stimulation or after stimulation with an exogenous compound or substance, such as before or after therapeutic treatment. 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 or set of subjects having some defining characteristic or biological condition. The baseline profile data set may also correspond to some ex vivo or in vitro properties associated with an in vitro cell culture. The resultant calibrated profile data sets may then be stored as a record in a database or library along with or separate from the baseline profile data base and optionally the first profile data set although the first profile data set would normally become incorporated into a baseline profile data set under suitable classification criteria. The remarkable consistency of Gene Expression Profiles associated with a given biological condition makes it valuable to store profile data, which can be used, among other things for normative reference purposes. The normative reference can serve to indicate the degree to which a subject conforms to a given biological condition (healthy or diseased) and, alternatively or in addition, to provide a target for clinical intervention.
Selected baseline profile data sets may be also be used as a standard by which to judge manufacturing lots in terms of efficacy, toxicity, etc. Where the effect of a therapeutic agent is being measured, the baseline data set may correspond to Gene Expression Profiles taken before administration of the agent. Where quality control for a newly manufactured product is being determined, the baseline data set may correspond with a gold standard for that product. However, any suitable normalization techniques may be employed. For example, an average baseline profile data set is obtained from authentic material of a naturally grown herbal nutraceutical and compared over time and over different lots in order to demonstrate consistency, or lack of consistency, in lots of compounds prepared for release.
Given the repeatability achieved in measurement of gene expression, described above in connection with “Gene Expression Panels” (Precision Profiles™) and “gene amplification”, it was concluded that where differences occur in measurement under such conditions, the differences are attributable to differences in biological condition. Thus, it has been found that calibrated profile data sets are highly reproducible in samples taken from the same individual under the same conditions. Similarly, it has been found that calibrated profile data sets are reproducible in samples that are repeatedly tested. Also found have been 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. Importantly, it has been determined 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, it has been determined 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.
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 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 (Precision Profile™) 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 or sets of subjects or samples, or for comparison to populations of cells. 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 nutraceutical through manufacture, testing and marketing.
The numerical data obtained from quantitative gene expression and numerical data from calibrated gene expression relative to a baseline profile data set may be stored in databases or digital storage mediums and may be retrieved for purposes including managing patient health care or for conducting clinical trials or for characterizing a drug. The data may be transferred in physical or wireless networks via the World Wide Web, email, or internet access site for example or by hard copy so as to be collected and pooled from distant geographic sites.
The method also includes 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, and wherein the baseline profile data set is related to the multiple sclerosis or conditions related to multiple sclerosis to be evaluated, with the calibrated profile data set being a comparison between the first profile data set and the baseline profile data set, thereby providing evaluation of multiple sclerosis or conditions related to multiple sclerosis of the subject.
In yet other embodiments, the function is a mathematical function and is other than a simple difference, including a second function of the ratio of the corresponding member of first profile data set to the corresponding member of the baseline profile data set, or a logarithmic function. In such embodiments, the first sample is obtained and the first profile data set quantified at a first location, and the calibrated profile data set is produced using a network to access a database stored on a digital storage medium in a second location, wherein the database may be updated to reflect the first profile data set quantified from the sample. Additionally, using a network may include accessing a global computer network.
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 Mj, where Mj 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 demonstrate relative efficacy, differences in mechanisms of actions, etc. compared to other drugs approved for similar or different uses.
The various embodiments of the invention may be also implemented as a computer program product for use with a computer system. The product may include program code for deriving a first profile data set and for producing calibrated profiles. Such implementation may include a series of computer instructions fixed either on a tangible medium, such as a computer readable medium (for example, a diskette, CD-ROM, ROM, or fixed disk), or transmittable to a computer system via a modem or other interface device, such as a communications adapter coupled to a network. The network coupling may be for example, over optical or wired communications lines or via wireless techniques (for example, microwave, infrared or other transmission techniques) or some combination of these. The series of computer instructions preferably embodies all or part of the functionality previously described herein with respect to the system. Those skilled in the art should appreciate that such computer instructions can be written in a number of programming languages for use with many computer architectures or operating systems. Furthermore, such instructions may be stored in any memory device, such as semiconductor, magnetic, optical or other memory devices, and may be transmitted using any communications technology, such as optical, infrared, microwave, or other transmission technologies. It is expected that such a computer program product may be distributed as a removable medium with accompanying printed or electronic documentation (for example, shrink wrapped software), preloaded with a computer system (for example, on system ROM or fixed disk), or distributed from a server or electronic bulletin board over a network (for example, the Internet or World Wide Web). In addition, a computer system is further provided including derivative modules for deriving a first data set and a calibration profile data set.
The calibration profile data sets in graphical or tabular form, the associated databases, and the calculated index or derived algorithm, together with information extracted from the panels, the databases, the data sets or the indices or algorithms are commodities that can be sold together or separately for a variety of purposes as described in WO 01/25473.
In other embodiments, a clinical indicator may be used to assess the multiple sclerosis or inflammatory conditions related to multiple sclerosis of the relevant set of subjects by interpreting the calibrated profile data set in the context of at least one other clinical indicator, wherein the at least one other clinical indicator is selected from the group consisting of blood chemistry, urinalysis, X-ray or other radiological or metabolic imaging technique, other chemical assays, and physical findings.
In combination, (i) the remarkable consistency of Gene Expression Profiles with respect to a biological condition across a population or set of subject or samples, or across a population of cells and (ii) the use of procedures that provide substantially reproducible measurement of constituents in a Gene Expression Panel giving rise to a Gene Expression Profile, under measurement conditions wherein specificity and efficiencies of amplification for all constituents of the panel are substantially similar, make possible the use of an index that characterizes a Gene Expression Profile, and which therefore provides a measurement of a biological condition.
An index may be constructed using an index function that maps values in a Gene Expression Profile into a single value that is pertinent to the biological condition at hand. The values in a Gene Expression Profile are the amounts of each constituent of the Gene Expression Panel that corresponds to the Gene Expression Profile. These constituent amounts form a profile data set, and the index function generates a single value—the index—from the members of the profile data set.
The index function may conveniently be constructed as a linear sum of terms, each term being what we call a “contribution function” of a member of the profile data set. For example, the contribution function may be a constant times a power of a member of the profile data set. So the index function would have the form
I=ΣC
i
M
i
P(i)),
where I is the index, Mi is the value of the member i of the profile data set, Ci is a constant, and P(i) is a power to which Mi 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 role of the coefficient Ci for a particular gene expression specifies whether a higher ΔCt value for this gene either increases (a positive Ci) or decreases (a lower value) the likelihood of multiple sclerosis, the ΔCt values of all other genes in the expression being held constant.
The values Ci 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, for example, may be employed the software from Statistical Innovations, Belmont, Mass., called Latent Gold®. See the web pages at statisticalinnovations.com/lg/, which are hereby incorporated herein by reference.
Alternatively, other simpler modeling techniques may be employed in a manner known in the art. The index function for inflammation may be constructed, for example, in a manner that a greater degree of inflammation (as determined by a profile data set for the Precision Profile™ for Inflammatory Response shown in Table 10) correlates with a large value of the index function. In a simple embodiment, therefore, each P(i) may be +1 or —I, 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{IL1A}+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 (Precision Profile™ for Inflammatory Response).
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 or set of subjects or samples or to a relevant population of cells, so that the index may be interpreted in relation to the normative value. The relevant population or set of subjects or samples, or relevant population of cells may have in common a property that is at least one of age range, gender, ethnicity, geographic location, nutritional history, medical condition, 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 or set 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 11n 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 or set of subjects. Since we have found that Gene Expression Profile values (and accordingly constructed indices based on them) tend to be normally distributed, the O-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
Still another embodiment is a method of providing an index that is indicative of multiple sclerosis or inflammatory conditions related to multiple sclerosis of a subject based on a first sample from the subject, the first sample providing a source of RNAs, the method comprising deriving from the first 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 constituent in a panel of constituents selected so that measurement of the constituents is indicative of the presumptive signs of multiple sclerosis, the panel including at least two of the constituents of any of the Tables 1-10. In deriving the profile data set, such measure for each constituent is achieved under measurement conditions that are substantially repeatable, at least one measure from the profile data set is applied to an index function that provides a mapping from at least one measure of the profile data set into one measure of the presumptive signs of multiple sclerosis, so as to produce an index pertinent to the multiple sclerosis or inflammatory conditions related to multiple sclerosis of the subject.
As a further embodiment of the invention, we can employ an index function I of the form
where Mi and Mj are values respectively of the member i and member j of the profile data set having N members, and Ci and Cij are constants. For example, when Ci=Cij=0, the index function is simply the constant C0. More importantly, when Cij=0, the index function is a linear expression, in a form used for examples herein. Similarly, when Cij=0 only when i≠j, the index function is a simple quadratic expression without cross products Otherwise, the index function is a quadratic with cross products. As discussed in further detail below, a quadratic expression that is constructed as a meaningful identifier of rheumatoid arthritis (RA) is the following:
C0+C1{TLR2}+C2{CD4}+C3{NFKB1}+C4{TLR2}{CD4}+C5{TLR2}{NFKB1}+C6{NFKB1}2+C7{TLR2}2+C8{CD4}2,
where the constant C0 serves to calibrate this expression to the biological population of interest (such as RA), that is characterized by inflammation.
In this embodiment, when the index value associated with a subject equals 0, the odds are 50:50 of the subject's being MS vs normal. More generally, the predicted odds of being MS is [exp(Ii)], and therefore the predicted probability of being MS is [exp(Ii)]/[1+exp((Ii)]. Thus, when the index exceeds 0, the predicted probability that a subject is MS is higher than 0.5, and when it falls below 0, the predicted probability is less than 0.5.
The value of C0 may be adjusted to reflect the prior probability of being in this population based on known exogenous risk factors for the subject. In an embodiment where C0 is adjusted as a function of the subject's risk factors, where the subject has prior probability pi of being RA based on such risk factors, the adjustment is made by increasing (decreasing) the unadjusted C0 value by adding to C0 the natural logarithm of the ratio of the prior odds of being RA taking into account the risk factors to the overall prior odds of being RA without taking into account the risk factors.
It was determined that the above quadratic expression for RA may be well approximated by a linear expression of the form:
D0+D1{TLR2}+D2{CD4}+D3{NFKB1}.
Yet another embodiment provides a method of using an index for differentiating a type of pathogen within a class of pathogens of interest in a subject with multiple sclerosis or inflammatory conditions related to multiple sclerosis, based on at least one sample from the subject, the method comprising providing at least one index according to any of the above disclosed embodiments for the subject, comparing the at least one index to at least one normative value of the index, determined with respect to at least one relevant set of subjects to obtain at least one difference, and using the at least one difference between the at least one index and the at least one normative value for the index to differentiate the type of pathogen from the class of pathogen.
The invention also includes an MS-detection reagent, i.e., nucleic acids that specifically identify one or more multiple sclerosis or inflammatory condition related to multiple sclerosis nucleic acids (e.g., any gene listed in Tables 1-10; referred to herein as MS-associated genes) by having homologous nucleic acid sequences, such as oligonucleotide sequences, complementary to a portion of the MS-associated genes nucleic acids or antibodies to proteins encoded by the MS-associated genes nucleic acids packaged together in the form of a kit. The oligonucleotides can be fragments of the MS-associated genes genes. For example the oligonucleotides can be 200, 150, 100, 50, 25, 10 or less nucleotides in length. The kit may contain in separate containers a nucleic acid or antibody (either already bound to a solid matrix or packaged separately with reagents for binding them to the matrix), control formulations (positive and/or negative), and/or a detectable label. Instructions (i.e., written, tape, VCR, CD-ROM, etc.) for carrying out the assay may be included in the kit. The assay may for example be in the form of PCR, a Northern hybridization or a sandwich ELISA as known in the art.
For example, MS-associated genes detection reagents can be immobilized on a solid matrix such as a porous strip to form at least one MS-associated genes detection site. The measurement or detection region of the porous strip may include a plurality of sites containing a nucleic acid. A test strip may also contain sites for negative and/or positive controls. Alternatively, control sites can be located on a separate strip from the test strip. Optionally, the different detection sites may contain different amounts of immobilized nucleic acids, i.e., a higher amount in the first detection site and lesser amounts in subsequent sites. Upon the addition of test sample, the number of sites displaying a detectable signal provides a quantitative indication of the amount of MS-associated genes present in the sample. The detection sites may be configured in any suitably detectable shape and are typically in the shape of a bar or dot spanning the width of a test strip.
Alternatively, multiple sclerosis detection genes can be labeled (e.g., with one or more fluorescent dyes) and immobilized on lyophilized beads to form at least one multiple sclerosis gene detection site. The beads may also contain sites for negative and/or positive controls. Upon addition of the test sample, the number of sites displaying a detectable signal provides a quantitative indication of the amount of multiple sclerosis genes present in the sample.
Alternatively, the kit contains a nucleic acid substrate array comprising one or more nucleic acid sequences. The nucleic acids on the array specifically identify one or more nucleic acid sequences represented by MS-associated genes (e.g., any gene listed in Tables 1-10). In various embodiments, the expression of 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 40 or 50 or more of the sequences represented by MS-associated genes can be identified by virtue of binding to the array. The substrate array can be on, i.e., a solid substrate, i.e., a “chip” as described in U.S. Pat. No. 5,744,305. Alternatively, the substrate array can be a solution array, i.e., Luminex, Cyvera, Vitra and Quantum Dots' Mosaic.
The skilled artisan can routinely make antibodies, nucleic acid probes, i.e., oligonucleotides, aptamers, siRNAs, antisense oligonucleotides, against any of the MS-associated genes in Tables 1-10.
While the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.
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
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
The results of the assay for inflammatory gene expression for each day (shown for 24 genes in each row of
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
The consistency between gene expression levels of the two distinct patient sets is dramatic. Both patient sets show gene expressions for each of the 48 loci that are not significantly different from each other. This observation suggests that there is a “normal” expression pattern for human inflammatory genes, that a Gene Expression Profile, using the Inflammation Gene Expression Panel (Precision Profile™ for Inflammatory Response) (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,
(again using the 48 loci of the Inflammation Gene Expression Panel (Precision Profile™ for Inflammatory Response)) also obtained from whole blood of two distinct patient populations (patient sets). One patient set, expression values for which are represented by triangular data points, is 24 normal, undiagnosed subjects (who therefore have no known inflammatory disease). The other patient set, the expression values for which are represented by diamond-shaped data points, is four patients with rheumatoid arthritis and who have failed therapy (who therefore have unstable rheumatoid arthritis).
As remarkable as the consistency of data from the two distinct normal patient sets shown in
In consequence of these principles, and in various embodiments of the present invention, population normative values for a Gene Expression Profile can be used in comparative assessment of individual subjects as to biological condition, including both for purposes of health and/or disease. In one embodiment the normative values for a Gene Expression Profile may be used as a baseline in computing a “calibrated profile data set” (as defined at the beginning of this section) for a subject that reveals the deviation of such subject's gene expression from population normative values. Population normative values for a Gene Expression Profile can also be used as baseline values in constructing index functions in accordance with embodiments of the present invention. As a result, for example, an index function can be constructed to reveal not only the extent of an individual's inflammation expression generally but also in relation to normative values.
Although the baseline in
Frozen samples were shipped to the central laboratory at Source Precision Medicine, the assignee herein, in Boulder, Colo. for determination of expression levels of genes in the 48-gene Inflammation Gene Expression Panel (Precision Profile™ for Inflammatory Response). 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
In
Each of
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.
A female subject with a long, documented history of relapsing, remitting multiple sclerosis (RRMS) sought medical attention from a neurologist for increasing lower trunk muscle weakness (Visit 1). Blood was drawn for several assays and the subject was given 5 mg prednisone at that visit. Increasing weakness and spreading of the involvement caused subject to return to the neurologist 6 days later. Blood was drawn and the subject was started on 100 mg prednisone and tapered to 5 mg over one week. The subject reported that her muscle weakness subsided rapidly. The subject was seen for a routine visit (visit 3) more than 2 months later. The patient reported no signs of illness at that visit.
Results of high precision gene expression analysis are shown below in
Samples of whole blood from patients with relapsing remitting multiple sclerosis (RRMS) were collected while their disease is clinically inactive. Additional samples were collected during a clinical exacerbation of the MS (or attack). Levels of gene expression of mediators of inflammatory processes are examined before, during, and after the episode, whether or not anti-inflammatory treatment is employed. The post-attack samples were then compared to samples obtained at baseline and those obtained during the exacerbation, prior to initiation of any anti-inflammatory medication. The results of this study were compared to a database of normal subjects to identify and select diagnostic and prognostic markers of MS activity useful in the Gene Expression Panels for characterizing and evaluating MS according to the invention. Selected markers were tested in additional trials in patients known to have MS, and those suspected of having MS. By using genes selected to be especially probative in characterizing MS and inflammation related to MS, such conditions are identified in patients using the herein-described gene expression profile techniques and methods of characterizing multiple sclerosis or inflammatory conditions related to multiple sclerosis in a subject based on a sample from the subject. These data demonstrate the ability to evaluate, diagnose and characterize MS and inflammatory conditions related to MS in a subject, or population of subjects.
In this system, RRMS subjects experiencing a clinical exacerbation showed altered inflammatory-immune response gene expression compared to RRMS patients during remission and healthy subjects. Additionally, gene expression changes are evident in patients who have exacerbations coincident with initiation and completion of treatment.
This system thus provides a gene expression assay system for monitoring MS patients that is predictive of disease progression and treatment responsiveness. In using this system, gene expression profile data sets were determined and prepared from inflammation and immune-response related genes (mRNA and protein) in whole blood samples taken from RRMS patients before, during and after clinical exacerbation. Samples taken during an exacerbation were collected prior to treatment for the attack. Gene expression results were then correlated with relevant clinical indices as described.
In addition, the observed data in the gene expression profile data sets was compared to reference profile data sets determined from samples from undiagnosed healthy subjects (normals), gene expression profiles for other chronic immune-related genes, and to profile data sets determined for the individual patients during and after the attack. If desired, a subset of the selected identified genes is coupled with appropriate predictive biomedical algorithms for use in predicting and monitoring RRMS disease activity.
A study was conducted with 14 patients. Patients were required to have an existing diagnosis of RRMS and be clinically stable for at least thirty days prior to enrollment. Some patients were using disease-modifying medication (Interferon or Glatirimer Acetate). All patients are sampled at baseline, defined as a time when the subject is not currently experiencing an attack (see inclusion criteria). Those who experienced significant neurological symptoms, suggestive of a clinical exacerbation, were sampled prior to any treatment for the attack. If the patient was found to have a clinical exacerbation, then a repeat sample is obtained four weeks later, regardless of whether the patient receives steroids or other treatment for the exacerbation.
A clinical exacerbation is defined as the appearance of a new symptom or worsening/reoccurrence of an old symptom, attributed to RRMS, lasting at least 24 hours in the absence of fever, and preceded by stability or improvement for at least 30 days.
Each subject was asked to provide a complete medical history including any existing laboratory test results (i.e. MRI, EDSS scores, blood chemistry, hematology, etc) relevant to the patient's MS contained within the patient's medical records. Additional test results (ordered while the subject is enrolled in the study) relating to the treatment of the patient's MS were collected and correlated with gene expression analysis.
Subjects who participated in the study met all of the following criteria:
Subjects are excluded from the study if they meet any of the following criteria:
These studies were designed to identify possible markers of disease activity in multiple sclerosis (MS) to aid in selecting genes for particular Gene Expression Panels. Similar to the previously-described example, the results of this study were compared to a database of gene expression profile data sets determined and obtained from samples from healthy subjects, and the results were used to identify possible markers of MS activity to be used in Gene Expression Panels for characterizing and evaluating MS according to described embodiments. Selected markers were then tested in additional trials to assess their predictive value.
Eleven subjects were used in this study. Initially, a smaller number of patients were evaluated, and gene expression profile data sets were determined for these patients and the expression profiles of selected inflammatory markers were assessed. Additional subjects were added to the study after preliminary evidence for particular disease activity markers is obtained so that a larger or more particular panel of genes is selected for determining profile data sets for the full number of subjects in the study.
Patients who were not receiving disease-modifying therapy such as interferon were of particular interest but inclusion of patients receiving such therapy was also useful. Patients were asked to give blood at two timepoints—first at enrollment and then again at 3-12 months after enrollment. Clinical data relating to present and history of disease activity, concomitant medications, lab and MRI results, as well as general health assessment questionnaires were also collected.
Subjects who participated in the study met the following criteria:
In addition, patients with known hepatitis or HIV infection were not eligible. The enrollment samples from suitable subjects were collected prior to the patient receiving any disease modifying therapy. The later samples were collected 3-12 months after the patients start therapy. Preliminary data suggests that gene expression can used to track drug response and that only a plurality or several genetic markers is required to identify MS in a population of samples.
Theses studies were designed to identify biomarkers for use in a specific Gene Expression Panel for MS, wherein the genes/biomarkers were selected to evaluate dosing and safety of a new compound developed for treating MS, and to track drug response. Specifically a multi-center, randomized, double blind, placebo-controlled trial was used to evaluate a new drug therapy in patients with multiple sclerosis.
Thirty subjects were enrolled in this study. Only patients who exhibited stable MS for three months prior to the study were selected for the trial. Stable disease is defined as the absence of progression and relapse. Subjects enrolled in this study had been removed from disease modifying therapy for at least 1 month. A subject's clinical status was monitored throughout the study by MRI and hematology and blood chemistries.
Throughout the study patients received all medications necessary for management of their MS, including high-dose corticosteroids for management of relapses and introduction of standard treatments for MS. Initiation of such treatments will confound assessment of the trial's endpoints. Consequently, patients who required such treatment were removed from the new drug therapy phase of the trial but were continued to be followed for safety, immune response, and gene expression.
Blood samples for gene expression analysis were collected at screening/baseline (prior to initiation of drug), several times during the treatment phase and several times during follow-up (post-treatment phase). Gene expression results were compared within subjects, between subjects, and to Source Precision Medicine profile data sets determined to be what are termed “Normals”—i.e., a baseline profile dataset determined for a population of healthy (undiagnosed) individuals who do not have MS or other inflammatory conditions, disease, infections. The results were also evaluated to compare and contrast gene expression between different timepoints. This study was used to track individual and population response to the drug, and to correlate clinical symptoms (i.e. disease progression, disease remittance, adverse events) with gene expression.
Baseline samples from a subset of patients were analyzed. The preliminary data from the baseline samples suggest that that only a plurality of or optionally several specific genetic markers is required to identify MS across a population of samples. The study was also used to track drug response and clinical endpoints.
Theses studies were designed a study for testing a new experimental treatment for MS. The study enrolled 200 MS subjects in a Phase 2, multi-center, randomized, double-blind, parallel group, placebo-controlled, dose finding, safety, tolerability, and efficacy study. Samples for gene expression were collected at baseline and at several timepoints during the study. Samples were compared between subjects, within individual subjects, and to Source Precision Medicine profile data sets determined to be what are termed “Normals”—i.e., a baseline profile dataset determined for a population of healthy (undiagnosed) individuals who do not have MS or other inflammatory conditions, disease, infections. The gene expression profile data sets were then assessed for their ability to track individual response to therapy, for identifying a subset of genes that exhibit altered gene expression in MS and/or are affected by the drug treatment. Clinical data collected during the study include: MRIs, disease progression tests (EDSS, MSFC, ambulation tests, auditory testing, dexterity testing), medical history, concomitant medications, adverse events, physical exam, hematology and chemistry labs, urinalysis, and immunologic testing.
Subjects enrolled in the study were asked to discontinue any MS disease modifying therapies they may be using for their disease for at least 3 months prior to dosing with the study drug or drugs.
In order, ranked by increasing p-values, with higher values indicating less discrimination from normals, the following genes shown in Table 1A were determined to be especially useful in discriminating MS subjects (all MS and 3-month washed out MS) from normals (listed below from more discriminating to less discriminating). A ranking of the top 54 genes is shown in Table 1B, listed from more discriminating to less discriminating, by p-value.
As shown above, ITGAM was shown to be most discriminating for MS, have the lowest p-value of all genes examined. Latent Class Modeling was then performed with several other genes in combination with ITGAM, to produce three-gene models, four-gene models, and 5-gene models for characterizing MS relative to normals data for a variety of MS subjects. These results are shown in
These data support illustrate that Gene Expression Profiles with sufficient precision and calibration as described herein (1) can determine subsets of individuals with a known biological condition, particularly individuals with multiple sclerosis or individuals with inflammatory conditions related to multiple sclerosis; (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. It has been shown that Gene Expression Profiles may provide meaningful information even when derived from ex vivo treatment of blood or other tissue. It has been 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.
Gene Expression Profiles are used for characterization and monitoring of treatment efficacy of individuals with multiple sclerosis, or individuals with inflammatory conditions related to multiple sclerosis.
Additionally, Gene Expression Profiles are also used for characterization and early identification (including pre-symptomatic states) of infectious disease. This characterization includes discriminating between infected and uninfected individuals, bacterial and viral infections, specific subtypes of pathogenic agents, stages of the natural history of infection (e.g., early or late), and prognosis. Use of the algorithmic and statistical approaches discussed above to achieve such identification and to discriminate in such fashion is within the scope of various embodiments herein.
Using a targeted 104-gene panel, selected to be informative relative to biological state of MS patients (shown in Table 4), primers and probes were prepared for a subset of 24 genes identified in the Stepwise Regression Analysis shown in Table 3.
Gene expression profiles were obtained using these subsets of genes. Actual correct classification rate for the MS patients and the normal subjects was computed. Multi-gene models were constructed which were capable of correctly classifying MS and normal subjects with at least 75% accuracy. These results are shown in Tables 5-9 below. As demonstrated in Tables 6-9, a few as two genes allows discrimination between individuals with MS and normals at an accuracy of at least 75%.
One Gene Model
All 24 genes were evaluated for significance (i.e., p-value) regarding their ability to discriminate between MS and Normals, and ranked in the order of significance (see, Table 5). The optimal cutoff on the delta ct value for each gene was chosen that maximized the overall correct classification rate. The actual correct classification rate for the MS and Normal subjects was computed based on this cutoff and determined as to whether both reached the 75% criteria. None of these 1-gene models satisfied the 75%/75% criteria.
Two Gene Model
The top 8 genes (lowest p-value discriminating between MS and Normals) were subject to further analysis in a two-gene model. Each of the top 8 genes, one at a time, was used as the first gene in a 2-gene model, where all 23 remaining genes were evaluated as the second gene in this 2-gene model. (See Table 6). Column four illustrates the evaluated correct classification rates for these models (Data for those combinations of genes that fell below the 75%/75% cutoff, not all shown). The p-values in the 2-gene models assess the fit of the null hypothesis that the 2-gene model yields predictions of class memberships (MS vs. Normal) that are no different from chance predictions. The p-values were obtained from the SEARCH stepwise logistic procedure in the GOLDMineR program.
Also included in Table 6 is the R2 statistic provided by the GOLDMineR program, The R2 statistic is a less formal statistical measure of goodness of prediction, which varies between 0 (predicted probability of being in MS is constant regardless of delta-ct values on the 2 genes) to 1 (predicted probability of being MS=1 for each MS subject, and =0 for each Normal subject).
The right-most column of Table 6 indicates whether the 2-gene model was further used in illustrate the development of 3-gene models. For this use, 7 models with the lowest p-values (most significant), plus a few others were included as indicated.
Three Gene Model
For each of the selected 2-gene models (including the 7 most significant), each of the remaining 22 genes was evaluated as being included as a third gene in the model. Table 7 lists these along with the incremental p-value associated with the 3rd gene. Only models where the incremental p-value <0.05 are listed. The others were excluded because the additional MS vs. Normal discrimination associated with the 3rd gene was not significant at the 0.05 level. Each of these 3-gene models was evaluated further to determine whether incremental p-values associated with the other 2 genes was also significant. If the incremental p-value of any one of the 3 was found to be less than 0.05, it was excluded because it did not make a significant improvement over one of the 2-gene sub-models. An example of a 3-gene model that failed this secondary test was the model containing NFKB1B, HLADRA and CASP9. Here, the incremental p-value for NFKB1B was found to be only. 13 and therefore did not provide a significant improvement over the 2-gene model containing HLADRA and CASP9. The ESTIMATE procedure in GOLDMineR was used to compute all of the incremental p-values, which are shown in Table 7.
Four and Five Gene Models
The procedure for models containing 4 and five genes is similar to the one for three genes. Table 8 and 9 show the results associated with the use of most significant 3-gene model to obtain 4-gene and 5-gene models. The incremental p-values associated with each gene in the 4-gene and 5-gene models are shown, along with the percent classified correctly. As demonstrated by Tables 8 and 9 the addition of more genes in the model did not significantly alter the ability of the models to correctly classify MS patients and normals.
TNF inhibitors, including ENBREL, HUMERIA, REMICADE, and other agents that inhibit TNF, have been associated with rare cases of new or exacerbated symptoms of demyelinating disorders including but not limited to multiple sclerosis, and optic neuritis, seizure, neuromyelitis optica, transverse myelitis, acute disseminated encephalomyelitis, HIV encephalitis, adrenoleukodystrophy, adrenomyeloneuropathy, progress multifocal leukoencephalopathy, and central pontine myelinolysis, and CNS manifestations of systemic vasculitis, some case presenting with mental status changes and some associated with permanent disability. These TNF inhibitors have been shown to accelerate the demyelination process causing nerve lesions. For example, cases of transverse myelitis, optic neuritis, multiple sclerosis, and new onset or exacerbation of seizure disorders have been observed in association with ENBREL therapy. The causal relationship to ENBREL therapy remains unclear. While no clinical trials have been performed evaluating ENBREL therapy in patients with multiple sclerosis, other TNF antagonists administered to patients with multiple sclerosis have been associated with increases in disease activity. As such, prescribers should exercise caution in considering the use of ENBREL or other anti-TNF therapeutics in patients with preexisting or recent-onset central nervous system demyelinating disorders.
The present invention provides a method for predicting an adverse effect from anti-TNF therapy in a subject. The method comprises obtaining a sample from the subject (e.g., blood, tissue, or cell), the sample providing a source of RNAs, assessing a profile data set of a plurality of members, each member being a quantitative measure of the amount of a distinct RNA constituent in a panel of constituents (e.g., two or more constituents from any of Tables 1-10), selected so that measurement of the constituents enables characterization of the presumptive signs of a multiple sclerosis, wherein such measure for each constituent is obtained under measurement conditions that are substantially repeatable to produce a patient data set. This patient data set is then compared to a baseline profile data set, e.g. a profile data set multiple sclerosis or inflammatory conditions related to multiple sclerosis, determined as previous described. A patient data set that is similar to the baseline profile data set indicates the subject is at risk for suffering an adverse effect from anti-TNF therapy. The sample is obtained, prior to, during, or after administration of an anti-TNF therapeutic regimen.
In particular, the method is useful for screening subjects suffering from an inflammatory condition for a demyelinating disease prior to the administration of anti-TNF therapy for the treatment of the inflammatory condition. The inflammatory condition may include but are not limited to rheumatoid arthritis, psoriasis, ankylosing spondylitis, psoriatic arthritis and Crohn's disease. The demyelinating condition may include but is not limited to multiple sclerosis, optic neuritis, seizure, neuromyelitis optica, transverse myelitis, acute disseminated encephalomyelitis, HIV encephalitis, adrenoleukodystrophy, adrenomyeloneuropathy, progress multifocal leukoencephalopathy, and central pontine myelinolysis, and CNS manifestations of systemic vasculitis,
Examples of anti-TNF Therapeutics and Indications
Enbrel, containing etanercept, is a breakthrough product approved for the treatment of chronic inflammatory diseases such as rheumatoid arthritis, juvenile rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, and psoriasis. Enbrel continues to maintain a leading position in the dermatology and rheumatology biologic marketplaces, ranking No. 1 in worldwide sales among biotechnology products used in rheumatology and dermatology.
Abbott's Humira has been approved for treatment of rheumatoid arthritis in 57 countries, and for psoriatic arthritis and early RA in some European countries and the US.
Remicade has now achieved approvals in the treatment of such inflammatory diseases as Crohn's disease, rheumatoid arthritis, ankylosing spondylitis, and psoriatic arthritis. First approved in 1998 for Crohn's disease, Remicade has been used to treat more than half a million patients worldwide.
Rheumatoid arthritis has a worldwide distribution with an estimated prevalence of 1 to 2%. Prevalence increases with age, approaching 5% in women over age 55. US prevalence of 2 million patients is 0.68%. The average annual incidence in the United States is about 70 per 100,000 annually. Over 200,000 new cases in US per year. Both incidence and prevalence of rheumatoid arthritis are two to three times greater in women than in men.
It is estimated that over seven million Americans (2.6%) have psoriasis, with more than 150,000 new cases reported each year.
Chronic plaque psoriasis represents approximately 80% of people with psoriasis with a US prevalence of approximately 5.7 million (2%). 10-20% of patients with plaque psoriasis also experience psoriatic arthritis.
This application is a continuation in part of U.S. Ser. No. 11/454,533, filed Jun. 16, 2006 and a continuation in part of U.S. Ser. No. 11/155,930, filed Jun. 16, 2005 and claims the benefit of U.S. Ser. No. 60/734,681, filed Nov. 7, 2005, U.S. Ser. No. 60/758,933, filed Jan. 13, 2006, and U.S. Ser. No. 60/831,005, filed Jul. 13, 2006, each of which are incorporated herein by reference in their entireties.
| Number | Date | Country | |
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| 60435257 | Dec 2002 | US | |
| 60141542 | Jun 1999 | US | |
| 60195522 | Apr 2000 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 11454533 | Jun 2006 | US |
| Child | 11827892 | US | |
| Parent | 11155930 | Jun 2005 | US |
| Child | 11454533 | US | |
| Parent | 10742458 | Dec 2003 | US |
| Child | 11155930 | US | |
| Parent | 10291225 | Nov 2002 | US |
| Child | 10742458 | US | |
| Parent | 09821850 | Mar 2001 | US |
| Child | 10291225 | US | |
| Parent | 09605581 | Jun 2000 | US |
| Child | 09821850 | US |
